How E.l.f. Turns First-Time Buyers Into Brand Loyalists

Customer retention is a beautiful thing.

Customer retention is a beautiful thing.

But it doesn’t happen without an ongoing investment in personalized customer experiences, said Ekta Chopra, chief digital officer at cosmetics brand e.l.f.

“We’re not just trying to reach our current customers,” Chopra said. “We’re looking to build long-term relationships with new ones.”

E.l.f.’s main mechanism to draw people in and keep them around is its loyalty program, with more than 2.7 million members. But there’s only so much a brand can do to maximize the value of a loyalty program as a retention vehicle without well-organized customer data.

“Members come from different places, they have different preferences and the data even lives in different systems,” Chopra said. “We’ve built our own homegrown systems to try and accommodate this, but as the data grows it becomes impossible to maintain.”

Role models

The challenge became more acute during the lockdown stage of the pandemic, when e.l.f., which hasn’t had a brick-and-mortar presence since 2019, saw a more than 60% spike in new customers visiting its site and making purchases.

E.l.f. hired a head of CRM and customer growth in July 2020, Shiseido vet Brigitte Baron, then gave her a team and empowered her to start kicking the tires on customer data platforms. RFPs were sent to the usual CDP suspects, including Salesforce – e.l.f. is a long-time Salesforce customer – but some of the responses were disappointing.

What certain CDPs hyped as their “secret sauce” felt more like a black box to e.l.f., Chopra said.

“They wouldn’t share their algorithms or models with us,” she said. “But we want to build our own models and we want to know how a partner does what it does.”

E.l.f. eventually alighted on ActionIQ as its CDP partner, in large part because of its data modeling capabilities. After centralizing its first-party customer loyalty data within ActionIQ, e.l.f. was able to white label ActionIQ’s predictive models and use the models it had built itself off of the platform.

“We have a business team that needs to be able to do things on their own and not have to constantly ask the data team to pull stuff together for us,” Chopra said. “Some platforms have these amazing technical ways to bring data together, but that’s not valuable to us unless the power can be put in the hands of the marketer.”

Look-alikes looking good

One of e.l.f.’s first orders of business was to develop a retention strategy for new customers who came to its site organically during the first year of COVID.

“These people were spending a lot of money, but we needed to entice them to stay with us,” Chopra said. To do that, she said the brands need data to customize email and paid media and to build look-alike models.

For example, e.l.f.’s main business is color cosmetics, but it also has a nascent skincare line.

Using ActionIQ, e.l.f. can get a better understanding of who comes to its site and the actions they take so it can deliver more personalized customer journeys and apply models to predict behavior. Is this someone’s first or second visit to the site, for instance? Is the person buying color or skincare, and if the answer is skincare, how likely are they to come back and buy the same product or a complementary product? Does offering a sample help?

“Being able to answer these questions helps us inform our engagement strategies through email, text and push. It helps us reach those audiences on paid channels and it lets us provide a personalized experience when people come back to the site,” Chopra said.

Going deeper

But e.l.f. also brings more sophisticated data sources into the platform, said Tamara Gruzbarg, VP of strategic services at ActionIQ.

Customer service logs are a good example.

“This allows you to understand the experience across all touch points, not just from the marketing or buying perspective or even just based on the answers you get back when you promote a survey,” Gruzbarg said.

Customer service information is the real deal, because it’s “proactive engagement from the customer, including the reason they called in, how the call was handled, the outcome and, importantly, how all of those things impacted their future lifetime value,” Gruzbarg said.

Now that e.l.f. has its data foundation in place – “Year One was all about getting our data sources together and getting people internally to start using it,” Chopra said – the next step is to find and add new data sources that enrich what it’s already got, including both third-party data and demographic data.

And some data sources, like online quizzes, for example, can even serve double duty as a data source and a real-time engagement tactic, Chopra said.

“‘Personalization’ has become a buzzword, but that’s what we want to do,” Chopra said. “And that’s true whether it’s part of our marketing strategy or whether we’re bringing this sort of information into our thought process as we build and test new products, which is something we’ve now started to do.”

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