Trying to assess the impacts of marketing on business performance is a daunting task, celebrated long ago by John Wanamaker’s famous quote regarding not knowing which half of his advertising was wasted. The root of this problem is that, for most companies, as much as 40% to 80% of their business results cannot be attributed to any specific marketing activity.
There are a number of reasons why some business results are non-attributable, data capture errors aside. In brief, they may be the consequence of a company’s prior brand building or represent the halo effects from competitor advertising, which might be stimulating demand generally in the category. Another problem with attribution is that some consumers are stimulated in several ways, often simultaneously, and they may simply choose to respond through one channel rather than another. A prime example of this problem is when consumers are drawn to a company’s website to investigate available products and services, but then opt to purchase through an offline channel. Clearly, with more companies encouraging consumers to contact them in more ways this has become a bigger issue.
The obvious concern is that, without knowing how to handle non-attributable results, assessments of marketing’s effectiveness are clouded and decisions regarding how to spend new marketing dollars may be seriously flawed.
Apportioning Business Results Across Multi-media Marketing Activities
One way to solve the attribution problem is to use an econometrically-based performance simulator to identify the various advertising and non-advertising factors that are found to be causally related to all business results, including the non-attributable results. Econometric techniques, especially time series regression, provide a well-structured means of evaluating the drivers of business performance by isolating key explanatory variables and holding constant certain variables that may mask the effects of advertising. For instance, sales may be related to the amount of spending in several media such as television, print, direct mail, and online advertising, during a specific time period. Special events and even significant public relations activities may also affect short-term sales. With adequate data, the performance simulator can estimate these influences as well.
In essence, what the performance simulator does is explain the up and down variations in business results, say, on a weekly basis by looking at variations in marketing spending and other factors that occur in proximity to those results. To do this, the performance simulator estimates the carry-over effects of advertising as well as saturation effects. While more difficult, it may also be able to isolate the effects of brand equity on results.
The performance simulator can also determine which media types directly affect business results and which “interact” with others synergistically. Separate effects are typically quantified for non-advertising influences as well, including economic trends, market factors, seasonality, competitive actions, and product changes. Taken together, these latter effects may be considered “the baseline.” So, this way, all the results, both attributable and non-attributable, are ascribed to one or more of these marketing investments or baseline factors.
An important advantage of using a performance simulator is that it can look retrospectively at what happened after a certain amount of marketing spending or can look prospectively at what is likely to occur based on planned marketing spending. In either case, the beauty is that the models can account for the full range of attributable and non-attributable business results.
Although we believe this is the best approach to understand what is driving non-attributable business results, it can be limited by certain factors. The approach requires a couple of years of reliable historical data and a high level of statistical expertise to develop the underlying models used in the performance simulator. There is also no guarantee that the performance simulator will always find statistically significant relationships or remain stable when radical changes in the marketplace disturb longstanding relationships.
Despite these potential problems, however, we have found that this approach provides important added information to the marketing decision making process.
A Brief Example
As an example, consider the case of an Internet company that serves as an online marketplace. This firm uses a multi-media marketing strategy, centered largely on television and online advertising. It also does a fair amount of outdoor advertising. While it has an active web site with large volumes of daily traffic, it is uncertain what is driving this traffic or the ensuing online transactions. Banner click through rates are modest and few individuals who click through actually transact during that same session. As a result, most of the key business results are non-attributable, forcing the company to make its marketing decisions primarily on intuition.
Using a performance simulator it was shown that the company’s site activity, while highly influenced by various non-advertising factors, is also affected in the short term by television advertising and even those dubious banner ads. The performance simulator also detected an interactive effect caused by the combination of television and online advertising, as well as other contributing influences.
Armed with these findings, the company decided that its major marketing efforts, in general, are worthwhile and that online advertising, specifically, should be expanded. It also decided to conduct more advertising tests and experiment with alternative media mixes now that it had a way to measure their impacts.
Summary
Having a way to account for non-attributable business results is extremely valuable for companies because these results often represent a significant portion, if not the lion’s share, of total business activity. Marketers need to understand what factors are responsible for producing these results since they may determine whether or not their marketing activities will achieve their business goals and be cost effective. An econometrically-based performance simulator can be a powerful tool to help marketers not only understand what caused certain results to occur, but also to estimate what results will likely occur based on new marketing spending.
Donald Ryan is Senior Partner and Director of Consulting Services for iKnowtion. Mr. Ryan has nearly 20 years of experience in the marketing field with intimate knowledge in direct and database marketing, customer behavior analysis, marketing strategy development, marketing performance management, and CRM consulting. Prior to iKnowtion, Mr. Ryan was Senior Vice President of the Marketing Consulting and Quantitative Analysis divisions at Epsilon, Inc., one of the leading database marketing firms in the United States. Mr. Ryan has also held senior management and consulting positions with Customer Development Corporation, Wheelhouse Corporation and Veridiem, Inc. He holds a BA in economics from Boston College and an MS in resource economics from the University of Massachusetts at Amherst.
