Commentary

Lookalike Targeting Is Not Prospecting

Most advertisers are faced with the challenge of finding new and relevant target audiences in an effort to expand reach and drive ROI. In one sense, the strategy to achieve expanded reach seems simple; get your ad out in front of as many consumers as possible.

The reality is, prospecting and engaging new audiences is about more than just reach; it should be based on a real-time understanding of your consumers. 

To do this, many advertisers turn to lookalike targeting, a proven approach to identifying new audiences from known segments of consumers who share a handful of attributes with known consumers who had previously taken a desired action.

These static segments consist of consumers who are thought to be the right consumers to target. In an age of information and social influence, date-driven insights are right at our fingertips. The old school approach to identifying new audiences using human analysis just doesn’t cut it.

At least not if you are looking to drive efficiency and ROI.

For advertisers that seek to understand and scale highly targeted audiences, simply tapping into CRM data (often historical data) to identify your target audience as female, city-dwelling, age 25-45 is not enough. To accurately model new audiences advertisers must take into account a complete and near-realtime view of high performing segments and how they perform together.

 
A holistic approach to prospecting

Advertisers have access to several types of data assets; campaign data, third-party, CRM, POS, website analytics and social. Taking a siloed approach to media buying by executing separate strategies across various types of data is becoming a thing of the past.

With current big-data capabilities, advertisers can break data assets out of their silos to create holistic views of consumers. Using data science, algorithms can be built to compute thousands of consumer data points to generate holistic consumer models.

These models address the challenge of true audience understanding and work to identify both new and existing audiences of high value users. By taking a holistic approach to audience modeling, advertisers can benefit from optimized prospecting campaigns that achieve both performance and reach.

Take for example, a cloud storage company seeking to target male IT professionals, ages 25-45 with cloud storage products. It is relatively easy for this advertiser to purchase static lookalike audience segments of consumers who fit the the standard segment criteria of male, IT professionals.

By using data science to dynamically model and find new audiences based on real-time campaign performance, the cloud storage company uncovers that their target audience isn’t quite who they thought they were.

The highest value audience segments are in fact made up of male and female high-end shoppers who have a penchant for travel, as well as yoga enthusiasts who use cloud storage products for photo archiving and sharing.

What does this mean for advertisers? Besides better informed media buys, advertisers can begin to see greater efficiencies in their prospecting efforts. Rather than allocating spend to reach a broad audience, imagine fine tuning targeting efforts to exclusively reach high performing audiences.

Similarly, imagine anti-targeting the audiences that are hurting the performance of your campaign reducing spend and boosting overall campaign efficiency.

 

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