Turning Attribution on Its Side

Attribution and predictive modeling is one of the hotter topics in marketing and advertising. With more dollars going toward online initiatives and the shift of marketing efforts toward social media, it is becoming more important to accurately decipher the role each media plays in a consumer’s purchasing decision. Marketers want to know how to spend their budgets and how to account for the efficacy of the dollars that comprise their budgets.

For the past 10 years, looking at the “last click” model may have worked; today, it paints an inaccurate picture given the decrease in clicks, the increase in retargeting and paid search efforts, the impact of social media, and the need to look at a more integrated marketing picture.

Several attribution modeling tools have standard default models from which to choose, where there are user-established weighted percentages including: last ad, even allocation, even allocation plus exclusions, time weighted, and pattern based. Additionally, you have regression analysis-based models. Both models, although quantitative based, have human elements first and foremost, deciding which model is the best for your brand given the objective, product lifecycle, customer lifetime value, etc. While the model and tool can always be optimized, the goal is typically a call to action, such as a sale.

We can understand what works to drive a sale, but are we capturing the entire picture?

What about a channel that drives a high propensity of consumers to abandon their shopping cart, generates a high volume of Facebook fans, or initiates the majority of live chat sessions? While it is not a sale, these actions are important to paint the picture of the customers’ various touch points with your brand, and what different media vehicles drive these actions. Understanding these actions allows you to tailor messaging and target consumers based on what action they are likely to take or information they are actively seeking out.

In an effort to see a more holistic, accurate picture, it is important to look beyond the incoming touch points to attribution of sale, and take into consideration all touch points – both quantitative (data driven) and qualitative. Much like media planning, while research tools are heavily used, it is both an art and a science. The numbers provide a black and white story, but when looking at the larger strategy, there is an art to determining which media, and at what levels, to include in the plan.

While many marketers and agencies continue to work on attribution, no one has perfected it.

First, it is important to look at all qualified actions. While the sale is perceived as the Holy Grail; qualified leads, friends, phone calls, downloads, etc., should not be ignored. Making informed decisions about initiatives will not be complete without taking into consideration both quantitative and qualitative information. This includes everything from website analytics to customer service feedback to user testing and brand studies.

The digital space – like consumer behavior – is continuously evolving. While data management is the new little black dress in online marketing, I believe the next “it” thing will be the marriage of strategic decisions to both budget allocation and true customer engagement.