Factors Blog

Understanding the B2B Sales Funnel 

When it comes to B2B marketing, qualifying your sales leads is not an easy job. Given the several steps involved in a B2B customer journey, visualizing each one as a funnel can be insightful (and actionable). It identifies what’s helping and hurting conversion rates along a prospect’s journey to becoming a customer. Which in turn, helps optimize the journey and improve conversion rates.

What is a B2B Sales Funnel?

A B2B sales funnel is a visual model that illustrates a prospect’s journey. The funnel graphically represents the proportion of prospects present in all stages. It can also represent customer engagement and break down each interaction from first-touch to deal-won. Here’s why a B2B funnel differs from a B2C funnel: 

* Unlike in B2C, a B2B prospect is composed of several decision-makers who would have to greenlight an investment.

* The sales cycle in B2B is considerably longer than a B2C one. This is not only because of the layers of approval required but also the meticulous research, review, and demos, and larger contract values.

* In a B2B endeavor, customer retention and the need to build a long-term relationship with clients are critical for long term success. Hence, brand building is placed on a pedestal for B2B customer engagement.

Breaking Down the Stages of the Funnel

Several terms exist for the different stages of the funnels. Functionally, however, most of them are relatively synonyms. For the sake of simplicity, a B2B sales funnel can be divided into 3 levels:

1. Top of Funnel (ToFu)

2. Middle of Funnel (MoFu)

3. Bottom of Funnel (BoFu)

Picture of funnel broken in three parts; top middle bottom. On the left of the funnel write down what is buyer intent? 

On the right side of the funnel; list down the marketing efforts commonly used at that stage; like top stage is content marketing and blogs. 

When guiding your users through the funnel, there are several  you can use to assess whether you’re doing it successfully. These metrics can help you evaluate past performance, predict future trends and optimize your current efforts. Some of these are click- through- rate, conversion, content shares and SEO metrics. Analytics software like Factors and Google Analytics can be used according to your campaign goals, content channels and campaigns.

Top of sales funnel

The top of funnel level deals with the awareness and interest stage in a prospect’s journey. The objective of this stage is to consistently bring in fresh, new traffic. At this stage, prospects may not be entirely aware of the problem you’re solving. From a B2B standpoint, this not only involves your advertising, but is heavily centered around content marketing, educational content creation, & building a strong organic presence.

B2B prospects commonly require significant nurturing before going further down the funnel. For example, a company like Salesforce revolves their content strategy around CRM among other things educating prospects on all things CRM related and more.

Common top-of-the-funnel marketing touchpoints include:

• Blogs
• Podcasts
• E-Books
• Webinars

And key top-of-the-funnel metrics to track include:

• Number of site visits
• Web session duration
• Bounce rate
• Keyword rankings
• CTR
• Mail open rate

Middle of Sales Funnel

"This level of the funnel corresponds with the engagement stage of a prospect’s journey. After creating awareness and defining the problem, prospects would now evaluate their solutions. At this stage, you would need to build your brand authority and elucidate how your solution is the superior option." - says Milosz Krasinski, Managing Director at Chilli Fruit Web Consulting.

The approach to marketing changes at this level. Here, content becomes increasingly brand-oriented and employs lead magnets or gated content to bolster your brand authority. This can also be ensured by hosting webinars, events, and live-demos. MoFu blogs also tend to be more product heavy as opposed to industry-specific.

Common middle-of-the-funnel touchpoints include:

• Comparison articles
• Retargeted ads
• Product reviews
• Trial sign-ups

Bottom of Sales Funnel

Not to be confused with the expression “being at the bottom of the barrel”. The bottom of the funnel is a crucial stage in the buyer’s journey. It’s where you would ultimately want to guide all your prospects towards. It is known as the conversion stage because at this stage prospects make a purchasing decision and possibly convert into customers.

It must be noted that bottom of funnel prospects can vary depending on your conversion goal. It could even include prospects that sign up for a demo, make an account, mail a product query, or anything that expresses high engagement with the brand or product. Based on historical trends, you could identify which conversion goal is conducive to a prospect becoming an MQL, an SAL, or an SQL. 

At this level, the sales team starts to get involved. It’s the combined effort of sales and marketing that ultimately onboard customers through promotional offers and strategies. Considering the B2B sales cycle, this is still a long, arduous process. The bottom of the funnel also helps form the ideal client profile which serves in identifying target accounts with ABM (account-based marketing).

How to Guide Users through the Sales Funnel 

The core objective of the funnel is to help guide potential B2B customers through the process, without spending too much or overdoing it and driving them away from making a purchase. However, once you have identified what stage your customers are at- what next? It is important to take advantage of this new information to adapt your content to target your customers better.

Along each stage of the sales funnel, content must be curated to drive up customer engagement. And the type of content that customers expect differs at different stages of the funnel. Let’s look at how buyer intent differs across the three stages of the funnel: 

1. Top of the funnel: The customer has arrived at your ad because there is a problem that they are facing. Present your content in a way that recognises their problem through educational webinars, blog posts and social media. 

2. Middle of the funnel: Remember that at this point, your customer is still looking for a solution. This is when you build trust through content marketing campaigns, blog posts. You want them to be assured of the quality of your product and have faith in you. 

3. Bottom of the funnel: In this last stage, make them aware that others before them have achieved the same goal with their products. Use testimonials, product USPs and case studies to drive your point home. 

Flipping the Funnel: An Alternate Way

Instead of using the conventional B2B funnel, Binnet and Field suggest flipping the funnel. This means to think of the funnel as ‘in market’ and ‘out market’ buyers. Instead of looking at your B2B customer journey as a funnel that has a narrower customer base at each stage, focus on different aspects like ‘activation’ or ‘branding’ at the in market and out market stages respectively. Find out more about this alternate perspective here. 

In Conclusion…

The stages of a B2B marketing funnel are diverse. Each stage adopts different types of content strategy, tactics, interactions, and analytics. This makes it all the more essential to compartmentalize efforts into an organized funnel, making the process disciplined.

The funnel not only keeps track of your prospects at each level but also identifies different pain points that limit prospects from moving down the funnel. Measuring your funnel helps distinguish your leads better too, which can be quite useful given that 79% of MQL are never converted to sales.

From a B2B angle, the funnel highlights the importance of efforts like SEO, building domain authority for TOFU and long-form product heavy blogs for MOFU, etc. Given the nature of B2B prospects, all these factors contribute to the movement down the funnel.

Imagine nurturing leads for months, only to find that most don't convert. For B2B marketers, this is a common issue: lost time, wasted budget, and missed revenue. The main problem? Decisions based on guesses instead of data. As competition grows and buyer journeys become more complex, relying on intuition alone puts you behind.

Predictive marketing analytics offers a solution. Using historical and real-time data helps B2B organizations forecast outcomes, focus on high-value leads, and improve every stage of the marketing funnel. This guide will cover the basics of predictive marketing analytics for B2B, helping you shift from reactive to proactive and turn your data into a competitive edge.

TL;DR

• Predictive marketing analytics uses past and current data to predict future marketing results, helping B2B companies make better decisions quickly.
• It allows for accurate lead scoring, effective account-based marketing, and precise sales forecasting, guiding marketing and sales teams to focus on the best opportunities.
• The process includes gathering quality data, creating predictive models (like regression and classification), and applying insights directly to marketing tasks.
• Success relies on setting clear goals, building cross-functional teams, selecting the right tools, and ensuring strong data management.
• Challenges like data silos, skill shortages, and model accuracy can be tackled with a good strategy and ongoing improvement.
• Measuring ROI is key. Monitor KPIs like conversion rates, customer retention, and campaign success.
• By using predictive analytics in B2B marketing, you can achieve higher efficiency, better customer experiences, and steady revenue growth.

What is Predictive Marketing?

As is evident from the name, Predictive Marketing helps marketers predict their marketing outcomes in terms of expected traffic, expected leads, conversions and impact on ROI at various touch-points

In other words, predictive marketing is the process of forecasting the influence of marketing campaigns and tactics with the help of:

• Historical data on audience behaviour
• Consumer research
• Purchasing history of target consumers
• Holistic marketing analytics

This forecasting is done using predictive analytics. B2C/E-commerce firms like H&M and Amazon already use this to predict products that their consumers would be interested in buying based on their current search keywords and products that they are clicking and opening in the catalogue, their past purchases, what other products similar consumers have purchased after similar search queries, purchases, items, etc

What is Predictive Marketing Analytics?

Predictive marketing analytics uses statistical models, machine learning, and past data to forecast future marketing outcomes and guide decisions. In B2B settings, it involves analyzing past customer interactions, campaign results, and sales data to predict which leads might convert, which accounts may leave, or which marketing activities will yield the best return.

Traditional analytics report what happened. Predictive marketing analytics answers ‘what will happen next?’ It can estimate the likelihood of a prospect becoming a customer or forecast future sales. With these insights, B2B marketers can prioritize resources, personalize outreach, and improve campaigns.

Predictive marketing analytics turns raw data into useful insights, helping you anticipate trends, reduce waste, and make smarter decisions. This approach is crucial for B2B organizations aiming to grow and stay competitive in a data-driven market.

How Predictive Marketing Analytics Powers Data-Driven Decision Making?

Predictive marketing analytics transforms how B2B organizations make decisions by focusing on evidence-based strategies. Instead of relying on gut feelings, you use predictive models to foresee buyer behavior, campaign results, and sales trends. For instance, by examining patterns in your CRM and marketing data, predictive analytics can show which leads are likely to convert or which accounts might leave.

This data-driven approach allows for better targeting, resource use, and forecasting. Marketing teams can focus on high-value accounts, time campaigns well, and tailor messages to predicted needs. Sales teams get more accurate forecasts and can target the best opportunities. Predictive marketing analytics helps you act ahead of time, cut unnecessary spending, and achieve measurable results. By using predictive insights in your decision-making, your organization can quickly adapt to market changes and grow steadily.

Key Components of Predictive Marketing Analytics

To succeed with predictive marketing analytics in B2B, focus on three main components: data collection and integration, predictive modeling techniques, and deployment with action.

1. Data Collection and Integration

Gather and unify data from multiple sources, such as CRMs, marketing automation platforms, website analytics, ad platforms, and sales databases. Clean, consistent, and enriched data is foundational for accurate predictions.

2. Data Enrichment and Quality Control

Before modeling, ensure your data is unified and enriched with external signals (e.g., firmographics, technographics, intent data). Remove duplicates, fill gaps, and standardize formats to increase predictive accuracy.

3. Predictive Modeling Techniques

Use machine learning and statistical methods to uncover patterns:

• Regression for forecasting values like revenue or deal size.
• Classification to predict the likelihood of conversion or churn.
• Clustering to group accounts or leads based on behavior or profile.

4. Deployment and Operationalization

Apply predictive insights to real workflows. Examples include:

• Lead scoring in your CRM.
• Triggered sales alerts for at-risk accounts.
• Personalization in campaigns based on behavioral predictions.

5. Continuous Feedback and Model Optimization

Predictive models are not ‘set and forget.’ Monitor performance regularly, gather feedback from sales and marketing teams, and retrain models with new data to maintain relevance and improve accuracy over time.

Here is a guide describing the process of implementing predictive marketing analytics to drive b2b growth. 

Measurement Models for Predictive Analytics

1. Cluster Models: These models are used to segment consumer based on behavioural data  (past purchases, brand engagement, etc) and demographic data. The most common predictive algorithms used for clustering are behavioural clustering, product-based clustering, and brand-based clustering.

2. Propensity Models: As the name suggests, these models are used to evaluate consumers’ tendencies or inclinations to act/engage in specific way. These model evaluate the likelihood of a consumer to purchase, convert, etc. 

3. Recommendation Filtering: H&M, Amazon and Netflix are some of the most common examples of firm's that use recommendation filtering. It refers to using past purchases or consumption history to find other sales/revenue opportunities. 

Use Cases for Predictive Marketing Analytics For B2B Marketers

Predictive marketing analytics provides real value in B2B by addressing key issues in sales and marketing. 

1. Lead Scoring and Qualification

Predictive analytics ranks prospects based on their likelihood to convert, using historical behavior and demographic data. This helps sales teams prioritize the most promising leads and reduce time spent on poor-fit prospects.

2. Account-Based Marketing Optimization

By identifying which target accounts are most likely to engage, predictive tools help marketers personalize campaigns and prioritize outreach. This improves engagement rates and increases efficiency in ABM strategies.

3. Customer Churn Prediction and Retention

Predictive models detect signs of customer dissatisfaction or drop-off risk. Teams can then take proactive steps, like personalized support or re-engagement campaigns, to improve retention and reduce churn.

4. Sales Forecasting and Pipeline Management

Predictive analytics estimates which deals are likely to close and when, enabling accurate revenue forecasting and smarter resource allocation. This supports more strategic planning and reduces surprises.

