What Can You Learn from Declined Store Fulfillments?

Zebra analytics
Scott Pethuyne
Scott Pethuyne

In the current retail environment, minimal contact ordering services like ship-from-store (SFS) and buy-online, pick-up-in-store (BOPIS) reign supreme. Many retailers who weren’t heavily invested in these strategies months ago have pivoted to them to increase sales while maximizing store inventory and associate productivity. The retailers that also focus their loss prevention efforts on mitigating risk and fraud around these critical services will hold the strongest competitive edge well into the future.

This may sound like a rather large, general area of focus, but there’s a way to hone it. LP professionals can uncover a wealth of invaluable insights around SFS and BOPIS by analyzing one single data point—declined in-store picks. The number of picks your store associates are declining (and why, if your system captures a reason for the decline) can reveal numerous opportunities to optimize the SFS and BOPIS experience for both customers and employees. Of course, the analysis and interpretation process can be greatly simplified by leveraging an advanced analytics solution, like prescriptive analytics, to comb through the data and determine what is happening and how to correct it.

Here are three critical key performance indicators (KPIs) that you can improve by looking at declined picks:

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Incentive Effectiveness

Many brick-and-mortar store associates originally took their jobs expecting to work on the sales floor with customers, so it’s little surprise that many are reluctant to fully embrace the concepts of SFS and BOPIS. To get around this, many retailers offer stores and/or individual associates an incentive for fulfilling SFS and BOPIS orders. Incentives can just as easily hurt BOPIS and SFS as they can improve them. For example, if a store associate isn’t getting appropriate sales credit for fulfilling these orders, he or she may increasingly decline orders in favor of devoting more time selling in-store merchandise that counts toward bonuses, awards, and/or metric goals.

On the flip side, it’s important to ensure your picking incentives are not too generous. Boosting incentives may lead to a decrease in declines, but if stores and associates are disproportionately incentivized for fulfilling BOPIS and SFS orders, they may neglect in-store customers or even structure order fulfillment and transactions in a fraudulent way to falsely inflate their fulfillment numbers. This can cause missed sales opportunities, not to mention a negative customer experience. If the customer cannot get in-store assistance as needed, the previously attentive customer experience begins to feel more like shopping in an active warehouse. Analyzing decline activity provides a window into the efficacy of these incentives as a factor in BOPIS and SFS excellence.

Inventory Accuracy and Availability

If your picking and fulfillment system includes reason codes for declines (such as not enough time or labor to pick, merchandise not available, merchandise not in salable condition), assessing this KPI is especially simple. The reason codes give you an indicator of potential phantom inventory. Phantom inventory is loosely defined as product that your inventory management system lists as available, when in reality it is out of stock. Whether the lack of availability is caused by product theft, merchandising issues, shipping errors, or other factors, all are real-time indicators of inventory inaccuracy and potential missed sales.

From another angle, depending on historical inventory accuracy and safety-stock levels, associates may also decline certain orders due to lagging availability. Given that in-store sales are typically their primary focus, they may choose to reserve the last few products for their in-store customers. Plugging the declines data into an advanced analytics solution makes short work of the analysis required to identify patterns of behavior indicating an ongoing issue.

Store Associate Performance

Does this sound like more of a store operations concern? Perhaps. But LP practitioners know a store that is exhibiting poor operational performance is often a store that will exhibit poor shrink performance as well. If a low-performing store also happens to have a high rate of declined picks, consider that your invitation to get involved, as either could be a leading indicator of fraud. Even if the culprit turns out to be something other than fraud, this is still a great opportunity to collaborate with your store operations counterparts and contribute to increased sales—something everyone can get behind.

For example, say you discover a certain store’s associates are declining picks simply because they’re poorly trained, struggle to find items, and take too long to fulfill orders within the promised timeframe. Thanks to your ability to flag the low performance within the data, store operations has an opportunity to address underperforming stores or associates, and potentially dedicate more BOPIS or SFS orders to stores with better picking metrics before the customer experience is greatly impacted. Everybody wins.

For more information on the use of prescriptive analytics to identify cases of shrink, visit the Zebra website.

About the Author
Scott Pethuyne is a member of the Industry Solutions team at Zebra Analytics. He comes from an accomplished background in asset protection with tenures at Justice, Ascena Retail Group, and Designer Brands (formerly DSW). He uses his intimate knowledge of AP strategies and the Zebra Prescriptive Analytics solution to show retailers how to combat fraud, drive efficiency, and strengthen revenue and margins with Zebra Prescriptive Analytics.

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