Factors Blog

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. 

In an age where the functionality of the B2B marketing landscape becomes increasingly volatile, account-based marketing (ABM) and Revenue Attribution rise to the occasion. The adoption of ABM as an alternative to traditional demand generation is becoming progressively prevalent in the B2B space. Despite its increased use in recent times, the conception of several new and complex channels is promoting the need for ABM practitioners to be able to appraise their investments and optimise their ABM strategies. The incorporation of Revenue attribution in account-based marketing deciphers this challenge.

Understanding Account Based Marketing

What is ABM?

Account-based marketing or ABM is a strategic marketing approach wherein marketing resources and campaign efforts are directed towards targeted/key customer accounts. More specifically, ABM earmarks Ideal Client Profiles (ICPs) that would generate the most revenue.

ABM is known to be collaborative in nature, as most functional ABM efforts work in conjunction with other teams such as sales, operations, customer success, etc. This collaborative work is done during the earlier and final stages of ABM, the former of which involves scrutinizing your target accounts by soliciting the data  (i.e. profitability, ACV, retention rates of customers, technographic characteristics etc) in order to build your ideal customer profile. With this data, one can identify target accounts as well as target contacts within those accounts. 

While businesses *could* work with this list of prospects, most marketers further compartmentalize these accounts and contacts into tiers that rank prospects based on ratings assigned for revenue potential. This, ultimately, would help distinguish your marketing approaches — one-to-one, one-to-few, and one-to-many etc. 

The final stages of ABM involve engaging with your preferred accounts. What’s important here is that you integrate other prominent teams like sales, customer success, and operations to ensure an aligned execution of efforts.

When is ABM Necessary?

Given the sheer magnitude of money, time, research, and personal campaigns invested into ABM, generating an ROI for your ABM strategy necessitates its investment. The problem is that the efficacy of your marketing efforts will not be the same for all key accounts, but that’s obvious. What’s noteworthy here is that your marketing efforts on key accounts should have the lowest risk and the highest viability. This however only becomes feasible depending on the quantity and mostly the quality of the target market. The higher the number of key accounts available to target, and the better the revenue potential of each key account, the more suitable ABM will become for your targeted accounts. There are a couple of ways in which you can measure this:

• Measuring the annual contract value of your key accounts will help gauge the potential ROI if you were to use ABM, the higher the better your ROI. 

• For account quantity, a larger number of key accounts accumulated is preferable — if ABM is your main/exclusive marketing strategy — as they increase the probability of lead generation per account. 

• The TAM or total addressable market will help you gauge if your target market is too broad or narrow for a manageable audience for personalized marketing efforts, the smaller the size of the TAM the more serviceable the personalized engagement becomes. 

The Relevance of ABM

While account based-marketing is not a novel strategy, its emergence over the last couple of years has been excellent thanks to its adaptation to technology, automation, and the utilisation of tools by an increasing number of businesses. Enabling better synergy for its collaborative prospects as discussed earlier.

As of 2021, over 70% of marketers reported the use of ABM, 15% of whom grew from the previous year alone.This is owing to an overhaul of your standard marketing approaches partly as a consequence of the global pandemic causing a loss in value for traditional lead generation and volume-oriented targeting. What made ABM stand out is its versatility and its adaptability to its customer needs. This is because ABM focuses more on quality than the quantity of your broader customer base. Prioritising retention and marketing efforts on their targeted accounts instead of a broader miscellaneous customer base that would have a higher chance of disqualification. The businesses that utilised ABM before and during the COVID-19 outbreak, adapted to the changes — relating to industries like tourism and food service that took a hit based on PD — by reconstructing their key accounts and ideal customer profiles based on new factors, showcasing its versatility and popularity in choice in a changing economic climate.

Attributing ABM

How does Revenue Attribution enhance ABM?

The following are ways in which revenue attribution can help overcome some of the shortcomings of ABM and maximise its utility in practice: 

Measuring ABM Activities and Tracking ROI:

One of the core principles of ABM is that it prioritises and invests in appeasing your best revenue-generating key accounts through personalised engagement programs, this warrants the need to measure the engagement and campaign’s success. A common challenge in ABM and legacy ABM tools is that they fail to provide these insights. That being said, the utilisation of revenue attribution and attribution models accommodates this need as it provides insights into what channels drive revenue and can highlight poor performing channels and campaigns throughout all your key accounts’ pipelines. Tracking your account-based campaign’s ROI, and optimising your customer acquisition cost through those insights are all part of its preliminary functions. Not to mention, identifying a reliable cost per lead (CPL), allowing ABM practitioners to set a more practical CPL limit on their channels for their key accounts.

Key Account Mapping:

The steps involved in an ABM strategy are complex, yet straightforward. Your plan of action is to identify your ideal customer profile (ICP) and use that as a blueprint to locate your key accounts. But what about the people or stakeholders within an account? — 75% of ABM practitioners can’t find the right contacts at companies matching target profiles. And along comes the next challenge. How do we identify the stakeholders involved in the buying process? The solution to this problem involves rigorous research into key accounts and organisational structure. Revenue attribution embellishes this process thanks to its sheer detail in the compartmentalisation of the customer journey by analysing several touchpoints mapping out a multi-stakeholder journey. Highlighting all the stakeholders involved in the buying process, which will facilitate better planning by engaging with the right stakeholders and the optimisation of campaigns based on these insights.

Incorporating Data Attribution in ABM:

The incorporation of data attribution facilitates the ability to measure the impact of account-based activity over the lifecycle of your key accounts or customers and help increase the productivity of these activities. Identifying the right data using a few metrics will make it possible to understand if you have targeted the right accounts. For example, the progression rate and pipeline velocity will illustrate the rate or speed at which your MQL or marketing qualified leads among your key accounts move through the pipeline in their life cycles. But before doing so, it is imperative to associate the right data with your attribution. A lot of the data solicited through various touchpoints are unstructured, identifying intent and buyer interest using metrics such as bounce rate, click-through rate, lead conversion rate, etc., are all essential in data attribution.

Aligning Sales and Marketing:

The functionality of ABM is highly dependent on the collaborative efforts of various teams involved in the approach, especially the sales team. 42% cannot effectively run their ABM program as they find it difficult to align their sales and marketing teams. Meanwhile 86.7% of marketers that utilize multi-touch attribution state that they have a good relationship with their sales team. Why is this? This is because of the lack of shared data and leads. A majority of MQL or marketing qualified leads that pass-through sales teams get disqualified. Only a small percentage (27%) of those leads turn to SQL or sales qualified leads due to not getting a hold of the right stakeholder or decision-maker in the purchase decision. As mentioned earlier, r attribution streamlines this problem through multi-stakeholder tracking aligning MQLs with SQLs. Revenue attribution also enables better communication between the teams through reporting. Through revenue attribution, marketers can report on revenue numbers instead of other marketing vanity metrics.

Implementation‍

The problem with implementing attribution in ABM is starting out. Laying the groundwork for attribution is usually a trial-and-error process if you want to find the most efficient way to utilize attribution. Deriving an attribution strategy, deciding on what models to implement, testing other models, etc., are all common problems faced when implementing attribution into anything. These are inevitable and will cost money and time. In order to stay one step ahead of the game there is a way in which a marketer can anticipate preferred campaigns by targeted accounts and stakeholders. It is through the use of intent data. Regardless of the manner through which it is obtained, it can be very insightful for understanding the channels your targeted account stakeholder is deriving their buyer intent from. This data will prove to be useful in the formation of your attribution models as will be able to premeditate your own channel activity due to the information obtained through the intent data.

Once you have laid the groundwork. It is time to start tracking your engagement. Using multi-channel or multi-touch attribution makes a big difference. Considering the proportion of the investment and the degree of personalisation being used in your account-based engagement, single-touch models will not do an effective job attributing all of your activities — keep in mind that this is dependent on several factors like the number of channels, opportunity cost of channels, the channel intent, etc. In fact, a lot of marketers focus on bottom-of-the-funnel attribution investing in sales enablement to convert customers, while not realising that there are so many other factors to consider. The goal here is to organise your customers into accounts and map out the complete customer journey through the pipeline of said accounts. Pairing this with data obtained from your tech stack will enable you to identify the stakeholders involved in buying decisions within each account.

As mentioned earlier the functionality of ABM is heavily reliant on the collaborative work of other departments, and the same holds true with the use of revenue attribution. While the use of revenue attribution itself facilitates this alignment, that alone should not give you a reason to disregard it. Ensuring that both the marketing and sales teams are working with the same metrics and also the same stakeholders play a vital role in your ABM’s campaign success. Revenue attribution tools also benefit from data across teams, as mentioned earlier, the utilisation of your tech stack which would include things like your sales data and CRM data, etc., are essential in the functionality of your revenue attribution in ABM.

