Retail Insights

How Can You Get There if You Can’t See It?

The Robin Report

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What performance measures and goal-setting metrics are you currently using? How do you know they accurately reflect individual store performance? Are they helping to create a high-performance culture? We are immersed in a data-driven environment and increasingly dependent on metrics and tools to measure and monitor our businesses. But most of us are willing to admit that transforming data into valuable insights is an ongoing challenge.

In the December Robin Report, I introduced a new customer-centered metric—Return-On-Visit (ROV)—that enables retailers to uncover hidden opportunities to drive incremental sales. Before we take a deeper dive into how ROV and other new metrics can provide better insights, let’s take a quick look at the limitations of traditional performance measures and goal setting.

Do Your Metrics Provide a Complete Picture of Performance?

There is no shortage of measurement tools and management reports. The era of “big data” has clearly arrived on the retail scene, principally fueled by:

  • Advanced POS systems that capture and disseminate vast amounts of transactional-level information.
  • The development of data-mining techniques that pinpoint the buying habits of customers.
  • Easily created custom reports at all levels of the organization that may or may not lead the end user to make the right decisions.

However, my experience suggests that these advances—as impressive as they are—have not delivered on the promise of enabling faster and more effective decisions at each level of the organization. To the contrary, they have often created information overload at all levels, with store managers struggling to balance operational tasks and customer service; field managers attempting to prioritize and filter competing corporate initiatives; and corporate managers wrestling with the fundamental questions of what really drives store performance.

Sifting through mountains of data and reports to identify actionable insights has become a full-time job for field and corporate managers, and the interpretation of the results is often unclear. More data doesn’t equate to better decision-making and can actually cause slower, less confident
decision-making, shifting priorities, and confusion about how to best replicate individual store successes across the entire fleet of stores. Unfortunately, in some cases, it can also lead to poor, or suboptimal, decisions.

In this data-driven environment, it’s important for retailers to step back and ask a few basic questions:

  • What do these metrics and reports really tell us?
  • Do they spotlight the opportunities and help us understand the reasons for performance?
  • Do they help store field teams know what specific actions to take to drive improvement?

Are Your Store Performance Goals Accurate and Credible?

The potential shortcomings of current measurement approaches come into sharper focus when we consider store performance goals. For most retailers, revenue goal planning is quite accurate at a company level on an annual and sometimes even quarterly basis. But don’t store teams also need specific daily goals so they can confidently make their week and their month? Won’t precise daily store goals, based on a ‘scientific method’, help each store team perform more effectively?

As an industry, we haven’t refined goals adequately at the store level.

There are many factors, when combined, which can distort the accuracy of daily store goals: differences in traffic patterns; seasonal influences; and other local factors just to name a few. Our research at Yacobian reveals that at a daily level, a store’s performance versus goal can often be greater than +/-50%, which obviously can be very troubling. Conversely, when we aggregate daily store data, a 200-store region measured over a month may see a variation of less than 5%. At a total company level, the quarterly variation might be less than 2%.

Based on our research, we have identified two typical patterns:

  • Rolling up overall results over a large number of stores and over longer time periods masks improvement opportunities that exist at a daily store level.
  • The high level of daily variation contributes to the belief among store teams that daily goals are based on “rough guesses” which will even out over time.
  • Based on these practices, store managers don’t give much credibility to daily store goals. Plus, when stores have daily goals that aren’t viewed by managers as reasonable, these goals generally aren’t seen as important or actionable, reducing accountability among store teams. When field managers drill down about the reasons for missing a daily goal, they often hear anecdotally, “it was just slow” or “customers are just looking.”

A related and perhaps more fundamental analytic issue is the use of comp sales targets to establish store goals. This results in all stores being assigned a similar modest annual increase (e.g., 3% to 6%) based on last year’s sales. This ‘non-scientific method’ does not take into account the many factors that influence each store’s ability to achieve their specific goal—e.g., the customer opportunity or the number of customers coming through the door.

Here’s a real-life example that sheds some light on the complex challenges. One of Yacobian’s clients was rewarding two stores with comp sales increases of 10% from the prior year. Store A had a 20% increase in customer visits, and Store B had customer visits decline by 5%. The leadership team quickly agreed that these stores should not be rewarded the same way, since their performances against customer opportunity were very different. Store A had a 20% larger opportunity for sales and yet sales were up only 10%. Store B had a 5% reduction of opportunity and yet the store team was still able to increase sales by 10%.

The difference in customer visit yield was clearly substantial; Store A’s yield was down 10% and Store B’s yield was up 15%. Senior management realized that its measurement system and goal-setting approach was not as accurate and effective as they had assumed.

This case study reveals that despite the proliferation of financial, operations, and customer metrics and reports, most retailers lack consistent processes for assessing performance and taking action. Different metrics on different reports are used by different people at different times. As a result, most senior managers have no clear “line of sight” between top-level objectives and store results; field leaders are unable to quantify and prioritize improvement opportunities; and the organization lacks a consistent understanding of what drives store performance.

One key question needs to be asked:

  • How can we expect to achieve consistent store performance improvement over time if metrics are incomplete, goals are inaccurate, and there are no standard processes for using this information?

A Better Answer

Given the shortcomings of traditional approaches, we can see that we need granular, actionable metrics that identify opportunities for growth. A new, more effective approach includes establishing:

  • A new foundation for measuring performance grounded in each store’s customer opportunity.
  • An integrated set of supplemental metrics that are holistic, accurate, granular, and actionable, on a daily basis, by the entire store team.
  • Goals and targets that are accurate, actionable, and readily accessible in every store, every day.
  • Consistent, integrated business rhythms for the entire organization—from a structured “store day management” process for store managers and periodic business reviews for field leaders—to annual planning and resource allocation for corporate managers.

If a retail organization is operating with sound measurements and actionable insights, it has a better opportunity for consistent performance improvement… more on this in subsequent articles. Or, to learn more right now, simply drop me a note at tyacobian@yacobian.com or call +1.978.461.4501.

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