Traditionally, marketers calculate the ROI of a marketing investment by measuring how much sales increased in its aftermath. This is a blunt metric: maybe the consumer had a different interaction with the brand that influenced them. Or maybe they had an intrinsic preference for the brand and would have made a purchase anyway.
Today the situation has changed. Marketers have access to data that allows them to track individuals’ various interactions with a brand before their purchase, and better understand what role each interaction — and individual preferences — played in the eventual sale.
This approach, called “attribution modeling,” allows companies to attribute appropriate credit to each online and offline contact and touch point in a customer’s purchase cycle, and understand its role in the revenues that ultimately result. A good attribution model should show, for example, precisely which ads or search keywords are most associated with actual purchases.
an attribution model is a gradual process. You can’t get there all at once. There are four key stages in the journey:
Stage 1: Prepare your data
You can’t have any kind of attribution model without data around touch points and outcomes. Many companies collect this data but often store it in different databases and in ways that make comparison difficult. Once companies can access and analyze data around touch points and purchases, they can detect patterns and are ready to apply simple attribution models. These involve applying rules of thumb, such as “give all credit to the last point of interaction” or “give equal credit to all points of interaction with the customer before a purchase is made.”
Stage 2: Experiment
As managers get more comfortable with a rules-based model they can begin to conduct experiments to fine-tune the attribution rules. Most importantly, you can start to assess the degree to which a given touch point depends on other touch points; you could, for example, test a search tool’s role in a customer’s cycle by turning display advertising on or off. This allows managers to identify clusters of touch points that might individually look less powerful but that collectively pack more punch than simply focusing on those that look individually strongest.
An insurance company we interviewed conducted several regional experiments to evaluate the synergy of television, organic search, and display advertisements. The company varied the exposure of its consumers to TV ads across the different regions they served. They found that organic visits to the website and display advertisement click-through all increased disproportionally in a region when consumers there were also exposed to TV ads. This experiment motivated the firm to start better coordinating their marketing campaigns across media channels.
Stage 3: Apply statistical models
Companies that are experienced in identifying and testing simple patterns that arise from the data soon become ready to try out more sophisticated attribution models, typically involving multivariate regression analysis, maybe even employing Bayesian estimation. These models offer formulae that allow marketers to determine with some confidence what touch points to invest in, the synergy obtained from multiple consumer exposures to the same media over time, and how much to put in relative to each other. Importantly, these models explain and predict. While they are not perfect predictive models, of course, following the attributions determined by the model will, as with the initial application of simple rules, usually deliver an improvement in ROI.
A retailer we worked with used multivariate regression analysis and Bayesian estimation to understand the effect of repeated exposure of consumers to their targeted offline communications. The analyses showed that the retailer needed to communicate in all channels but could decrease the rate of communications over the three months following the first touch, rather than maintain a constant stream of messages. Acting on this finding boosted the ROI of their offline communications by at least 10%.
Stage 4: Expand the scope of analysis
So far, the marketer has been making attributions solely on an analysis of the customer’s purchase journey – how the company has touched the customer from the start of the purchase cycle to its conclusion. But a customer’s choices are also strongly determined by experiences that take place outside that journey — both in terms of time and in terms of who the customer is actually interacting with.
To gauge the effects of out-of-time interactions or interactions with other parties companies can turn to the fancier creatures in the bestiary of statistical methodologies. Panel vector-autoregression (Panel VAR) models, for example, can be used to model the effect of a company’s television advertising in the current time period on the effectiveness of other media channels (e.g., paid search click-through) in future time periods. Of course, these statistical models then can be combined with subsequent experiments to test the very recommendations from the statistical models in the field.
A software company we advised developed such a statistical model, and improved it over iterations in order to understand the attribution rules across offline (such as TV and radio) and digital media (branded and unbranded search, display, and so forth). While a “last point of interaction” rule would have given all credit to branded search for this company, an advanced statistical model like a Panel VAR accurately showed this company that clicks coming through branded search were boosted by TV advertising. Subsequent to this analysis, the company in fact increased their investment in TV rather than decreasing it. Implementation of this new strategy led to a substantial improvement in total marketing ROI for this company.
Dealing with complexity is an unavoidable necessity for today’s marketers — making informed and advanced media and channel allocation decisions in a multichannel and technology-mediated business environment is a demanding task by any standard. Attribution modeling is perhaps the best navigation tool for companies negotiating complex cause-and-effect environments but using it work requires a willingness to build the right capabilities over time. And remember that you have to learn to walk before you run.