5. Campaign Performance Prediction

By analyzing past campaign data, predictive models forecast which future marketing strategies will deliver the best ROI. This allows for better budget allocation and more effective campaign planning. 

6. Automated social suggestions

Predictive analytics can also analyze audience engagement trends across social channels to suggest the best times to post content, provide content suggestions, and conduct granular A/B testing of two or more variations of content to predict which one performs better.

7. Predictive SEO

In addition to improving traffic and  SERP rankings, predictive analytics like search trend insights can also prevent the loss of SEO momentum and ranking. Essentially, predictive SEO helps you determine if a webpage is about to lose its SERP rankings and predict topics for blog posts that your audience wants more of. 

By applying predictive marketing analytics to these areas, B2B organizations can work more efficiently, increase conversion rates, and achieve steady growth throughout the customer lifecycle.

How to Build a Predictive Marketing Analytics Strategy?

Here’s how to build a predictive marketing strategy for your B2B business:

1. Define Clear Business Goals

Start with specific goals tied to marketing and sales outcomes, such as increasing qualified leads, improving win rates, reducing churn, or boosting customer lifetime value. These goals will shape how you use predictive analytics and what success looks like.

Also, read the blog on lead tracking methods to learn how to qualify leads and prospects. 

2. Set Measurable KPIs

Identify performance indicators that align with your goals. Common KPIs include lead-to-opportunity conversion rate, average sales cycle length, customer retention rate, and forecast accuracy. These help track progress and measure the impact of predictive efforts.

3. Assemble a Cross-Functional Team

Bring together data analysts, marketers, sales leaders, and RevOps professionals. Data experts handle modeling, while marketers and sales teams offer context and ensure insights translate into action. Collaboration keeps your strategy grounded in real-world needs.

4. Choose the Right Tools and Platforms

Select predictive analytics tools that integrate well with your existing tech stack, especially your CRM (like Salesforce or HubSpot), marketing automation tools, and customer databases. Look for features like lead scoring, campaign forecasting, and segmentation modeling.

5. Ensure Data Quality and Compliance

Reliable predictions require clean, consistent, and compliant data. Establish clear rules for data collection, validation, and storage. Ensure your practices comply with regulations like GDPR or CCPA when handling sensitive customer data.

6. Test and Refine Predictive Models

Predictive models need regular tuning. Run tests on model performance, gather feedback from your teams, and adjust inputs or thresholds as needed. This helps prevent bias, adapt to changes in buyer behavior, and improve model accuracy over time.

7. Enable Cross-Team Collaboration

Encourage open communication between marketing, sales, and analytics teams. Share insights regularly, translate findings into action plans, and ensure accountability. A shared understanding improves execution and boosts adoption of analytics-driven decisions.

8. Focus on Actionable Outcomes

Don’t just build dashboards, ensure your predictions lead to real changes. For example, prioritize outreach based on lead scores, adjust targeting based on churn risk, or personalize content based on account behavior. The value comes from using predictions to act smarter and faster.

Common Pitfalls in B2B Predictive Marketing Analytics

Implementing predictive marketing analytics in B2B has its challenges. Some are:

1. Fixing Data Quality and Integration Issues

B2B data is often fragmented across CRMs, marketing automation tools, and external platforms. This leads to inconsistencies that hurt prediction accuracy. Start by consolidating your data into a unified system and use data cleaning protocols to maintain accuracy and consistency.

2. Dealing with Limited Sample Sizes

B2B businesses typically have fewer transactions than B2C, which limits the volume of training data for models. To overcome this, focus on quality over quantity and supplement your internal data with third-party intent or industry data to enrich your insights.

3. Breaking Down Organizational Silos

Lack of collaboration between marketing, sales, and data teams slows down adoption. Promote cross-team collaboration by sharing dashboards, insights, and KPIs. A shared view of customer data boosts transparency and trust in analytics-driven decisions.

4. Avoiding Overfitting to Outdated Data

Predictive models trained on outdated or limited historical data may fail to reflect current buyer behavior. Regularly retrain and validate your models using the latest data to keep them aligned with changing market conditions.

5. Managing Privacy and Compliance Risks

Using personal and behavioral data requires strict adherence to regulations like GDPR and CCPA. Make sure your team understands compliance rules and uses secure tools. Invest in systems that allow for consent tracking, anonymization, and audit trails.

6. Starting with Pilot Projects

Instead of deploying predictive analytics across your entire operation, begin with small-scale pilot projects, like lead scoring or churn prediction. Show quick wins to build confidence internally and gather feedback for refining your strategy before scaling up.

7. Building Internal Trust in Predictive Insights

Skepticism from stakeholders can hinder adoption. Share results clearly, explain how predictions are generated, and show real business impact. Involve end-users in the feedback loop to increase trust and buy-in. 

By addressing these challenges directly, your B2B organization can fully harness predictive marketing analytics and achieve more reliable, actionable results.

Best Practices in Adopting Predictive Marketing Analytics

Some of the best practices you can use while adopting predictive marketing analytics are:

1. Start with a Clear Business Problem

Begin by identifying a specific challenge, such as improving lead quality, reducing churn, or forecasting revenue. Avoid applying analytics without a purpose. Tying models to real business goals increases focus and impact.

2. Involve Sales and Marketing Early

Predictive success depends on cross-functional collaboration. Involve both sales and marketing teams from the start to ensure alignment, shared ownership, and consistent adoption of insights.

3. Prioritize Data Quality and Integration

Accurate predictions require clean, unified data. Integrate key data sources, like CRM, marketing automation, and web analytics, to implement ongoing processes for deduplication, validation, and accuracy checks.

4. Choose Scalable, User-Friendly Tools

Select analytics platforms that align with your team’s technical capabilities. Look for solutions that are easy to integrate and scale with your organization’s growth.

5. Invest in Continuous Training

Educate teams on how predictive analytics works and how to use the insights in their day-to-day decisions. Ongoing training builds confidence and fosters adoption.

6. Define and Track Clear KPIs

Establish measurable success metrics such as MQL-to-SQL conversion rate, churn reduction, or forecast accuracy. Use these KPIs to assess performance and guide optimization efforts.

7. Share Insights with Dashboards

Use real-time dashboards to make predictions and results accessible across departments. Visibility fosters transparency, encourages collaboration, and drives action.

8. Continuously Update Your Models

Market dynamics and buyer behavior change. Regularly review and retrain models using fresh data and feedback to keep predictions relevant and accurate.

9. Encourage a Culture of Experimentation

Support a test-and-learn mindset. Run small experiments, measure impact, and iterate based on results. This agile approach ensures continual improvement and greater ROI.

Wrapping Up: Why Predictive Marketing Analytics is a Must-Have?

Predictive marketing analytics is key for B2B companies to stay competitive and make smarter decisions. By using past and current data, you can foresee customer needs, improve campaigns, and focus on valuable accounts more accurately. The real benefit comes from linking your data, teams, and technology to build a smooth, insight-driven marketing system. This journey needs investment in data quality, the right talent, and ongoing model updates, but the rewards are clear: better conversion rates, improved customer retention, and more efficient resource use. 

Predictive marketing analytics is not a one-time task but a process that grows with your business. By integrating predictive insights into your daily work and decisions, you'll not only boost marketing results but also support steady growth. See predictive marketing analytics as a strategic tool, and you'll be ready for the challenges and opportunities in today's B2B world.

As digital marketing takes on new complexities, it’s essential for your marketing plan to incorporate content analytics. This will help you have a more detailed understanding of how customers interact with your company. You’ll be able to make more informed, data-driven decisions to effectively reach and convert your target audience.

What is content analytics?

Content analytics, or content intelligence, is a set of technologies that analyzes digital content in order for you to understand which content is performing well with your target audience. This enables you to create the most effective digital content possible to drive more conversions.

How does content analytics work?

B2B companies often offer highly specialized solutions. Accordingly, businesses must target their niche audiences with dedicated marketing initiatives and content that address relevant paint-points and use-cases.

You can generally figure out which content works best for your clients by analyzing certain metrics for the unstructured content your business has put out. Analytics and attribution tools like Factors.ai help marketing teams with granular insights into content performance and bottom line impact.

It’s important to remember that content analytics and attribution isn’t a linear process. We got in touch with B2B content industry leaders to ask how many metrics they use to measure content performance. Here’s what they had to say:

We also asked them which metrics they think are the most important in measuring the effectiveness of content. Here’s what they had to say:

• Clickthrough rate (CTR): Sophia Madhavan at GrowthMakerz and Vitaliy at Videowise include the CTR in the list of metrics they use to evaluate how well their content is doing. The CTR allows you to see how many times visitors to a certain page organically search for your content or click on your ad as a ratio to the total number of visitors to that page.
• Impressions: Impressions give you insight into the level of engagement your content is generating. This content could be a web page, advertisement, or blog post, for instance. Madhiruma Halder at Recruit CRM lists impressions among the metrics they use to understand how their social media campaigns and search engine marketing are performing.
• Marketing qualified lead (MQL): Not all leads are created equal. A lead that takes any high intent actions like scheduling a demo or signing up for a trial is far more likely to convert than others. Karishma Chopra at Hiver believes that the effectiveness of content should be measured in terms of its influence in driving MQLs.
• Time on page: The time a prospect spends on a particular page is indicative of their interest in the solution you’re offering. Praveen Das at Factors then uses these insights to create marketing campaigns informed by the content their prospects and clients are interested in.
• Scroll depth: Scroll depth is a measure of how far your website visitors scroll down a certain page. As a general rule of thumb, if most of your website visitors are scrolling at least halfway down the page, it indicates that your content has substantial value.
• Unique users: Chelsea Downing-West at The Martec finds unique users a crucial metric. The number of unique users may be challenging to calculate. Effective visitor identification allows you to see how many unique visitors your website receives by counting each visitor only once, no matter how often they visit the website.
• Bounce rate: The bounce rate of your website indicates the ratio of visitors who access your website and leave instead of clicking on and going through other webpages.
• Engaged Accounts: Understanding which specific accounts are consuming the content is another intuitive way for content marketers to analyze its effectiveness. Visitor Identification softwares like Factors can help marketers identify the account and its properties (employee range, industry) even if the user does not fill up a form. This helps Content Marketers plan content efforts tailored to specific industries or scale of employees.

Why is content analytics important?

Your website content is the first thing that prospects see when they’re evaluating your company. It creates a lasting impression about your services. Content analytics helps you understand the types of content that perform well among your target audience, which in turn is crucial to designing a successful customer experience.

Moreover, marketing teams spend a substantial amount of time and budget to create content that prospects find valuable. A few of the experts we surveyed responded that they hire in-house teams for content creation and distribution. This indicates how valuable creating effective marketing content is to increasing overall revenue; B2B companies are willing to invest in salaries, 401(k)s, and insurance to generate engaging content. Respondents stated that they spent anywhere between $1000 and $10,000 a month towards content creation and distribution.

This expense makes sense when you consider how much content contributes to the overall pipeline. Respondents stated that the monthly investments they make towards content creation reaps significant rewards. Although responses varied greatly, most of the experts cited that around 30-40% of the pipeline is influenced by content creation and distribution.

Creating valuable content for prospects is almost entirely dependent on content analytics. Content analytics offer your sales and marketing teams multiple benefits:

• Helps marketers redefine their strategy based on how current content is performing
• Calculates the ROI for each piece of content, which in turn guides future content strategy and content repurposing.
• Superior prospect experience by focusing on the most relevant content and elimination of guesswork
• Quicker and easier conversion for prospects by offering them content that is relevant and important for them at each stage of the funnel
• Cuts down on redundant content by immediately finding out when certain pieces are underperforming

How can content analytics contribute to a better customer experience?

Understanding each interaction a prospect or customer has with your website and other marketing collateral enables you to improve your content offerings. You’ll be able to offer customers a far better experience by analyzing and iterating based on content data. Content analytics helps you:

Offer relevant content

You can offer clients valuable content depending on where they are in the conversion process. Content analytics help you anticipate which content is relevant at every stage of the funnel, thereby streamlining communication with prospects and clients alike.

Address specific client needs

You can also create a 360-degree buyer persona for existing clients. An integrated analytics software like Factors allows you to have a holistic overview of each client so you can see every interaction they’ve had with your company. You have access to each touchpoint and all their past behavior, thereby enabling you to make educated guesses about their pain points.

For instance, let’s say you run a company that creates CRM software. You could find through trends in content analytics that customers from the tourism industry are interested in the customer service features it offers. On the other hand, customers in the tech industry are more interested in its customization and workflow automation features. You can then use these trends to offer them content that best fits their needs.

Hyper-personalize your content

Prospects today expect a highly customized experience tailored to their needs. It’s essential to curate a personalized experience in order to create a lasting client relationship. Understanding which channels your client base uses, the keywords they’re interested in, and the time they spend trying to solve a pain point on a webpage are all crucial to personalization.

