I’ve been heads down at work for the past several weeks rolling out a significant operational change (supported by new technology) to a segment of pilot stores in our retail fleet. This rollout required me to visit stores on both coasts listening for feedback, observing things that need to be improved and iterating quickly to deliver value in near-realtime. It’s been invigorating and has brought back to the surface all the elements I love about product management as a practice!

Ultimately, the problem space we’re operating in is this: How might we make the products in our stores easier to find for our employees and our customers?

The big change we are attempting to deliver is the introduction of product location data in-store. It’s a big, gnarly problem to solve for a legacy co-op like REI, mainly due to the extreme variability in our store layouts. If you’ve been to more than one REI, you know that some locations are in historical buildings – an old train station in Denver, for example – while others inherit a simpler big-box & strip mall retail feel. This variability in warehouse size & organization, combined with diverse floor sets across markets, create complexities that make standardizing a process and delivering tech that works across all locations extremely difficult.

One store workflow this new product location data will help improve is the sales floor restocking process. For the first time at REI, we know how many units of a SKU are on the sales floor, how many units are in the warehouse, and where those units are in the store. My team is also ingesting several data elements from our Visual Merchandising team and we wrote a machine learning algorithm to forecast a Target Sales Floor Quantity for every SKU in the store.

So with the raw location data and the algorithm telling us what should be on the sales floor, we were able to develop a new restocking tool in REI’s employee mobile app (Ascent) that is centered around one key hero metric: Sales Floor Percent Stocked. Store employees can now get a real-time snapshot of their sales floor stocked rate, along with a prioritized list of products that need to be restocked, on their mobile devices. This is a big step forward for our store teams.

One of the things I’m most proud of related to this pilot rollout is the feedback we’re getting from users about the Ascent features. Because we lean into co-creation mindset, the product team was able to deliver an initial version that delighted store teams out of the gate and we continue to iterate as we learn more about usage. The Ascent team obsesses over quality and store employee experience, and I think that’s evident in the product we deliver. I mean, tell me this is not one of the most elegant employee-facing app interfaces you’ve ever seen.

Employee-facing apps don’t need to suck. The Ascent team is lean: 1 front-end engineer, 1 back-end engineer, 1 QA analyst, shared product designer, shared product manager (me). The lean-ness of the team presents some hurdles, but it also affords us the ability to take both an an agile approach that prioritizes speed-to-market and an artisanal approach that prioritizes craft. I believe this mode of operating is our sweet spot.

As we enter REI’s holiday code freeze, we’ll be hands off on production changes but we’ll be working hard behind the scenes on the next version of Ascent (ETA January) that will power location data enablement across the enterprise.