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.
If you have ever invested in SEO services to drive inbound traffic to your website, then you are probably familiar with the charts and graphs coming out of analytical tools such as Google Analytics (and the latest version of that tool, GA4, must be adopted by July 1st of this year).
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 3 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 (DuckDuckGo) 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.
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. For example, successive SEO consultants can create a new profile in Google Analytics every time that does not automatically wipe previous tags off your website which can result in double counting things like button clicks and field entries and result in misleading reporting. 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.