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Unlocking customer data through unique identifiers

Article by Daniel Resnick, SATOV Principal

I know Demar Derozan, Auston Matthews and Beyoncé…and they should know me too!

The whole world has gone data-crazy. Companies are making massive investments to capture customer information. A CMO survey conducted by Duke, for example, indicates that spend allocated to capturing, analyzing and using customer data is expected to grow from 4.6% to 22% of marketing budgets in the next 3 years. The idea is, if businesses have data, they can use it to target customers with personalized ads, build the best customer experience, and ultimately increase sales.

This is all well and good, but to get the most value out of all these data points, companies have recognized the need to be able to collect and attribute them directly to a customer. That’s where unique identifiers come in. Think of every piece of customer data as a single brick…the unique identifier is the mortar that holds them all together to create a useable wall. Once constructed, the wall tells a broader picture of what that individual really cares about, and how their dollars and loyalties are subsequently directed. Marketers can use this information to efficiently create personalized offerings that increase lifetime customer value.

Think about the power of a unique identifier for companies with multiple properties, like MLSE. The same person can take their clients to Leafs games, their friends to Raptors games, their kids to TFC games, and their spouse to the Beyoncé show. They spend money on merchandise, they have dinner before and drinks during, and they take road trips to see their favourite teams and bands. All of these individual transactions are linked to the portion of their wallet that is allocated to entertainment, and the amount that they spend on entertainment will be a function of all the other things going on in their life. The challenge for an organization like MLSE is that without the unique identifier, they don’t know anything beyond the fact that individual transactions occurred.

What’s true for MLSE is also true for retailers with multiple banners, CPGs with multiple brands, telcos with multiple product lines, and non-profits with multiple events. Having a unique identifier allows these organizations to link information across all their properties, and augment it with 3rd party datasets that have more information on their customer, not to mention other like customers. The fulsome view this creates unlocks new opportunities to attract, retain, cross-sell and upsell in a way that is actually valuable for the customer.

So, what unique identifiers are out there, and how are they being used? Credit cards, emails, phone numbers and loyalty programs are typical options, but they all have drawbacks. People have different cards and emails for different purposes, and frequently change them. Phone numbers are consistent, but consumers don’t have a natural reason to share them. Loyalty programs are truly unique, but are expensive to start up and maintain in a way that drives engagement.

As organizations continue to figure out what data to capture, they must also grapple with the more challenging questions of how to capture it, and what to do once they have it. To answer these questions, companies must balance trade-offs between time (it can take years to build integrated CRM systems), money (it can cost tens of millions of dollars) and culture (even with a great system to capture and manage customer data, there are no guarantees that customers will feed it and employees will use it).

With unlimited time and money, building the infrastructure to capture and analyze data is easy. Culture inevitably becomes the barrier to success. We talk to telcos who can’t get anyone to pay attention to their data-driven segmentation, CPGs who don’t have the structures and permissions to share data across brands, and financial institutions who have to secure buy-in on data integration one silo at a time. These organizational impediments dramatically delay the creation of a complete and actionable view of the customer.

From our experience, a key to capturing, linking, and analyzing unique identifiers is showing quick wins that demonstrate tangible benefit before investing in a large-scale CRM initiative. There are near term workarounds available to make this possible. AI and machine learning companies have emerged that can piece together disparate points of data and create accordingly contextualized offerings (see inset). These solutions are static, and do not displace the need for permanent infrastructure and training to get the most out of data. If deployed properly, however, they provide the clearest case to management, front line employees, and high priority customers that sharing and using identifier data is mutually value creating.

Let’s go back to sports. Right now, people spend their money on Leafs, Raptors and TFC tickets because they’re all good, and winners sell themselves. As any Toronto sports fan knows, the winning won’t last forever, at least not across the board. So how can MLSE get me to keep spending money across their properties in the long run? By giving me a reason to share my data, and by linking all that data together to create cross-platform offers and experiences that get me excited in all facets of my life. In short, know me! Just have Auston Matthews give me a call and we’ll go from there…

Third party sources:

  • Forbes

Case Study: Creating a single view of the customer

We collaborate with an innovative software company that can pull together data in pretty much any format and structure and cross-reference it to create unique customer signatures. Our partner used this capability to help a top 5 US bank build a consolidated picture of their customers, pulling information from multiple databases and millions of PDFs.

Our partner’s software leveraged machine learning and AI tools to match customer names, company names, addresses, and other semi-unique identifiers to extract a specific list of high net worth (HNW) clients. The bank was previously unable to truly understand these customers, as data on their different assets had been buried in disparate systems.

Creating a single view of the HNW customer helped the bank understand the breadth of products that they had sold to each individual customer. This insight allowed them to effectively optimize offerings and manage risk. Let us know if you’d like to learn more.