Omnichannel retail demands cross-functional analytics

Old Retail Hierarchies Don’t Fit the New Retail World

In my previous post, Retail’s Urgent need for Multichannel Analytics, I talked about the need for retailers to adopt new KPIs to truly understand the modern, digital-first customer journey.

But solving the multichannel retailer’s analytics challenges goes beyond redefining KPIs. Your hierarchies—the way you organize your information, how you group and roll up the “who, what, when, where and why” of your business—matter, too. Traditional retailers limit their perspective due to the rigid merchandise and location hierarchies inherited from brick and mortar days. Although these hierarchies promote useful comparisons of plan and actual results to prior years, the perspective is myopic in the face of digital dynamics.

For instance—today’s shopper is typically online before, during and after their visit to the website, app or store. Amazon has extensive knowledge about these interactions. But most multi-channel retailers don’t, and can’t, because their rigid information hierarchies won’t handle the breadth of data needed. This means that these multi-channel retailers have no shot at leveraging the potential advantage of their physical-plus-online presence to the fullest.

These legacy hierarchy definitions—and their siloed design—are major contributors to the problems retailers face relative to being “disconnected” from the customer. Redefined hierarchies designed for cross-functional analytics (more on this in a moment) are the key to seeing retail the same way customers do.

Progressive retailers who organize their information using modern hierarchies are empowered to manage their success by customer segment, path to purchase, fulfillment practice, social media buzz, promotional events, brand, assortment and many other factors.

Cross Department Collaboration is a Critical Success Factor

The onrush of digital retailing has brought extraordinary complexity into retail’s traditional departmental orgs: marketing, store operation, planning, HR, and supply chain management. In each case these departments have introduced new processes supported by robust data flows in pursuit of digital opportunities. But these flows typically stop at the department door because traditional analytics provide no path for them. This results in information silos, overspecialization, and a trip to “Excel Hell” whenever someone needs to bring together data across departments.

Let’s take a brief tour of what data each department possesses and why retailers should be leveraging this information across the enterprise.

Siloed Data and Who Else Needs It

To get a holistic picture, if possible, a cross departmental data integration effort is required. Progressive retailers are regaining the unified view by killing off information silos through enhanced analytics infrastructure.

You Can’t Unify Your Business through Excel

When your existing analytics processes can’t handle today’s business questions, you can still get answers—but at great cost. Your top talent—the businesspeople and information workers who have the drive, context and knowledge to ask and answer the right questions—will build the analytics required to meet their immediate needs. This activity, though impressive, only exacerbates the fundamental problems of an inadequate analytics, with dozens of hours spent doing manual data integration via copy-and-paste, metric construction and other data heroics, all in Excel.

These are your best people, trapped in a grind of enervating brute force work. They aren’t freed up to solve higher value problems. And the one-off nature of each time consuming analysis effort means the business agility you need to compete in the digital-first economy is crushed. No higher level thinking about anticipating the customer’s next desire, no spare cycles for innovation and testing.

Remember—when you see “Excel Hell” happening in your organization, it’s a symptom of a much bigger problem you need to address immediately. You can’t unify the business though Excel.