Updated: Jan 14, 2019
Better measurement and management of retail store employees can provide brick & mortars a competitive advantage over online stores and there is a manageable six-step method to perform these optimizations effectively… so goes the argument of Wharton Professor Serguei Netessine in the most recent edition of Harvard Business Review…
Prof. Netssine's arguments derive from the conclusions of his research and experiments with several large retailers.
Why you should keep on reading...
Prof. Netessine's experiments suggest that by following his prescriptions, retailers can add as much as 20% to the revenues to existing stores.
Prof. Netessine makes two prescriptions in his article entitled Retailers Are Squandering Their Most Potent Weapons: First, retailers should rightsize their individual store staffing to the point of maximize marginal profit. Second, retailers will likely see improved sales performance from low-to-zero cost investments in training staff.
Step 1: Use historical data on absenteeism to estimate the effect of staffing.
Premise: "Retailers may not realize it, but they already have a way to calculate how staffing influences store revenues: crunching the numbers on what happens when associates don’t show up because of illness, personal problems, decisions to quit, and so on. For example, if 30 people are scheduled to work in a store and only 27 come in, how do actual sales compare with forecasts? If they meet forecasts, the store is probably overstaffed. If they’re down 10%, increasing staffing by 10% would most likely raise revenues by 10%."
Actions: Prof. Netessine recommends "us[ing] the most granular data from the longest time period a retailer can conveniently provide it for—typically, one year of weekly sales and payroll data [in order to have a sample size that can produce statistically significant results]. But keep in mind that other forces—such as advertising and the weather—affect sales. We collect data on those too and with machine learning create a demand model that can predict sales in a store as a function of its staffing level and other drivers. Then, using the analysis from that model, we sort a retailer’s stores into three tiers: those that could benefit from more labor, those that could live with less, and those that are appropriately staffed."
Step 2: Validate the results by running an experiment
Premise: The data across all the store locations may not be homogenous enough to rely on so a test should be run.
Actions: "[R]un tests with a sample of stores from the first two tiers. Change the staffing levels in a selection of stores and compare the results with what happens in control stores—similar outlets whose staffing levels are kept constant. If you increase payroll in 25 test stores by 10% and leave it unchanged in 25 control stores, and revenues go up 8% and 1%, respectively, in the two groups, you can conclude that the net impact of the staffing increase is 7%. This lets you account for revenue drivers other than payroll, which in this example must have caused the 1% increase in the control group. Since the profitability of the additional revenues is obviously important, you should track that, too, calculating the gross margin on the incremental sales minus the cost of the additional payroll needed to generate them."
Step 3: Optimize staffing chainwide and measure the results.
Premise: Implement the changes suggested by the conclusions of your findings.
Actions: "Add labor to the stores that your analysis showed could benefit from more labor, reduce it in stores that need less, and leave it alone in the rest. Then, again, because your experiment’s results are still not as precise as you need them to be, evaluate the impact of the changes to confirm they produced benefits. This new and improved labor plan is not an end point, however, because all the factors that influenced it will change over time. Retailers need to repeat this three-step process, perhaps annually, to adjust as the world around them changes.
As we noted earlier, when staffing increases in some stores are matched by staffing decreases in other stores, the resulting additional revenues won’t cost the retailer anything. Net increases in labor should be made only if they increase profits significantly, however. And though it takes time to reap the benefits of adding workers in many industries, the payoff is immediate in stores that can productively use extra staffing. So retailers need not fear that they’ll experience an initial period of lower profitability."
WHY DOES THIS WORK: "The retailer’s customer surveys revealed why: The two most important drivers of customer satisfaction were the ability to find an associate who could provide assistance and whether that person was knowledgeable—exactly the factors we’re addressing here."
TRAINING FOR PRODUCT KNOWLEDGE
"Rightsizing store labor is only part of the story. The quality of associates matters immensely as well. An incompetent salesperson might be worse than no one at all... Associates can benefit from two types of programs: process training on how to perform such tasks as restocking shelves and executing a customer return, and product knowledge training about the features of the store’s offerings, so they can help customers decide which items to buy." Prof. Netessine's experiments and prescriptions focus on product knowledge training."
Step 4: Track sales by associate.
Step 5: Identify sources of product information.
Premise: Make brands pay for product knowledge training! "If you’re selling branded products, the brands are your allies and may pay for training about their offerings. After all, they care even more than you do about having their products’ features described accurately to customers. If they foot the bill, your contribution should be providing on-the-job time for associates’ training."
Actions: Contact Suppliers. Use ExpertVoice (formerly Experticity) which provides free training for hundreds of brands across 30+ categories. (I've used ExpertVoice to train on the Carhartt brand myself - for completing the modules you get a certificate for 50% off a Carhartt product.)
3. Collect data on training activity and compare it with data on individual employees’ sales.
Premise: "The idea is to determine if associates who train more also sell more."
Actions: You can measure the hours people spend in training, but it’s even better to measure the objective knowledge they’ve acquired, which you can do with online tests. But again, it’s important to consider other factors that affect sales.
Research in Action: "When [Prof. Netessine's research team] followed this process at Dillard’s, a department store chain with nearly 300 locations in the United States, the results were telling. Dillard’s partnered with ExpertVoice (formerly Experticity), a firm that provides online, voluntary self-guided training modules for retail associates. Sponsored by the makers of goods that Dillard’s sells, the modules taught associates about the features of each brand’s products. Associates were paid commissions—giving them an incentive to learn how to sell more—and the brands that developed the training offered associates discounts on their products, which were based on how many modules people had taken. Since each module lasted only about 20 minutes, many associates did more than one."
"After assembling data on the training history and sales productivity of the associates over a two-year period and on their years of experience and other influential factors mentioned earlier, we created a model to assess the effect of training. We found that for every online module associates took, their sales rate increased by 1.8%. Since training was voluntary, not all associates engaged in it, but the average hourly sales of people who took it were a whopping 46% higher than those of people who abstained. The associates who did the training were already selling more per hour than those who didn’t, and this accounted for about half the 46% difference. However, a comparison of the sales rates before and after the training showed that it accounted for the rest. Given that associates took the modules on their own time, most of the gross profits resulting from the online training fell straight to the bottom line."