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“Too much data.”
“It takes me all week to figure out what I was supposed to start last Monday.”
In our research into the relationship between brands and their stores, we hear these grievances often. Auto dealers, bookstores, apparel chains, shoe retailers, restaurants, and more—businesses across all industries just can’t get enough data. And for good reason: Brand organizations need data to illuminate what’s going on in their enterprise of stores, and to steer future initiatives. In our professional opinion, the addiction to data crosses a line when organizations focus on uncovering massive volumes of information with too little focus on what it all really means.
Our prescription has two ingredients. To reveal the truth and truly drive change, data must be customized and actionable. Here’s why both are vital:
“Customized” data is tailored to the user and the store.
Each user in an organization needs something different from the data.
- A corporate analyst needs detailed dashboards, spreadsheet exports, and sophisticated data science. Their domain knowledge brings insights out of the data.
- The field leaders—the regional vice presidents, area managers, and District Managers—need exception reports and visuals that identify the outliers in their districts. Data should direct them to the stores that need their help.
- Store Managers need an extremely curated view. Not because they’re not data savvy; most of the managers we’ve met love working with numbers. They’re just incredibly busy. Their time is better spent on the floor coaching associates and assisting customers, not stuck in the back office searching for the opportunity of the week. When they have straightforward, easily shareable insights, numbers start to move.
Customization must also happen at the store level. Take in-store fulfillment, an emerging omnichannel trend, as an example. Fulfilling an online order from a nearby store instead of a distribution center hundreds of miles away shortens shipping times, improves customer loyalty, and encourages repeat purchases—potentially amounting to a $500,000 opportunity across the brand. That opportunity isn’t usually spread evenly across the stores, however; an organization with 1,000 storefronts won’t have a potential $500 at each store. The brand more likely has a $50,000 opportunity at just 10 of those stores. Sending the opportunity alerts to just the 10 that need it focuses management’s attention, and reduces noise for the other 990 (who each have a customized handful of metrics that matter to their operations).
Data means nothing if it isn’t tied to actions. While an organized dashboard takes the burden off Store Leadership, it’s still just data on a screen. People must react to the data in order to make progress toward business objectives. To do that, data points often need to be broken down into actionable pieces.
Imagine a big-box department store chain wants to increase profit (because don’t we all?). The underlying analysis of profit may show that cosmetic sales are a key driver across the board in these stores. This insight into the profit metric already enables a more specific action to increase profits, but we can take it a step further: What is driving cosmetic sales?
Breaking down a metric into its drivers can reveal specific actions that each store and category manager can take to improve the bottom line.
The answer may vary by the stores’ demographics, suggesting differentiated next steps. Luxury brands may sell better at high-end shopping centers and so warrant special placement and promotions, while in-store demonstrations of affordable brands may drive sales with younger customers at the mall department store. Breaking down a metric into its drivers can reveal specific actions that each store and category manager can take to improve the bottom line.
It is important to note that a correlated piece of data doesn’t always mean it is actionable. However, correlated metrics can reveal what drives top-level metrics, giving organizations insight into what’s really impacting store performance (and subsequently, what actions will move the metric). A Store Manager needs to know that on-the-surface measures like Phone Hold Time, Reset/Revision Execution, and Total System Outs seem to be correlated, but sophisticated algorithms might find the common root cause is Insufficient Weekend Staffing. With this added insight to correlation, store leaders have an Ultra-actionable way to improve the drivers of NPS without taking hours to do all the analysis themselves.
In a sea of data, it’s too easy to get swept up in trends, comparisons, breakdowns, and red herrings of correlated measures—none of which are actionable.
HOW TO UNCOVER CUSTOMIZED, ACTIONABLE DATA
We’ve seen it all: handwritten numbers on a whiteboard, a sheaf of printed reports tucked into a binder, a grayscale PDF provided by a District Manager, a live on-screen portal. We’ve even seen all four in one office with different numbers in each document, creating uncertainty about accuracy. The first step toward conquering a data addiction is identifying and sharing a single source of truth.
It’s not enough to just share data—it has to be the right data for the right person, with clear next steps rooted in best practices.
This customized and actionable data fosters accountability. It’s not enough to just share data—it has to be the right data for the right person, with clear next steps rooted in best practices. Pair that with a lightweight set of tools that encourage follow-up, reward engagement, and support coaching and you’ve connected the dots on actionability and accountability.