Harsha Potapragada

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.

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.

Outline:

• Introduction
• What is B2B Marketing Attribution and how is it different from B2C Marketing Attribution? 
• 7 Challenges with B2B marketing attribution

1. Tracking The Website Activity And Identifying Users Using Form Submissions, 
2. Identifying Accounts On The Website Even For Anonymous Users Using A Reverse IP Solution. 
3. Stitching Website Data With Map And Crm Data Using Email Ids (Specifically Unifying CRM Data Across Objects - Lead, Contact, Campaign Member, Activities Into A Single Timeline)
4.  Tracking And Defining Offline Touchpoints At The Same Level As Digital Marketing Touchpoints
5. Long Sales Cycles Implying Need To Track This Data Over Many Months And Years
6. Sales Marketing Alignment - Bringing In Sales Data
7.   Ability To Do All Of This At An Account Level                                                                         

• Takeaway

The B2B customer journey includes multiple people and touchpoints in the decision-making process.

On average, 6 to 10 people are involved in the B2B buying process. And for 33% of B2B organizations, the sales cycle is extended beyond six months. 

Overwhelming, isn't it?

In a B2B business, there are multiple stakeholders at different stages in the buying journey. And it is essential to have content that appeals to them. Hence it becomes hard to build content pieces that provide educational value. 

However, it is not an excuse that hinders your growth. In this blog, we will discuss the seven main challenges with B2B attribution and how factors can help overcome them. 

How Is B2B Marketing Attribution Different From B2C Marketing Attribution? 

71% of Marketers believe optimizing the customer journey across multiple channels and interactions is crucial. This optimization can improve customer satisfaction and drive business growth. 

However, 50% of B2B marketers report limitations with their current analytics solutions. These reports are not providing them with adequate visibility into what channels or campaigns work best. 

The following are two reasons why traditional marketing analytics solutions fail to achieve this. 

1. Multiple stakeholders are involved in decision-making, and the buying journey is non-linear. It makes it difficult to predict the impact of marketing-driven interactions.
2. Sales cycles are longer and involve multiple online & offline touchpoints for educating and influencing the buyer's decision. 

Let's understand this with an example. 

A customer journey for a B2C brand that is selling chocolates will look like this: 

Clicks on an Instagram ad → go to the website→ to make a purchase. (Yes, that's it!) 

On the other hand, a B2B customer's journey will look something like this.

Visit website→Read product reviews→Attend a webinar→Engage with a sales representative→Make a purchase decision. [For example's purpose only]

Now, from the customer journey, it is clear that it has both online and offline touchpoints. A more detailed depiction of a customer journey in the B2b business is added below for your reference. 

Furthermore, users now tend to browse anonymously, making it harder to piece together the accurate buying journey. Website Visitor identification capabilities can help throw light on these otherwise untrackable touchpoints. 

Challenges With B2B Attribution

Here are the seven challenges faced by the marketing teams with B2B attribution and how to overcome them. 

1. Tracking Website Activity And Identifying Users 

• How many people visit my website, and who are they? 
• Which page are they landing on?
• Which content is driving maximum engagement? 
• Which traffic sources - campaigns, referrals are driving high-quality traffic to the website? 

These are some of the questions that cross the mind of a B2B marketer. Websites are the sales epicenters for B2B marketers. Why? Because all the lead generation and conversions happen via the website. 

At every stage of the buying journey, your prospects are consuming your content and comparing it with your competitors. They want to understand whether you can solve their problems faster and better. 

So, it is vital for you to track and identify the website visitors to prepare customer-centric marketing strategies. However, tracking a user's journey from the first interaction to conversion across months is a technically complex task. It includes

• Managing cookies,
• Tracking traffic sources via utm parameters, referral parameters, or click ids,
• And stitching that with the respective ad platforms.

How Can Factors.ai Help?

Factors.ai is an analytics solution purpose-built for B2B marketers. It has an inbuilt capability to track a user's journey from the first interaction to conversion and beyond. 

The solution is configurable, wherein marketers can set up their utm definitions and channel configurations. It also comes with the following

• Ability to track utm parameters and click ids.
• Native integrations with the main ad platforms, providing a cost-to-revenue view seamlessly.

2. Website Visitor Identification

The key to driving effective marketing is targeting the right audience with the right message at the right time.

