Imagine you are the marketing leader of a $100M cloud-based software company selling your productivity solution to other businesses.  Your company has worked hard to find a winning product-market fit and your revenue team has been able to scale past the $50M mark in annual revenue by expanding its reach to cover the majority of its Total Addressable Market while focusing on, and refining, an Ideal Customer Profile.   

To continue to grow profitably, however, one of your key responsibilities is to ensure that every dollar spent on marketing activities is accounted for and justified. This is where marketing attribution comes in – the process of identifying and assigning value to the touchpoints that lead to a conversion or sale. Attribution measurement helps you understand which channels and tactics are most effective in driving business outcomes, and can inform your decision-making on how to allocate your marketing budget each quarter. 

However, the question remains – how much attribution measurement is sufficient? How much time and effort should you invest in tracking attribution, and at what point does the data become too cumbersome to manage and not worth the effort? In this article, we’ll explore the different ways to track attribution and discuss when the amount of time and effort needed to obtain attribution data exceeds its impact on decision-making. 

What is Marketing Attribution and How Can it be Measured? 

Marketing attribution is the process of identifying the touchpoints or interactions that led to a specific business outcome, such as a conversion or sale. It helps marketers understand which channels, campaigns, and tactics are most effective in driving business outcomes, and how to allocate marketing budgets accordingly. 

There are various ways to track marketing attribution, each with its own strengths and weaknesses. Some can only be used in digital marketing campaigns and others can only be exercised once a prospect becomes a customer.  Some of the most commonly used methods include: 

Cookies – Cookies are small text files that are stored on a user’s device when they visit a website. They track user behavior and can be used to attribute conversions to specific channels or campaigns that initially drove that user to visit your website. 

UTM Codes – UTM codes are parameters that can be added to the end of a URL to track traffic from different sources. They allow marketers to see which channels and campaigns are driving traffic and conversions.  Unfortunately, a tool such as Google Analytics can only report on one UTM parameter at a time so there is usually some manual aggregation of data that needs to happen to evaluate all the campaigns you may be running. 

Performance Marketing – paid media sources that provide mid-to-bottom of funnel leads are easy to track in a CRM system and, to the extent they are considered net new sales opportunities, credit can be attributed directly. 

Survey Questions – Survey questions (asked either on a registration page or verbally by an Account Executive) can be used to ask customers how they first heard about your company’s product or service. This can help attribute conversions to “hidden” influencers such as word of mouth from peers or a highly visible Public Relations (PR) motion involving a company executive. 

Each measurement technique has its own strengths and weaknesses. Cookies, for example, are easy to implement and provide a high level of granularity, but they rely on users accepting cookies and can be blocked by ad blockers.   

Client device companies (Apple, in particular) and alternative search engines (Duck, Duck, Go) have recently made market inroads based on their mission to better protect the anonymity of their users.  While UTM codes may also be easy to implement, they require consistent tagging and can be subject to errors. Survey questions, while useful for understanding customer behavior, can be subject to bias and are dependent on customers accurately remembering how they first heard about your company brand.    

Only performance marketing campaigns (based on a Cost Per Lead pricing structure) are designed to provide direct attribution because the leads they generate flow directly into a process that results in sales-accepted opportunities.

Comparing Attribution Models 

When it comes to classifying attribution, there are various models that have been employed.  The most popular method is first-touch attribution. This is probably because it is synonymous with the initiation of first-party data, whereby a suspect, employed by either a qualified account or a target account (if an Account-Based Marketing approach is pursued) agrees to receive further marketing communications from your company.  

Last-touch attribution gives credit to the last touchpoint before a conversion, linear multi-touch gives equal credit to all recognized touchpoints that led to a conversion and multi-touch weighted gives credit to each touchpoint in proportion to its perceived impact on the eventual conversion or sale.  

First-Touch Attribution 

First-touch attribution gives credit for the conversion or sale to the first touchpoint that a prospect had with your brand. This could be anything from a social media ad to an email marketing campaign. The primary benefit of this model is that it’s easy to implement and understand, making it an ideal starting point for businesses that are new to marketing attribution. 

Pros: 

Clear attribution: With first-touch attribution, it’s easy to see which marketing efforts are driving initial awareness and interest in your brand. 