Challenges with ABM and Attribution

A lot of the challenges that arise from attributing ABM have to do with problems and mistakes marketers face when using attribution. Finding the most efficient model that is applicable for your ideal customer profile is not an easy task and has several hurdles. Identifying stakeholders will also only get more difficult considering the constant increase of the number of stakeholders involved in a B2B buyer decision due to sales cycles becoming increasingly bigger in size. Multi-touch attribution, in general, is a complicated and tedious process with more complex channels arising convoluting the entire journey. To overcome this, advancements in marketing technology have enabled us to accompany the right attribution tool that consolidates complex information into useful insights that will save time and effort in practice. Better yet, an AI-powered attribution tool that will eliminate the skill gap required to effectively utilize an attribution tool. With all the necessary tools and know-how available, you should be well equipped in attributing your account-based marketing.

Marketing’s transformation from a cost-centre to a revenue powerhouse — coupled with a boom in digital channels — means that marketers, now more than ever, require a granular account of their influence on pipeline and revenue.

Enter: Revenue Attribution.

B2B companies are prioritizing revenue attribution to measure their marketing performance and ROI, and track customer journeys. In fact, 76% of all marketers find that they currently have or will have in the next 12 months, the capability to employ a robust revenue attribution platform (Think with Google). Conceptually, the function of attribution is straightforward, but there are several mistakes that could easily skew your results and limit your progress when it comes to accurate, actionable revenue attribution analysis.

With that in mind, here are 8 common mistakes to avoid for your revenue attribution regime:

1. A lack of an attribution strategy

Despite the automation solutions that are embedded in most attribution tools today, it becomes easy to forget that your input plays a huge part in producing relevant results. Formulating a strategy is essential in being able to derive actionable insights from your attribution. At the end of the day, the relevance of tracking different channels and campaigns in a customer’s conversion journey is incumbent upon you.

Get organised! Start by cataloguing relevant channels to track as per your conversion goals. Label your channels and campaigns and assign budgets so that all your data across all your tools is coherent. Tracking irrelevant channels (or not tracking relevant ones) is a part of trial and error, but reliance on such incomplete data is a big red flag. One common example of this is: tracking only the performance of ad campaigns without testing its performance relative to other channels.  

Communicating with the appropriate personnel and others involved in the strategy to gain better insight on what to track and what not to is a good start.

2. Excessive reliance on preliminary revenue attribution models

The tendency to rely on preliminary attribution models — single-touch models like first and last touch or the popular last-click model — may produce quick and simple results to measure your ROI. This, however, can be an expensive mistake. Don’t get it twisted, single-touch models have their use cases — attributing PPC and short sales cycles to name a couple. But relying solely on preliminary models for all your marketing decisions will likely do more harm than good. Single-touch models are linear in nature, which is not conducive to most customer behaviour. Attribution is more effective when you strive to get as close as possible to analysing a customer’s journey across several touch-points. And having one touchpoint attributed to a customer’s conversion gives a vague, and often inaccurate, image of their journey.

3. Not testing multiple attribution models

This mistake is likely to be a consequence of the previous point — excessive reliance on preliminary models. But why is it important to test other models? When it comes to rule-based attribution and multi-touch attribution models, the general reasoning behind adopting a model is the nature of the product, the number of marketing channels, the length of the sales cycle, etc. While there’s nothing explicitly wrong with this, we cannot only rely on those factors.

There are several omitted variables around the intent of your attribution — measuring the functionality of different campaigns in conjunction with other channels, the relative probability of channel interaction, opportunity cost of campaigns, or just simply mapping out the most influential channel and ROI. Even the type of campaigns and the medium through which the customer interaction occurs could affect your decision in choosing a model. Some models are more applicable than others in producing reliable results, and the only way we’ll identify this is by testing out what works and what doesn’t.

4. Not understanding the limits of rule-based modelling

In practice, administering a combination of rule-based attribution and data-driven attribution is an effective way of producing reliable results. That being said, if you’re for the most part dependent on rule-based modelling, you’re unlikely to have transparent results. Rule-based modelling is limited, as the weights in the models would need to perfectly represent the influence of each channel in a customer’s conversion journey. This is highly unlikely as no two customers are the same. For example, a time decay attribution model will assign credits in ascending order regardless of the type of campaign or prospect’s actual behaviour. So, to help identify your most influential channels on average, data-driven attribution can be used to give credibility to different channels by assessing their KPI’s. This in turn will help you draft a custom model that makes sense to your attributing pattern.

5. Misaligning attribution data and customers/lead quality

In the pursuit of using your attribution data to aid your marketing decision making, sometimes you forget to categorize our data considering the customers involved or their lead quality. To make better sense out of your attribution data, we need to pair the interactions with customer IDs to avoid duplication of leads and accurate credit distribution across marketing channels.

Tracking our customers even helps assess the quality of their leads. What this means is some customers are likely to be more interactive and engaged with your brand than others. This even dictates if some of them become recurring customers or only ever interact with your business once. Tracking customer interactions helps you distinguish the quality of their leads. These values also contribute to calculating the LTV (Lifetime Value) of your customers.

6. Ignoring the bias

These mistakes have to do with certain biases that might compromise your decision-making pertaining to attribution. The most common ones are:

Correlation Bias

When attributing credit to different channels along your customer journey, there is a possibility for certain interactions to conceive other interactions (or at least a level of other interactions). One could over/underestimate the influence of channels with other channels simply because of the natural conversion of targeted customers. A conscious consideration of correlation vs causation must always be kept in mind.

Confirmation Bias

‍A confirmation bias is the proclivity to seek out information, and the interpretation of said information, to favour your results and personal beliefs. This type of bias is prevalent in attribution as it involves having to attribute your channels in accordance with the result that favour you. This would eliminate the organic element of attribution to favour your marketing ideals, ultimately leading to inaccurate findings and conclusions.

7. Failure to understand the channel intent

When you fail to recognize your channel’s intent, you fall short in gauging how much it facilitated a customer’s conversion. This could lead to poor decision making as a consequence. Some channels facilitate interactions with other channels more than they do sales — like a blog versus a targeted email campaign. Hence, it would be unfair to discredit the channels that did not directly contribute to sales — or other predominant goals — but probably contributed significantly to a customer’s decision to convert.

8. Attribution is not the Be-All End-All of your marketing analytics journey

As convenient and resourceful as attribution is, they will never provide a holistic, extensive picture. While attribution is valuable in showcasing a blueprint of your campaigns, channels, and marketing performance. You still require other analytics tools — Funnel analysis, Anomaly detection, SEO optimization, CRM, and other web analytics tools that help assess channels using premeditated metrics. These tools will ultimately compliment your data-driven attribution for a far more comprehensive analysis of your campaign and channel performance. In order to do this effectively, you will have to use these tools cooperatively and in real-time.

Acknowledging these limitations and making a conscious effort to mitigate them will help equip and optimize your marketing attribution journey. Don’t let the idea that there is so much that could go wrong make you apprehensive about trying out marketing attribution to begin with. Undoubtedly, it’s a steep learning curve, but the rewards far outweigh the risk involved.

And there you have it! If you’re interested in understanding how some of the most popular single-touch and multi-touch attribution models work, you might enjoy this blog piece. 

‍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

If you find ROI measurement of your content marketing efforts a challenge, you’re not alone. Only 8% of B2B marketers believe they are successful at gauging their content's ROI and influence on revenue.  With the content marketing industry constantly growing, making up between 25%-40% of B2B marketing budgets, it only seems fair to understand its metrics and incorporate ROI measurement into your content marketing strategy.

Ends That Justify Your Means — Why Do You Need To Measure Your Content Performance?

If It Won’t Convert, It Won’t Matter:

Content marketing has contributed substantially to the B2B marketing sphere. Blog posts, podcasts, infographics, etc. all play a major role in a business’s marketing efforts.  But there’s a fine line between good content and content that promotes lead generation. The end goal of content marketing is generating traffic and influencing the conversion of said traffic. So, a conscious effort to measure your content helps lay the groundwork for a content marketing strategy that prioritises the goal and justifies the cost of doing so.

If It Does Convert, By How Much?

‍When it comes to B2B marketing, your prime audience is pretty specific. Hence, your content is likely to have a larger impact on pipeline and revenue. 71% of B2B customers consume a blog before making a purchase. Quantifying information like this is effective in distinguishing your leads from your sales. The difference and variety of metrics available for your content provide valuable insights. Understanding the extent to which each metric attributed your leads is an essential aspect of painting a clear picture of your ROI. A classic example of this is to resort to vanity metrics such as organic search traffic to evaluate your content’s success rather than its bounce rate or impressions made which are more conducive in assessing an MQL — marketing qualified lead.

What You Could Expect for The Future:

‍Trial and error is an expected component of your content marketing track record. The data you amass by monitoring your metrics will prove to be insightful in the formation of your content marketing strategy and budget — including the provision of answers to common questions like “what type of content generates the most traffic?” “Which content influences the most revenue and pipeline?” and “Which content had the most effective link building and/or SEO rankings?”

