63% of consumers say that they would not buy from a brand with poor personalization tactics (Forbes, 2020). Effective personalization relies heavily on the single customer view, the holy grail of customer profiles. It aims to assemble an accurate representation of a customer by merging their data into one consistent and comprehensive record. That way, you can be sure you know what you need to know to craft personalized messages and experiences that really speak to people (or even that the people you’re speaking to are who you think they are). Unfortunately, building a unified view of the customer is trickier than it seems.
At Amperity, we often talk to CMOs, CCOs, and IT leaders who are trying to unify their customer data. Nearly all of them tell us that despite months (or years) of effort, expensive consultants, and heroic efforts by their IT teams, they still lack the single view they set out to achieve.
What’s really holding everyone back?
What’s the real problem? Is customer data unification really so hard? In our experience, customer data is often siloed in difficult-to-link technologies. But rather than the data being poor quality, we find that often enough the data itself is rich and valuable. When brought together in an intelligent way, it forms a deep and useful customer knowledge base that can power advanced analytics, personalization, and targeting.
So while data quality and access to siloed data can be an issue, there are two more fundamental blockers that we encounter when we work with brands to unify and make their customer data usable:
The problem of identity resolution has never been so complex as it is today, due to the explosion of customer data, channels, systems, and applications. To achieve a single customer view, data has to first be brought together from these disparate systems, which requires building, maintaining, and monitoring data pipes into and out of a centralized system.
Without a common key between records from disparate systems, it’s nearly impossible to tell for sure when two records belong to the same person.
But after data is co-located, the real challenges begin. Customer identities must be resolved across tens of sources and billions to trillions of records, even when there is no common key to link them. This requires massive computing power, machine learning intelligence, and customer data expertise.
Lack of ground truth
There is another related, but deeper, complication. Customer data is inherently uncertain. Without a common key between records from disparate systems, it’s nearly impossible to tell for sure when two records belong to the same person. And yet choices must be made: there is no way to be certain, and yet choices must be made: either leave records separate when they have a high likelihood of belonging to a single individual (producing false negatives), or unify records when you can’t be sure if they do (producing false positives).
The business impact of these choices is huge. False negatives in customer data unification mean duplicated lists, multiples of the same emails and direct mailings sent to a single individual, and ads shown to customers who have already purchased the products you’re promoting. A lack of unified data also makes effective site, app, and email personalization difficult to achieve. All this rolls up to lower revenue, poor customer experiences, and loss of loyalty.
False positives, on the other hand, mean that some of the information in a customer’s profile is false. Using profiles built with potential false positives runs the risk of sending order confirmations, shipment notifications, and information about returns to the wrong individual, with disastrous consequences.
A better way
We believe brands shouldn’t have to make this difficult and expensive choice. That’s why Amperity invented patented AI-powered processes to resolve identities and unify records without a common key. Solving this root-level problem brings a single view of the customer within reach, as well as providing the foundation for a range of other solutions based on a robust first-party data foundation.
For example, by linking together and making the most of all the data provided directly by customers, brands no longer need to turn to third-party identity solutions that are costly, error-prone, and increasingly constrained by privacy regulations.
It also forms the basis for matching digital engagements to anonymous records from offline transactions as a way to expand the marketable audience, which is critical as third-party cookies are phased out.
Accurate data unification and identity resolution allow for both single and household views of identity for aggregate analytics and targeting alongside individual personalization. This means isolating individual identities among people who live together and shop for groceries as a household, or people who travel together under one reservation, or even people who share the same Peloton bike. And then of course, there’s personalization at scale — customizing content and experiences to individuals throughout every step of their engagement with the brand.
These are just a few examples of the use cases unlocked by having a single view of the customer, which itself is enabled by comprehensive identity resolution. Starting from the beginning to solve this challenge opens up a world of possibilities for brands to transform the way they serve their customers.
To learn more about unified customer profiles and multiple customer views, get in touch.