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ARTICLE

7 Factors That Influence Retargeting Success

May 21, 2013 — by Ari Buchalter

Many people think retargeting is just about placing a pixel on an advertiser’s site to cookie users, and then buying ads against those users. If it were that simple, everyone would see the same results in all of their campaigns. The truth is that successful retargeting is more than a two-step process.

So, while nearly every advertiser sees a lift in conversions, two partners both doing retargeting may see very different results. The following factors all come into play when bidding on retargeting impressions, and each one could affect campaign performance.

The breadth of supply integrations. This includes media across display, mobile, video, social and across premium as well as remnant supply. The more impressions there are flowing into a system, the greater the chance of finding the retargeted user. Some platforms can support over 1 million queries per second, while others maybe 1/5th or 1/10th of that.

Match rates. Dropping a cookie is only half the story. You still need to sync that user with supply sources in order to bid on them in media. Match rates speak to the percentage of users that are successfully synced, and can vary from 70-80%, down to as low as 20-30% for some platforms (it will always be less than 100%, given that some users either don’t accept or delete their cookies).

Error/timeout rates. Just because an opportunity to bid on a retargeting user comes in, doesn’t mean the DSP will actually be able to bid. Platforms that fail to respond within the allotted time window of 50 to 100 milliseconds will timeout (e.g., because the system simply cannot handle the flow coming in), making their bid ineligible. Some platforms have timeout rates as much 20 times lower than their peers.

Optimization Finding a retargeted user is great, but knowing what to bid to achieve your ultimate goal is a different story. Not all retargeting users are of equal value, and even the same user could be of greater value when appearing on one type of site vs. another. Yet surprisingly, many platforms employ a simplistic fixed-bid approach (bidding the same amount every time, perhaps with some random variation), which is never the right answer. Why? Because any fixed bid will be either below a publisher floor (and never win that inventory) or above other floors (potentially overpaying for that inventory).

Moreover, most exchanges and supply side platforms (SSPs) employ dynamic price floors, altering the floor price depending on the bids submitted. For this reason, it’s critical to have a machine-learning algorithm that can bid dynamically on retargeting. This means bidding higher when you need to, and lower when you don’t.

Recency & frequency management. Building on the optimization point, it’s not enough to know who to bid on and what to bid. Advertisers must also know when to bid, as well as how often. Performance can vary significantly as a function of recency and frequency, and knowing how to incorporate those factors into a retargeting program may be the difference between decent performance and breakaway performance.

Dynamic creative. Retargeting users have recently interacted with the advertiser’s site, so leveraging dynamic creative to tailor the ad based on those interactions (e.g., showing specific products viewed, as opposed to generic ads) ensure greater relevance and dramatically lifts performance.

Fraud detection. The trade press has recently highlighted how many retargeting impressions available — more than many people realize — aren’t actually against humans, but rather against bots that have intentionally visited advertiser sites to trick unsuspecting buyers to bid for them. Sophisticated buying platforms should have algorithms that look at multiple factors to identify fraud and avoid bidding on suspect bot traffic. The net effect is a reduction in impressions and lower click-through rates (by eliminating fraudulent clicks associated with bots – actual human click-through rates are much lower), but a dramatic improvement in cost-per acquisition and order quality.

Ari Buchalter

Ari is the President, Technology, at MediaMath. Ari oversees MediaMath’s Product & Engineering teams, focused on designing, developing, and supporting best-in-class product solutions and platform capabilities for MediaMath’s customers. Ari brings a unique combination of business leadership, strategic vision, practical marketing knowledge, and quantitative expertise. Whether it’s designing MediaMath’s proprietary Brain optimization algorithm, unifying marketing approaches across digital channels, innovating new targeting technologies, or building the open marketing platform of the future, Ari is impassioned by the chance to work on industry-transforming solutions and humbled by the opportunity to lead the best technology team in the business.