Understanding The Metrics 

Historical Data and Monitoring:

‍A common barrier to entry for content marketing ROI is your access to customer historical data. To elaborate, your access to said data also includes the cost of acquiring it, the risk associated with losing it, and the availability of precise data when needed — relating to interactions with content. Most software available to track customer metrics like the touch attribution of content, the number of contacts from email, the revenue generated per customer, etc., are fragmented across different software with limited storage of customer data and are behind a paywall. There is even the risk of losing this data because of these stipulations. For this, it is recommended that businesses house their customer data using a data warehouse to retain the historical data of their customers and to use a customer data platform that will organise customer data and behaviour across various software in real-time into a comprehensive format suited for content ROI.

Lead Conversion:

‍The first step in measuring your content’s ROI is to establish what your lead conversion is. Or in other words, identify what customer action is considered a worthy result of your content’s purpose. This would vary depending on the product and what business it is being targeted to — so organising your leads or conversion goals in conjunction with your products is crucial. Some examples of conversion goals would be — signing into your website, downloading a demo, subscribing to a newsletter, or even a sale, etc.

Lead conversion rate: The number of leads relative to the number of visitors on a webpage. Divide the total number of leads by the total number of visitors.

Landing Page:

‍Your landing page is the first page of your website which is visited by a prospective customer. There are certain metrics that can be used to assess the attribution of your landing page to your conversion goal. Your landing page’s page views indicate the number of visits that have occurred on your landing page. The number of unique visitors helps you identify the number of people visiting your landing page, this is different from page views as it only counts the number of visitors and not the number of their visits.

Other useful metrics for evaluating attribution in your landing page include your bounce rate — which is the number of visitors that navigated out of your page after viewing only one page. Your average session duration is the average time lapsed during a session — a session being a user’s regular interactions — on your landing page. These metrics illustrate the authenticity of your content’s applicability for conversions.

Email Traffic:

81% of B2B marketers utilize email newsletters as a part of their content marketing strategy — making it the third most popular form of B2B content. If your business sends out newsletters, these metrics are important to track: An email’s open rate measures the percentage of emails opened, and if you link your content webpage in your email, a click-through rate distinguishes the number of users who’ve clicked on the aforementioned link and those who did not.

Social Media Traffic:

The most popular form of content (95%!) implemented in a content marketing strategy by B2B marketers is organic social media posts. On channels such as Twitter, LinkedIn, Instagram, and YouTube, Audience engagement on your posts in the form of Likes, Shares, Comments, and even Follows are useful metrics to assess the influence and engagement of your posts. Of course, click-through rate may be tracked as well.

The Nitty-Gritty — Measuring Your Content’s Influence and ROI:

Once we have gathered all the relevant data, we can now measure our content’s ROI. But before doing so, we need to assign a monetary value to your MQLs. If your conversion goal is a sale, then it is the revenue generated from that customer’s sale. If it is a campaign goal like demo scheduled, it is the forecasted revenue from prospective customers that’s most relevant.

Once this is established, organise this data in a coherent manner to measure its ROI. Start by isolating landing pages or content pages to measure them individually. Then we will allocate their respective data to them. For the sake of comparison and future content marketing strategy, it is imperative to distinguish your MQLs from your visitors. The last step is to assign your revenue to your MQLs, whether it be the revenue generated from sales or the forecasted revenue of a particular lead or conversion goal. And finally, we can calculate the ROI with our MQL revenue — the ROI calculation here would be the revenue generated from the MQL divided by the cost of production of the landing page’s content.

To illustrate — let’s say that you were measuring the ROI of one of your landing pages at the end of the month. Perhaps a blog in your payment gateway service company. Organically your blog has amassed 500 unique visitors, and around 300 through social media posts and email campaigns. Out of the 800 visitors, 60 of them signed up for a demo, whose forecasted revenue amounted to around $5000. Using the formula mentioned above and dividing the $5000 with the cost of the production of the content, you will measure your B2B content’s ROI.

Evidently, measuring the ROI of your B2B content is a tough nut to crack, and as I mentioned earlier, trial and error is an expected component of your content marketing track record. While quantifying your means will expedite your strategy, functional results take time and mistakes, and if you’re patient enough, they’ll yield.

Search Engine Optimization

It is reported that 75% of users never visit the second page on their search results.  As search results become increasingly concise and filtered, it’s easy to forget how ruthless and saturated search engine rankings can be. Hence, it isn’t an understatement that the accessibility of your page on a search engine should be an integral precursor for your marketing value proposition. Accordingly,  marketers are  prioritising SEO as part of their inbound efforts. This post expands upon the theory, practice, and importance of SEO in an ever-growing digital marketplace. 

What is SEO?

SEO, or search engine optimization, refers to the process of increasing the likelihood of your website, content, product, etc. appearing close to the top of  your SERP — search engine results page. The objective is to direct  more traffic to your webpage by increasing its ranking on a user’s search engine index, either organically or with minimal monetary investment.

Search engine results page or SERP is a constantly evolving geography.  Search results — especially those pertaining to inquiries now feature quick answers and knowledge panels that direct clicks away from low-ranked domains. For instance, if you were to google ‘marketing attribution’, you would be presented with a quick answer in the form of a short description directly below.  Additionally, other relevant, consolidated information is presented on the right within knowledge panels. Note that Google and many other search engines prioritise having their users spend more time on their SERP without having them navigate away as much. This is why marketers need to capitalise on their rich results and SERP ranking.

CRAWLABILITY AND INDEXING

Before we look at what your search engine prioritizes when ranking, it’s well worth understanding what crawlers are and how search engine indexing works:

Crawling is the process of your search engine sending out crawlers, which are bots that are used to discover new web pages. The crawlers start by following a certain number of web pages followed by then routinely navigating content and new links within these web pages. Thereby discovering a series of new web pages which it reports back to its respective server. A website’s crawlability thus refers to a crawler’s viability in a website or web page. More on increasing crawlability below.

All this information that the crawlers obtain is then stored in a database known as a search engine index. The data is then organised, analysed, and prepped for retrieval on a search engine results page — this process is known as search engine indexing.

The Ranking Algorithm: PageRank

Before indexed information is retrieved into a search engine results page, it is ranked by several factors in order to obtain the most relevant sources of information. While this piece will cover some critical success factors for your SEO, it is important to understand that Google ranks their websites based on relevancy and an algorithm. Understanding the algorithm is fairly complicated as it is continually evolving. That being said, PageRank is an algorithm that is still being used by Google to rank websites and will help provide an idea of how the ranking algorithm works.

PageRank uses an algorithm that helps rank web pages based on their relative importance. It does this by estimating how many times a web page is visited or linked from other web pages and also measures the quality of these links. For example, your web page is more likely to be ranked higher if it is linked by  relatively important web pages — like Forbes or the NYT — than it is if it was linked by many less "relevant" web pages — like The Onion or ArticleIFY.

The importance of a web page is assessed using a random surfer model and a damping factor that estimates how many times a web page is visited by a random surfer and assigns a percentage to all web pages visited. All you need to know is that the model and damping factor helps eliminate any way in which people can artificially inflate their web page’s importance.

SEO CSF — CRITICAL SUCCESS FACTORS

This segment will explain a few critical success factors for your SEO in the form of good keyword practices, indexing and crawlability, and more:

Keyword CSFs

Keywords play a surprisingly significant role when it comes to SERP ranking. Certain niche keywords could be the reason your web page is ranked higher in a SERP. But what keywords should you use? Before you choose your keywords, you need to establish your search intent. Understanding your web traffic, and what they’re looking for is key when it comes to search intent. Ask yourself what people would specifically search for and what words or phrases they’d use — for instance, 8% of all search queries are in the form of questions.
Once you have an idea of some appropriate keywords, it would help to know what their search volume is. You could administer the help of a keyword research tool — like Jaaxy, GrowthBar, SEMrush, Google Keyword Planner, etc., which are tools that  help gauge how popular/relevant certain keywords are. They could even compare and recommend other related keywords.
The largest barrier here is the competition of high volume and short-tail keywords — or search phrases consisting of only one or two words. Industry-leading brands are often ranked higher for short-tailed keywords due to their relative importance. However, there are some advantages in using long-tail keywords (i.e. search phrases that are longer with three to five words). The consensus is that, while high volume and short-tail keywords tend to involve highly-competitive broad search queries, long-tail keywords account for more convertible traffic as their search phrases are specific. Hence, you’re likely to garner more traffic with niche low volume, long-tail keywords than if you were to compete using high volume keywords. For example, you’re more likely to earn traffic from a search phrase like ‘Accounts receivable automation software’ than you do for ‘Accounting software’. Remember, if your keywords are too obscure, you risk losing your spot on a SERP.
LSI or latent semantic indexing keywords may also be  useful. LSI is a tool used by Google to understand synonyms and can contextualize keywords by linking them with relevant ones. This means that a synonym does not necessarily have to be an LSI keyword, and can be anything relevant in the context of your content. For instance, Googling ‘demand generation’ would have related searches for strategies and comparisons with lead generations. LSI has helped Google in identifying and contextualizing content on web pages better, which is a win when it comes to SEO.

