As much as consumers like to believe that their behaviors are largely unique, research and reality have shown that we often behave in very predictable ways under certain circumstances. As a parent, for example, I behave in ways today that would've been truly inconceivable to me before having children. From a marketing angle, the good news is that we can predict with a certain amount of confidence how some consumers will behave in the future based upon current life milestones. On the other hand, not all consumers will follow the same needs path in the same order.
I bring this up because I've been thinking a lot about the idea of look-alike targeting. Look-alike targeting is based upon the premise that if an existing customer exhibits specific behaviors (or using more psychographic-based targeting tendencies like identifiable opinions, beliefs, and ideals), then finding other consumers who share those characteristics makes them good candidates to become customers as well.
While look-alike targeting in online campaigns isn't new (veteran targeting company AudienceScience used look-alike targeting as its foundation when it started), it is appearing more frequently as an added service or line item on digital media buys. Which begs the question, "If I were to add look-alike targeting to my next campaign, just what am I buying, and can look-alike targeting allow me to measure consumer actions as well as consumer types?"
From a media buying angle, I would definitely want to know how look-alike targeting would positively affect my campaign's conversion rates. Using the assumption that "look-alike" consumers will behave like existing customers should mean that these type of targeted campaigns should have a higher conversion rate. But is this always the case?
According to a case study done by Quantcast, buying look-alike profiles is on par with the conversion results of retargeting campaigns. While retargeting focuses on revisiting consumers who have already had meaningful contact with an advertiser's brand, look-alike targeting acts more as a direct response tool to get a message in front of the best candidate consumers and the expectation that they will be more likely to take action immediately.
In its study, Quantcast worked with a financial advertiser that traditionally had received its greatest results from retargeting campaigns. Using that conversion rate as a benchmark, the advertiser ran two campaigns; one focused on retargeting efforts and the other on look-alike targeting. According to Quantcast, the look-alike targeted campaign delivered higher conversion rates than the retargeting campaign for one of the ads run and significantly increased the number of probable applicants. Overall, the case study points out that by using look-alike targeting, advertisers are able to convert nonperforming inventory into an effective way to target and convert new prospects.
While the significance of these results is meaningful, we should also keep in mind that in most cases when looking at positive bumps in conversion, it is in relationship to some sort of baseline. In the case of many campaigns, the most common baseline is against run-of-network distribution. The simple truth is that targeting a consumer based on some measurable criteria will always do better than a "hope you saw my ad" campaign.
Hugh McGoran, vice president of sales at Turn, echoes this concept. "Effective advertising is often less about finding an audience than it is about finding an outcome. It may not matter that a consumer is male or female or within a specific age group. What matters in the end is what gets those consumers to take the necessary steps toward a point of conversion."
Turn offers its buyers what it calls "Act-a-likes," and by using targeting pixels is able to continentally retool the targeting criteria being used to identify the "optimum'" customer. By using previous conversion and targeting criteria as a benchmark, advertisers can then scale their targeting to include additional points of criteria that may be relevant in locating and speaking directly to those key consumers.
As with any smart media buy, the first step is to clearly identify the criteria that best defines the optimum target audience. This often means starting with assumptions and through measurement and optimization working toward a meaningful targeting reality. In short, just because you think you know who your customers are doesn't mean that's who they are.
Rob Graham is the CCT (chief creative technologist) of Trainingcraft, Inc., where he heads up development of customized training programs for a wide range of digital marketing, entrepreneurial development, and digital media clients.
A 20 year veteran of digital media, Rob has served as the CEO of a multimedia development company; an interactive media strategist; a rich media production specialist; a Web analytics consultant; a corporate trainer and seminar leader; and a chief marketing officer.
When he isn't on the road presenting training workshops, Rob teaches at Harvard University, Emerson College, and the University of Massachusetts - Lowell where he teaches classes on Digital Media Development, Web Store Creation, Software Programming, Business Strategies, and Interactive Marketing Best Practices.
He is the author of "Fishing From a Barrel," a guide to using audience targeting in online advertising, and "Advertising Interactively," which explores the development and uses of rich-media-based advertising. He has been an industry columnist covering interactive marketing, digital media, and audience targeting topics since 1999.
May 22, 2013
1:00pm ET / 10:00am PT
June 5, 2013
1:00pm ET / 10:00am PT