And data is what you need to convert the hot lead! The more you know about your prospect, the more you can personalize their experience.

However, collecting user data is challenging for the B2B segment. According to a report by 6sense, only 3% of B2B website visitors will fill out any form. And the rest, 97% of them, will be labeled as anonymous traffic. 

But it would be misleading to say that 97% of anonymous users did not influence the decision-making process of the known 3% of users. 

Let's unpack this with an example now. 

For instance, six people from the same company visited your website, but only 1 filled out the demo form. Therefore, attributing all the marketing efforts to that single identified person and his touchpoints will be wrong.

All the users from that account and the campaigns/content they interacted with should be considered when building an attribution model.

How Can Factors.ai Help?

Collecting user data is crucial. But you can do that only with their consent, which means your anonymous visitors stay hidden. Therefore, you need a solution that tracks the data on the website, even for anonymous users. 

Factors.ai has an OEM partnership with 6sense to provide the best-in-class visitor identification to its customers. Thus, stitching together the entire account journey across all users. 

They use a reverse IP solution and get data on an account level rather than at an individual level. It further enables you to understand the companies the users are from and know more about your anonymous users.

3. Putting The User Data In One Place 

B2B Marketers today leverage multiple channels to promote content downloads, webinar registrations, and demo requests. It helps them engage buyers as per their preferences. 

However, with many campaigns, ads, and other marketing activities happening simultaneously, it becomes challenging for marketers to measure the influence of each of these efforts on pipeline and revenue. In many cases, the customer journey is siloed across multiple tools. For example, the Marketing Automation Platform captures the website activity, while CRM captures the post-sales hand-off events.

Most Marketing Automation Platforms also are not sophisticated to capture traffic sources accurately. Furthermore, CRMs keep the user data fragmented across multiple objects such as Leads, Contacts, Campaign Members, and Activities.

Hence, it isn't feasible to stitch together the user journey across all these tools at an account level. Therefore, to make result-oriented marketing strategies, you need to unify this data - both at a user level and then at an account level. 

How Can Factors.ai Help?

Factors.ai has out-of-the-box integrations with Marketing Automation and CRM platforms. And it can stitch all data with the website activity based on the user's email ID.

Also, Factors pulls in all the engagement data across both Hubspot and Salesforce across individual objects. 

For example, in Hubspot, Factors can pull in the Contact, Engagement, Form Submission, and Add to List activities. Within Salesforce, Factors unifies data across Lead, Contact, Campaign Member, and Activity objects.

It makes it easy for the decision-makers to get a 360-degree unified view of customer activities and behavior in one platform.

4. Tracking And Defining Offline Touchpoints At The Same Level As Digital Marketing Touchpoints

Both online and offline touchpoints are equally involved in the lead acquisition process. Hence, B2B marketers need to track them in a single timeline. 

Online touchpoints are easier to track through the well-established digital marketing ecosystem. However, offline touchpoints like events, workshops, meetings, and direct mail are difficult to keep track of. 

Therefore you need a solution that allows you to keep track of both touchpoints simultaneously and build an exhaustive account timeline. 

How Can Factors.ai Help?

Factors automatically track offline touchpoints, which are recorded in the MAP or the CRM. 

Further, Factors allows you to configure and define your offline touchpoints with a simple UI. It enables Marketers to map all their touchpoints at a user and account level for making data-driven decisions.

5. Long Sales Cycles Implying the Need To Track This Data Over Many Months And Years

Longer sales cycles are one of the unfortunate realities of the B2B buying journey. Due to the multiple stakeholders involved and shifting priorities, most buyers take much longer to make a purchase decision. On average, a customer conducts nearly twelve searches before interacting with a brand. 

With this and the complexity involved in the decision-making process, it becomes challenging to accelerate the sales cycle. As a result, the customers could take weeks, months, or even years to close the deal size. 

Therefore B2B organizations would need a solution that can manage voluminous data running into many years of interactions with their prospects. 

How Can Factors.ai Help?

Factors.ai allows you to keep a record of all the interactions across all the platforms, like websites and campaigns, within one platform. In addition, you can seamlessly store data for an extended period (no limits) and reflect back on it at any point to decide what really helped.  

