Sohan Karuna

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.

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.

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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.

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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.

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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.

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.

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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.

‍4. 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.

5. 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.

6. 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. 

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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.

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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.

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

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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.

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.  

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.

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.

What’s New?

Over the course of the last month, Google has announced new updates with regard to Google Ads and its API. While there are several new feature announcements, this piece will focus on a few of the overarching updates and changes. These include:

1)     API support for Performance Max and Keyword Planner

2)     Demand forecasts on the insights page

3)     Performance Planner new features

4)     Spend fluctuations with tROAS and maximize conversion value

What Are The Implications? 

API support for Performance Max and Keyword Planner:

Google Ads API as described by Google is the “next generation of AdWords API”. This holds true as Google makes it clear that AdWords API will no longer be available as of April 2022. And as users make the shift to Google Ads API, they’ll find that the keyword planner can now be integrated with API support, which enables more flexibility with data and aids in keyword research. The API features the option to generate keyword ideas, historical metrics — such as average monthly searches over the past 12 months, an approximate monthly search volume. Also, marketers will now be able to forecast metrics for existing campaigns — such as impressions, click-through rate, cost, etc. API support for Performance Max campaigns will be available as well. This will expedite the management of your campaign along with making adjustments easier for developers.

Demand Forecasts on the insights page:

Google Ads now feature demand forecasts on the insights page. The demand forecasts will predict upcoming trends of search interests for products and services with the help of historical data. The forecast will generate data for up to the next 180 days.

The search trend insight is of two types — an “Account” insight which relates to demand forecasts generated through your existing ads, and a “Suggested” insight, whose demand forecasts inform you on other relevant opportunities. These forecasts will enable you to better understand the factors that promote the demand behind major trends.

Performance Planner new features:

The performance planner tool has a few notable updates mainly around efficiency optimization. Starting with a “Suggested Changes” column which offers bid suggestions and budget recommendations. Along with the option to add columns to a performance plan which will display secondary metrics which help reveal insights outside your key metrics. Users now can choose a specific time range for their historical conversion rate with the aim of using a historical conversion rate that has a similar time or date range that’s in line with their plan. Ineligible campaigns — which are campaigns that were deleted, drafted or had been changed to become eligible but have not been running for more than ten days — can now be added to your plan by using its past performance or manually adding in its forecasts so as to plan with your entire account.

Spend fluctuations with T-ROAS and maximize conversion value:

On the 2nd of December 2021, Google Ads users that were using the tROAS — target return on ad spend — and the maximize conversion value bid strategies experienced “spending fluctuations”, which were reported to Google Ads and later confirmed by a Google spokesperson. Google’s support agent reported that “the issue has been identified by the team and is resolved.” Google Ads liaison Ginny Marvin confirmed that affected users will receive credits for the spending fluctuations.

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. 

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.