Dollar Shave Club, the direct-to-consumer grooming brand owned by Unilever, has enhanced its data capabilities so that it can operate in the most nimble, impactful way when it comes to tapping the power of this information stream.
Amber Hameed, vp/information systems at Dollar Shave Club (DSC), discussed this subject at the Association of National Advertisers’ (ANA) 2019 Data & Measurement Conference.
“Have you heard the term, ‘Data is the new beta?’” she asked. (For more, read WARC’s in-depth report: How Dollar Shave Club is making smarter use of data.)
“It really is true,” she proposed, not least because of the opportunity this incoming flood of numbers can provide to unlock new modes of revenue generation.
Hameed joined Dollar Shave Club in 2017 having previously served as a director at Publicis Groupe, the agency holding company. And the direct-to-consumer brand, she soon discovered, needed to enhance its data capabilities.
“We had basically what we called a ‘database’. It was essentially a hybrid between a dump of data in a swamp of information that one or two analysts were digging into on an ad-hoc basis and just moving data together to show some analytics to our C-suite,” she reported.
Today, by contrast, “It’s all data all the time. Data never sleeps. We’re constantly generating information that’s coming in from the consumer, from our audiences, and from people who interact with our value proposition.”
And the organisation’s “nimble” approach to data is based on clear principles. “We use very lightweight streaming technology to process our data from the application layer down to specific frameworks,” Hameed explained.
“That allows us to parse the data and push the data out quicker. We are constantly generating and collecting information, and we also want to keep it as budget-friendly as we possibly can.”
It is equally important to have “purposeful data,” Hameed insisted. For Dollar Shave Club, for instance, revenue recognition and finance data have been part of its customer-growth, acquisition, and marketing datasets.
“Every philosophy at every company is different,” noted Hameed, “and you may want to prioritise what kind of data you want to collect and rationalise, depending on your budget profiles, your consumption models, and how your company is morphing that information, and how quickly it’s absorbing it.”
Sourced from WARC