6. Sales Marketing Alignment - Bringing In Sales Data

An alignment between marketing and sales can maximize the ROI of a business. But this alignment between the teams is often absent in B2B businesses. Each team believes their efforts were the reason for closing a deal, which could be one reason for this. 

Emphasizing that each team is part of a larger go-to-market function is one way to make them work together.

Once you form a synchronization between them, it will allow the marketing heads to get a unified overview of the data across both marketing and sales touchpoints. 

Furthermore, each team can review and analyze the attribution data to see which of their strategies are working and which are not. 

How Can Factors.ai Help? 

Factors.ai pulls in all your sales interactions from the CRM and treats them at par with marketing touchpoints. And it also provides a clear and consistent view of the customer journey. On top of the unified data foundation, both teams can get answers to questions such as; 

• How many touchpoints did it take to convert a deal? 
• How many of these were sales vs. marketing touchpoints?
• Were marketing efforts able to drive engagement with the right stakeholders in these accounts?
• When is the right time for sales teams to intervene to convert an account?

7. Ability To Do All Of This At An Account (company) Level

The most significant pain point of B2B marketers is the involvement of multiple stakeholders in decision-making. 

The person who made the purchase is not usually the one who initiated the process of buying the product. Instead, multiple people across different departments (technical support, finance, marketing) must have come across the different stages of the buying journey. 

The traditional methodology would want you to attribute all the credits to the person who bought the product. It makes sense because he is bringing in the revenue. 

However, tracking customer journeys at an account (company) level rather than at an individual-level is what your attribution strategy requires. 

How Can Factors.ai Help?

Factors.ai will give insights at a granular level by breaking down the customer journey at the account level. It will simplify and visualize the customer journey by giving you an optimized overview of every touchpoint that drives the velocity of conversions & pipeline. 

Do B2B Marketing Attribution The Right Way! 

To keep up with the competitive marketplace, you need a differentiated analytics tool that helps you connect the dots from initial interaction to conversion. 

While B2B Attribution is technically and organizationally a complex problem, overcoming these challenges is critical to ensure your efforts are well directed. Hence tools like Factors.ai can tremendously simplify the B2B attribution process and elevate your ROI. To get your B2B marketing attribution game on point and cost-effective, sign up now for a free demo today. 

Here's a handy beginner's guide on the basics of A/B testing that covers what A/B testing is, why it's important, how to perform a robust test, and more! This should be a great introduction for those looking to dive into the world of optimisation.

‍What Is A/B Testing? 

A/B testing is a strategy that, simply put, allows you to compare two versions of something and find out which version performs better. 

Marketers use this technique to compare two or more versions of their websites, adverts, emails, pop-ups, or landing pages against each other to see which version is most effective. In A/B testing, A refers to ‘control’ or the original version and B refers to ‘variation’ or the new version. A/B tests can provide both qualitative and quantitative insights for the marketer. It usually falls under the larger umbrella of Conversion Rate Optimization or CRO.

To illustrate an example, you might test two different Google Ads to see which one drives more purchases or you might want to test two versions of a CTA button on a webpage to see which version leads to more webinar sign-ups. The version that drives more visitors to take the desired action (click on the ad, sign up for the webinar, etc) is the winner.

Why Does it Matter?

A/B testing is a great way to field-test ideas before finalising implementation. A/B testing helps you track impact of the changes on key metrics like conversion rates, drop off rates, etc. Thereby providing key insights on how effective the changes are going to be. Secondly, leaders don’t want to make decisions unless there is strong evidence for them, particularly when they have to incur costs. A/B testing helps databack ideas and decide where and how to invest the marketing budget. It is a great tool for creating effective marketing strategies. 

Where do marketers use A/B testing?

Almost any style or content element that is a customer-facing item can be evaluated using A/B testing. 

Some common examples include:

• Website design and layout
• Email campaigns and personalised emails
• Social media marketing strategies
• Paid Adverts
• Newsletters
  

In each category, A/B tests can be conducted on multiple elements. For example, if you want to test your website design, you can test the colour scheme, layout, headings and subheadings, pricing page, special offers, CTA button designs, etc, amongst many other elements.