Crawlability and Indexing CSFs
It is essential to know what affects your crawlability. The first is your site and internal link structure. It is imperative to make your search engine’s job of locating your site as easy as possible. For this, you must ensure that your site structure has an appealing UI and makes navigating across different pages intuitive. This way,  crawlers will not find it difficult to get by. For the crawlers to do a comprehensive search on your website, ensure that a fair amount of internal link resources are prevalent for the crawlers to fully cover your website. It is also important to block crawlers’ access to irrelevant pages to avoid saturating the context of your content.
Besides your site and internal structure, making sure that other interferences such as slow site loading speeds are resolved as they add to the crawlability of your website. If you are unsure about your site’s visibility on a SERP, using tools like Google Search Console will help monitor your site’s presence on Google SERP.

Other Important CSFs

Recalling the mechanics of Google’s PageRank algorithm, you will know that your web pages’ networking with other pages is of paramount importance. Having external links from other sites that link to your site — especially higher quality links that come from important sites — along with understanding your competitors’ backlinks and utilizing them will help improve your ranking.
Rich results is a feature that showcases information that is not only important in giving a brief description to a user but also helps crawlers identify your site and the purpose of the content because of its metadata. Rich results have a title, meta description, favicon — and depending on what the page is about it could even show pricing, specifications, and a rating. All of which aid in the crawlability and a user’s understanding of the web page.
Another simple but effective factor is the quality of the content on your page. The use of unique, engaging, and informational content with ample visual representations in the form of high-quality images and video. Google prefers sites with content, and good content at that. The better the quality of the content is, the more favorable you become in Google’s algorithm.
With these factors in place, you’re one step ahead in your SEO journey. When it comes to SEO, being consistent, putting out new content, and following good practices will be sure to help out in the long run. Just remember that SEO is always changing, and if you want to take the bull by the horns — keep updating your methods, and stay ahead of the status quo.

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.

Think about spending a lot on marketing but not knowing which efforts actually lead to sales. Many marketers face this problem when trying to improve strategies and justify spending. Marketing attribution models can help. They show the journey from first contact to final sale, highlighting the role of each step.

Attribution modeling is a key approach to measuring marketing performance, especially in the complex, long sales cycles typical of B2B contexts. It provides a framework for assigning credit to various interactions throughout the customer journey, helping businesses identify which touchpoints contribute most to conversions. While no attribution model is perfect, each offers different levels of usefulness depending on the context. In B2B marketing, where customer interactions are numerous and extended over time, the right attribution model offers invaluable insights into which channels drive sign-ups and what content influences conversions, allowing businesses to better understand and optimize their marketing strategies.

Today, customers connect with brands in many ways, using different platforms and devices. Knowing their journey is more important than ever. Marketing attribution models give you a clear way to examine this journey. They help you spot key steps and adjust your strategies.

This guide will cover the top 9 marketing attribution models. Each one has its own strengths and uses. By learning about them, you can pick the one that fits your business goals and customer journey. Whether you are experienced or new to marketing, understanding these models is vital for boosting ROI and growing your business.

TL;DR

Attribution modeling evaluates how different marketing touchpoints contribute to conversions. In B2B with long sales cycles, this can be complex, and while all models have limitations, they offer valuable insights. Single-touch models like First-Touch and Last-Touch give full credit to one interaction, while Multi-Touch models distribute credit across multiple touchpoints. Time-Decay models emphasize recent interactions and Influence Attribution credits, all touchpoints that impacted the deal. Choose a model based on your sales cycle, business needs, and desired insights.

How Attribution Models Benefit Your B2B Marketing?

Here’s how your business benefits from using marketing attribution models:

1. Smarter Budget Allocation: Identify high-performing touchpoints and channels to invest your marketing budget where it matters most.

2. Deeper Customer Journey Insights: Understand how customers engage across different channels and which interactions influence their decisions.

3. Personalized Marketing: Use insights from attribution data to tailor messages, improve the customer experience, and build stronger brand loyalty.

4. Data-Driven Decision Making: Evaluate the effectiveness of each channel to optimize current campaigns and plan future strategies more effectively.

5. Improved Team Collaboration: Align marketing and sales with a shared view of customer interactions, helping both teams work toward common goals.

6. Increased ROI and Efficiency: Focus on strategies that drive results, reduce waste, and improve overall marketing performance.

In summary, marketing attribution models are vital tools for businesses aiming to refine their marketing strategies and achieve lasting success.

What are attribution models?

Attribution models are frameworks that help analyze the customer journey and assign credit to the various touchpoints prior to the conversion. The method for assigning the credit is different for each attribution model depending on either the position of the touchpoint in the customer journey or a data-driven estimation of the significance of that touchpoint. 

Additionally, businesses may need to configure these attribution models to suit their unique circumstances - such as considering an attribution window of, say, 60 days or 365 days depending on their sales cycle or performing the attribution analysis at a contact or opportunity, or account level depending on their sales motion.

With the help of these models, marketers are able to identify channels and tactics that drive more conversions and revenue, driving higher ROI for the business.

The following are some of the main reasons why attribution modeling is important. 

• They provide insight into channels and campaigns that drive conversions and revenue
• They help plan and distribute spending to the right marketing channels
• Also, they help us identify the most influential channels and campaigns for each stage of the marketing and sales funnel.

There are different types of attribution model available for marketers, and we will dive into each in the coming sections.

Categories of Attribution Models

Before delving into how some of the most popular attribution models work, it’s worth understanding the mechanics of attribution modeling. A general categorization of attribution models would include two types. They are - 

1. Rule-based attribution models
2. Data-driven attribution models.

1. ‍Rule-based attribution models

These models use predetermined rules for assigning attribution credits to touchpoints. These pre-defined rules determine the weightage or credit for a touchpoint primarily based on its position in the customer journey. Hence, these models are also called Position based Attribution Models. 

In addition to the position, you can also define custom logic to assign differential weights based on the seniority of the customer representative involved in the touchpoint (say Director and above gets higher weight) as well as the amount of effort expended by the buyer in that interaction (attending a webinar required higher effort from a buyer than clicking on a paid search ad). 

2. Data-driven attribution models

These models assign attribution credits to touchpoints based on an algorithmic estimation of the significance of that touchpoint in converting the customer. Some of the popular algorithmic techniques are Markov Chain models and Shapely value-based models. Whilst data-driven attribution is seen as the north star of Multi-Touch Attribution, they are also more expensive to compute, require a large volume of conversions and touchpoints not to be biased, and are harder to debug. 

Whilst each approach has its own pros and cons, a combination of these models may be leveraged to identify marketing leakage and improve ROI.

What are the different types of attribution models?

Single-Touch attribution models

Single-touch attribution models are among the most straightforward approaches used to evaluate marketing performance. These models focus on one touchpoint in the customer journey and assign all credit for the conversion to that one. While straightforward, these models might only sometimes provide a complete picture, especially in scenarios involving complex sales cycles.

Some of the most common types of single-touch attribution models include:

1. First-Touch Attribution

‍The first-touch attribution model assigns full credit to the initial interaction that brought the customer into the marketing funnel. This model is particularly useful for businesses with simple, transactional sales processes, such as SaaS sign-ups. By understanding which initial touchpoints are most effective at attracting prospects, marketers can better focus their efforts on top-of-the-funnel activities.

However, the limitation of first-touch attribution becomes apparent in longer sales cycles. For example, if a potential customer interacts with a brand through a blog post, attends a webinar, and finally makes a purchase, first-touch attribution would credit only the initial blog post. This approach overlooks the influence of subsequent interactions that may have been crucial in nurturing the prospect through the funnel.

‍

Key benefits:

• Ideal for campaigns focused on lead generation and brand discovery.
• Helps you evaluate which channels introduce the most prospects.
• Misses the influence of nurturing and closing interactions.
• Works best for businesses with short sales cycles or fewer touchpoints.

2. Last-Touch Attribution

Conversely, the last-touch attribution model gives full credit to the final interaction before the conversion occurs. This model is beneficial when trying to identify what specifically triggered the conversion. For instance, if you want to determine whether a blog post, a LinkedIn ad, or a webinar was the last factor that led a prospect to book a meeting, last-touch attribution can provide clarity.

While last-touch attribution can offer valuable insights into what ultimately led to a conversion, it has drawbacks. This model can skew results by ignoring the role of earlier touchpoints. For example, in a long B2B sales cycle, if a prospect finally signs a contract after several months of interaction, attributing the entire credit to the final step—such as a contract-signing tool like DocuSign—may not accurately reflect the contributions of earlier interactions. This can lead to an incomplete understanding of the marketing efforts that influenced the final decision.

‍

Key benefits:

• Good for identifying conversion-focused channels like retargeting or email.
• Simplifies reporting and is easy to implement using most analytics tools.
• Ignores the impact of earlier touchpoints that shaped intent.
• Often leads to over-investment in bottom-funnel efforts.

3. Last Non-Direct Touch Attribution:

This model assigns 100% attribution credit to the last non-direct touchpoint. A non-direct touchpoint is an interaction that is guided by a specific source the business sets up (like an ad, email campaign, newsletter, etc.). 