Make better decisions

Content reporting can help you make deeply informed decisions with respect to pricing, sales, organizational goals, and communication. You’ll never have to rely on guesswork again; all you need to do is gather user data through an analytics tool and leverage it relentlessly.

Your clients have needs that are continuously evolving, so you have to continuously utilize real-time data to create adaptive strategies that help you get the most out of your investment.

What are the biggest challenges of content analytics?

Customers’ preferences and expectations from content are constantly changing. Jess Cook from LASSO puts it best: content analytics isn't an exact science. Here are the biggest challenges that marketing and sales teams encounter in the process of analyzing content data:

Data silos

Your sales and marketing teams need access to a unified customer data infrastructure. End-to-end account journeys are of the utmost importance when you’re streamlining the sales and marketing processes. If your sales and marketing teams work independently of each other, there’s a huge chance that they have access to disconnected data.

Data silos lead to an underwhelming customer experience. The sales team should have access to all the marketing touchpoints the user has been through to avoid repetition, tedium, and misunderstandings in the sales process.

Visitor identification

De-anonymization is crucial to effective analytics, since you need to see how many unique visitors your website is drawing and the firmographic characteristics of these users such as company name, industry, employee range and revenue range. However, all users on the internet want anonymity, and there are laws to protect user data from illegal tracking.

Free content analytics tools have limitations

Tools like Google Search Console (GSC) offer users limited insight. Saffia Faisal at Userpilot believes that GSC is inadequate for dedicated content analytics and reporting. B2B companies require an in-depth analysis of how their content is performing. GSC’s algorithm limits accuracy in reporting, providing just a signal of how content performs. GSC also imposes limitations on the number of rows of data that can be exported at a query level.

How content analytics works in Factors

Factors.ai connects the dots between web sessions and CRM events to answer this question through automated form captures, customer journey funnels, and AI-powered inflection analysis to determine what content is helping/hurting larger objectives

“How are my website resources driving form submissions, MQLs, SQLs, Opportunities, Deals, and ultimately, Revenue?" 

Content marketers and sales strategists need access to real-time, relevant data that provides a holistic overview of content and customers alike. These insights can help tailor new content based on what works, thereby driving greater revenue.

Customizable dashboards

Remember the data silos we mentioned earlier? They slow down the progression of the accounts into the sales funnel and may lead consumers to drop out of it altogether. With Factors, however, you have all your customer information present on the same dashboard. Your sales and marketing teams will have access to consistent information, and will be able to see all your clients’ touchpoints. You can choose which metrics you want to view on the dashboard.

Easy account identification

With Factors’ reverse IP lookup, you’ll be able to identify which companies are interested in your solutions through their website visits. If an individual in a company visits your website, Factors will match its IP address within its existing database and identify the company name and domain, industry, annual revenue, and employee headcount. Since the database relies on publicly available data and the data you draw from your website, this process is fully compliant with user privacy protections.

You’ll also be able to see which stage of the funnel a particular prospect is at by the type of content they consume. Once you know the identity and needs of your prospects, you can target them in a personalized manner.

Automated analytics and attribution

Your sales and marketing teams won’t need to crunch numbers or look for missing information. Factors provides all the data you need, such as time on page, page load time, button clicks, scroll percentage, and page views. By automating the process of content data collection, your teams can focus their energies on strategizing and creating quality content for clients.

Factors’ multi-touch attribution model helps your company understand the customer journey and give credit to the touchpoints involved in the conversion process. It provides more detail with respect to user behavior when compared to single-touch models. A multi-touch attribution model aims to highlight which touchpoints have the greatest influence in the account’s journey, and how they work together.

Adaptation based on new insights

Your customers’ requirements are constantly evolving. It’s necessary for your analytics and attribution systems to respond to these nuances. Factors allows you to obtain instant insights about which content assets are redundant or performing well. You can use these content performance reports to refresh older content and push pieces that are doing well across more channels.

Get in touch with us today to find out how Factors can help your company improve its content analytics and reporting.‍

____________________________________________________________________________________________

Dive in Deep with Factors

Factors.ai takes content analytics one step further with extensive breakdowns + filters, custom dimensions + KPIs, and Content Groups.

 As a content marketer, you may be interested in granular insights into your resources. “What geographies are consuming most of my work?”, “Is my blog being read more frequently on a phone or on a desktop? Should I optimize accordingly?”, “What campaigns, channels, and sources is web traffic originating from? “What about my SEO efforts and organic traffic?”.  

Factors.ai answers these questions and more with an extensive range of filters and breakdowns. Additionally, there’s ample flexibility to create your own custom events and KPIs if you’d like to track tailor-made metrics.

Finally, with Content Groups, you can group a collection of logically related URLs that make up your overall website content. For example, core web pages like “features” “pricing” and “product” may be defined under one group to measure holistic performance. Similarly, blog articles written with specific intent can be analyzed all in one go. No doubt, a handy feature for content marketing analytics.

FAQs

How do you use analytics in content marketing?

Content analytics include reporting on website, marketing, and sales metrics. Understanding how customers interact with your content allows you to personalize your marketing campaigns in order to drive more conversions. Measuring these metrics also helps you address specific pain points and therefore improve the overall consumer experience for your solution.

Which metrics should I measure for content performance?

We asked experts about the metrics they keep track of to ensure high content performance. Their responses included clickthrough rate, impressions, marketing qualified leads, scroll depth, time on page, unique visitor identification, downloads, funnel movement, influenced demos, and bounce rate.

And there you have. Our customers regularly use Factors.ai to make sense of their content performance and guide their B2B content marketing strategy with the help of granular web page analytics, end-to-end customer journey insights, and flexible content reporting. If you’d like to learn more about our work at Factors, schedule a personalized demo today.

B2B Marketing Attribution (or B2B Revenue Attribution) empowers demand gen teams to map out their customer journeys and connect the dots between marketing and revenue. At a high level, attribution weaves the story that your marketing data is trying to tell about the influence of each touchpoint on core business objectives. As multitouch attribution technology improves, B2B attribution is becoming an increasingly powerful tool for CMOs to wield. Here are 5 way in which CMOs and marketing leaders can take advantage of B2B marketing attribution.

1. A Bird’s Eye View Of Marketing Efforts 

B2B marketing attribution empowers marketers to capture nearly every touchpoint across the customer journey. This is valuable information as most B2B buyers are already halfway through the sales cycle before they explicitly engage with a sales rep. 

Your customers have likely interacted with plenty of marketing channels and content before being picked up by the sales team. Moreover, many of these customers become high intent buyers even before sales or marketing identifies them as such. In such a case, it becomes important to know:

• Which touchpoints help them make their decisions 
• What content or marketing activity influences them to further pursue a product or engage with your company 
• What content helps users narrow down your product over your competitors
• At which touchpoint do customer generate buyer intent,
• At what touchpoint do customers lose this intent

This helps CMOs understand user journeys as well as the efficiency of various marketing efforts in influencing customer decisions. It gives insight into the precise point in the funnel during which to target customers and optimize conversion rates, which campaigns to allocate budget to, which touchpoints are weak links in the buyer journey, and more.

2. Achievable Targets

Marketing attribution, being the data-driven technique that it is, helps CMOs undertake goals in terms of achievability and feasibility. More importantly, attribution uses metrics that can be used to track the progress as well as the success of various campaigns across various channels. This also helps in planning larger goals as well as yearly sub-goals with forecasting, tracking and analysis of campaigns and their impact on revenue. Such goal-setting is not vague as it is thoroughly backed by data. 

3. Improve Productivity and Alignment Across Demand Gen

As your business grows and your marketing campaigns and sales processes start to scale, it can be challenging to track which campaign brought in which leads. Sales and marketing activities tend to become more siloed and communication gaps between the two teams can widen. This can lead to a lot of inefficiencies in the handing over of leads from marketing to sales. Marketing may have insights on which touchpoints impacted most positively to a certain lead that can help sales reps during their engagement. Conversely, sales reps may have insights through their engagements on what information or campaign content helped customers make their buying decision. CMOs can use marketing attribution to align the processes of these teams and improve the productivity of each campaign and each SDR by unifying customer journey reports and touchpoints onto one platform. 

4. Accountability and Reporting

With attribution, marketing leaders can easily generate reports of the most important metrics for their business and board. Moreover, it’s convenient for CMOs to track the performances of their various teams and understand the contributions of each team on conversions, pipeline, and revenue. For example, if a certain blog posts incurs recurring URLs for all leads that have converted, then it is a good idea to give more resources to the content team and perhaps even hire more writers. Attribution gives you hard data on metrics like website traffic and what pages they visited and how much time they spent, whether they filled a form or if they left without any activity, whether they clicked on a discount code or a free whitepaper or if they were not able to notice it — this can give a CMO a good idea on the interface and content of the website. In essence, attribution helps you hold each team accountable by getting a data-backed view of their performances.

5. Driving Growth

Marketing attribution recognizes trends and makes sense of the confusing quagmire of touchpoints in any marketing and sales funnel. Data is unequivocally important in driving sustained, scalable growth. If there is seasonality to when you get more qualified leads or there are specific blog posts, ad campaigns or social media platforms bringing in higher traffic and driving growth, attribution makes it easy to identify these high performing channels and take advantage of them. Most attribution tools have built-in integrations for various ad platforms, social media sites, CRMs and website tracking tools that ensure that regardless of how big you grow, you always have a handle over your customer tracking and don’t lose out on important insights that may get lost in high volumes of data.

In conclusion,

B2B Marketing attribution is a powerful tool for any CMO in 2022 to get the best insights from both internal and external data sources that an organization has. Forecasting, tracking trends, revenue impact, ensuring accountability, saving time and human resources on reporting to focus more resources on analysis and implementation, ensuring accuracy in reporting — are all foundational to building and executing powerful marketing campaigns. With marketing attribution, CMOs can make data-driven, informed decisions and enable their teams to deliver more with less spending and better, useful insights.  

GA4: End Of The Road For Google Analytics?

Over the last decade, Google Analytics has dominated the marketing analytics space. This uncontested reign can be attributed to a couple of key reasons: One, GA is free. And two, GA maintains a monopoly over historical data for an enormous install base. While the former isn’t likely to change, the introduction of GA4 is set to crush 10+ years of backward compatible data for millions of websites — including yours.

By breaking history and forcing a migration from Universal Analytics to GA4, Google Analytics loses an important advantage. This, coupled with a host of issues around UI, functionality, and privacy has resulted in a dramatic turn in tides. If users are going to lose out on their historical data by next year anyway, there’s little incentive for them to stay — especially when the best Google Analytics alternatives are readily available.  

“We will begin sunsetting Universal Analytics — the previous generation of GA —  next year. All standard UA properties will stop processing hits new hits on July 1st, 2023” - GA Support

“GA4 feels like a huge step backwards. Tons of functionality in the previous version is missing or replaced with smart “insights” — which always goes wrong. A good time to move on to something better” - Hacker News

With this in mind, The following article compares Google Analytics (specifically, GA4) with Factors to demonstrate why, now more than ever, the latter is better suited for B2B marketing analytics, web analytics & CRO, multi-touch attribution, and more. 

____________________________________________________________________________________________

Data Integration

To be of any value, B2B marketing analytics requires substantial data from a wide range of sources. This includes granular metrics across all your ad campaigns (Google, Bing, Facebook, Linkedin, Capterra, etc.), website, and CRM (HubSpot/Salesforce) events. Ideally, this body of information is unified under one platform to ensure normalized data and alignment across demand gen teams. So how does Factors compare with Google Analytics when it comes to data integration? Here’s the rundown:

Ad Campaign Metrics

Google Analytics can report basic traffic data from most commonly used social platforms — Google Ads, Facebook/Meta, Linkedin, etc. Integrating non-Google sources, however, is significantly more challenging on GA. It requires the construction of custom campaigns using URL builders (read: a time-consuming, laborious chore). Factors, on the other hand, deliver immediate no-code integrations. 

The real issue with GA, however, is that it cannot pull metrics that matter from any source that isn’t Google Ads. Commonly tracked figures like impressions, engagement, click-through rates, etc cannot be reported, let alone linked with website or CRM data. This is a headache for users looking to consolidate all their marketing efforts under one roof. Factors solves for this by automatically pulling granular metrics across the board.

CRM Integration

This is another big point for Factors. Unlike GA, Factors provides robust integrations with HubSpot, Salesforce, and soon, Leadsquared. The implications of this are significant. Marketers will have the ability to connect the entire customer journey from first touch to deal won. In turn, marketers can determine exactly how their efforts across ad campaigns, web content, and offline events are contributing to larger business objectives like pipeline and revenue. This is simply not possible on Google Analytics as it does not integrate with any CRM whatsoever.

