There’s a near obsession with achieving the 360-degree customer view. With a single, centralized customer record, marketers believe they can achieve their dreams of perfectly orchestrated marketing across channels. Customer data platforms (CDPs) are typically the system of choice, serving as the global ID or ID of record.
Identity resolution has become a key part of the process, merging data from disparate sources into the central file.
The reality is actually different from the dream. The problem is not that achieving a unified 360-degree customer profile is hard (which it is), the problem is that identity resolution actually reduces a marketer’s ability to deliver relevant marketing because of the way the process works.
A new approach called identity recognition solves for the problems caused by identity resolution, preserving more data, distinguishing important information for individual channels, and enabling faster reactions to customer behavior.
What Identity Resolution Gets Wrong
Identity resolution merges information that a marketer collects across different environments. The premise is that if a marketer can merge the data, they will get a 360-degree view of the customer which can inform multichannel marketing.
However, merging information takes away context. A global ID doesn’t preserve the data that’s associated with specific mobile behavior or desktop behavior. The actions someone takes on their mobile phone are different from the actions they take on their desktop. Someone might browse for a couch on their phone but wait until they’re on their laptop to buy one, for example. But identity resolution isn’t architected to preserve the information needed to treat people differently on the two platforms. Luxury and high consideration purchases in particular suffer from this process.
How Identity Recognition Helps
“Identification” is a process gaining traction with retailers, which helps them recognize visitors to their website so that they can deliver more relevant messaging and experiences. Recognizing someone on a desktop or mobile app would trigger different experiences based on the context. This requires a channel-specific ID, something that a global ID doesn’t have.
A better approach is to find a way to connect identities without eliminating channel-specific information. While many CDPs create a hierarchy with one primary ID of record, retailers really want to store customer data in such a way that there’s no hierarchy. The idea is to recognize an individual, and then access data to determine what action to take — including channel-specific data.
Building a Schema Can Help
To achieve this, some retailers have created a schema themselves. Brands can build a structured approach to saving data that combines customer data by channel, product data, and specific high-value events such as adding a product to a wish list, signing up for an important newsletter, or coming in for a free offer as well as purchases. Data is saved in such a way that identities for each channel are preserved and connected, but not “resolved.”
Too many brands have weighty processes to resolve data into their CDP and then don’t know how to “unlock value” from the data. The data gets old and doesn’t do the job of driving really great marketing experiences in specific channels. What’s more, it doesn’t connect to those real-time events that are so critical in understanding a consumer’s likely next action.
Retailers don’t need to throw their CDP away, nor do they have to rip out their channel-specific processes. CDPs work very well for what they were originally designed to do — centralize reporting and maintain data hygiene. However, retailers will see a market lift in channel-specific performance if they free their identification process from the CDP and embrace channel-specific information that can drive better performance.
Sherene Hilal is the chief product officer at Bluecore, a retail shopper identification and customer movement technology that turns anonymous shoppers into known customers and repeatedly moves them through the purchase funnel more efficiently than any other customer data solution in the retail stack.
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