When your website traffic doesn’t come from a known source, they are considered direct traffic (traffic that came from prospects directly entering the company URL into the browser, for example). 

Let’s assume that a lead interacted with your brand 5 times, each touchpoint is as given below.

• Touchpoint 1 - Prospect clicks on a PPC ad
• Touchpoint 2 -  Prospect arrives at your site’s landing page
• Touchpoint 3 - Prospect subscribes to your newsletter
• Touchpoint 4 - A week later, your prospect clicks on a newsletter campaign
• Touchpoint 5 - Prospect directly visits the website and initiates a free trial before purchasing a subscription 

Touchpoints 1, 2, 3, 4, and 5 constitute all the prospect’s interactions with your brand that led to them purchasing your product. Keep in mind that, in reality, businesses deal with numerous prospects interacting with several touchpoints, making the process of mapping the customer journey far more convoluted.

So if we consider the above-given example, this model would assign 100% sales credit to touchpoint 4 or the newsletter campaign clicked on, as that was the last non-direct source before the sale. This model assumes that every interaction is a consequence of the non-direct campaign, hence making it the most influential.

Key benefits:

• Useful for identifying the performance of non-branded campaigns (e.g., PPC, referral).
• Reduces bias from loyal repeat visitors or brand-aware customers.
• Often used in tools like Google Analytics for more realistic insights.
• Still ignores multiple other influential touchpoints.

4. Last AdWords Click Attribution:

This model credits the last interaction with a Google Ads campaign before conversion. It’s designed to help marketers optimize their paid search investments.

Let’s say a customer journey looks like this:

1. Clicks a Facebook ad
2. Visits via organic search
3. Clicks a Google ad
4. Converts

Last AdWords Click Attribution will assign 100% of the credit to Step 3 (Google ad click) — ignoring the earlier touchpoints.

Key benefits:

• Highlights which specific search campaigns drive conversions.
• Helps maximize ROI from PPC spend.
• Ignores contributions from organic search, social, or email.
• Can lead to siloed decision-making if used alone.

Is Single-Touch attribution an INEFFECTIVE model?

Many businesses and marketing aficionados are of the opinion that single-touch attribution is not an effective model on its own. It is often considered to be a one-dimensional approach that fails to faithfully represent a customer’s conversion journey down the funnel. 

As we have discussed, while single-touch models may have their own relevant use cases (like for products with shorter sales cycles), it may not be as effective in identifying the most influential touch-point in a B2B customer journey. 

If big data in marketing has proved anything, it's that customer journeys can be non-linear, sophisticated paths spanning several channels and mediums. Assigning 100% of the credit to a single touchpoint will rarely be sufficient.

Multi-Touch Attribution Models

To address the limitations of single-touch models, multi-touch attribution models distribute credit across multiple touchpoints in the customer journey. These models offer a more nuanced view of how various interactions contribute to conversions, making them particularly useful for complex sales processes.

Linear Attribution

The linear attribution model assigns equal credit to every touchpoint the customer interacts with along their journey. This approach highlights the importance of each interaction, providing a balanced view of how various touchpoints contribute to the final conversion. In a B2B context, where a customer may engage with a company through several channels before making a purchase, linear attribution helps ensure that no single interaction is undervalued.

However, linear attribution can also have its drawbacks. By giving equal weight to all touchpoints, this model may overvalue less significant interactions and fail to capture the varying levels of influence each touchpoint has on the conversion. For example, if a customer interacts with a blog post, attends a webinar, and then downloads a white paper before making a purchase, linear attribution would attribute equal credit to each of these touchpoints, potentially overlooking the unique impact of each interaction.

U-Shaped Attribution

The U-shaped attribution model provides more weight to the first interaction and the touchpoint that leads to conversion while giving less credit to intermediate interactions. This model strikes a balance between acknowledging the importance of initial engagement and recognizing the significance of conversion-driving touchpoints. For B2B businesses with longer sales cycles, the U-shaped model can offer valuable insights into which early touchpoints attract prospects and which final touchpoints are crucial in closing the deal.

The U-shaped model is particularly useful when you want to understand the relative importance of initial and final touchpoints. However, it may not fully account for the influence of touchpoints in between, which can also play a crucial role in nurturing the prospect through the sales funnel.

W-Shaped Attribution

The W-shaped attribution model adds more granularity by assigning credit to the first touch, the lead conversion touch, and the final deal-closure touchpoints. This model is designed to provide a comprehensive view of the customer journey, capturing the influence of key stages along the way. In a B2B setting, where a prospect's journey may include various touchpoints such as content downloads, webinars, and sales meetings, the W-shaped model ensures that significant interactions at each stage receive appropriate credit.

While the W-shaped model offers a detailed view of the customer journey, it can also be complex to implement and interpret. The model’s emphasis on multiple key touchpoints may lead to a more detailed understanding of the customer journey but may require more sophisticated tracking and analysis.

Time-Decay Attribution Model

The time-decay attribution model assigns more credit to touchpoints closer to the conversion event, assuming that later-stage interactions significantly impact the final decision. This model recognizes that earlier interactions are essential but less influential than those closer to the conversion point.

The time-decay model can help identify which touchpoints are most influential in the final stages of the customer journey. For instance, if a lead interacts with various marketing channels over several months, the time-decay model would attribute more credit to the interactions that happen closer to the conversion date while still acknowledging the role of earlier touchpoints.

However, it may undervalue early interactions that played a crucial role in initial engagement. By focusing more on recent touchpoints, this model may not fully capture the cumulative impact of the entire customer journey.

5. Linear Attribution

‍A linear attribution model assigns attribution credits evenly among all touchpoints. While this model is far more illustrative than any of our single-touch attribution options, it's a relatively simplistic approach when compared to its nonlinear variants. 

Let’s assume that the total number of touchpoints in our PMS example is four: An advert, a blog, a review, and a retargeting campaign. Linear attribution would reward 25% of attribution credits to each of these touchpoints.

Of course, in reality, the number of touchpoints a B2B customer goes through is significantly higher — so the weights for each one are likely to be far smaller.

6. U-Shaped Attribution

‍The U-shaped model assigns attribution credits to all touchpoints — but assigns higher credits specifically to the first and last touchpoints. This would imply that your customer’s first and last interactions prior to the conversion milestone are the two most valuable touch-points in their journey. 

Consider the same four touch points as with the previous example (Ad, Blog, Review, and Retargeting campaign). This time, maybe 40% of the credits will be assigned to the first and last touch points each. The two touchpoints in-between will receive only 10% each as they are deemed less influential to the conversion decision.

The model laid out in a bar graph takes the shape of the letter ‘U’, hence the name.

Key benefits:

• Balances the value of initial awareness and final action.
• Great for mid-length customer journeys with 3–6 touchpoints.
• It can be customized depending on your funnel structure.
• Doesn’t consider lead qualification or deeper CRM stages.

7. Time Decay Attribution

‍ Time decay attribution assigns attribution credits in an ascending cascade. 

What this means is that each touchpoint is given progressively higher credit, with the first touchpoint having the least credit and the last touchpoint having the most. This is an effective tool in mapping out a customer’s conversion journey. 

The model works on the assumption that touchpoints closer to the conversion were far more influential than touchpoints further away from the conversion. Again, using our handy four touchpoint PMS example, a time decay model would assign attribution credits in this manner: 5% for the advert, 15% for the blog, 20% for the reviews page, and 60% for the retargeting campaign.

7. W-shaped attribution

This type of attribution model is similar to the U-shaped model we discussed earlier. 

The first and the last touchpoints are also given importance in this model, just as in the U-shaped model. But during the middle of the sales funnel, if you generate quality leads, then that touchpoint is also considered influential. And therefore is given equal importance as that of the first and last touchpoint. 

So, if there are 5 first touchpoints in total, the first, middle, and last touchpoints will be given 30% each and the rest only 5%.

To give you a clear-cut idea, take five touchpoints. For example, an advert, a blog, a case study, reviews, and finally, retargeting campaign. 

A prospect got in touch with your business through an advertisement, prompted to read your blogs, where they decide to subscribe to your business’s newsletter. Thereby generating a lead towards the middle of the process. The lead then continued to follow up on their research by constantly staying in touch with the business through newsletters. And finally, the lead converts by signing up for a free trial. Following is an example of a graphical representation of the W-shaped attribution model for the given example. 

8. Lead Conversion Touch Attribution

This model attributes full credit to the touchpoint that converted a visitor into a lead, such as filling out a form or subscribing. It’s especially relevant for lead generation campaigns. It credits multiple touchpoints across the buyer journey, not just one.

It's commonly used by marketing and demand gen teams to understand what activities helped generate leads, even if those leads haven’t converted to customers yet.

For instance, consider this user journey:

1. Clicks a LinkedIn ad → No lead
2. Downloads a whitepaper from organic search → Still not a lead
3. Clicks a retargeting ad → Fills out a demo form → Becomes a lead
4. Sales calls and closes the deal

Lead Conversion Touch Attribution would distribute credit across Steps 1–3 (touchpoints before lead conversion) but exclude Step 4, since it happens after lead creation.