While the metrics for conversion are unique to each website, A/B tests can be used to collect data and understand user behaviour, user actions, the pain points, reception to new features, satisfaction with existing features, etc. The metrics however depend on the industry and type. For example, the metrics for B2B (new leads or deals won) will be different from their B2C and D2C counterparts (cart abandonment rate, total purchases, etc). 

The Primary Types of A/B tests:

1. Split URL testing:

The simplest in concept — in split URL testing, two versions of a webpage url are compared with each other using webpage traffic to see which performs better on key metrics. It is the primary testing method for most organisations vying for website optimisation. However, this is not the best method to compare between two changes. It is mostly used to compare the original version with the new version that has some changes. More importantly, you can’t learn more about how different changes or elements interact with each or what combinations perform best.

2. Multivariate testing (MVT):

Multivariate testing allows the experimenter to compare multiple variables in the same test. This helps further what split URLs can do by overcoming their main limitation. Here, you can compare various combinations of the elements whose impact you’re trying to test. Good multivariate tests can combine all possible permutations to find which combination produces the best results. However, a large traffic is needed to be able to divide the traffic to face all the permutations of the webpage that is created by the traffic.

3. Multi-page testing:

Multi-page, as the name suggests, implements the changes being studied over multiple pages instead of a single page as is seen with simple split A/B tests. This helps understand how the changes impact the visitors in terms of how they interact with the different pages that they encounter on the website. This also helps maintain consistency when a visitor is met with a new variation that is being tested. 

How to perform an A/B test

The A/B testing process can be summarised as follows...

1. Data Collection:

In the first stage, the marketers or experimenters collect data from their analytics softwares to look out for numbers like high and low traffic areas, pages with high and low conversion rates, and or drop-off rates. This helps understand how the webpage is currently performing.

2. Decide what features you want to test:

Here marketers decide what features on the website or webpage they want to track and identify the goals. In other words, the determining the key conversion metrics that they want to improve for those features.

3. Formulate hypothesis:

Here, one starts generating A/B testing ideas and formulating a hypothesis for why the changes will perform better in terms of impact on the metrics being tracked.

4. Create variations:

After the hypothesis has been created, giving direction and clarity to the marketer’s goals, create variations that will be tested against the current version. This is where the marketer will choose the method of testing as well as the A/B tool used for testing. 

5. Run test:

After everything is in place, the only thing left to do is to run the test. Most A/B testers suggest around two weeks of testing on average. However, it varies based on the campaign, industry and traffic. 

6. Analyse results:

Once the test is complete, the experimenter can interpret the results given by the A/B test. It is important to ensure that the result is statistically significant. In other words, if one version saw better results than the other version, the changes can be confidently attributed to the new changes (and not coincidences).

7. Make changes:

Finally, now that the marketer has data backing their new ideas or proposed changes, they can go ahead and implement them to reap the reward of a more effective variation on metrics such as conversion rates, drop off rates, click-through rates and so on.

How do A/B testing tools work?

In short, every A/B testing tool has a piece of code that decides which variation of the webpage, email or ad each visitor sees. It also collects the data for the visitors of each variation which helps you compare and analyse visitor behaviour. 

This code works by incorporating the URL of the page(s) that are being tested. It also incorporates the metrics that you want to test. The results from this will determine which variation performs better. The tool’s cookies track visitors and opt them into the experiment. It will divert the traffic where half the visitors see version A (the control) and half see version B (the variant). The cookies track which version a particular visitor is opted into and measures their actions on the webpage towards the specified goal. 

There are several tools on the market today for A/B testing including Hubspot’s A/B testing tool, Google Optimize, VWO, and Optimizely.

‍Finding the Relevant KPIs for your Business

Identifying KPIs that are relevant to your marketing team depends on your particular type of business. For D2C businesses that sell directly to customers, website traffic and cart abandonment rate are two essential KPIs. The former helps guage how successfully a given marketing campaign is able to encourage customers to click on desired CTAs and advertisements, while the latter helps figure out possible pain points for customers that may be hindering their completion of purchases. If your cart abandonment rate is high, retargeting ads on customers’ social media feeds with their in-cart products can serve as useful reminders to complete a purchase. Alternatively, it can help identify customers’ pain points like contentions with shipping or exchange policies, pricing, etc. Such insights are useful in determining next steps. Similarly, for B2C companies, customer retention rate is an important KPI. Unlike B2B businesses, B2C deals seldom involve long term contracts and a continual inflow of revenue from paying customers. Finally, for B2B companies, a KPI like Customer Acquisition Cost (CAC) is a useful measure of the overall cost involved in onboarding a customer. 

