This article originally appeared on Search Engine Watch.
One of the biggest challenges facing digital advertisers is the ability to accurately and effectively measure the impact of search marketing on display, and vice versa. Much like a billboard wouldn’t be measured in the same way as a direct mail campaign, the effectiveness of display and search tactics must be measured separately, yet considered comprehensively.
Measuring display clicks and impressions doesn’t give the full picture, but there’s no doubt it plays a critical and cumulative role in brand awareness, search volume, and ultimately, conversion.
Unfortunately, no single solution is the best fit for all advertisers, so we are frequently left to attempt to measure the correlation between search and display ourselves. In many cases, some simple and well-controlled testing can yield results.
Compare Lookalike Geographies
To measure the impact of search on display and vice versa requires an experiment whereby we analyze the results of each campaign on two demographically similar geographies. By comparing the results of search only, then search with display layered on top for one market, we can see how the display effort influences the overall campaign effectiveness.
Here’s how to do it:
1. Identify Two Similar Geographic Areas to Target
Using data from the U.S. Census Bureau, Nielsen, comScore or other third-party demographics service, select two areas that, based on relevant criteria for your audience, would likely show similar behavioral patterns. Consider the typical data like household income, age distribution, etc., but don’t overlook qualitative issues like weather and other factors.
For example, if you want to compare results between Los Angeles and New York for an ice cream brand, it’s a safe bet that New Yorkers won’t be nearly as responsive in wintertime, especially if the weather is anything like we’ve experienced this season. If one market is a college town, this will also significantly impact consumer behavior patterns, depending on whether the experiment is run when class is in session or not. And, even if both are college towns, the results may skew based on their specific schedules.
Be aware of these qualitative factors before you begin.
2. Run a Search Campaign in Both Markets For 2-3 Months
Identify patterns and gather a baseline. If you’ve done your homework correctly, the patterns should be very similar for each market.
3. Layer in a Display Campaign in One of the Markets For At Least a Month
The other will serve as the search-only baseline or “control” market. Continue to measure results on the search/display market for at least one month after the display campaign is discontinued. Because most branding campaigns often have a cumulative effect on audiences, this will enable you to continue gathering data throughout the typical response lag time.
4. Analyze the Results
The goal here is to see measurable change in the search results for the market where display was layered on. Just as you should experience when you run an offline brand campaign, you should see an uptick in the search results for the brand keywords you’ve selected for measurement. Examine the search volume and display delivery and find patterns that you can quantify to show measurable results.
5. Tweak the Mix to Achieve the Optimum Response
Now that you’ve established the relevancy and impact of display on search, roll out this highly repeatable operation in other comparable geographies wherever appropriate for your brand and target audience.
While this is by no means a foolproof method, a similar tactic has been used to measure the effectiveness of offline spending for years. It is just another way that digital marketers can leverage the same principles to uncover hidden results for their own efforts.
The key factor in the effectiveness and accuracy of lookalike measurement is to begin with geographies that are as identical as possible.
While you may see some impact from competitive marketing efforts, in terms of both cost and response, the experiment is well worth it for helping to establish the relative effectiveness of search and display.