Third-party Integrations

Another reason Factors has the edge over Google Analytics is third-party integrations. Factors integrates with the likes of Clearbit Reveal (Deanonymization), Segment (CDP), Drift (Chatbot), and more to ensure an actionable web + marketing analytics experience. Again, this is impossible on Google analytics because GA4 does not integrate with third-party platforms. Consequently, this affects the quality of customer journey insights. Even with sophisticated manual analysis, the data derived from Google Analytics is likely to remain superficial at best. In later sections, we’ll explore the implications of this issue in detail. 

___________________________________________________________________________________________

Implementation & Onboarding

So far, we’ve established that data integration (including third-party) makes more sense with Factors. Next, let’s examine how the two marketing analytics tools compare with regards to implementation and onboarding. For context, if you’re currently using Universal Analytics, you either have to upgrade to GA4 by next summer (June 2023) or find a GA alternative like Factors. 

What does it take to upgrade to GA4?

12-steps. That’s right. It requires 12 steps before you can upgrade from Universal Analytics to GA4. What’s more? As previously mentioned, integrating with non-Google ad platforms needs elaborate orchestration with custom campaign URL/UTM builders. Additionally, GA4 requires users to manually tag each event they want to track. In short, this means significant labor effort, engineer dependency, and time-spent waiting for incomplete datasets.

What does it take to set-up Factors.ai?

Maybe about 30-min. In fact, it’s probably closer to 10 with Google Tag Manager. Simply place the superlight SDK onto your website and integrate (no-code!) with your ads platforms, CRM, CDP, Chatbot, and more. Before you know it, all your marketing + web data will begin pouring into a single, normalized platform. Easy as pie.

____________________________________________________________________________________________

B2B Marketing Analytics

Now, let’s discuss functionality. Here’s how Factors compares to Google Analytics when it comes to core use-cases of a B2B marketing analytics tool.

Right off the top, it should be noted that GA isn’t built for B2B marketing analytics. It struggles to support tracking for journeys that involve several months, touch-points, and stakeholders. Unfortunately, these are the precise characteristics of a standard B2B sales cycle. Customers often experience countless (well, on average, 7) touchpoints — both online and offline — across ads, emails, webinars, blogs, web sessions, etc before converting. These touchpoints can occur within a window of one week, one month, or sometimes, even longer than one full year. Unlike Factors, GA4 isn't designed to analyze or attribute lengthy, sporadic interactions for B2B marketers. 

Additionally, and as previously mentioned, GA is unable to track and measure granular campaign or event metrics from non-Google platforms. Since B2B marketers target (and retarget) marketing efforts to their audience across several channels, it’s nearly impossible to consolidate these figures on GA alone. 

Factors eliminates this data silos issue for marketers and demand gen folk with the help of aforementioned third-party integrations. Once data has been collected, Factors delivers an end-to-end marketing analytics suite that’s tailor-made for B2B teams.
This includes web analytics, funnels, custom events and KPIs, multi-touch attribution and more. Let’s explore why Factors has the edge over GA across these use-cases.

Data Accuracy, Marketing Funnels,
Revenue Attribution, & More

At its core, Google Analytics is a website analytics service. And to be fair, GA does a half decent job at it, especially for a free tool. That being said, there are significant limitations with GA that Factors solves for:

Data Accuracy

‍Certain web metrics are not precise on GA. Let’s say a lead lands on a blog on your website. Before they can start to read the article, their doorbell rings and they leave to answer with the blog page open on their laptop. The lead returns in about 8 minutes, clicks out of the blog to schedule a demo. Google Analytics would inaccurately assume that the blog played a massive role in this conversion because of the significant (8 min) time spent on page. In reality, however, the lead did not scroll even a little to read the rest of the piece. Factors solves for such issues by tracking granular details like cursor activity and scroll depth percent to ensure your data, and the insights derived from that data, is as accurate as can be.

Marketing Funnels

‍On GA4, you’re limited to page-to-page funnels. That is, GA4 only considers funnels wherein each webpage is a separate step. As such, GA4 struggles to track multi-session engagement. Let alone a funnel across ads, web, and CRM. Funnels on GA may only be used to reliably measure drop-offs and conversions that occur in the length of a single web session (Eg: Blog -> Pricing -> Schedule Demo in one web session). Additionally, event funnels are not supported on GA4. Hence, if you’d like to track how specific website content is contributing to MQLs, leads, & revenue, GA4 won't suffice. 

Customer Journey + Revenue Attribution

As previously mentioned, GA does not integrate with CRMs like HubSpot, SalesForce, or Leadsquared. This severely impedes cogent customer journey analysis and revenue attribution. Without laying the entire map from ads, offline efforts, web sessions, and CRM events, you are left with an incomplete picture of what’s driving revenue and pipeline. Consequently, this affects data-driven decision making, which ultimately results in suboptimal marketing strategy and ROI. It is not feasible to derive end-to-end marketing insights into what’s working and what’s not with Google Analytics. While GA might be able to track the source of traffic, it cannot determine the cause.

Usability - UI and UX

What’s surprising  is that even with these significant drawbacks, users complain about GA4’s useability. It can be overkill — unintuitive, excessive, and far too technical — for marketers looking for basic reports. And completely irrelevant to marketers looking to dig deeper into their data. Let’s let twitter expand upon this…

Website owners, is it just me or is the new GA4 just HORRIBLE? It's like it's designed only for retail sites or something, very hard to get the basic info that I used to rely on... Think I'll switch back! Awful!

Trevor Long (@trevorlong)

I usually can find my way round any piece of software quickly. But Google Analytics 4 is making me cry...I've never seen a tool upgrade that made simple things sooo complicated :face_palm: Non-tech business owners were already struggling to use it. But now they have NO chance.

Gill Andrews (@StoriesWithGill)

1. The UX of Google products in general suck
2. GA4 is a new level of suckiness
3. I get the feeling Google doesn't have a UX team and/or never tests the usability of their products
4. If you are not a monopoly you would never be able to get away with this

Tom Kasperski (@TomKaz)

Factors is simple by design. Users go from onboarding to creating powerful (and basic) marketing reports in a matter of minutes.

Now Is The Best Time to Switch Over From Google Analytics To Factors.ai

And there you have it. A non-exhaustive set of reasons as to why Factors.ai makes far more sense for B2B marketers over Google Analytics. With the imminent arrival of GA4 (and the consequent break in years of historical data), now is the best time to make the move to Factors.

‍Correlation vs. Causation

Correlation occurs when no cause and effect can be established between two variables that have a relationship. For example, the level of education of parents is positively correlated with the salary levels of their children. In other words, higher levels of education of parents has been observed in higher salary levels of their children. However, this does not mean that a direct causation can be established. If that were the case, to increase your salary level, you would simply have to get your parents in schools and universities. Another such example of correlations exists between heights and weights. Your height is not causing your weight but taller people tend to be heavier than shorter people. 

Causation means that there exists a cause and effect relationship between two variables. In the education example, a direct relationship may exist between education level of a child and the average salary he earns. Someone who just completed an undergrad and someone who just finished an MBA might get different salaries even at the same experience level regardless of their parent’s education levels. 

Correlation  ≠ Causation

It is important to be able to distinguish between causations and correlations. The best way to differentiate the two is to consider all other factors that are involved in the outcome. For example, there exists a strong correlation between the data for ice cream consumption and murders. This correlation is a complete coincidence. But if you were to apply causation, it becomes worse because then it implies that ice cream consumption leads to murder. 

Applying causation in less subtly absurd correlations can be even more harmful, especially if budgeting decisions are based on cause and effect relationships between touch-points. Ideally, most data analysts avoid establishing causations. First, because its hard and correlations are easier to establish. Second, direct causations are very rare. 

Correlations in B2B Marketing Analytics

Establishing correlations and causations is fundamental to any and all data analysis. Marketing analytics is no exception to this. Correlation insights help marketers make sense of their data points. In turn, this contributes to optimizing marketing efforts and determining the impact of marketing on KPIs and revenue.

In other words, correlation analytics identifies valuable patterns within the story, your marketing data is trying to tell you. Here’s how:

1. Understand the impact of your SEO/PPC 

2. Test campaign decisions during implementation

3. Determine the revenue impact of customer touchpoints 

There can be several pitfalls to correlations data, particularly in cases where coincidences can be mistaken for statistically significant relationships. Some can be very obvious, others are not so much. For example, there exists a strong correlation between the number of pool drownings and films that Nicholas cage has appeared in through the years. Another perfect correlation is between total revenue generated by arcades and CS doctorates awarded in the US. But as is plain, these events have nothing to do with each other. 

Let’s take a marketing example. Say a company decides to mail catalogs of their retail products to their target audience in Karnataka. Soon after, they Ef a stark rise in orders placed from Odisha. Intuitively, the right move would be to send more catalogs to Odisha to support the growing demand for your product. However, as a result of the strong relationship between the two touch-points, correlation analytics would suggest shipping catalogs to Karnataka instead.

Best Practices for Correlation and Causation in Marketing Analytics. 

Avoid confirmation bias

Confirmation bias in correlations data occurs when your data inaccurately confirms a bias. Say, a preferred channel is performing better than another and a correlation that confirms your belief, you are likely to assign causation that isn’t there. 

Anish is the marketing head of Company X. He recently had a celebrity promote X’s product. He worked hard on getting them on board and was sure that it will drive sales. Soon after, he noticed a spike in the number of website redirects from Facebook and immediately assigned the causation for this increased traffic to the celebrity’s campaign. Expecting similar results, he invests further resources and runs another ad with the celebrity. However, there is no change in performance. There is something amiss in the marketing head’s correlation analytics. Instead of checking for causation, he let your subjective assumptions take over. This is confirmation bias in play. 

To assign definitive causation, it is necessary to check for coincidences. In this example, tracking performance data for the campaign across channels is a good way to assign cause to the campaign. Simply put, if the celebrity is affecting more people to click on this ad, then there should be a percentage increase in clicks in all channels that carried the ad with the celebrity. So Anish should’ve tried to corroborate the results, keeping all other things (like the intent of the target audience) constant across all platforms (Google, Facebook, Instagram, etc). On running such an analysis, he notices that only Facebook had a spike in traffic after the first ad, which wasn’t replicated across other platforms or even on Facebook itself when the second ad was shared. On further research, he learns that the platform had made changes to its algorithm around the same time, which seems to have impacted all ads on Facebook, including X’s. 

Using quantitative data from all channels can help avoid making decisions or causations around subjective assumptions. 

You can use a marketing analytics tool like Factors can help you check how a touchpoint is helping or hurting pre-determined conversion goals. The funnel feature allows you to customise your queries to check for specific correlations. Funnels can be created for website redirects, and in this example, the celebrity ad could be compared across channels in a few clicks and Anish could check whether to attribute the change to the celebrity ad or if there’s something else at play.

A/B testing

One of the best ways to establish effective correlation is A/B testing. Let’s say you’re revamping your website homepage and want to test the impact on traffic and conversions. A/B testing involves testing a variable (for example, the position of a “schedule demo” button). This change is tested across two-time frames — pre-change and post-change. 

Let’s change the previous example and assume that the spike in Facebook redirects did not happen immediately after running the ads but happened a few weeks later. In the absence of a proper pre and post analysis, it is human nature for Anish to attribute it to the ad campaign. But if he did a pre-and post-analysis of the impact of ad campaign on redirects, he might find that the cause for the change is something else. 

You can use tools like Factors.AI to record changes like new ads when they occur and use data from the various channels like Facebook as well as your website or conversions to A/B test campaigns. The funnel feature allows you to use campaign naming conventions to get data pre-change and post-change. 

Analyse the impact of correlations across channels.

Looking out for correlations and establishing possible causations can help understand how a specific touchpoint is affecting pre-determined conversion goals. If you want to check impact on goals like say, web event sign-ups, white paper downloads or even deals won, you can use correlation and causation analytics to figure out what touchpoints are saying, helping you schedule demos, what touchpoints on your website is driving down form fills, etc. 

Factors allow you to compare metrics on a week on week basis to catch changes in any of the metrics. The explain feature allows you to check for what URLs or web pages your users have visited before submitting a form. Apart from identifying URLs that have influenced the users to convert, you can also see which webpages aren’t performing well. Weekly sessions data can help see short term changes, apart from A/B testing. Correlations can also be checked at a segment level, like demographics, industries, business model types, etc. 

Choose the right graphs for correlation analysis/reporting

Data collection is only the first step to understanding correlations. The second step is to read the data and share the insights. After getting the insights, you act upon the data as well as build data-driven strategies. To understand how a touchpoint is interacting with each other and the impact of a change on your conversion metrics and revenue, you can use graphs.

There are several kinds of graphs that can be used for correlation analysis.

Time-series graphs:

These reports compare metrics over time periods. They are most appropriate for trends or changes in metrics post a change in a touchpoint or campaign strategy etc.

Distribution Graphs:

These graphs can easily show when there is a correlation. They show changes in distribution against a mean.

