Proper Matching: The Missing Piece to Identity Resolution

In this article, Christine Frohlich, EVP of Product, Marketing Services at Experian, explains why organizations that make identity resolution the foundation for marketing activities, experience greater impact in their campaigns.

Brand advertisers recognize the value of identity, particularly when it comes to the customer journey, and more importantly, customer retention. Most advertisers recognize the value, so much so that identity has become a part of their everyday vernacular. The ability to properly identify individuals in a way that improves certainty opens the door for them to deliver true omnichannel campaigns; the opportunity to deliver relevant content that tells a consistent story across multiple devices. It’s the foundation for any strategic initiative. Yet, despite its importance to the advertising process, recent research from the Data & Marketing Association and Winterberry Group shows only 15 percent of businesses can identify their audiences accurately and consistently. And if businesses struggle with proper identity resolution, it could affect brand reputation with consumers, costing them hundreds of loyal customers, and thousands, if not millions of dollars in potential revenue.

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The challenge for many brand advertisers is the sheer volume of data. There is a seemingly infinite volume of offline and online attributes, such as name, address, email address, cookies, date of birth, transaction history, mobile device identifiers and the list goes on. Each one of these attributes alone gives you a glimpse into the customer, but the ability to connect these disparate data sets in a privacy compliant manner, can create a 360-degree view of the customer and help marketers make the right decisions. And it all starts with the right resolution and matching process.

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Most advertisers rely on deterministic and probabilistic matching methodologies. Deterministic matching leverages basic information, such as name, email, phone number, etc. to help identify the person behind the screen, giving the advertisers a high level of confidence that they know who the customer is. On the other end of the spectrum is probabilistic matching. Advertisers tend to use signals, such as device type, IP address, time of day and frequency of events to predict, with some level of confidence, who the individual is behind the screen. For example, let’s say you own a laptop, smartphone and tablet, and typically use each of them at home. Seeing as all three devices would register the same IP address, and probably access the same types of apps and websites, advertisers can infer that it is probable that the same individual is using all three devices – but cannot necessarily verify with 100 percent confidence.

There are clear benefits and limitations to each approach. At the most basic level, deterministic matching provides a higher-level of confidence, but it can be quite difficult to implement the approach at scale. But, it goes deeper. Let’s say an individual logs into their music streaming account and listens to a pop playlist, yet a friend logs into the same account the next day and listens to a country playlist. It would be difficult for an advertiser to determine which type of music the individual is most interested in, and thus could throw off algorithms that recommend new music for the customer – a nightmare for the perfect customer experience.

Probabilistic matching suffers from the inverse. If a laptop is connected to an IP address at 8 p.m. every night, there wouldn’t be a way to accurately understand the differences between devices and their actual owners. It could be a friend or family member that used the laptop. It wouldn’t be any more than a highly-educated guess. That said unlike deterministic matching, the educated guesswork could be implemented at a larger scale.

The ideal solution: Get the best of both worlds.

Advertisers need to have a hybrid approach that maximizes the power of all of the available identifiers to help them better understand their target audiences and make the right marketing decisions. To deliver a truly personalized and relevant communication to an individual, advertisers need to have a full and accurate view of the consumer. Fortunately, the technology exists to help advertisers turn the need into a reality. Deterministic matching works well for managing core customer data assets. Combined with quality data, advertisers have access to technology to give them the confidence that the individual who saw an advertisement on their mobile phone is the same individual that went into the store and made a purchase. Probabilistic matching plays well when there is a need for reach and scale in targeted advertising events - maximizing the opportunities to locate consumers across devices and locations.

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Business success often comes down to a positive customer experience. The ability to attract and retain customers drives any business’s bottom line. And proper identity resolution plays an integral role in building and maintaining a positive experience with customers. Advertisers need to leverage every data point to build an individual’s identity – partial attributes tell an incomplete story. If advertisers can find the happy medium between deterministic and probabilistic methodologies, they will have an opportunity to make the right marketing decisions and better connect with customers.