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Three Better Ways to Quantify Unmet Demand in Specialty Retail
This blog post continues our discussion on unique challenges faced by specialty retailers in quantifying traffic and workload. In our last post, we discussed the hidden work content implicit in customer traffic and the limitations of point-of-sale (POS) transactional data in specialty retail. Point-of-sale data alone misses the work demand associated with customers who, for a variety of reasons, shop and require support but elect not to make a purchase. Today, we continue on this journey, elaborating on labor standards modeling and forecasting to quantify hidden demand for planning, scheduling and performance optimization.
Getting it Right
Getting the workload right for specialty retailers is the first priority for labor modeling and store planning. Mathematically, workload is the product of volume group occurrences (i.e., transactions, cases, etc.) by the engineered standards, which reflect the time to perform work processes. Those engineered standards incorporate store characteristics, which modify the right standards for unique store variables like equipment, store layout, walking distances, and more to create accurate location-specific standards and store-specific work requirements.
The process for quantifying workload in non-specialty retail is simpler. Grocery and big box operators identify all the activities required to accurately reflect business processes by location, then leverage POS or receiving data to define workload volumes. It is a bigger challenge in specialty retail because there are many hidden components (called “unmet demand” by one author1) that derive from the conversion challenge, which we discussed in last week’s post. If you have not read through part one and two of this series, we encourage it before moving on.
Digging for Data
So where does the data to drive workload volumes come from? There are several options, and some are better than others. A typical approach is to augment the standards using special allowances or frequencies to estimate the hidden demand. The basis of the allowance may be anything from gut-level estimates to quick adjustments deemed within the budget.
But are the hidden volumes the same every day, throughout the day? Are promotions unchanging? Traditionally, the answers to these questions are, “no.” Thus, beware of simple spread percentages. Typically, one or more allowances within the standard grocery approach will not solve the problem, but it will bandage the root issue. We believe the same for specialty retail.
Electronic means of capturing customer traffic can be helpful, including beacon technology, area sensors and video interpretation. However, this technology tends to be expensive, and it can be problematic during interpretation.
For those not yet ready to dive into customer-counting technology, a secondary but equally advantageous option is to address this on the labor standards side. Observational studies and analyses can be completed to inform the labor standards and capture the full picture of stores’ workloads. Two kinds of studies are valuable to enhance standards modeling: utilization and customer journey studies. Below, we uncover the intricacies and benefits of both.
Who is Doing What?
Typically, utilization studies capture facts about how effectively associates are using their time. Are they working? What area are they working in? What type of work are they performing? In grocery and big box retailing, utilization studies offer data into how well the workforce is directed between tasks, how employees focus during their work and where employee time is spent.
Utilization studies are a great way to identify low-priority activities absorbing time but adding little value. Once found, these obstructions can be targeted to eliminate or reallocate wasted time. With the right modifications for specialty retail, we can learn about critically hidden activities like recovery and service interactions. This makes better, more accurate standards modeling possible, which includes the hidden activities not recorded by POS transactions.
Join the Journey
The second data source for augmenting specialty retail comes from customer journey studies, where an engineer tracks a customer through the store from entrance to exit to log their activities without attaching personal data. These take skill to execute in small-store environments but can yield major insights about customer behavior. They often generate ideas concerning store layout, brand merchandising, display effectiveness, customer experience and purchase decisions.
They are also a key data source for hidden workload. For instance, customer journey studies often answer questions like: how many items does one customer touch? How many items require recovery based on the customer’s journey? Does she use a fitting room or consultation services? Does the customer buy? If not, what was the probable cause?
If we can understand the patterns behind customer travels, we can model the required labor content using better data and standards. As Gartner Vice President Tom Enright noted in 2017, the goal is no longer understanding what customers want to buy, but how and why customers are buying2. This starts with data collection and leads to standards management. Solidifying these foundational elements opens a wealth of opportunities for cost-benefit analyses, scheduling optimization and wasted-hours reduction.
In the fourth installment of this five-part series, we will dive into task-level work planning and how vital it is for scheduling the right people at the right time, particularly in specialty retail.
- Glatzel, C., Hopkins, M., Lange, T., and Weiss, U. (2016, November). The secret to smarter fresh-food replenishment? Machine learning. McKinsey & Company: Retail. Retrieved from https://www.mckinsey.com/industries/retail/our-insights/the-secret-to-smarter-fresh-food-replenishment-machine-learning
- Sillitoe, B. (2017, June 15). Retailers urged to change approach to demand forecasting. com. Retrieved from https://www.computerweekly.com/feature/Retailers-urged-to-change-approach-to-demand-forecasting