Funnel comparison graphs:

These graphs can be used to see a side by side comparison of funnel queries. Say you want to see how ad 1 and ad 2 have impacted the conversions, you can see a side by side strategy comparison of the two. You can also compare the same funnel before and after a specific time period. 

There are also other graphs like relationship graphs that help see the relationship (positive, negative or nil) between two or more metrics. 

In closing...

In the age of data-driven marketing, it is important to know how to treat your data. Every customer journey and every touchpoint weaves a larger story where the channels are connected and touchpoints impact each other to influence each potential customer to convert. Correlations can help bring forth these insights that are invisible to the naked eye and can help you craft a winning marketing strategy for your organisation.

The following is the second half of a two-part series about the factors involved in building and buying a B2B marketing analytics and attribution solution. This post deals with the cost and time requirements for an in-house and an off-the-shelf solution. It also compares the opportunity costs of building and buying a solution.

Be sure to check out part one which talks about the need for a B2B marketing analytics and revenue attribution solution. Along with a breakdown of the technical requirements for each solution.

Costs Involved

This segment is an overview of the costs involved in building an in-house B2B marketing analytics and revenue attribution solution. While there are certainly other costs involves, we cover 5 of the most prominent ones:

·        Cost of ETL

·        Cost of Data Warehousing

·        Cost of Data Processing

·        Cost of Data Visualization

·        Cost of Staff

Cost of ETL 

Extract, Transform and Load (ETL). Extracting structured or unstructured data from a source — this could be data from your CRM or Google Ads. Transforming includes processes like cleaning, duplication, sorting, etc. and ensuring data integrity and compatibility. Loading involves placing all of the transformed data into a data repository or a data warehouse. The data could be either loaded completely or at predetermined intervals.

This is the cost of maintaining a data pipeline. While it is possible for your engineering team to set up a data pipeline, some companies find it cost effective to use an ETL tool. These tools include tools such as Hevo, Fivetran, Google Dataflow, Pentaho, etc.

Fivetran, foe example, lists a range of pricing tiers. The starter tier’s price (which is for a small team’s data stack or the bare minimum for an in-house solution), would depend on the number of rows you update. This will range anywhere from 2 million total rows at a monthly estimated fee of $120, to 500 million total rows at an estimated $4,628 per month.

Cost of Data Warehousing

In the previous blog we talked about the need for a data warehouse from an analytics perspective. We discussed why relying on application databases is not scalable. While you could invest in a local data warehouse, there are a multitude of benefits to investing in a cloud data warehouse. Ultimately, this will prove to be convenient when building an in-house solution. Scaling operations would be expensive as it requires more ram (and in all likelihood, a dedicated database manager). That being said, cloud-based data warehouses like Google Cloud Storage, AWS Redshift, Microsoft Azure, and Snowflake fit the bill.

Cloud data warehouse storage prices vary depending on a range of factors. Google Cloud Storage, for example, has options varying in region and class — the class of storage, standard, nearline, coldline, and archive is determined by the frequency of access to the storage. In the region of US-central Iowa, at the standard class, warehousing will run you about $0.020 per GB per month.

Cost of Data Processing

Most cloud-based data warehouse services also include processing data. This is the cost to process SQL queries, scripts, functions, and more. This is in addition to  the cost of loading data that you are processing in storage. Processing data is usually handled by a database management system like Bigquery, AWS Redshift, Oracle, Singlestore, etc. These services offer Cloud database as a service.

The cost involved in the pricing of these services includes the use of vCPU, Memory and cloud storage. Singlestore, for example, on its standard plan has a starting price of $0.65 per hour and will increase depending on the number of vCPUs and memory used. A vCPU of 16 and 128GB of memory will cost you $3,796 per month.

Cost of Data Visualization

In the previous blog, we talked about the presentation of your reports to your end-user. This requires a data visualization tool. A skilled engineer could purchase data visualization libraries and build them out. But for the sake of time, a lot of businesses resort to data visualization tools like Tableau, Looker, and PowerBI.

A  data visualization tool like Tableau will cost you $70 per month per license.

Cost of Staff

Staff  will, by far, be your most expensive costs. To build a marketing analytics and attribution in-house solution, you would at the very least require a small team of 3 full time data engineers and 1 data scientist. You will require experts with  experience across programming language and ETL. In the US, the break down is as follows: on average a data engineer’s CTC is $116,772 per annum, along with a $5,000 cash bonus and other non-cash benefits as of 2022. The average CTC of a data scientist in the US will cost $102,865 as of 2022. (Indeed.com).  These costs will have to be multiplied by the number of data engineers and scientists hired.

In terms of cost an off-the-shelf solution like Factor.ai will as of this date cost you $1,188 per annum on the starter plan which includes web analytics, multi-touch attribution, funnel mapping, Metric reports and more. Their growth plan on the other hand will cost you $5,988 an AI powered “Explain feature”, automated weekly insights and a dedicated customer success manager

Time

To build a fully operational in-house B2B marketing analytics and revenue attribution solution, with a team of 3 full time data engineers and 1 data scientist will take anywhere between 9 to 12 months.

An off-the-shelf solution like Factors.ai can be set-up in minutes. It requires no professional services for onboarding either.

So...Build or Buy?

Now, we're all caught up about the resources required to build an in-house B2B marketing analytics and attribution solution, as well as what to expect from an off-the-shelf solution. So should you build or buy? This section runs through the opportunity cost of building and buying. Essentially, what are you missing out by choosing whether to build or buy.

Opportunity Cost of Building:

By choosing to build an in-house solution you forgo the benefit of:

·        The cost savings earned from buying a solution

·        The time saved from not having to set up an in-house solution

·        No-code integrations and developer dependency

·        Maintenance and innovations handled by the service

·        Using an advanced SDK, and not having to optimize SDK

·        Data cleansing handled by the service

·        Data visualization within the same product

·        Unified Dashboard

Opportunity Cost of Buying:

By purchasing an off-the-shelf solution you incur the following opportunity costs:

·        Product may not fulfill very unique analytics aspects of your business

·        Product may not deliver on their promises

·        Certain products may not fulfill your data privacy requirements (learn more)

·        If a vendor liquidates or gets acquired, you cannot ensure data ownership and continuity of business

In Conclusion…

The most important point to take away from this is that when you build an in-house solution, you would have to weigh the risk of doing so. The average tenure of a CMO is about 40 months. Would they prefer to spend the first 9 to 12 months of their tenure waiting on a solution that isn’t proven to meet their need, or have a solution that is up and running within a week for a fraction of the cost of building one?

In my opinion there is too little to gain and a lot to lose when buying. Most of the opportunity costs of buying could be avoided with modern solutions like Factors.ai. Where custom plans can be built to fulfill your business’s unique needs. A demo of the product can be requested to ensure if the product delivers on its promise. Factors.ai uses first party cookies and is GDPR, CCPA, PECR and SOC2 compliant. And Factors.ai can send their client’s data to their Bigquery instance on demand giving full data ownership to the client.

Still on the fence? Book a demo with Factors.ai now.

The following is a two-part blog on aiding your decision between building and buying a B2B marketing analytics and attribution solution. Part I deals with understanding the need for B2B marketing analytics and attribution. We also breakdown the technical requirements for an in-house solution, and what it would take for an off-the-shelf solution to deliver a similar experience.

Part II takes a logistical standpoint. There, we explain the practical resources required for a robust B2B marketing analytics and attribution solution. We also break down the opportunity cost of building and buying such a solution. Hopefully, this leaves you with valuable insights in cementing your build vs buy decision. 

1. DEFINING THE NEED

Before we get into the nitty-gritty, we need to understand the need for marketing analytics and attribution under a business’s marketing function. This in turn necessitates the requirement of an off-the-shelf or in-house solution. This can be boiled down to two needs — tracking and optimization.

Tracking: In recent years, marketing has been losing its gravity as a core function of a business’s operations. As a result, justifying its revenue contribution is becoming increasingly vital. With lengthy B2B sales cycles that stretch across several months, waiting on revenue is simply not an option. This is where the use of marketing analytics with indicative metrics and KPIs (such as CPA, CTR, CPL, and web traffic analytics, etc) help in tracking and justifying the ROI of the marketing function before having to wait on your closed-won revenue contribution.

Optimisation: The optimisation need can be summarized as the need to quantify performance at a channel/campaign level and determine what to invest more or less in. Multi-touch attribution is used to facilitate this need. The requirement to optimize B2B marketing efforts will vary depending on the length of your sales cycle — but is nonetheless indispensable. Attribution is a tentative requirement, while Tracking is usually an everyday thing.

2. UNDERSTANDING YOUR REQUIREMENTS

Now that we have defined the need for a marketing analytics and attribution solution, choosing to build or buy comes next. The problem here is acknowledging that when you’re considering a solution, different marketers have different needs and resources. While some marketers only need the bare minimum, others may opt for more sophisticated solutions. 

The presupposition here is that the following part of the blog on your technical requirements will always highlight more than just the bare minimum, while also zeroing on what an off-the-shelf alternative has to offer.

TECHNICAL REQUIREMENTS

Tracking and Collection

Your primary requirement for an in-house marketing analytics and attribution solution is to track user data and user interactions. There is a wide array of data that is to be tracked — page views, URL changes, web events, web sessions, CTAs, button interactions, form downloads, demo scheduled, and more. While you could limit what you track, it’s always advisable to track as much as you possibly can, as long as it is relevant to your analytics.

Ideally, your in-house solution should be able to track all of the above. There are two ways of building this. You could either opt for a CDP like Segment to automatically collect this data or construct a solution with developer dependency.

The developer will need to be able to create an event into your analytics stack every time an action occurs. The biggest concern with this approach is that it is risky. Especially when there are changes on the website, as it is too meticulous to be able to scale. That being said, you want to have a solution where your marketing team is not too dependent on your engineering team. Else this will only cause more harm than good.

SDK Requirements

The general concern with using an SDK is understanding that it will have to work with different website frameworks — like single-page applications (SPC) and normal web applications. Additionally, as websites are developed using different technologies like React JS, WordPress, CMS HubSpot, you’ll have to ensure that your SDK is sufficiently compatible. 

Your SDK will need to be fine-tuned to be able to send data to your server. Webpages have to load A LOT of content including images, animations, text, etc. Your SDK will need to be able to capture events and put them in a queue to be sent at a later time. It will also have to be optimized for different internet speeds — mobile internet vs broadband. These factors should not be taken for granted. Failing to optimize your SDK could either crash the client website or result in the collection of incomplete data. Your goal here is to engineer a light yet effective SDK that captures and sends data without a compromise on user load time.

Tools like Segment and Google Analytics will help you track the events. However, these solutions are not fully automated and will require developer dependency. Factors.ai is tailor-made for marketing analytics. We also facilitate an advanced SDK that empowers robust web tracking, zero developer dependency, and more.

Data Handling and Cleansing

For a marketing analytics use-case, your in-house solution will need to be able to handle different types of data. More specifically 3 types of data:

     1. Campaign reports from ad platforms

     2. Event tracking data from your website

     3. Objects from your CRM

Your goal is to build a solution that can handle all 3 of these data types. Doing this will prove to be challenging. At a base level, you will need to understand the objects of your CRM and how they are connected. Salesforce, for example, records accounts, contacts, opportunities, leads, products, campaigns, users, and dashboards as their standard objects. You can also create custom objects. You will then need to model your web analytics events against the users on your CRM customer data. Not to mention a separate data stack for ads data from Facebook, LinkedIn, Google Ads etc.

Data cleansing could be best explained with an example: In most off-the-shelf web analytics solutions, a web session will have some parameters set to distinguish itself from other sessions. For example, when a user is inactive for more than 30 minutes on your website, it will be tracked as a new session. Another one is when a user visits your website through an ad and a session begins, but then clicks on another ad to the same site shortly after. In this case, they will be considered as separate sessions as they come from different ad sources. 

In other words, designing web sessions based on a period of inactivity or distinct UTM parameters are examples of data cleansing. Failing to do this, and other such data cleansing practices, will result in a lot of nuanced difficulties.

While most off-the-shelf solutions will handle such cleansing and data categorization, note that most of these analytical solutions usually handle only one of the three  data types. Factors.ai, on the other hand, consolidates all three.

Data Storage and Warehousing

As a business running data analytics, it is important to acknowledge that data warehousing is a core need. Unfortunately, companies that adopt a data warehousing solution are still in the  minority. One could argue that they could rely on application databases. This, however, will result in processing constraints and logistical difficulties. Alternatively, most data warehousing solutions process analytical queries in a more effective, columnar fashion. They also serve as a centralized data hub for all your workflow data. Modern data warehouses also make it cost-effective to scale your data warehousing. Therefore, it would be preferable if your analytics solution had an export to a data warehouse like Google BigQuery.

