History belongs to those who survive to write it, and data belongs to those who created companies to compile it. When companies have commodities they know to be hot, the market becomes their playground, and that playground can be prohibitively expensive and constricting. But what if companies could create their own data playground?
Mark Clarke from Unilever and Shawn O’Neal recently with the same company spoke to attendees about how their organization did just that: brought data compilation, analysis, and insights completely in house. Clarke noted the immediate value of bringing company data back to home turf, namely that companies don’t always want consumer data to be public. Data relating to sales, media spend, product trials, and the like can be sensitive and are meant to be confidential. When a company controls that data from where they sit, as opposed to using a software product or service to do it for them, they’re able to ensure data security and privacy on a deeper level.
While Unilever is a global company with large budgets and plenty of offices serving as data sources and warehouses, creating their own in-house data center was not without its challenges. Each Unilever-owned brand, each global office, each function had to believe that data would really affect them in a positive way. How did the company get the buy-in from all these different parties? For starters, they made each of these offices and brands literally buy in: “We never gave our data away for free. Each brand had to make the decision to purchase the data from our mother company–that way, you know they’re invested in what the data can do for them,” said O’Neal. Clarke also explained the challenge of convincing these different department heads of the value of data: “On the one hand, you have digital marketers who are just on digital because they know their customers are there, so they feel like they don’t need to know anything else. On the other extreme, you have marketers who have been trained since traditional marketing school to only care about the ROI of any specific decision, and they don’t see how you can show them the ROI of investing in this data.”
But Unilever proved both parties wrong: by collecting data from surveys, social listening, and search listening, they’ve been able to determine that people talk about the same thing in different ways at different times. On the topic of stains, for instance, their Consumer Insights department found that when people filled out surveys about problematic stains they’d had in the past, they were often what Clarke jokingly referred to as “mythological memories” of stains past–the information might not be accurate. On social, people would talk about stains as they were happening: “Ketchup just dripped down my shirt; what do I do?” And still, on search, people were engaging about stains when the problem became a crisis: that ketchup won’t come out of the search. These insights, derived from their own in-house data center, helped brand managers and marketers understand how to market to consumers about stains in different environments, at different stages in the ketchup stain life-cycle.
Finally, Clarke and O’Neal noted that it is not necessary that an in-house data center operate entirely in-house. How is that possible? Unilever keeps the data oversight management and data consolidation functions in-house, but they outsource some of the bigger labor efforts: data collection and social listening, for example.
Keeping a data center in-house allows a company to have better control over their data, ensures an owned responsibility in security and privacy, and customizes insights important to them, instead of being stuck with the insights an outside product “believes” is most important. While not every company may have the option of building an in-house data team, Clarke and O’Neal provided a compelling case for trying to keep control of specific aspects inside the company, as opposed to entrusting that control to outside parties.