Influence Attribution

Influence attribution, or custom attribution, is a flexible approach that assigns credit to all touchpoints that have influenced the deal. This model allows marketers to analyze the impact of different channels and interactions on the final conversion, providing a comprehensive view of how various touchpoints contribute to the customer journey.

While influence attribution offers valuable insights into channel impact and the relative effectiveness of different marketing efforts, it carries the risk of double-counting revenue. By assigning credit to all touchpoints involved in the conversion process, this model may attribute more value to each touchpoint than is warranted, potentially leading to inflated performance metrics.

Choosing the Right Attribution Model

Selecting the right attribution model depends on several factors, including the complexity of your business, the length of your sales cycle, and the specific insights you want to gain. Here are some key considerations to keep in mind:

1. Business Complexity and Sales Cycle Length

Single-touch models may provide sufficient insights for simple, transactional businesses. For more complex B2B sales processes, multi-touch and time-decay models offer a more detailed understanding of how various touchpoints contribute to conversions.

2. Key Insights

Determine what questions you want to answer. Are you interested in understanding what drives initial sign-ups, or do you need to know which touchpoints are most effective in closing deals?

3. Ease of Implementation

Choose a practical and feasible model for your marketing and sales teams to implement. While multi-touch models provide more detailed insights, they may require more sophisticated tracking and analysis.

4. Goals and Metrics

Adapt your attribution model based on whether your goal is to track revenue, measure the effectiveness of touchpoints, or evaluate overall marketing performance.

Here’s a summary table to help you choose the right attribution model based on your needs:

‍

Limitations of Attribution Models

Single-touch attribution models (like first-touch, last-touch, and list non-direct touch) are simple to implement but have several disadvantages. They oversimplify the customer journey by assigning credit to a single touchpoint, ignoring the contributions of other touchpoints. Similarly, these models also neglect the aggregate effect of multiple touchpoints over time. What results is inaccurate credit allocation, because the model disregards individual customer behavior and other factors. 

On the other hand, multi-touch attribution models are definitely more complex because they work with complicated algorithms and technology. This often requires expert knowledge and pro- marketing knowledge of marketing software. The impressions from data can be misleading because of shortcomings like wrong assumptions and wrong weightage assigned to each marketing activity. To add on, while multi-touch attribution models are efficient for data- rich digital marketing campaigns, they are not equipped to measure external factors like word-of-mouth, seasonality or pricing.  

Like single touch attribution models, multi-touch attribution models can also miss out on giving the full picture. Linear attribution models assume that all touchpoints have equal influence on customer behavior, which is not always the case. U-shaped, W-shaped and Time-Decay models run the risk of oversimplifying the customer journey since they assign more credit only to some touchpoints, while neglecting others. This could cost the model some valuable insights and paint an incomplete picture. The time-decay attribution model considers the recency of the customers close to the conversion event, but it can still overlook the significance of earlier touchpoints.

5 Marketing Attribution Tools For 2025

To use marketing attribution models well, you need the right marketing attribution tools to gather and analyze data from different channels. These tools help you understand the customer journey and improve your marketing strategies.

1. Factors.ai 

Factors capture intent signals and automate tasks, with no-code integrations and strong support. It's useful for businesses wanting to simplify their attribution without needing technical skills.

Features:

• Multi-touch attribution with easy setup.
• Lead scoring and buyer journey mapping.
• No-code integrations with CRMs and ad platforms.
• Intent signal tracking from anonymous and known users.
• Automated reports and insights.
• It has a free version. The paid plan starts at $399 per month.

2. Google Analytics 

It is a common choice. It shows where traffic comes from and how users behave. It supports basic models like Last Click and Linear Attribution, making it good for businesses new to attribution analysis.

Features:

• Supports Last Click, Linear, and Time Decay attribution models.
• Real-time website traffic monitoring.
• Integration with Google Ads and Search Console.
• Funnel and goal tracking capabilities.
• Free and accessible for businesses of all sizes.
• Public pricing is not available.

3. Adobe Analytics 

It offers advanced modeling, including multi-channel analysis and data-driven insights. It's ideal for large businesses needing detailed analytics across many touchpoints.

Features:

• Multi-channel and cross-device tracking.
• Custom attribution modeling and segmentation.
• Predictive analytics using AI and machine learning.
• Real-time data visualization and reporting.
• Seamless integration with Adobe Experience Cloud.
• Pricing details are not available.

4. LeadsRx 

LeadsRx focuses on multi-touch attribution and customer journey analytics. It provides a clear view of how different channels lead to conversions. It's great for businesses wanting to understand the full customer journey.

Features:

• Unified view of marketing channels and conversions.
• Cross-device and cross-domain tracking.
• Integration with CRM, ad, and marketing automation tools.
• Real-time attribution and performance reports.
• Clean, visual journey mapping interface.
• Paid plan details are not publicly disclosed.

5. Wicked Reports

Wicked Reports specializes in revenue-focused marketing attribution, particularly for e-commerce and subscription-based businesses. It helps marketers link marketing activities to actual sales, focusing on long-term ROI.

Features:

• Tracks full customer journeys with multi-touch attribution models.
• Measures true ROI using customer lifetime value (CLTV) tracking.
• Offers detailed attribution for email, paid, and organic channels.
• Integrates with platforms like Shopify, Klaviyo, Google Ads, and Facebook Ads.
• Provides cohort-based analysis to track marketing effectiveness over time.
• Paid plan starts at $500/month.

Choosing the right tool depends on your business size, budget, and specific needs. Look at these tools based on their features, pricing, and integration abilities to ensure they fit your marketing goals and data setup.

Also, read this guide on common challenges in B2B attribution and their solution.

‍

Takeaway

Needless to say, all attribution models are not appropriate for all use cases. Different attribution models aid different types of marketing campaigns and can reveal different insights into the customer journey.

In the end, a lot of the use cases for these types of attribution models are subjective. The decision to opt for a specific model can be based on several reasons spanning from the nature of your product to the extent of your brand equity. It may also vary based on the specific kind of insight you want to achieve. 

More often than not, you will find yourself using more than just one model with several stipulations and custom values for each variant. Fortunately, the progressive ingenuity of AI and constant innovations around attribution modeling will render your experience less of a trial by fire and more of an intuitive, insightful practice. 

Leveraging the right marketing analytics platform will be the first step in deciding the attribution model required for your company/business. As we said, it's best to rely on more than one model to improve your desired results. And for that, you will need an expert team, like Factors, that understands your requirements and guides you in leveraging the right techniques. 

With Factors.ai, you can easily track the effectiveness of your campaigns and content, identify which channels are driving the most conversions, and optimize your marketing efforts for maximum results. The tool also offers a user-friendly interface and customizable dashboards, making it easy for you to access and interpret your data.

Interested? Sign up here for a FREE trial, or contact our team to get a Free consultation now. Here is the contact email for your reference - solutions@factors.ai

Bonus FAQs

1. How do I choose the right attribution model for my business?

In order to choose the right attribution model, you will need to know the target market, the target audiences, and so on. And once you have everything set, consider the following.

• Define your business goals. The attribution model you select must align with your business goals. Is it sign-ups? Leads? SQLs? Or just organic traffic.
• Once you have defined the goal, understand the types of attribution models and how each model allocates credits to the touchpoints.
• Evaluate the data you have to get an idea of the current touchpoints where your business is driving conversions [goals].
• Test out different models to see which is more effective.
• And finally, constantly review the results and update the models according to the business needs. 

‍2. How do attribution models help find the gap in the customer journey?

As we discussed earlier in the blog, each attribution model provides insights into your customers' touchpoints with your business. Which itself gives the different paths each customer has taken to reach your service. 

Thereby helping you understand the customer journey and find the touchpoints you missed during your initial marketing campaign.

3. How do attribution models help in improving the conversion rate?

Attribution models help improve the conversion rate by identifying which touchpoints in the customer journey are most effective in driving conversions. 

They enable data-driven decisions helping businesses optimize their marketing budget and allocate resources efficiently to boost conversion rates.  

As an organization, in any industry, it's important to understand the audience behavior on websites and what gets them to convert or drop-off. These insights help optimize website content and improve its overall effectiveness.

The D2C (Direct-to-Consumer) industry is no exception. With tens of thousands of visitors logging sessions each day, knowing what exactly they do on the website, what pages they visit and what influences them to convert is crucial. But how do you go about doing this?

Let’s dive into the kinds of analyses that can be performed to truly understand the user journey on a D2C website.

Page Funnels:

For this, let’s consider a common buying process seen on D2C websites:

1. Select the items to purchase
2. Visit ‘Cart’ to review items and proceed to ‘Checkout’
3. Complete payment on the ‘Checkout’ page
4. On successful payment, the order is placed
   

While this seems to be a fairly straightforward process, there is a lot that goes on behind it. Here are the questions that you need to ask:

• What pages do users visit before they reach the checkout page?
• How much time does it take for users to place their order after reaching the checkout page?
• What pages do users visit before they place their order?
• What pages accelerate the buying process?
• What pages do users visit based on the marketing campaign they came from?
  