Attribution and Presentation

The previous tracking and handling data requirements form the basis for a robust marketing and web analytics. But what about marketing attribution?  

For an attribution solution, you will first have to refer to the contacts on your opportunity account in your CRM. Then identify all data touch-points you encountered with those contacts — these could be webinars attended, demos scheduled, white paper downloads, field events, etc. These are all website sessions that are driven from different ad campaigns, email campaigns, etc. After accumulating all this data over a certain time frame, there will be an X amount of opportunity value that can be attributed to these touch-points. You will have to credit them and to do that you can utilize several existing multi-touch attribution models — refer to this blog to learn more. You could even implement a custom model — for example a model that attributes more credits to contacts with job titles past a certain level, and fewer credits for one below that level.

Presenting your marketing analytics and attribution reports requires breaking down, summarizing, and visualizing an extensive amount of data. For this, you would have to set up a dashboard and operate a data visualization tool — like Tableau and Looker. This requires a fair amount of expertise to assemble. What makes this process challenging is building out all the SQL queries for these reports.

Ensuring the correct data and the right quantity of data is being delivered to your dashboard is key. An overcomplicated dashboard or several superfluous dashboards won’t run efficiently. Ideally, you don’t want your team of data engineers to be preoccupied with operating existing data pipelines.

Factors.ai comes with a powerful attribution engine. With it, you can use, compare, and customize several single-touch and multi-touch attribution models. Factors attributes touch points across ad platforms, website events, and CRM. Factors also supports real-time reports and insights, a unified and customizable dashboard, and a wide range of data visualization under one roof.

Maintenance

When you build an in-house marketing analytics and attribution solution, there is a need for continual maintenance to ensure operational efficiency. Especially the maintenance of integrations across your SDK, CRM, Ads platforms, API version updates, etc. 

Optimizing your SDK from an engineering perspective as mentioned before is a trial-and-error process. You will have to adjust your data pipeline to effectively deliver data to your dashboards. You’ll even have to keep your techstack up to date. Not to mention that there will always be tech debt and bugs to troubleshoot over time. This is never a one-and-done situation. As time passes you would have to modify your queries while you bring in more data all while optimizing your process.

Integrations, data concerns, troubleshooting and all of the aforementioned maintenance can be administered without developer dependency with Factors.ai.

3. INTERPRETATION

The purpose of part I is to illustrate the challenges in setting up the technical requirements for building a solution — optimizing your SDK, building a data layer and a solution that is compatible with all types of data, building a solution that can export to a data warehouse, assembling a dashboard, maintenance, etc. This way you can have a fair idea of not only what to decide on, build or buy. But also know what to look for when opting for an off-the-shelf solution.

If you were looking for a comprehensive conclusion, then look no further than part two. The next part highlights the opportunity costs for an in-house and off-the-shelf solution and takes into account the cost, time, planning, and even the technical requirements of this piece. 

Most B2B marketers will accept that the success of any marketing plan depends crucially on marketing budget allocation. It is the key to effective strategy implementation. The best-laid plans fall short if you do not have the right resources in the right places. Strategic budget allocation is necessary to make the move from meetings to real execution, iteration, and conversions. The following post discusses best practices when constructing a B2B marketing budget.

Why is marketing budget allocation core to marketing’s success?

Considering that all budgets come with the caveat of spending limits, getting your budget allocation right is key to having adequate reserves to efficiently implement plans. Marketers will often spend a lot of time validating their budgetary requirements because no organisation wants to misspend its revenue or capital. Resultantly, marketing budgets usually require inputs from multiple stakeholders across the organisation. 

What should your marketing budget include?

Marketing budgets include everything that you and your team need to positively reach your target audience. This includes expenses related to campaigns, channels, platforms, wages, marketing technologies (CDPs, social media, data analytics, design, automation), advertising, PR, freelancers and consultants, conferences, trade shows, etc. Each of these elements needs to be accounted for in your budget with wriggle room for other revenue generation tactics. 

How much should you spend on marketing?

Although the revenue spent on marketing differs a lot from industry to industry (and company to company), on average about 7-15% of a company’s revenue goes towards marketing. So all of your company’s unique requirements in terms of your revenue model, stage, funding, amongst other things factor into how much to spend on marketing. The ROI from your marketing activities also plays a role in budget allocation. As per a CMO survey conducted in 2019, on average, B2B firms allocate about 10-11% of the firm’s total budget toward marketing. 

Another common question amongst marketers is: how to allocate across channels? 

 A common rule of thumb is the 70/20/10 rule-

• 70% of the marketing budget for channels goes towards proven strategies
• 20% of the budget for channels goes towards new strategies for growth
• 10% of the budget for channels goes towards experimentation with new or alternative channels as well as emerging channels. 

How to create a marketing budget?

1. Establish your overall marketing goals

The first step to creating a budget is to determine your overall marketing goals. This involves setting your larger strategy and breaking it down to substeps. Make the steps you need to reach these goals as detailed as possible and determine the overall length or schedule of the plan. They say that the overall strategy and all its steps need to be specific, measurable, attainable, relevant, and time-bound (SMART). Elaborating on the acronym SMART and determining goals for each term is a preferred place to start.

2. Outline your plan for the year

The second step to creating your budget involves outlining the plan for the year for which you are budgeting. This involves determining the channels and strategies to be used over the year and includes SEO, PPC, web redesigns, social media, new employments — connect them with your overall marketing goals. Essentially, if the previous step is determining the long term goals, this step is all about determining your yearly goals.

3. Determine your budget

In the third step, you determine the spending to be allocated for each element of your strategy (marketing channels, SEO, PPC, etc). The process involves looking at past data of expenses to get a comprehensive roadmap of how much to allocate and then calculating the future expenses in light of your current goals. Calculate the expected costs for each initiative, account for potential expenses that could occur. Finally, divide the total budget into quarterly and monthly budgets. 

4. Allocation

Allocation of the marketing budget across various channels, platforms, human resources, tools, and other marketing spending is where best practices come into play. Being efficient when determining how much to spend and what to spend is essential to reaching your marketing goals and getting in that ROI. We’ll be exploring the best strategies and practices for allocation in the next section.

5. Track your progress + Refine your strategy

This step becomes important during the actual implementation of the year’s marketing plans. Tracking your marketing activities in tandem with your budget is crucial in ensuring that you’re hitting your goals. If you find that your predictions don’t align with your actual outcomes, you can fine-tune or rework your plans to course-correct them. A marketing budget tracker essentially helps you see how your marketing plan is progressing. Moreover, comparing your progress against the predetermined goals helps ascertain the efficiency of the plan. To track progress on channels, channel-specific data like number of users, clicks on ads, website traffic, number of forms filled, registrations for webinars, downloads for whitepapers and more, can be used to check if your spends are giving you the desired returns. 

6. Measure the ROI

Ultimately, your budget was created to improve revenue. So, apart from tracking your marketing budget and channel-specific metrics, one must also track and measure the ROI — this helps to see how successfully the marketing plan is progressing. If the money spent on items in the marketing plan is bringing in more returns, you can increase the budget allocation for that item next year. Vice-versa for items that are bringing in low returns. 

Best practices for marketing budget allocation

Allocate more budget where you have a larger audience 

A key step to creating a good budget is knowing your buyer’s journey — that is the steps that your potential customer takes on their journey from being a prospect to a paying customer. Understanding your buyer’s journey will give you key insights into which platforms and channels work best to reach your ICP (ideal customer profile), what forms of marketing ads and social media platforms your target audience prefers, and how they interact with your marketing. A few important questions to ask is how do your customers come across your product or service? What information do they need before they make their purchasing decisions? What is the cost of generating new leads and conversions? What is the revenue from each lead? — answering these questions can help you know where to allocate your budget and to better reach your customers. 

The best way to ensure your buyer’s journey and what channels and touchpoints are more efficient is by investing in a good attribution system — may it be an in-house system or an attribution tool that saves both the time and effort that goes into mapping a customer journey so that the marketing team can focus on the strategy and execution of marketing’s goals. 

Diversify your strategy with multi-channel campaigns + Experimentation

In the previous point, we mentioned the importance of allocating more funds to channels and platforms where your audience already exists or has a proven success rate. However, the world of digital marketing is ever dynamic with new channels and audience migrations being a regular phenomenon. In that case, diversifying your strategy with omnichannel campaigns becomes extremely important. The previously discussed 70/20/10 rule for channels is a good rule of thumb to ensure that all your eggs are not in one basket and your campaign strategies remain forward-looking.

Look out for hidden marketing costs

If you’re not careful with budget tracking and keeping an eye on where your money is going it is easy to miss out on marketing costs that may not be very evident to the campaign. Spending on product launches, promotional activities, market research, etc are critical in shaping campaigns and it is a good idea to account for additional marketing tactics. 

Leverage your data: use data-driven marketing to guide your decisions

We spoke about using previous years’ data while determining your budget. However, apart from past data, the current data from tracking your metrics can be useful in determining what’s working and what isn’t. If something is not working, it is okay to cut losses and redirect those funds to strategies that are performing well. A data-driven marketing approach can help with efficient budget breakdowns as well as with course corrections where necessary. Use all the metrics available to determine the best channels as well as the potential of emerging channels.

Prioritise BO-FU marketing: this can minimise risk and improve your chances of better returns (ROI)

Prioritising BoFu (Bottom of the Funnel) marketing can minimise the risk and improve your chances of better returns or ROI as this involves targeting the bottom of the conversion funnel. The audience here is in that part in their buyer journey where they are closer to becoming paying customers and have higher intents for purchase. Ensuring that you allocate enough resources to BoFu marketing helps increase potential ROI and also minimises the risk associated with spending too much on the top of the funnel which is usually characterised by more misses than hits. 

In Closing...

Budget allocation is a process that requires data and insights to figure out what channels should be allotted funds and how much. Relying on historical data and having a data-backed strategy is integral to getting desired returns from the budget allocated for marketing. Good attribution tools can simplify reporting for budgetary asks as well as clarify which channels and touchpoints are performing well and deserve more funds. 

We hope this article helps you with your marketing budget allocation and helps you implement some time-worn budgeting best practices that can translate to better returns.

We all have heard the saying: “Change is the only constant,” which translates to the fact that everything changes, every time. While that is too philosophical, and since marketers – more so data-driven B2B marketers – are not really fans of philosophy, there is a need to “measure” change. And not just that, a business needs to “attribute”, “rank”, “predict”, “explain”, and hopefully even “bring about" change using data analytics. Since statistics is the mother of all real-world measurements, and probability is the grandmother thereof, the science of measuring change too lies somewhere close by.

Since surface (i.e., not deeply thought-out) definitions are easy to make but difficult to follow with real-world data, the real challenges are solved by taking support from the well-researched areas of data science and probability. One extreme is to discuss the topic of change with its extreme roots, but that could easily fill-up a book (which is under way), but we stick to measuring change w.r.t. marketing analytics.

The questions

The main questions from a marketing perspective are: “What main factors changed from last week to this?”, and “By how much did these main factors change?”. But before that, let us understand something even more basic.

Why to measure?

Marketers take various targeting decisions on a daily basis – which audience to target, how frequently, and with what kind of marketing campaigns. They also have to keep track of various prospective customers, their journey, their overall statistics, and most importantly, reasons why a particular technique worked (or didn’t work) – be it a campaign or a strategy. And there are various goals when it comes to marketing. While some techniques could be motivated towards increasing reach (where it’s important to maximize the number of eyeballs a webpage could get), others increase conversions (at various points in a funnel – form-fills, email cold/warm calls, prospects, pre-sales, sales, etc.).

The foregoing marketing goals are achieved by measuring both static marketing performances (i.e., what happened today / this week), and also more dynamic, time-aware ones. While analyzing performance over time is a comprehensive view of marketing goals’ achievements / shortcomings and hence makes it cumbersome to digest multiple metrics of interest in a single frame, summaries of the same are preferred. And one of the summaries of a dynamic measurement of marketing performance comes in the form of change – that is, what changed, by how much, and why. In this article, we would focus on measuring overall changes, and would dig deeper into measuring the causes for the same in a follow-up blog.

What to measure?

Based on the business requirements, marketers focus on tracking and eventually improving relevant KPIs (key performance indicators). For the scope of this article, we would explain change analysis taking the example of one important metric marketers are concerned about – the number of leads they generate every week. They achieve this by driving relevant visitors to their website every week. Any change in the number of leads that they get as compared to what they expected calls for an investigation on the reasons for the difference between expected and actual metrics.

Since the first step into any such investigation is to measure and compare (with last week) some global performance indicators measuring the reasons for change, the same is the focus of the current article. Hence, keeping a webpage in mind, we take the example of measuring the number of visitors (those who reached the website), the number of leads (visitors who reached target), and the conversion rate therefrom (leads per visitor).

