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Identifying and Replicating a “Good” Store: How Theatro Moves Beyond the Obvious

At Theatro, my job is to help retailers maximize Theatro by leveraging the new analytics field of study we’ve discovered; store behavior insights.  We can track and measure employee communication, movement, and behavior patterns to tell a story about a store’s ecosystem.  This being a net new field and all, one of the interesting challenges here has been pulling apart correlation vs. causation.[i]  While this is a well-known and basic analytic concept, the approach to isolating and investigating this is a challenge, and that challenge is amplified quite a bit when there is no real established baseline.  This is the mountain we need to climb in the world of the store behavioral analytics. The payoff at the top of the mountain, however, is massive.  Using Theatro to help identify, scale and replicate ideal store behavior patterns will yield a gold mine of value.

We “know” that a store is better when the manager is on the floor shoulder to shoulder helping their associates succeed.  We “know” that a store is better when managers communicate and drive a daily action plan.  We “know” a customer is happier and more likely to make a purchase when associates are knowledgeable and responsive to service requests.  While we intuitively believe these concepts, how do you quantify and validate the performance of stores that check all the boxes?  Performing these activities should have an underlying correlation AND causality.  But what do we compare it to? 

A truism in retail is that a top volume store can succeed in spite of themselves, while a low volume store may be maximizing its potential.  Given that, what metrics make a store “good?” VOC[ii]?  Sales?  % to Plan?  Comp?  Turn?  The influencing factors are endless, and therein lies the challenge.  We not only need to establish those key metrics to measure a good store, we need to determine the key Theatro benchmarks that reveal beneficial store behavior patterns.  This will establish a scalable set of employee strengths that drive the behavior patterns which will maximize each store’s sales potential.

The first, and most obvious, way a retailer can rank a good store is the Voice of the Customer.  Giving an ear to customer feedback is unquestionably valuable, but there are challenges here.  Is the data stable?  Is your sample set large or consistent enough?  Are only certain types of customers providing feedback (overly happy or overly upset)?  So, VOC is an important, but potentially unstable, input into what makes a good store.

Secondly, let’s not miss the big prize here… sales.  It’s all about making money, one way or another.  This must be a factor in overall store performance.  The challenging aspect with sales is a large new store in Times Square is going to have a massive advantage in traffic, available product breadth, depth and funding for associates to support this.  Do we know that the large volume is associated with excellent customer service and a well-run store or do you just need warm bodies to run the tourist’s credit cards?  This is an area Theatro Analytics can provide previously unknown insights to help get that extra dimension of performance.  Response time, floor coverage, problems that have to be escalated, etc…

The inverse is just as true.  In my experience in the buying office of a luxury retailer, there came a point where one of our stores was, quite literally, the last store in a struggling location.  There was even barbed wire surrounding the rest of the mall.  They struggled to get any real sales, but they sure maximized what we gave them.  The associates were helpful, they did what they could to establish loyal customers who relied on them, but the foot traffic in the area was non-existent, rendering their efforts futile.  We knew they were one of the best run stores in the company, but looking at sales, margin, labor, or any metric in isolation would never show that.  Especially sales to plan.  We all knew it was dying, and when you plan for failure it tends to become a self-fulfilling prophecy.

The lesson so far is that while all this data can be manipulated to tell whatever story you want, is at times unstable and in isolation lacks any real context to the big picture…  It’s quite the conundrum.  Theatro can help address that.  Combining metrics from a retailer with Theatro insights create the context that most retailers are lacking.

I believe customer loyalty is at the heart of identifying “a good store.”  It is the primary force behind the key metrics.  Bain Consulting illustrated this point very well in their white paper Prescription for Cutting Costs.  Loyal customers translate into cost savings; they buy more over time and the cost to serve them declines. While selling a great product is important, it isn’t the key to building a lifelong loyal customer—customer service and experience is what ensures they keep coming back.  Establishing loyal customers.  Not customers who fill out surveys, not customers who come in the door every week, but customers who have a positive association with the store and the brand. 

Instead of focusing on a single metric, such as VOC, to measure customer loyalty, the problem really requires a retailer to combine multiple metrics to inform a single aggregate value.  Sales revenue, conversion rates, growth trends and profitability with stable VOC results is our most likely winner to vectoring in on a “good” store.  Low sales but high margin % combined with good VOC results, for example, would be surfaced in an equation like this. The tremendous opportunity before Theatro here is on two fronts:

1) Leverage our analytics to provide added context to isolated retailer metrics, helping understand how employee behavior drives customer loyalty

2) Take the retailer driven combined metric and store rank to determine and compare and contrast to what Theatro Analytics says about each store

Both fronts provide something no one else can: established, data driven correlation and causality between store behavior patterns and stores with strong customer loyalty.  Thanks to Theatro’s insights, this can be applied to all stores in the chain.  Just imagine understanding the ideal characteristics of a “good” store’s employee behavior and applying it to all of your stores!

We have a whole host of proprietary data that paints a multi-layered view of how a store performs and operates.  With analytics built around our unique insights, understanding what drives a good store, we can help the customer replicate the right combination of managers, associates and benchmarks to achieve a highly efficient ecosystem, regardless of circumstances beyond their control.  Correlation, causality and customer loyalty.  All things possible with Theatro!

[i] Correlation is a statistical measure (expressed as a number) that describes the size and direction of a relationship between two or more variables. A correlation between variables, however, does not automatically mean that the change in one variable is the cause of the change in the values of the other variable.

Causation indicates that one event is the result of the occurrence of the other event; i.e. there is a causal relationship between the two events. This is also referred to as cause and effect.

[ii] VOC: Voice of the Customer– a term used to describe customer’s expectations, preferences and aversions.

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