Retargeting is a commonly used advertising tool, with one in five marketers claiming to have a dedicated retargeting budget.
Not surprisingly, it’s often associated with high conversion rates, as retargeting serves specific messages – such as additional product information or special offers – to consumers who have already demonstrated interest in a brand, product or service, and therefore have a higher propensity to convert.
Yet despite its popularity, retargeting has a number of flaws. A key drawback is that the size of the audience reached is relatively small. Generally speaking, a retargeting pool consists only of those consumers who have previously visited a brand’s website but have yet to convert. Moreover, different measurement approaches can also unfairly reward retargeting.
For example, when using last-click methods, retargeting ads receive full credit when they are the last ad presented to a converting consumer, regardless of any other touch points that may have influenced the eventual conversion. As a result, marketers often struggle to determine the incremental performance of their retargeting efforts in driving conversions, versus what part of those conversions would have happened regardless.
Fortunately, there are new approaches for reaching likely converters that can help to overcome some of the issues experienced with retargeting, including predictive segmentation.
Simply put, predictive segmentation enables marketers to identify and score users who are more likely to convert based on their previous behaviour, including those who have never visited a brand’s website before, and deliver relevant advertising to those with the highest scores.
So how does predictive segmentation work in practice?
Any marketer using a sophisticated measurement system is able to track the behaviour of their entire universe of users based on propensity to convert. These advanced attribution techniques are able to correlate the behaviour of certain users with future conversion activity. Because the marketer understands the value of each user and their inclination to purchase, they can use that information to create a group of “likely converters” and deliver the most effective ads to them in order to entice conversions and achieve maximum lift.
While the principles of predictive segmentation may seem similar to retargeting, the two are actually quite different. Retargeting only targets a small percentage of people who have recently demonstrated interest in a product or service, by visiting a brand’s website for example. With predictive segmentation, the potential pool is much larger, since segments are based on the probability that a given user will convert in the future.
Moreover, using predictive segmentation, marketers can select as large or small a sampling of individuals to target based on their available marketing spend. For instance, marketers with smaller budgets can target the top percent of those “most likely to convert” for the most effective results. Those with larger budgets can target as high a percentage as they’d like – broadening their reach of potential conversions well beyond retargeting methods.
Retargeting was a significant trend of 2014 due to the perceived ROI, but the performance of this advertising approach is hard to measure and it can limit the potential audience too far. More sophisticated models including predictive segmentation take audience targeting to new levels, allowing brands to increase their reach, engage with a wider pool of likely converters, and dramatically improve conversions and ROI.