When we perform these measurements for two given periods of time (say consecutive weeks), we could compare them. Let V1, L1, and C1 represent the number of visitors, number of leads, and conversion rate of week 1 respectively, and let V2, L2, and C2 represent the same measurements from week 2. 

How to measure?

The four simple change measurements one could perform are the following:

1. Total change: This is the most basic measurement of interest that preserves both the “unit” and the “sign” of change. For example, if the number of website visitors who filled out a form changed from 50 last week to 75 this week, we get an absolute change value as +25 form-fillers. In short, it answers the question: “How much more/less?”.
   More formally though, one could measure absolute change in visitors (𝚫V = V2-V1), leads (𝚫L = L2-L1), and conversion rates (𝚫C = C2-C1).‍
2. Relative change: While absolute change remembers the unit of the entity one is measuring, it is – more often than not – more convenient to adopt a normalized change score. Taking the same example as before, change in the number of form fillers from 50 to 75 means a change value of +25, but the same is true if form-fillers had increased from 150 to 175 (viz., +25). What separates the two cases is relative change (i.e., how much did the unit change per unit original), which is +0.5 (=25/50) for the former (50→75), and +0.17 (=25/150) for the latter (150→175). The specialty of this score is that it remains a fraction between -1 and +1, and helps in comparing two “changes”. In a day-to-day language, a “percentage” variant of this metric is used by marketers.
   Again, a formal representation of relative change in visitors (𝚫rV = 𝚫V / V1), leads (𝚫rL = 𝚫L / L1), and conversion rates (𝚫rC = 𝚫C / C1) would also help engrave the idea.‍
3. Percentage change: As described earlier, it’s easier to understand when one says “the number of visitors saw a 50% increase” (as opposed to saying the relative change of visitors was +0.5). Therefore, as a human-friendly change metric, the percentage variant is more popular than its math-friendly counterpart (relative change).
   A notable caveat is as follows. “From X to zero” would mean a “100% decrease” (and vice-versa), but “from zero to X” turns out to be an “infinite% increase”, which is absurd. One workaround to rectify this is to call “from zero to X” as “from min to X”, where min could be set based on the metric of interest (e.g., min could be just 1). Another workaround is to call “from zero to X” as a “100% increase”. Another interesting point is that “no change”, it’s called a “0% change” – even if it is “from zero to zero”.
   From the perspective of our current example, percentage change in visitors (𝚫pV = 100 x 𝚫rV), leads (𝚫pL = 100 x 𝚫rL), and conversion rates (𝚫pC = 100 x 𝚫rC) could be computed by simply multiplying the relative change by 100.‍
4. Factor change: Some marketers (and sometimes marketers) like to express change in percentage differences, and sometimes in factor increments/decrements (and some do both), and this is purely a personal/company-wide choice. Picking the same example from above, whether it is convenient to say “the visitors increased by 50%” or “this week’s visitors are 1.5x last week’s” differs from use-case to use-case, but is only a different way of expressing relative change.
   Although one has to be cautious, however. 

1. “1x” means no change (a “0% change”). For example 100→100, 1→1, 0→0, etc.
2. “0x” means a “100% decrease”, from, say 100 to 0, 5 to 0, but not from 0 to 0 (since we choose to call it a “1x” change.
3. “2x” means a “100% increase”.
4. “1.5x” means a “50% increase”.
5. In general, “kx” means a “100*(k-1)% increase/decrease”, where it’s an increase when k > 1 and a decrease when k < 1.
6. When saying “kx”, k never goes negative.

Going by the earlier example of website visitors, we could be interested in the factor change in number of visitors, (𝚫fV = V2 / V1), leads (𝚫fL = L2 / L1), and conversion rates (𝚫fC = C2 / C1).

Measuring overall change

Depending upon the business and the audience, there are multiple combinations of possibilities. We only cover some of them, summarizing overall (global) change. And as mentioned earlier, our next article on this topic would dive deeper into digging up the “reasons that drive this overall change.”

No change

Let us start with a simple world, and slowly drift towards complex scenarios. Suppose our website had 1,000 unique visitors last week, and 1,000 new unique visitors this week (i.e., there was no change in the number of visitors). Of the 1,000 users last week, 20 signed-up for our newsletter, and the same trend continued this week as well. In other words, both last week’s and this week’s conversion rate was 2% (20*100/1,000). What does this tell us about this week’s performance over last? That it remained the same! In other words, there was a 0% increase/decrease in both reach and lead conversion rate.

Proportionate increase in visitors & leads

Now, if we had more visitors (say 1,500) this week as compared to the last, with a proportionate increase in leads so as to maintain the 2% conversion rate, it would amount to a 50% increase in visitors and leads, but a 0% change in conversion rate.

Increased leads, retained visitor count

On the other hand, if the total number of visitors had remained the same, and leads would have increased by 50%, this would increase the conversion rate by 50%.

Increased visitors, no change in leads

If, however, the number of leads would have remained the same despite an increase in visitors (by, say, 50%), we would see a 33% fall in conversion rate.

Disproportionate increase in visitors & leads

It is also possible that the number of leads increased by a disproportionate amount, which led to an increase in conversion rate.

Measuring change factors

We just discussed how measuring overall change is straightforward: simply report the signed/relative/percentage difference or factor change in visits, leads, and conversion ratio between the two weeks. But this is only half battle won. What is ideal is to measure the “causes” for the change. For example, we know that a 2x increase in visitors (V2 = 2V1) and 1.5x increase in leads (L2 = 1.5L1) – and hence a 25% drop in conversion rate (C2 = L2/V2 = 0.75L1/V1 = 0.75C1) – happened. But why it happened is one of the most important questions change science has to answer.

What causes change?

A seasoned marketer can quickly understand the main factors that led to a drop or a rise in scale (#visitors) or conversion (leads/visitor). But where data analysis comes in is in short-listing such factors from the rest, and hence help the marketer with her weekly (or periodic) decisions. In this article we only give an intuitive idea of what causes change (and how we measure it). In part 2 of this series on Change Science, we discuss the exact procedures and methods to measure factors that cause change periodically.

The more we know about our customers, the more our analysis benefits. While measuring change, as it was mentioned above, we usually track the number of visitors (V), the number of leads (L), and the respective conversion ratio (C). Along with mere counts, one ought to measure the profiles of such visitors and leads – in both weeks. For example, with a 2x and 1.5x raises in visitors and leads, to know what caused it, one has to track how the properties of visitors and leads have changed. The properties we are referring to are usual user/event based properties – from simple demographic ones such as Location (country, city, etc.), User Agent (browser, OS, etc.), etc. to marketing-oriented ones such as Referrer (domain, campaign, etc.) – along with their values (a “property=value” combination looks like “country=India”, for example). In summary, in addition to tracking overall statistics (visitor and lead counts), we also track counts “grouped-by” their properties.

Needless to mention, such properties are too many to count, and property=value combinations are even more. This is where Factors.AI comes in. We track-down each factor, measure the change attributed to the factor, and surface top insights. And we repeat the process for any event pair combination (in say, a funnel), for every property, and for every consecutive period of interest. We urge you to follow-up with us in the next article that describes how exactly we surface the main change factors via our very own Weekly Insights feature.

Stay tuned.

It’s no secret that data privacy is a macro trend that’s here to stay, and with good reason. As social interactions and business operations increasingly take place in digital spaces, users are rightfully concerned about the safety of their sensitive information.

Accordingly, government bodies and security experts have established comprehensive privacy guidelines to ensure the protection of user data. Privacy laws such as GDPR, CCPA, and PECR limit the extent to which websites and businesses can track user activity without explicit consent. While there’s no doubt that this is a win for end users, it may seem like a cause for concern to data-driven marketing teams.

In fact, 73% of GTM teams believe that data privacy regulations will negatively affect their analytical approach to marketing. This article highlights why this is not necessarily true. Let’s explore how privacy-first solutions like Factors empower data-driven marketers to flourish in 2024 and beyond.

Marketers need data. Here’s why. 

Marketers need data to understand and improve the customer experience. This, in turn, results in better conversions and revenue. With data, analytics, and testing marketers can target the right audience with the right message and persuade prospects to become customers. Ideally, it's a win-win situation: marketers spend their budgets efficiently on campaigns that work, and buyers receive relevant promotions as opposed to spammy, spray & pray advertising. In truth, this is nothing new. 

Data has been leveraged by marketers and advertisers since the days of Ogilvy, and with sweeping digital transformation, data tracking has become all the more prevalent. For example, mobile phones today constantly transmit precise gro location as a common user identifier across consumer apps. In comparison, B2B tracking has remained relatively benign — yet effective. B2B marketers have the ability to identify companies visiting their website, track their page visits, scroll depth, and other noninvasive metrics to be able to understand and improve the customer experience. 

The dawn of privacy-first analytics

So far, this sounds great. However, while the intention with which marketers collect data is rarely malicious, the tools and techniques used in this process have been, until recently, without guardrails.

Fortunately, we’ve been seeing a dramatic improvement in data privacy and security in recent years. Today, privacy-first marketing intelligence and analytics tools (Like Factors 😉) honor privacy principles to ensure that data is used only for its intended purpose — to improve the customer experience. Even widely used tools like Google Analytics are having to rework their architecture to comply with regulations.

With tools like Factors, there’s no risk of data being collected without consent, shared with third-parties, or sold to advertisers. Even with this secure approach, marketers can continue to access everything they need to discover new prospects and optimize their performance without intruding on privacy. 

The most important aspect for marketers is to be able to draw the line between reasonable and intrusive tracking. Collection of PII without consent or the ability to identify individual users across websites is illegal and would fall under the latter. As an important practice, marketers should vet their technology vendors keeping this in mind. 

That being said, Factors and other privacy-compliant tools are secure by design. Customer information is protected without compromise on the quality of data, analytics, or insights derived. The following sections cover the basics of what you need to know about the most important marketing data privacy regulations — each of which should be considered when investing in marketing technologies.

1. First-party cookies

First-party and third-party cookies play important roles in the collection of user information. Here’s a quick overview of what cookies are and how first-party and third-party cookies differ from each other.

Cookies or HTTP cookies are tiny pieces of data that are sent to your browser from a web server. This data is stored locally on your device so that the next time you visit a website, it can identify you as the same user. So what’s the difference between first and third party cookies? 

‍First-party cookies: FPC are set directly by the website you are browsing. Their primary purpose is to collect analytics data such as page views, button clicks, and form submissions to improve website functionality and enhance user experience. Without first-party cookies, a user would have to sign in to an account every time they visit a new page on the website or app. Even the most basic preferences like language setting would have to be reconfigured on every page without first-party cookies. In short, they’re entirely harmless and collect basic website data to help marketers eliminate areas of friction and improve website usability.

Third-party cookies: Third-party cookies are tracker cookies which are set by third-party servers (or ad servers) independent of the website a user is browsing. Third-party cookies are accessible to any website that can load the server’s script. More often than not, these cookies are used for unsolicited advertising and are set by ad networks like Google’s AdSense program.

Websites that accommodate ad spaces from servers such as Google’s “DoubleClick” also allow them to place third-party cookies. These cookies can track your browser history and identify interests to facilitate retargeting. That way, when you visit a website that also hosts a similar ad server, it will display a targeted advertisement using the same third-party cookies.

Factors.ai only uses first-party cookies to enhance your user experience with zero intention in building an interest profile or a third-party context with first-party cookies. More information on the usage of cookies here. Third party cookies are  generally considered to be questionable and in some countries, illegal. This is because there’s no certainty as to what data these cookies are collecting and how that data is being used. Accordingly, it’s best to avoid tools that use third party cookies.‍

By design, Factors only uses first-party cookies to track visitor activity and enhance user experience. Tools like Factors have no ownership rights over your user data. They do not share or monetize first-party data collected from users in any way, shape or form.

2. GDPR Compliance

GDPR (General Data Protection Regulation)

General Data Protection Regulation is a privacy regulation standard that covers data protection andp privacy in the EU and European Economic Area. Under this regulation, businesses are required to receive voluntary or opt-in consent to collect personal information of customers, which needs to be clear and unambiguous. 

Personal information is defined by the GDPR as “any information which is related to an identified or identifiable natural person”. Information like IP addresses or any other data that can be traced back to a person is required for analytical purposes will require the user’s consent under the GDPR. This is why you may have noticed several privacy-compliant websites request consent on tracking personal information when you visit.

It is important to note that the consent of collecting personal information cannot be preordained or implied like in the form of pre-ticked boxes. Instead, the user must choose to opt-in to the collection of data and provide adequate detail on the information being tracked.