Simple to implement: First-touch attribution is straightforward to implement, especially if you’re using a marketing automation platform. 

Cons: 

Limited perspective: First-touch attribution doesn’t account for any subsequent touchpoints that may have played a role in the conversion or sale. This means that it may not accurately reflect the full impact of your marketing campaigns. 

Overemphasis on top-of-funnel activities: First-touch attribution tends to overemphasize top-of-funnel activities such as brand awareness campaigns, while undervaluing the role of middle- and bottom-of-funnel activities in the conversion process. 

Last-Touch Attribution 

Last-touch attribution assigns credit for the conversion or sale to the last touchpoint that a prospect had with your brand before becoming a sales-accepted lead. This model is popular in industries with longer sales cycles, where prospects may interact with your brand multiple times before converting. 

Pros: 

Easy to implement: Last-touch attribution is also easy to implement, and can be done using most marketing automation platforms. 

Reflects the most recent touchpoint: Last-touch attribution accurately reflects the touchpoint that was most recent and influential in the conversion process. 

Cons: 

Limited perspective: Like first-touch attribution, last-touch attribution doesn’t account for other touchpoints that may have influenced the prospect’s decision to convert. 

Overemphasis on bottom-of-funnel activities: Last-touch attribution tends to overemphasize the importance of bottom-of-funnel activities such as sales calls and product demos, while undervaluing the role of top-of-funnel activities in driving initial awareness and interest. 

Linear Multi-Touch Attribution 

Linear multi-touch attribution gives equal credit to all touchpoints that a prospect had with your brand, regardless of where they fall in the conversion process. This model is useful when you want to give equal weight to all touchpoints, and avoid overemphasizing any one activity. 

Pros: 

Fair representation of all touchpoints: Linear multi-touch attribution ensures that all touchpoints receive credit, providing a fair representation of the role that each touchpoint played in the conversion process. 

No bias towards top or bottom of the funnel: This model avoids bias towards either the top or bottom of the funnel, and gives equal weight to all touchpoints. 

Cons: 

Oversimplifies attribution: Linear multi-touch attribution may oversimplify the attribution process by giving equal credit to all touchpoints, even if some touchpoints had a greater impact on the conversion than others. 

Doesn’t account for diminishing returns: Linear multi-touch attribution doesn’t account for the fact that some touchpoints may have diminishing returns as the prospect moves closer to conversion. 

Multi-Touch Weighted Attribution 

Multi-touch weighted attribution assigns credit to each touchpoint based on the perceived impact that it had on the prospect’s decision to convert. This model takes into account the stage of the conversion process, and assigns greater weight to touchpoints that had a greater impact on the eventual conversion or sale. 

While each reporting method has its own benefits and drawbacks, the intensity of conviction in the prospect’s mind typically increases when touchpoints are located farther along the buying journey.  Also, the sale of complex B2B technology involves many members of a buying committee and the first-touch attribution model may “steal” credit away from other campaigns that increased engagement with several other committee members in the same target account. Hence, the best choice of model will depend on the business objectives and the type of marketing activities being tracked. 

Finding the Right Balance 

While tracking attribution is important, it’s also important to find the right balance between the time and effort invested in obtaining attribution data and the impact it has on decision-making. Investing too much time and effort in tracking attribution can lead to data overload and make it difficult to make informed decisions. On the other hand, not investing enough time and effort (for example, relying too much on a first-touch model when many touchpoints are involved) can lead to inaccurate attribution and misallocation of marketing budgets. 

To find the right balance, it’s important to identify the key touchpoints that are most likely to drive conversions and focus on tracking those touchpoints. Secondly, you must get buy-in from your sales management partner for an attribution schema that you can both live with.  Whatever model is chosen, adoption is key and those pipeline meetings with the sales team will be much more comfortable when everyone embraces a more data-driven approach to marketing spend.  Finally, it’s important to regularly review the data and adjust attribution models as needed to ensure that the data is accurate and relevant. 

In conclusion, marketing attribution is an important tool for understanding which channels and tactics are most effective in driving business outcomes. While there are various ways to track attribution, each with its own strengths and weaknesses, the choice of how much is enough comes down to cross-functional alignment that promotes adoption rates and visibility to the algorithms behind the reporting of data.