The answers to these questions will help you understand the success and failure paths on your website. For example, you might see a huge percentage of users visiting the ‘Reviews’ section right before checkout indicating the need for validation. Hence you must highlight the ‘Reviews’ section clearly.

Another insight would be users from, let’s say, an Instagram campaign tend to follow a particular path before placing an order. This can then be used to tweak ad communication and landing pages for the campaign to improve CTRs and possibly conversion rates.

Measurement of Experiments:

Experiments are a key part of any marketing activity whether it’s changing website banners, re-positioning items, highlighting content, or simply changing colors.

However without a measurement framework, you will never know the true impact of an experiment. Performing such analyses is necessary to measure the outcome of an experiment.

Let’s say you have recently changed the home page banner and re-positioned a page link from the footer to the top. The questions that you should be asking here are:

• What has been the impact on the conversion funnel after changing the banner?
• Are users spending more time on the website after re-positioning the page link?
• Is the re-positioned page link playing a crucial role in the conversion funnel? And so on.
  

This will help you know what experiments should be scaled and the ones that should be halted.

User Attributes and Behaviors:

Understanding how different types of users behave on the website helps personalize content and optimize marketing campaigns.

For example, you observe that new website visitors from Mumbai tend to spend more time on one of your blog pages than any other. Or, visitors who use an iPhone have a 30% higher funnel entry rate than other visitors using other devices. As an actionable, you would promote the blog in campaigns running in Mumbai and increase bids/budgets when a user using an iPhone is searching for your product.

Similarly, uncovering other such insights can go a long way towards amplifying your marketing ROI.

Multi-Touch Attribution:

Knowing how different marketing touchpoints play a role in a user journey is crucial especially when it's time to scale marketing campaigns.

The questions that you should ask here are:

• How do I know if my Facebook/YouTube/Google campaigns are working?
• How do different keywords affect the conversion funnel?
• Is everything being attributed to ‘Brand’ campaigns? If yes, how do I know the influence of other campaigns?
• What would the scenario look like if I were to change the attribution model (for example from last touch to linear touch)
  

The answers to these questions will help you understand the impact of marketing touchpoints and their cost effectiveness.

Asking yourself the right questions and being equipped with the right tools will help you uncover hidden insights with the data you always had.
Factors.AI helps you get critical insights into marketing activities and decoding customer behaviors.

What are Lead Magnets?

Lead Magnets are ‘gated’ content pieces that are created with the aim of providing useful information to users in exchange for their contact details (Email IDs/Mobile Nos). Content pieces such as newsletters, guides, white papers and other informational documents are used for this purpose.

The captured leads are then nurtured through customized email sequences in order to improve funnel progression and conversion rates.

Here’s a classic example of a lead magnet:

Let’s say you’re browsing a business website that provides sales intelligence to other companies. Just as you reach the middle of the page, you see a link to an insightful and informational guide. To access it, you click on the Call-To-Action button ‘Download Now’, which then triggers a popup asking for your email ID. Upon successfully entering the ID, you’d have access to the guide while the marketing team at the other end would add your ID to a mailing list for the purpose of nurturing.

Why Don’t We Give All Content For Free?

To answer this question, let’s analyze the pros and cons of not having any gated content on the website:

1. Pros:
   

- High Accessibility: With no form in place, users will be able to access content without filling any form, thus reducing drop-offs

- Helps with SEO: If you’re not gating content, it means it lies in its full form on the website. This helps improve SEO score through strategic keyword placement within the content.

- Better Content Tracking: Since the content is not gated, metrics such as session time duration, page time, bounce rate and so on can be calculated to gauge the effectiveness of the content

2. Cons:
   

- No Leads Acquired: The contact details of the user reading the content will not be known. Thus, the nurturing process cannot take place.

- Losing out to competitors: It is highly likely some other competitors will be using gated content with structured nurturing sequences and could end up winning a customer even though their content may not have had a similar impact.

Thus, while the accessibility and visibility of your brand increases with ungated content, you lose out on leads which other competitors may be able to capture. 

Mixed Gating Strategy - Intent Driven

A mixed gating strategy involves using both gated and ungated policies based on the type of content.

When your focus is on improving the top funnel such as website visits, content that indicates low intent can be ungated. For example, a document on ‘What is Marketing Analytics?’ would be considered low intent since the vast majority of the users would be in the exploration phase and not ready for a sales call yet . This helps in avoiding the generation of leads with low quality who are not in the buying process yet thus allowing Sales Reps. to focus on high quality leads.

When you’re focusing on generating high intent leads, gated content can be utilized. For example, a guide on ROI analysis . Since such content indicates high intent, you can expect the lead volume to be low and the quality to be high.

A good way to connect the two gating policies would be re-marketing campaigns. All users who have visited the ungated content can be re-targeted with promotions for high intent gated content. Leads generated from these campaigns can be then expected to have higher quality in terms of conversion rates.

Account Level Tracking - Isn’t It Sufficient?

Many businesses that have adopted account-based marketing would argue that a mere visit to the website on any of the content pages would be enough to create a user profile with details such as location, company of the user, etc via IP address identification tools. This would seem to solve the gated/ungated content conundrum.

However, there are two points to be considered here. One, most IP address tools are not 100% accurate leading to missing or sometimes even wrong data. Two, even if you have been able to correctly identify the user’s location and company, how would you go about contacting them? It could be a marketing specialist belonging to a large corporation with 10K+ employees or a software engineer of a mid-sized company. Either way, with no email address, there would be no way to determine who read your content.

Thus, account level tracking gives limited understanding of who is reading your content, but is not enough to get contact information that can be used for customized nurturing sequences. 

Finally, it’s important to focus on the content quality and the value it adds to the readers. There should be a strong enough reason for a user to submit their Email ID in exchange for the content. Good quality content will leave a lasting impression on the reader and aid towards brand recall.

Webinars are a great way to communicate with business prospects. They empower you to demonstrate value to hundreds of people whilst sitting in the most remote parts of the world. They enable you to deliver memorable presentations from all across the globe without leaving your desk.

But how should you go about hosting a webinar that, in addition to adding value to your audience, converts them into paying customers?

Let’s go step-by-step and list the key stages of planning a successful webinar:

1. Topic and Audiences:

The very first step towards executing a successful webinar is to identify a topic — and the right target audience for it. Pick a topic that adds value to your target market and makes attendance worth their time. Perform extensive market research to truly understand the challenges and interests of your audience. Needless to say, you should only choose a topic you and your company are proficient in. In the case of Factors.ai, for example, this might be presentations related to marketing analytics, multi-touch attribution, etc.

2. Communication and Promotion Channels:

Once you’ve set your topic and identified your target audience, it’s important to make a concise communication plan for the same. This would involve shortlisting  channels to be activated for promotions, content buckets/themes, and a timeline of when you plan to engage the audiences. Here’s an example:
Channels: Social Media (FB, LinkedIn), Google Ads, Email, Slack Communities, etc
Content Theme: Brand of Speaker, Virtual Summit, Value proposition, etc
Timeline: Ads to be run from N-30 till the webinar date, Customized Email sequences to be sent to website subscribers on N-20, N-10, N-7 and so on.

Key metrics for measuring performance will depend on the type of channel. For instance, in the case of Social Media, link clicks and CTRs will be good metrics. For emails, metrics like open rates and click through rates may be better suited.

3. Pricing and Offers:

If you plan to monetize your webinar, a pricing model that changes based on the promotion channels may be employed (the reason being user intent).

For example, while promoting a business webinar, a user browsing Facebook (lower intent) may need more convincing than a user browsing LinkedIn (higher intent), given the latter is a business platform. This is where price fluctuation will  convert even low intent users into webinar registrants. You may look to promote the entry fee for the webinar on Facebook at 10-12$ while promoting the same on LinkedIn at 18-20$.

Another variation that may be added to the webinar are offers. If you have a product/service that would be promoted before/during the course of the webinar, create a custom offer just for webinar promotions. This way, you will also be able to better measure the performance of the webinar

For email sequences to already subscribed users, specific offers (based on their profiles and funnel stage) can be created to improve attendance and pipeline velocity.

4. Creatives and Targeting:

The quality of your creative copies and designs will make or break the performance of your online promotions. The best way to approach this is to create a custom copy and design for each set of audiences to increase viewer connect. If this proves to be resource-heavy, you could experiment with 2-3 creative variations to see what works best.

Always take note of these learnings and implement the best practices for future webinars. It’s generally best practice to highlight the webinar takeaways as well as details about date and venue.

5. Landing Page or Lead Generation Form?

It's now time to decide how and where a user will be able express interest for your webinar. There are two ways to go about this:

A) Lead Generation Ads

These simple in-line forms open instantly when an ad is clicked without taking users to a separate page. Users can fill in the required fields and move on.

‍Pros:
i) Quick and easy
ii) Does not require website development, making execution faster

‍Cons:
i) Very little content can be put into these forms to educate users about the webinar
ii) Website cookies will not be generated making re-marketing campaigns less effective
iii) Integration for payment getaways (in case of a paid webinar) will prove to be a difficult task

‍B) Landing Pages

Creating short and crisp landing pages with concise content is a great way to get users to register for the webinar.