When complying with the GDPR, businesses must also comply with a set of rights with regards to personal information being collected. These include:

• The right to disclose and access the information collected
• The right to request for a correction of the information
• The right to request the erasure of personal information
• The right to register a complaint on the handling of personal information
• The right to request a restriction in the processing of personal information
• The right to object to the method in which your information is being processed
• The right to retrieve personal information and transfer it to another party, and
• The right not to be subject to a decision that is based on automated processing and has an adverse legal effect on the user  

‍Factors is aligned with GDPR rules and regulations. At present, Factors stores its data in US-based cloud-company servers. Note that the GDPR does not mandate the storage of data of EU citizens and residents within the EU. Additionally, while Factors collects IP addresses for high-level enrichment such as coarse geolocation (city, state-level) and account identification, this data is purged. We do not store IP or firmographic data in our database.

CCPA (California Consumer Privacy Act)

The California Consumer Privacy Act is a state-wide data privacy law that regulates how organizations handle personal information (PI) of California residents. Under the CCPA, the collection of personal information does not require opt-in consent for adults. That being said, just like the GDPR, users under the CCPA have the right to access personal information being collected and the right to opt out of the sale of personal data to third parties. 

‍Factors is CCPA compliant. In fact, by design, we do not have the ability to share, sell, or store personal data to third parties.

PECR (Privacy and Electronic Communications Regulations)

The Privacy and Electronic Communications Regulations (PECR) represents the UK's law on how businesses are allowed to market to UK consumers using electronic technology. ‍This regulation deals with unsolicited marketing, which includes things like cold calls, fax, text and emails, etc. PECR does not apply to solicited marketing — or marketing messages that are voluntarily requested. Even if a person has opted-in for marketing from your businesses, there are still instances that are defined as unsolicited, which would have to comply with PECR. As a marketer that relies on email marketing, detailed information on the consent must be provided to the person you are emailing. Consent must be received in the form of an action, whether it is written or ticked on a box. 

The rules of PECR slightly differ for B2B, where there is an exception to retrieving consent for emails and text messages. If you intend on the processing of personal information of corporate subscribers (B2B) or/and individual subscribers (B2C), the rules of UK GDPR apply.

Surprise, surprise — Factors is also aligned with PECR regulations.

SOC2 Compliance‍

While marketing under the aforementioned regulations would advocate a fair degree of privacy assurance to your users and necessitates consent. A Service Organization Controls 2 or SOC 2 compliance raises the stakes on the safety and confidentiality of customer data. SOC 2 is a set of criteria that define how a business should go about managing customer data and the examination of relevant controls in accordance with those criteria. While it is not legislation for data privacy, an SOC2 certification is the cherry on top of your data privacy practices and the forefront of establishing security standards as a part of being a privacy-first organization. It works on 5 trust principles:

1. Security: This involves the use of tools such as application firewalls and two-factor authentication for the protection against unauthorized access of systems.
2. Availability: This refers to the software, systems, or information that is available and is being maintained at a minimum acceptable performance level.
3. Processing integrity: This ensures that a system completes its objectives in a valid, timely and authorized manner with no errors in the system processing.
4. Confidentiality: Using encryption and limited access of data to ensure its disclosure is only restricted to a few people.
5. Privacy: This refers to the personal information of the system that is being collected, retained, used, disclosed and disposed of in compliance with the organization’s privacy notice and GAPP (Generally Accepted Privacy Principles).

‍Factors.ai is also SOC2 compliant.

Playing the long game — B2B Marketing Privacy In 2024 & Beyond

As more intent and uses of personal information by businesses get discovered, postmodern norms for regulation on the safe collection of data gets more rigid. Falling short on the compliance of these regulations will lead to the obstruction of marketing efforts. Here are some reasons as to why marketers should consider becoming privacy-first:

1. Data privacy and being privacy-first is bound to become an industry standard for marketing considering that web analytics is more of a necessity than a value adding requirement.
2. The legality of data privacy regulations will severely affect the operational efficiency, and even the going concern of the business. Data privacy under legislation is an obligation.
3. The conception of regulation for data collected and processed by artificial intelligence caused by an inevitable surge in automated workload is well underway.

Today, Google Analytics is illegal in Austria, Italy, Sweden, Denmark, and other European countries because the CLOUD Act allows US authorities to demand personal data from Google, Facebook, Amazon, and other US providers — even when they’re operating in external locations (like the EU). Regulation will only get more stringent — like the new revisions of the CCPA under the CPRA which goes into more detail on the sharing or disclosure of personal information. Being compliant early will help you stay ahead of the game.

More businesses will need to prioritize being privacy-first by building a decision framework around the management of personal information. This means making data privacy, its regulation, and the control of user data for the long haul the cornerstone of your business and marketing efforts.

In this article we’ll cover,

1. What is Revenue Intelligence?

2. Why are teams increasingly opting for Revenue Intelligence?

3. Revenue Intelligence to Optimize Conversions

• Breaking down silos between marketing and sales
• Solves for uncaptured data
• Solves for outdated and stale data
• Targeting entire accounts with ABM
• Give sales leaders total visibility/Access to the larger picture
• Accelerate sales cycles with more efficiency
• Forecasting

4. The Emergence of Revenue Operations and Intelligence (RO&I)

Revenue intelligence (RI) is a popular buzzword in today’s marketing landscape. This enthusiasm may be warranted. RI is revealing itself to be a powerful tool for marketing and sales teams to derive powerful data insights that were hitherto unforeseen. RI uses AI to gather data that would otherwise remain uncaptured.

Let’s start with an example. 

GrowNow is a marketing agency for start-ups. They focus on both digital and event services. Their content team has put out several articles on how marketers should approach scaling at various stages of growth. 

Akshat is the marketing head of Company X that has a fintech product. They’ve found their product-market fit and now they are looking to scale. He is searching online for ways to scale marketing and branding efforts. He comes across GrowNow’s website and finds the information that he is looking for.

He is not a lead yet but marketing has the information on how he came upon the website and what pages he’s engaged with. He finds his way back to the website a few days later whilst searching for more information on what tech stack his team would need. He downloads a free report on GrowNow’s website on the latest trends in martech. 

Finally, after a few weeks, Akshat comes back to GrowNow’s website, this time with a direct search and the intent to check out the services that GrowNow provides. He even fills a form for a preliminary call. 

Now that Akshat has been converted, he is pushed to Sales and GrowNow’s CRM has the information that he filled on the form: his name, email address, title and company. They might also have other information like the report downloaded by him. Marketing directs a few more adverts towards Akshat over the next few weeks. Soon sales gets on call with Akshat, they use this information to convert him and they are successful.

Later on, Deepti, the CEO of clothing brand Y which has several pop-up stores finds GrowNow in an article on up-and-coming marketing agencies and clicks on the link which redirects her to their website. She spends some time looking through the website and fills a form. On receiving a call from an SDR, she learns more about their services. Marketing continues to send the same adverts based on Deepti’s website activity. However, after a few calls, they quickly realise that Company Y and GrowNow do not have a good fit. Sales had the same basic information about Deepti as they did with Akshat. 

Both Akshat and Deepti’s customer journeys were a little different which sales were unable to access — like the data on their journeys pre-form fills. Similarly, marketing was unable to personalise websites based on Deepti and Akshat’s activities once they went down the funnel to SDRs. This in part, came about due to different locations of this data. Marketing has its data on first touch, web pages visited, time spent on webpages, adverts clicked on Google Analytics or other marketing platform while sales has its data on its CRM like Salesforce. Both departments were unable to access the other’s platform nor did they have an integration in place that allows for seamless flow of this information. 

This is where Revenue Intelligence comes in.

What is Revenue Intelligence?

In its simplest terms, revenue intelligence refers to the process of leveraging AI to collect, sync and analyse data across sales, marketing and customer success to produce critical insights and generate revenue. 

It is a powerful revenue operations tool that helps companies bring synergy between their customer-facing teams (marketing, sales and customer success) and make decisions that are powered by metrics. 

Why are teams increasingly opting for Revenue Intelligence?

More and more companies are increasingly realising the limitations of human intelligence in identifying important data points as well as the limitations on relying only on CRM data for insights on customer journeys.

The solution to this, has been to look at AI to collate and identify data that humans cannot. Furthermore, RI helps teams coordinate and capture data at the right time, before data decay diminishes value - 

1. Breaking down the silos between marketing, sales and customer success

Data silo is a problem when there is a lack of seamless coordination between teams, especially in terms of data collection and storage. A huge chunk of insights get lost when the data captured by these teams remains limited to their own teams. This is propelled by storing of data on different locations and difficulty in cross-departmental access of this data. All three of these departments are interacting with customers and have intelligence on customer trends and opportunities that get lost with interdepartmental misalignment with data getting siloed.

A revenue intelligence system captures and integrates the data from all these teams in real-time and creates a single, consolidated platform for the entire organisation. This ensures that everyone is on the same page and allows for seamless coordination between teams that helps create a unified strategy.

2. Solves for uncaptured data

Sales and customer success teams have to manually enter customer data like contacts, engagements, etc into their CRM. Two problems arise with this:

1. Manually entering data for each and every customer interaction is time consuming. 

2. This leads to negligence as many sales and customer success fail to enter all a lot of this data. Around 55% of salespeople admit that they do not enter all lead and customer data.

Resultantly, a lot of available data remains uncaptured and the company relies on this incomplete data for reporting, planning and forecasting.

RI solves for uncaptured data by automatically capturing contacts and engagements data from all customer facing teams, solving for both time and incomplete data, leading to more accurate and reliable sales reporting and forecasting.

3. Solves for outdated and stale data

Sales and marketing data is susceptible to becoming stale. 

Relying on manually entered contact details and the fact that people change jobs and positions and do not update their linkedin profiles leads to databases and CRMs being outdated and filled with errors. Good, high intent leads are very critical for both sales and marketing to reach their conversion goals. 

Then there is also the consideration for the hidden cost of redundant data. Bad or outdated data can muddle up research, competitiveness and accuracy of forecasts. Poor data leads to the wastage of sales’s time and IT’s time in syncing systems. It causes frustration when data-backed decisions fail to execute results.

RI solves for this by automatically tracking and updating changes to the leads in the CRM. This ensures more up-to-date and reliable prospect data.

Revenue Intelligence To Optimize Conversions

1. Capturing missing sales activity

We’ve spoken about the problems of unco-ordination and data silos between sales and marketing. When marketing is unable to access sales data, it prevents potential for improving marking activity and checking for inefficiencies in the existing process. As discussed earlier on the Factors Blog, getting multitudes of leads won't have a positive impact on revenue unless they are good, qualified leads. Infact, it may just lead to a waste of the sales efforts. In such a case, RI helps marketing access sales data that is pertinent for marketing’s processes and planning for more efficient campaigns.

Auto-creating of leads based on sales’ experiences, auto-removal of leads that sales has already dealt with or are low-intent based on previous experiences — both lead to coordination of data as well as a more seamless process of lead identification and capturing of contacts.

Furthermore, automated opportunity association of leads and tracking of interactions (emails, meetings, etc) helps get more insights from available data.

2. Attributing Marketing Touchpoints

Apart from sending better leads to sales, RI also helps paint a clearer picture of how marketing is helping sales acquire leads that lead to conversions. This helps in both having a better understanding of customer journeys and measuring the impact of marketing in the organisation’s overall functioning.

Revenue intelligence helps with marketing attribution reports that highlight marketings total impact, impact in each channel and the creation of first-touch, last-touch and multi-touch reports. RI also simplifies visualising the opportunity journey with easy spotting of marketing email and campaign touchpoints and deal updates as leads move through the funnel.

3. Enhances ABM 

Revenue Intelligence helps optimise ABM by improving the data quality of the contacts that are captured for the various accounts. With automation, more contacts can be captured. These contacts are also of better quality due to the improved tracking of customer engagements. 

RI also allows you to pursue better personalisation and target marketing efforts based on an account’s firmographic features and funnel position. So teams can get more meaningful insights from CRM and build improved target account audiences.

4. Giving sales leaders access to the larger picture

RI helps sales leaders have a better understanding of the customer journey and gain insights into the prospects that are coming in. Furthermore, having a real-time system of data relating to sales helps with insights into the sales process.

5. Improved sales pipeline

Better prospects, higher intent leads determined based on historical and real-time data improves the quality of leads entering the sales pipeline which in turn leads to higher conversions. Apart from higher output, RI also helps SDRs close deals faster and improve productivity. 

6. Forecasting

Revenue Intelligence helps sales forecasting by solving for outdated and uncaptured data to improve the reliability and accuracy of predictions. 

The Emergence of Revenue Operations and Intelligence (RO&I)

RO&I is a tech category that leverages AI to perform the principal task of revenue operations: integrating sales, marketing and customer success. In other words, RO&I is technology that allows the integration of sales technology, marketing technology and customer success technology to provide an end-to-end solution from customer acquisition to retention and expansion.

Revenue Intelligence tools help teams get the best out of revenue intelligence and empower their Rev Ops efforts with better data and more improved efficiency in mapping customer journeys. Knowing when to reach out to potential customers with the right information at the right time is critical to improving experience and conversions.