Your Data is Always Changing. Are You Keeping Up?

Ask any retailer and they’ll tell you it’s rough out there. Scaling profitably has become the persistent headache most brands can’t overcome. There’s not a lot of patience in the market for retailers that are losing money. Digital-native brands that went public, like Allbirds and Warby Parker, are down 70 percent to 90 percent from their IPO prices. In addition, there are countless midsize brands stuck in purgatory, caught between having substantive eight-figure revenue and lacking escape velocity or best-in-class designation. As a result, there are many brands that are getting acquired for little or folding, despite their best efforts to stay afloat.

The lifeblood of retail businesses right now is achieving healthy unit economics and keeping rising customer acquisition costs at bay. Brands are asking themselves if they can scale effectively and profitably, both in-store and online. The short answer is that customer acquisition at scale is getting harder — this is true for brands of all sizes. As we know, acquiring a new customer can cost five times more than retaining an existing customer.

Furthermore, competition is fiercer than ever, making it increasingly difficult to find a competitive edge. It seems like any time a retailer unlocks something exciting, there are three copycats waiting around the corner. While many brands have leaned into influencer or celebrity marketing to help gain differentiation, this isn't a foolproof strategy.

So, how can today’s retailers face these challenges head-on? They must make the most of the data available to them.

For retail giants like Walmart and Target, this isn’t as big of an issue. They have greater volumes of data and more resources to invest in the infrastructure and data specialists required to help them analyze what their customers want. But tiers of businesses below are struggling because of their more limited datasets and resources.

This is where strong data intelligence and technology can even the playing field. To get ahead, retailers need to take a good, hard look at their data strategies.

First, they must understand their core strategy around customer acquisition. Without this figured out, it’s almost impossible to be successful. Be very clear about how you're going to acquire new customers. Find all the channels your customers are using, and then make it easy for them to engage with and purchase from you. It’s not enough to engage with customers on one channel (e.g., Meta); an omnichannel approach is essential.

Second, brands must invest in data intelligence technology until they have the means to build it themselves. Look for technology that can provide differentiated insight about who your best customers are and how to find more just like them. There's best-in-class audience technology that can lower the cost of customer acquisition by more than 25 percent, helping improve your marketing funnel efficiency. At the same time, look for tools that provide valuable benchmark data — how are you doing relative to similar businesses in your category? This will help you gain a more holistic view of your business health and hopefully gain the competitive edge you need to succeed.

As brands seek to lower their customer acquisition costs and improve competitive differentiation, they must get in the habit of looking at and leveraging their data and insights every single day. When growing brands have greater access to what's actually happening across the ecosystem, they can better understand where their highest-value customers are, when to pursue them, and how to increase customer lifetime value. They can then set their sights on not just acquiring new customers, but retaining them for the long haul. With access to broader datasets and advanced data analysis tools, scaling profitably can become a reality.

Alex Song is the founder and CEO and founder of Proxima, the data science company that leverages a proprietary database of anonymized consumer data to help brands better reach the right consumers across all major platforms.