Know Your Audience

Using Audience Measurement Data to Optimize Digital Display Campaigns

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 is 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 optimize 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 audience composed of 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 46-65 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 18-45.

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 life cycle. After collecting brick and mortar sales data for years, the retailer knew that the average life of a mattress was approximately 7 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 (>7 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 towards individuals reporting over seven years 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.

Fig 5Comparing 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 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.

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: home owners, 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:

  • Know Your Data: In today’s technology-driven marketing world, knowing how to leverage the data available to you is critical to both understanding and targeting your audience. Make sure your marketing investment decisions are driven through the analysis and usage of 1st party data, including registration data for demographic modeling; 2nd party data, such as ad server and search data for behavioral modeling; and 3rd party data, such as available audience segments from providers like Nielsen and Datalogix, for audience validation, matching, and lookalike modeling. Data is not just about buying audience segments for targeting; it’s about trying to get a 360-degree view of your ideal customer.
  • Choose the Right DMP: There are DMPs for every marketer, so be careful to choose the right one. Big Data needs call for pure play DMPs that can stitch together highly disparate data sets that include all data types, and make both insights, audience segments, and lookalike modeling available in real-time. Marketers looking to buy from a variety of 3rd party audience segment providers should choose a data marketplace such as Exelate, or be willing to access a more limited number of data sources inside a DSP such as AppNexus.
  • Leverage Audience Measurement: Finally, there is a lot that audience segments can bring to the table in terms of audience insights. Understanding the audience composition of who saw, clicked on, and converted after seeing your campaign gives you the ability to learn about your target customers, their online behaviors, and (most importantly) find more of them. Your DMP should have the ability to marry audience and campaign data to give you a highly granular level view of your best (and worst) performing audience types—down to the creative level.

Learnings from this case study, and other valuable information, can be found in my upcoming “Best Practices in Digital Display Media,” coming in January 2012 from eConsultancy.com.

[This article originally appeared in ClickZ on 1/4/2012]

 

Digiday:Daily Interview (Repost)

The right exit?

During the past year, the number of tech companies has exploded. There are some 245 logos on the (in)famous Luma Partners slide. Some of those have been acquired, but many have not and, in the eyes of several observers, are more features of products than standalone companies. In an economic downturn, with its focus on wringing efficiency, how many will be able to argue for a small slice of ad buys?

“People are all talking about the Luma slide,” said Tom Deierlein, managing director of Tagman, an ad technologies firm. “And they all want to pat themselves on the back and say, ‘Yeah, we made the Luma slide!’ If you go back to the original presentation of the slide, the point is that the industry is too cluttered. Too many people have their hands out, and there are not enough dollars to support all of the companies, and all of the tollbooths being put up. There is not enough money to support the ad ecosystem.”

Deierlein’s sentiments are echoed by analysts within and outside of the ad technology industry. The ad technology herd will “absolutely” be thinned, according to Erin Hunter, evp of media at Comscore. The proliferation of ad technology companies, some offering little or no transparency on their operations, won’t continue in an economy where brands want results, not simply assurances, according to Hunter.

“Brands want to know that there is a human on the other side of that impression,” said Hunter. She believes that advertisers will soon start to demand a system of “checks and balances” that will make ad technologies back up their product stack with verifiable results. Those firms not able to illustrate their value with transparency will eventually implode. At the end of the day, with the dizzying array of ad technologies, from DSPs to DMPs to SSPs to AMPs, there is still the question of what value each player is bringing to the table.

“When everyone has access to the same tools, there tends to be little differentiation and, consequently, value in an industry,” said Chris O’Hara, svp of marketing for Traffiq, an ad technologies firm. “Ad tech provides a highly robust example of this. Take DSPs and agency trading desks, for example. Everyone buys the same exact inventory from Google, Right Media, Microsoft, OpenX, PubMatic, Admeld and Rubicon, for example, and uses data that spring from the same sources such as IXI, Experian, Acxiom, etc. It’s extremely simple to get access to technology that enables you to leverage those things. So, what makes the companies that do real-time bidding valuable? They don’t own the inventory or the data. Many of them use the same machine-learning algorithms licensed by IPonWeb to drive optimization, and most deploy a roomful of smart account managers to help their clients manage and optimize their campaigns. As an investor, what value do you ascribe to those companies?”

And then there’s the simple fact of M&A: that there aren’t enough chairs to go around. There’s clearly the need for consolidation — Google’s vp of display advertising Neal Mohan regularly stresses this — but it’s hard to find a home for all those logos on the Luma chart.“Is there an explosion in the number of ad tech companies? Yes. Is that going to continue? No,” said venture capitalist Mark Suster in a recent Digiday interview. “In a bull market the number of startups in a category multiplies by as much as ten, and then when the markets collapse then they consolidate or shut down, and that is normal.”

The problem isn’t simply a general me-too mentality among startups, according to Deierlein. It’s that many companies bring nothing new to the table, often because they aren’t expected to. Companies are forgetting the history of the technology markets, Deierlein believes, and so many companies are simply plunging headlong into a complicated market without assessing whether or not their business model is an improvement on what is commonly offered in the “already cluttered ecosystem.”

“The clutter has come from the amount of money to be made in the ad technologies industry,” said Josh Kraft, marketing director for data analytics firm InfiniteGraph. “Eventually we will see companies being pruned, in a sense. Companies will have to back up their claims with actual client references. It requires a significant capabilities with data to compete, survive and thrive now.”

[This post appeared in DigiDay:Daily on 8/16/2011]