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5 Questions with TruSignal

March 7, 2016 — by Aruna Paramasivam

We recently partnered up with TruSignal to premier a new pilot solution: On-Demand Audience Filtering.  Using more than 2,000+ offline data points and a sample of your best customers, this solution calculates a predictive 0 to 99 score that identifies a brand’s highest to lowest converters, to enable targeting in various stages of the buying funnel. I recently was able to chat with Pete LaFond, the VP of Marketing, to learn more about their technology and how to best utilize their data strategy.

  1. Your president David Dowhan says on your website: “The era of 1:1 targeted marketing has tremendous potential. The challenge is knowing what data is relevant and how to use it effectively.” How do you see the next evolution of that potential playing out?

The access to data—and advances in technology to make data actionable—have dramatically changed the way marketers advertise. Only five years ago, marketers were just beginning to experiment with exchanges and programmatic advertising. Fast-forward to today, and more than half of digital advertising has become programmatic.

In addition, marketers are now able to tap into massive amounts of data that have historically been offline and out of reach. The pieces are in place for marketers to use this data with programmatic technology and to better understand which people to target and which people to avoid, based on a more comprehensive view of the consumer and across channel. With that said, we still have much opportunity to do even more.

All marketers at the core want to target people, however, historically, they have been relegated to targeting cookies or generic segments. A consumer’s identity is the only constant, and it is very difficult to reliably predict who someone is without it. The consumer identity is now the basis for matching offline profiles to online users in a privacy-safe way.

  1. Can you explain more about how your predictive technology works?

Our TruAudience® platform combines your first-party customer data with powerful offline data to score 220 million  U.S. adults and determine which people to target and which people to avoid. Our predictive scoring process analyzes thousands of data attributes, weighting them to their relative importance. High scores identify the people highly likely to be your next customer, and low scores flag people who are highly unlikely to convert and you should avoid targeting in your existing campaigns. All our audiences are people-based, built using known people, not anonymous cookie profiles or generic segments. I don’t believe any other company is creating predictive audiences in this way.

  1. What are some tips you have for getting the most out of “who data?”

Behavioral or “what” data tracks a person’s online activities, such as searches, clicks and page views. “Who” data consists of demographics, financial summaries, past purchases, lifestyle, hobbies and more. Consumer insights become incredibly powerful when marketers use both “what” and “who” data to tailor their advertising.

Marketers using a TruSignal audience have access to the full set of scores, thus the first step is to understand how they will measure success.  For direct response campaigns or last-touch performance metrics, using an Audience Filter of the lower scores provides tremendous results. Audience Filters can be applied to any campaign and provide immediate results by avoiding the non-converts. Similarly for branding campaigns, marketers can improve efficiency and reach using the top scores that pinpoint the right consumers to target and find great consumers they are missing today.

  1. How can advertisers use data signals to attract and retain the loyalty of millennial shoppers as they continue to grow up and cycle through different stages of life?

There is lots of talk about targeting specific segments such as “millennials”—people of a certain age range. However, segments have some challenges. Segments assume everyone with the single attribute (in this case, age) are all the same. Put that together with overlapping segments to increase accuracy, and marketers are left with a targeting strategy that is binary—consumers are either in or out. The end result is that segments include consumers that fit the segment, but are not ideal prospects, and they miss great prospects that aren’t checking all the boxes on segment criteria.

This is best illustrated with an example—a “green” hybrid car. The marketer believes a majority of millennials may prefer their car model because this group tends to be more eco-conscious. Thus, targeting a millennial segment sounds like a solid strategy. The reality is not all millennials are eco-conscious and would be interested in the hybrid nor do they all have the financial capacity to afford the car. The result is wasted impressions. At the same time, the campaign may also be missing those people who are NOT in the millennial segment—perhaps people over 40—who are truly avid hybrid car enthusiasts and could be the perfect customers for them. Using predictive scoring may provide better results in this regard.

  1. . What are your top three prospecting tips for 2016?
    1. Embrace offline data. Cookies provide a means to target consumers based on real-time behaviors. But cookies capture only a disparate and limited amount of data: only about 50 data points per cookie, on average. In addition, the life of a cookie is between seven and 14 days, which constrains scale. Targeting solely with cookie data gives a limited and fleeting picture of the consumer. Offline data, by contrast, provides a reliable and stable data resource with a longer life span. With coverage on more than 97 percent of U.S. adults, offline data sources aggregate thousands of data points.
    1. Use an audience filtering solution to avoid non-converters (and wasting money) in your targeting campaigns. TruSignal scores 220 million U.S. adults based on each person’s likelihood to buy a specific brand’s product. These scores determine which people marketers should target, and which people they should avoid (audience filtering). For lower-funnel campaigns focused on conversion, filtering out bad customers is just as important as finding new ones.
    1. Expand your prospect base instead of limiting it. Look beyond segments and behavioral clues to find your ideal customers, using powerful “who” data including demographics, financial summaries, past purchases, hobbies and more. As illustrated with the millennial car example referenced earlier, don’t miss those people standing outside of a segment (i.e. the eco-friendly drivers over 40) who might be the perfect fit for your brand.

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