By: Werner Reinartz and Rajkumar Venkatesan
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.
Developing 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.”
They may sound simplistic, but even simple rules-based models can deliver immediate results. This was the case at one company we recently advised. Only after considerable efforts to get data for each touch point aligned in one repository could the company begin to figure out sensible rules of thumb to guide marketing investments. It began by simply allocating resources to each touch point as a direct function of its marginal ROI. Even this rather rough and ready approach sharply improved the company’s overall marketing ROI.
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
