Beauty brands are using data to target specific consumer segments.

Trends come and go in the beauty business but one direction that keeps growing is the need to address smaller, more specific market segments. Customer needs include relevant palettes for expanding ethnicities, premium science-based features, of-the-moment statement colors, (periwinkle lipstick, anyone?) natural/non-toxic ingredients, eco-packaging…and more every day. The best brands are leveraging the explosion of data to further segment their customer base, and better meet their needs.

Getting a lot of data to further segment your customers might seem like the finish line, but the more data you collect and leverage, the more you need. Big data is not a finish line, but a starting line of a new way of doing business.

For example, the modern beauty assortment is broader than ever. From large established brands to up-and-coming innovators, the best beauty brands are trying to address more specific needs of more granular customer segments. One size or color never fit all and now customers have the power to demand a more relevant experience.

Beauty bloggers and vloggers are creating more content faster than ever before. It is highly splintered and customized to very small groups. Beauty education is no longer from a few experts to the many. It is from many different types of experts to many different types of customers. They can get beauty content anywhere, anytime, and exactly what they need. So, they expect their products to be equally as accessible and convenient.

To meet this need, savvy brands capitalize on data mining to target specific market segments and demographics. They can develop products for specific segments. They can message, market and price them for specific segments. To do this, they must analyze traditional and emerging data sets such as purchase baskets, loyalty cards, social media, competitive pricing and online behavior.

With better analysis they often develop more stockkeeping units, faster assortment changes and store-level assortments to cater to local demand preferences. They can localize promotions and displays for specific segments. This is a win-win as the customer gets a more relevant experience and the brand can attract new customers.

However, all this complexity creates challenges in the store. It’s harder to keep inventory and presentation minimums in store without store-level data. It’s harder to get your promotion plans executed. All the while, your competition is doing the same thing and trying to take over your shelf space.

Other problems arise as well. For example, segmentation and big data analysis has led many brands to offering more premium products in mass channels. This is great for margin and addressing an affluent customer segment. However, this leads to some stores installing security devices which can hurt customer access to your product. Brands seldom know which stores have these and so it’s hard for them to address the problem.

To keep up, brands must keep new product and color palettes coming at a brisk pace while broadening the assortment, serving more customer needs and doing it all at a localized level. But this creates a complexity that results in terrible in-store execution. Display compliance is less than 50 percent in drug stores. Out of stocks, where no product is on the shelf for purchase, ranges between 5 percent to 42 percent for health and beauty categories. And the worst part is that most brands have no way to know.

Traditionally, brands get in-store condition information from either an algorithm or a store survey. They both have a role to play, but neither one gives you the customer view, at the moment of truth as they decide to buy — or not to buy — your product.

Kathleen Egan is vice president of services and analytics at Quri.

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