In this article however, we deal with a primary KPI(s) that impacts all businesses: Conversion Rates.

Conversion Rates

Conversion rates may refer to different concepts. It can mean conversions per activity; which measures how many customers perform the desired activity  (clicking on an ad, signing up for a webinar, downloading a free booklet, etc) — all of which can be a part of an overarching campaign or strategy. Conversion per Activity is an important metric in it's own right when it comes to determining what works in your overall strategy. 

While these activity conversions contribute to the ultimate success of the marketing campaign, the actual success is measured by sales conversions — How many people actually converted to paying customers?

Hence, conversion numbers usually fall into two categories:

Category 1: Lead Generation

These include conversions per activity, website traffic, social engagement, etc. Sometimes these indicators receive a bad rap for being some what superficial. However, they have their own value to marketers in understanding the overall efficacy of a strategy. 

For example, Website traffic may not directly measure the impact of a strategy in acquiring new customers, but it can help determine impact of a strategy on brand awareness. This can be particularly useful when there is a strong correlation between awareness and sales. If 20% of your website traffic has converted to paying customers, improving the website traffic may have a positive impact on the final conversion numbers. Alternatively, if boosting website traffic does not seem to have any positive impact on sales, it can be a sign of potential customer pain points or inefficiencies in the overall marketing strategy. 

Category 2: Sales Conversions

These are conversion metrics that measure for concrete, direct impacts on revenue. Here are three influential metrics to keep an eye out for:

I. Campaign Conversions or Conversions per Campaign:

‍This determines what percentage of traffic to a certain campaign landing page/webinar/new subscribers to a newsletter — turned into a customer. 

How to measure: To find the campaign conversion rate, divide the traffic by the customers attributed to that traffic. For example, out of a 100 attendees to a webinar, 7 convert to paying customers, the conversion rate is 7%. Or if your ad had 200 interactions that can be tracked to 15 conversions, then you divide 15/200 to find the conversion rate of 7.5%.

Having a proper attribution model or platform in place is key to finding accuracy in such conversion numbers.

II. Website Conversion Rates:

It is safe to say that almost all B2B or D2C companies have websites which are their primary point of contact with potential and returning customers. So, the conversions from the website becomes an ultra important KPI. Although this indicator is calculated pretty much the same way as the campaign conversion ratio, it can get tricky as the customer journey gets complicated. There might be other touch points that impact the customer’s conversion decision even before they visit the website. Again, having a good attribution system is key to understanding the true impact of website traffic on conversions. It can help understand customer journeys and isolate the impact of the website on conversions. More importantly, it can help identify what works for the website and what doesn't. Insights like what pages converted users visited, how long they spent on those pages, what CTAs they acted on, etc can help figure out possible pain points and improve website conversions.

One thing to remember is that regardless of how customers make their way to the website and when they made the decision to buy, a website has important consequences for the conversion. In the digital age, a business’ website is essentially its storefront. It influences the customer’s perceptions and opinions of the business. In other words, it plays an important role in the customer journey. As such, the website conversion numbers are all too important to ignore for online businesses. 

How to measure: The most common and direct way of measuring the website conversion rate is to divide the number of conversions in a given timeframe by the total number of people who visited the website in that timeframe. For example, if in the past week, a site had 100 visitors, and 10 visitors converted to customers, the website conversion rate is 10%.

III. Lead-to-Close Conversion Ratio:

The Lead-to-Close Conversion Ratio, more popularly known as CVR, measures the number of sales that were made in comparison to the total number of leads the marketing team started with. This indicator helps marketers focus not only on creating leads but also on actually closing them. In other words, it helps create quality leads who will actually make the purchase. The effectiveness of the various components of the marketing strategy can be measured with the CVR. It gives the all important insight of which campaigns convert leads to customers and which do not.

How to measure: Similar to the aforementioned, the CVR is calculated by dividing the number of sales by the number of leads generated. For example, if you started out with 1000 leads from webinar attendees or newsletter sign ups or holiday ad campaigns and 170 of them convert to paying customers then you have a CVR of 17%.p

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.