How do we use it to drive performance improvement?
In my last few articles, I have focused on new metrics and processes available to retailers that help them create and energize a far more participatory and collaborative organization. CEOs and store operators alike are intrigued by the concept in theory, but are increasingly curious about execution. How can we simplify data and make it more actionable? Execution is everything!
When you look closely, retailers are overwhelmed by the amount of data they are collecting and unsure how to use it. Often when we start working with a new retailer, we find they are sifting through 150 to 300 reports and looking at a sea of metrics. What is even more concerning is that their field teams have little consistency regarding how they use these reports; they “cut and paste” on a daily basis to get individualized information to stores within their region or district. In addition to being difficult and inconsistent, this approach rarely provides the right information needed to effectively coach their teams and measure store performance in ways that are actionable by store teams. As one CEO told me recently, “we are expecting our field teams to be analysts rather than the executers we need them to be.” This is how big data is backfiring. Rather than saving time and driving the right outcomes, it is time consuming and leads to confusion.
In order to put this new data to work, retailers need to see, and act on the strong connection between their customer opportunity, store team performance, and the actions that can improve performance. Below are a few of the customer and store team “retail myths” that we have exposed after having studied a broad range of retailers with tens of thousands of stores, hundreds of thousands of retail employees, and millions of ‘store days.’
Myth #1 ‘Transactions’ are a good proxy for foot traffic.
Reality When we look at store performance only through the lens of transactions, we are blind to the number of customers that were in the store on a given day or time of day and how a store team performed relative to this opportunity. Let’s look at two similar stores. Both store A and store B generated $10K in sales and each store had 100 transactions on a given day. Without insight to customer visit data, the story is incomplete. In this instance, store A generated their transactions from 250 customer visits vs. store B making their day off of 500 customer visits. This tells us that store A did a better job at converting its customer traffic into buyers (40% vs. 20% conversion). Retailers should want to know what store A is doing differently from store B to convert more traffic. We know there are best practices in high performing stores that can be replicated in low-performing stores to improve performance.
Myth #2 Customer visits to retail stores are ‘random.’
Reality Customer visits to retail stores can be accurately measured using correct methodologies and algorithms. While retailers talk about weather and holidays impacting performance, there are far fewer outlier days than retailers think. Furthermore, a store’s future or ‘expected’ customer foot traffic is highly predictable. When we internalize that foot traffic has distinct patterns that can be identified, it means store teams can be prepared for the number of customers who will come to their store and they can be held accountable for the number of customers they are expected to convert.
Myth #3 Corporate should reward and recognize store team success based on ‘comp store’ growth.
Reality While important for Wall Street, ‘comp store growth’ is not a reliable indicator of store team success. Often we find that a store that has +10% comp is praised and one with 0% comp is reprimanded. Both stores are similar with the same number of annual customer visits, but the first store increased from $2.0M to $2.2M (i.e., +10%) and the second store – with the same customer opportunity – had flat sales of $4.0M each year. Should the second store be reprimanded or recognized for maintaining a higher yield from its foot traffic? Rewarding performance relative to customer opportunity (vs. comp sales) can be far more effective and motivational.
Myth #4 Conversion rates and dollars-per-transaction (DPT) are NOT controllable by store teams.
Reality While merchandise mix and promotions contribute to conversion rates and dollars per transactions (DPT), these metrics are also highly influenced by store team actions. To us, conversion and DPT measure the “behaviors” of our store teams. We can see these behaviors in the data and observe them on the retail floor. Store teams need to be educated on the specific actions that drive conversion and DPT. Is our team available and interacting with customers? How is this impacted by callouts, truck day, a manager’s day off? Is our team presenting full solutions and attaching add-on products for customers? If retailers are able to make the connection between store team actions and these key drivers of sales, they will systematically improve results.
Myth #5 Improving store associate effectiveness is not scalable and sustainable, consequently they are not core components of the business model.
Reality Is this a self-fulfilling prophecy? Most people want to be successful in their jobs and in life; retail employees are no different. With vision and clear expectations of “why” they are being asked to do something along with the necessary tools and coaching to know “how” to become successful, retail employees can thrive. Sales skills must be ‘operationalized’ as part of daily store activities just as ‘store merchandising’ is. For example, both Starbucks and Apple have built a business model in which their store associates use daily tasks to create energy in the store while interacting with customers, rather than allowing those same tasks to become a distraction that causes associates to avoid customers. Both retailers get that the human interaction is a key driver of customer value.
When we recognize that customer behavior is far more predictable than we think, and we begin to look at store performance through this lens, it becomes evident that the performance variability across similar stores and the day-to-day variability within each store are often caused by the actions of our store teams. Here are a few more myths that retailers need to internalize if they are going to put big data to use to drive revenue.
Too much time is spent analyzing and talking about data rather than using data to take actions that solve the critical, individual issues regional vice presidents, district managers, store managers and store associates face daily. I know that once these “retail myths” are debunked, there will be a fundamental change in how leaders lead and how value is delivered in retail brick-and-mortar.
To learn more about how to activate your store team with your existing data, simply drop me a note at firstname.lastname@example.org or call +1.978.461.4501.