The Unilever U.S. team had identity challenges: For years customers received emails and visited the website, but not always by clicking through Unilever’s emails. Those website visits were being read as net-new interactions with the brand, even though many were from loyal customers across the Unilever portfolio.
For many brands, it’s not easy to solve for that disconnect—especially in an organization like Unilever, with 64 brands in its portfolio. Unilever had a lot of information on each customer, but they lacked a single, actionable customer view. And they had two primary challenges to solve in trying to achieve it: 1) confidence that data from disparate sources could come together to show that the data belonged to the same person and 2) a lack of ongoing website recognition because asking customers to sign in didn’t make sense from a value exchange perspective.
Resolving this disconnect is challenging, but not impossible. In the course of six weeks, Unilever closed the gap by working with Epsilon to integrate real-time identity recognition into the brand’s customer data platform (CDP) strategy, enabling Unilever to see its customers much more clearly. From a metrics perspective, Unilever saw a five-fold increase in customer recognition, which allowed the brand to then better personalize its customers’ journeys.
Rosa Pantoja, data-driven marketing lead at Unilever (U.S.), led the effort to build the organization’s new identity solution: an enterprise CDP based on first-party data across the company’s full portfolio. Read on to learn about Unilever’s “before-and-after” approach to identity, its road to success and what advice Pantoja has for marketers looking to tackle similar identity projects.
Dana Moroze: Let’s start with the past vs. present, both in terms of Unilever’s starting point for customer data storage and any shifts you’ve seen in terms of how that information is broadly viewed by the organization.
Rosa Pantoja: In the early 2000s, email became the channel of choice so we shifted to email address capture. That was the first database for profile storing. Now, we have more than 40 brands contributing data across every single touchpoint, where they’re collecting customer data. We have over 25 different data providers who are integrated and sending data in—all in compliance with privacy policies and consumer opt-ins.
The model of the past was having a center of excellence, or a core group of people, whose focus was data. One of the bigger shifts I’ve seen is that the “core group of people” of the past has become part of our business and embedded across our organization. That brings data more to the forefront in every conversation—it isn’t an afterthought at all. It’s integral to every conversation.
What was the impetus for the shift to a different way to understand and connect customer data across brands and touchpoints, and what were the initial steps?
Well, we noticed gaps. Until we launched our new identity solution (powered by Epsilon PeopleCloud Customer), we really didn’t have line of sight to be able to attribute a website visit back to an individual.
We certainly started by doing a lot to build out our internal data science and analytics, to actually work with the data. When I think about the journey for our first-party data in the past three to five years, we’ve been focused on bringing more and more of that data in-house and really having that ownership sit with Unilever. It makes internal conversations much easier when there’s a sense of ownership.
We’re connecting interaction data to actual individuals and consumers in a way that we couldn’t before.
Rosa Pantoja, data-driven marketing lead, Unilever (U.S.)
But of course, sometimes it sounds like I’m talking out of both sides of my mouth. I’m saying, “We need to have a commitment to building our first-party data asset, to have a laser focus on our internal capability and to make sure we have the flexibility to integrate with any partner.” But at the same time, we also don’t want to necessarily build a capability that’s already existing in the market.
Even if you wanted to build something that could handle those customer alignment capabilities, or work with a number of partners to build something like that, it wouldn’t be at that level. We don’t want to be in the business of building those solutions, especially when it’s not our core competency.
As you partnered with Epsilon, what were you looking to build, what was the buy-in process like across the business and what did you accomplish?
We knew we had gaps to fill. Interestingly, getting buy-in from our business and marketing stakeholders to build a solution that fills those gaps has been less of a push than I anticipated. I think it’s because data is so top of mind across our organization. And because we’re already doing so much around unlocking additional channels, finding consumers where they are, and having those conversations with our retailers and customers. This idea just clicked in a way that I didn’t anticipate.
We partnered with Epsilon to build a different identity solution capable of bringing together our known and anonymous data and extend how much we know about our consumers in a privacy-protected environment. From a metrics perspective, we’ve seen a five-fold increase in our ability to recognize the consumer—and we’re able to personalize that interaction. That’s huge for us.
The response internally has been an overwhelming demand to really nail down these customer journeys, which we weren’t able to unlock before. We’re connecting interaction data to actual individuals and consumers in a way that we couldn’t before. So now we’re having conversations around “What are the other use cases? How do we bring this to life? How do we change how we’re engaging consumers?”
It’s also given us an opportunity to educate around data transparency and consumer privacy across the organization. Everyone is aware of the behind-the-scenes work and vigor behind managing all of our data securely, making sure that we’re being good stewards of that data.
What would you say to someone else if they were pursuing a project similar to what you’ve just accomplished?
I would definitely suggest a “crawl, walk, run” approach. It’s important to have a clear vision for where you want to end up. For us, it was being very clear that we were building an enterprise-level asset. We were going to have a single view of the customer across all of our brands and stop having siloed information across brands. From there, it was putting all of our energy into nurturing our identity graph, which meant making sure we had robust data management at the core of everything we do.
I also would say choose your partners carefully. Establish a practice for vetting. And, especially when you’re bringing in data, vet the data quality; not every provider or source is created equal. If you’re putting a lot of energy into building your first-party data, you want to make sure you protect it. And then, challenge your partners. Just keep challenging them whenever possible. Be creative and take advantage of any opportunity you get to test and learn. Make enough time for that—it’s a good practice.