‍Pros:
i) More content can be accommodated to tell a story and convince users
ii) Integration with payment gateways is seamless
iii) Re-marketing campaigns are simple to execute through cookies for users who have visited the page but are yet to register
‍Cons:
i) Resource-heavy since it involves website development thus increasing overall execution time and cost

So, depending on the resources and time available to you, either one can be chosen.

6. Practice Run and Hosting the Webinar

No matter how well you promote your webinar, if on the day things don’t go as planned, you may end up losing all your potential prospects.

Therefore, practise the entire webinar flow and everything you plan to cover on the day. Make sure your content is validated from other team members to ensure accuracy and relevance. Finally, ensure your webinar is interactive as you do not want to lose out on participants mid-way.

7. Reaching Out To Webinar Participants

Do you plan on reaching out to each participant after completion of the webinar to check whether they’ll be interested in your offerings?

While it’s not a bad idea to do this, a poor execution strategy could leave a bad impression on your brand. It always helps to be subtle and strategic.

‍Here’s how:

• Create a checkbox in the webinar registration form asking users whether they’ll be open to calls from the Sales team to know more about your offerings. This way, you’ll know who are the ones interested to know more beforehand.

• During the webinar, take up questions from participants to understand challenges they face in their business'. Use this moment to talk about the specific features that your product/service solves for these challenges and ask the participants to directly reach out to you via mail for documents like case-studies or feature specifications. The Sales team can then take the discussion forward and drive the funnel.

• After the webinar, share an event replay that can be consumed by participants who couldn’t attend the webinar and by those who would prefer to go through the content at their own pace for insights. Add a Call-To-Action here asking if they would open to be contacted by the Sales Team.

• When contacting participants, begin every conversation with takeaways of the webinar and how useful it has been for them rather than directly asking for a demo. This would help structure the conversation around the challenges faced by the participant and how your product/service could solve it.

• It is important to understand where a webinar participant is in the buying funnel. For example, if someone is simply exploring products/services, first understand their problems and use-cases, suggest ways they can solve them, and then proceed to the next stage of engagement. This would ensure you’re not too early or too late with your sales pitch.

8. Reporting and Analytics

Finally, how do you plan to measure the success of the webinar? Are you measuring the right metrics and tracking impact on the pipeline? This data is critical to understanding how much the webinar has resonated with the prospects and what needs to be tweaked to make future webinars a success.

Most teams would be measuring performance across different data silos such as Facebook Ads, Google Analytics and an MAP/CRM such as Hubspot/Salesforce.

Let’s a take real-life scenario to understand this better:

Jay, a marketing manager, has recently concluded an important webinar for his organization that develops SaaS products. He now wants to:

• Understand the impact of the efforts that were put in to set up and promote the webinar.
• Understand how the webinar participants progress through the buying funnel to focus future promotion efforts on channels that produce quality prospects

Jay’s team would be able to give a performance report on:

• Ad Platforms such as LinkedIn Ads in terms of which ads worked best, got the highest clicks, CTR and other metrics.
• Landing page visits and drop-offs across channels
• Hubspot contacts and Salesforce leads created from the webinar

While Jay will be able to gather insights on individual platforms for the webinar, more importantly, he will need a complete view into user journeys right from the first user visit all the way up to their status in the buying funnel.

This would help Jay make informed decisions for planning future webinar promotions better in order to acquire quality prospects.

At this point, Jay’s team were unable to find a way to stitch these data silos together to give Jay what he wanted.

Human beings are social animals. Over thousands of years, we’ve developed gestures, languages, and tools to express ourselves to those around us. Our exceptional ability for communication has empowered us to exchange ideas like no other species on the planet. Given that this dialogue is at the heart of the human experience, it’s of little surprise that Conversational ABM is becoming an increasingly effective engagement technique for the modern-day marketer.

TL;DR:

• Conversational ABM is a marketing strategy that uses chatbots or live chats to actively engage with target accounts.
• It is crucial to identify and segment your prospects since the demography of each prospect could vary.
• Set proper boundaries when assigning SDRs and ensure that the visitors are routed to appropriate SDRs. 
• Ensure you’re running personalized ads to each prospect and provide relevant and consistent messaging throughout. 
• One of the best platforms to converse with your prospects is LinkedIn.
• Be ready for your prospect at any time by using AI-powered chatbots.

What is conversational ABM?

Conversational ABM is a marketing strategy that uses chatbots or live chat to engage actively with target accounts. 

With real-time conversations, businesses can build strong relationships with their target audience and address specific needs. In addition, it creates a more human connection with prospects, leading to a higher likelihood of closing a deal. 

And because 90% of prospects identify live messaging as their most favored channel of business communication, conversational ABM is a strategy worth considering.  

How to implement a Conversational ABM strategy?

1. Identify your target accounts

As is the case with any ABM strategy, your first step should be to align marketing and sales through a collaborative identification of accounts. 

The target list is usually determined by a few specific firmographic characteristics such as industry, revenue, and geography. Once generated, this list will dictate the tone and language of your messaging, content, and campaigns. So getting it right is pretty important. 

2. Identifying and segmenting prospects

Once you’ve created a fresh list of target accounts, the next step is to identify individual users at these target accounts to reach out to within this list. Maybe you want to target CXOs, or maybe managers, or maybe engineers, or maybe a combination of a variety of such roles.

Regardless, the optimal approach for each demographic will undoubtedly vary. Hence, it would make sense to segment this list of prospects further by customer life cycle, sales stage, pain points, and, most importantly, intent. Then the person in charge allocates this segmented list among Sales Development Representatives, who can work out distinct marketing strategies for their targets.

3. Building boundaries

In an ABM approach, it is important to assign individual Sales Development Representatives to build a strong relationship with each prospect. 

When assigning SDRs, always keep in mind to set strict ownership boundaries. It helps route the visitors to appropriate SDRs and eliminate any engagement overlaps. 

4. Personalizing ads

Okay, now you know whom you’re contacting and why. Now it’s time to think about the approach for each prospect. This stage involves an intricate balancing act between personalization and scale. 

Of course, every individual in every role across every company you’re targeting has their own unique preferences — but personalizing ads at that level isn’t feasible. Instead, customizing ads on a higher level — say, by role or industry, is the way to go. This entails running campaigns based on prospect-specific pain points, and value adds. 

A CMO may care about marketing’s influence on revenue, while a marketing manager may be interested in improving workflow and automation. Your campaigns should resonate appropriately with all such use cases.

5. Sentry Surveillance

Your target list is ready, and your personalized ads are running. Now, the second a prospect from your list is on your website, your marketing + sales teams need to be conversation-ready. 

The first step here is to make sure everyone has access to all the information they’ll need. It means all your CRM data, marketing automation data, and intent data should be consolidated, organized, and easily accessible. Once equipped with all relevant information about the visitor and their company, your SDR team is all set to engage with the prospect.

6. Complete consistency

Personalization is the most important aspect of conversational ABM when a prospect is currently on your website. 

Assuming your prospects love your ads and visit your website, they should be landing on a homepage that’s relevant to them. Any decent content management system (CMS) will be able to identify a contact when they land on your homepage and cater to the web flow in a manner that ensures a personalized experience. 

7. Chit-Chat

A relevant landing page will definitely help direct prospects toward your product. But a lot of the time, this won’t be sufficient. 

A target will stay on your website only for a few precious minutes, and it’s important to make the most of it. Sure, you could wait until they make their way to the demo form and submit their details — but Conversational ABM encourages marketers and SDRs to proactively reach out through a relevant live-chat message. 

References to the contact’s role, the company’s signals, or a prominent pain point are all great ways to get the conversation going. This is the meat and potatoes of the Conversational ABM process. SDRs utilize target data to provide a genuine, relevant, and personal dialogue with their prospects to confirm a demo and push accounts through the funnel

8. Conversational ABM - Around the clock

Conversational ABM involves interacting and connecting with prospects around the clock. While thorough research and proactive interactions are valuable tactics, you may want to employ AI-powered bots to render the process air-tight. So when you do happen to get that one inbound demo at 4 in the morning, you can trust that your chatbots will be up to schedule that demo for you. 

Oh, and another thing — conversational ABM doesn’t top conversations on your website. Linkedin is your friend when it comes to interacting with your target’s content posts. Feel free to leave likes, comments, and, if appropriate, connection requests with prospects. 

Conclusion

And there we have it. When executed well, conversational ABM can be a valuable strategy to bolster your marketing efforts and improve conversions. Though it’s definitely a lot more effort than traditional marketing techniques, conversational ABM pays its dividends in the long run. Prospects form stronger associations with the product and are almost certainly more likely to convert from a distant target to a tight-knit customer.

Factors.ai enables easy integration with CRM platforms like HubSpot and Salesforce. This  can help you generate a more effective ABM campaign. Signup for free or book a demo to start your Conversational ABM campaign today.