Use audience measurement data to optimize digital display campaigns. See how with this real-life example.
These days, advertising and data platforms are giving marketers a wealth of information that can be used to validate their strategies and optimize their digital campaigns for better performance. There's a lot of data to sort through - some more useful than others. Sometimes, good campaign optimization comes down to the basics: understanding who your audience is, and why they are doing what they are doing.
Let's look at a real-life example of a digital display campaign, run through the digital ad agency of a popular mattress retailer. The agency wanted to test new inventory sources for the campaign by running broadly on general interest sites, evaluating the demography of audiences that showed purchase intent, and optimizing over the course of the campaign to maximize impact.
A theory being tested was that older audiences, who report more difficulty sleeping than younger demographic groups, would respond more favorably to the retailer's online display ads. Campaigns were initially skewed to sites that over-indexed against an audience composed of ages 50 and older.
Figure 1: Age of ad viewer, by impressions.
As Figure 1 shows, a bulk of impressions during the discovery portion of the campaign were delivered to visitors aged 46 to 65 years of age, which was the desired demographic. After analysis of those who viewed or clicked on a display ad, and then went on to purchase, the audience composition was remarkably different. As shown in Figure 2, the bulk of conversions came from those aged 18 to 45.
Figure 2: Age of mattress purchaser (conversions).
The agency adjusted the ad buy to heavy up on sites that over-indexed for a younger audience, and opted out of buys tailored to the older demographic. As wasted impressions were trimmed down in the overall plan, conversion rates increased dramatically. Testing and validating your instincts with data on an ongoing basis is the key to success in digital display advertising. The mattress retailer, who experienced better sales from older store visitors (offline), found a more responsive younger audience online. Although it seems obvious, having the initial data means being able to smartly allocate marketing capital, and having access to ongoing data means not having to rely on old insights in a changing marketplace.
Another offline theory the mattress retailer sought to validate was the mattress lifecycle. After collecting brick-and-mortar sales data for years, the retailer knew that the average life of a mattress was approximately seven years, and that the single greatest life event influencing the purchase of a new mattress was moving. Therefore, it made sense to target audiences based on length of residence (less than seven years), and target content around buying or renting a new home.
Inventory was bought from a wide range of home-specific and moving sites, and measured using Aperture audience measurement populated with data sets from Experian, IXI financial, V12 demographic, and Nielsen PRIZM data.
Figure 3: Length of residence, by impressions.
Figure 4: Length of residence, by click.
As Figures 3 and 4 amply demonstrate, the mattress retailer was targeting the bulk of impressions toward individuals reporting over seven years of residence in a single location, and clicks among that group indexed the highest in aggregate. That data validated the approach of buying into sites with a strong audience of self-reported homeowners. However, a deeper look into audience data revealed a strong distinction between renters and buyers.
Figure 5: Comparing impressions and conversions by home ownership status.
As noted in Figure 5, although the bulk of impressions in the campaign were served to homeowners, renters were the ones buying the most mattresses. This learning did more than any other data point to drive campaign optimization.
Naturally, the next step in the campaign optimization process was to focus inventory delivery to sites that promised a concentrated audience of home renters. Sites such as ForRent.com, ApartmentGuide.com, and Renters.com were added to the optimization plan.
More insights came as the Aperture data was collected. Despite purporting to have a heavy concentration of renters, two of the more popular sites actually index much higher among homeowners, as shown in Figure 6. It looked as though homeowners that were looking into renting made up the majority of the audience - a fact that helped the retailer tailor specific messaging to them.
Figure 6: In this example, a media site aimed at renters, over-indexes against current homeowners.
For this particular campaign, the ability for the retailer to validate certain audience assumptions using real demographic data was critical, as well as the ability to leverage the distinction between two types of potential customers: homeowners, and renters. Additionally, getting real audience metrics beyond a publisher's media kit or self-declared audience information enabled the retailer to craft its creative and messaging in a highly specific way that increased conversions.
When it comes to audience validation and campaign optimization, here are three keys:
Learnings from this case study, and other valuable information, can be found in my upcoming "Best Practices in Digital Display Media," available January 2012 from Econsultancy.com.
Chris O'Hara is an ad technology executive, and the author of "Best Practices in Digital Display Media," a contributor to ClickZ, and the author of the new whitepaper "Best Practices in Data Management." He can be reached through his blog at www.chrisohara.com
2015 Holiday Email Guide
The holidays are just around the corner. Download this whitepaper to find out how to create successful holiday email campaigns that drive engagement and revenue.
Three Ways to Make Your Big Data More Valuable
Big data holds a lot of promise for marketers, but are marketers ready to make the most of it to drive better business decisions and improve ROI? This study looks at the hidden challenges modern marketers face when trying to put big data to use.
December 2, 2015
1pm ET/ 10am PT
Wednesday, December 9, 2015
5pm HKT / 5am ET