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Up, Down, and All Around: Communication Loops Across Retail Brands

SEPTEMBER 14, 2017

Competition and technology are changing how retail functions both behind the scenes and on the front lines, making feedback loops within large brands increasingly important. Most brands have found ways to ensure that top-down communication—where feedback, strategy, and data move from corporate to stores—happens.

However, there is a second feedback loop that has proven trickier and is often ignored: the loop from the stores back to corporate. In the second feedback loop, communication and data move from every individual store up to corporate. This is an important, and complex, challenge that can differentiate a retailer in an extremely competitive landscape.

KNOWING YOUR STORES

The second feedback loop is critical in understanding and improving customer experience at the store level. Customers have more choices and information at their fingertips, and if they have a bad experience with your brand… they won’t stick around for long.

The second feedback loop is critical in understanding and improving customer experience at the store level.

Customer experience is by far the most effective lever an individual Store Manager (SM) can pull, and also the most difficult solution to provide—as it’s never one size fits all. It’s up to each store and every SM to communicate this.

Sometimes stores across varied locations share a brand name, and not much else. Knowing the stores—the geography, the location, the culture, the neighbors—can provide granular information that makes all stores more efficient. SMs know their stores best. By listening to them, Square Root is able to manage and drive better performance specifically for each store.

For example, consider two different grocery stores. Location A sees beer and chips as their top sessions. However, Location B sees pain relievers and greeting cards as their most popular. Store management knows what they need to stock and display in their respective stores, as Location A is located next to a college campus, and Location B knows they must handle things differently because they are just down the block from a hospital.

FACING THE CHALLENGE

The first feedback loop is straightforward because you are sharing singular messages from one point (corporate) to many (stores). But this second feedback loop requires thousands of distinct messages (more beer! more chips!) to be received and digested by corporate in order to drive decisions. Brands have started to tackle this with data analysis and business intelligence, but they frequently cannot decipher the signal through the noise.

When corporate looks at rolled up performance metrics from different stores, important contextual pieces of information (like being located next to a college or a pharmacy) get lost in the data summaries.

The challenge of establishing a successful feedback loop from individual stores to corporate contains two major roadblocks: too many variables and too little feedback.

HARNESSING THE POWER OF MACHINE LEARNING

At Square Root, we’re tackling this second feedback loop with machine learning and natural language processing to identify meaningful signals that corporate can use and act on. Machine learning within the CoEFFICIENT® platform is about:

1) Listening to store managers

2) Understanding store context to boost performance    

3) Getting the right data to the right people

We can tell whether a district’s sentiment is good or bad, and can drill down quickly to figure out why (e.g. not enough pharmacists on staff during rush hour!). With this workflow, District Managers and corporate retail operators do not have to manually review feedback for 1,000+ stores to uncover insights. They get a clear image of granular store information by letting machine learning do the heavy lifting.

Additionally, machine learning algorithms get smarter over time. So as we collect more data, it optimizes for better results. This workflow allows corporate to develop best practices (send more beer to stores like Location A) that empower store managers to react to their specific environments. It ensures corporate users don’t lose the pulse on their stores, which leads to higher performance and happier employees.

Contextualized data are an important way for big brands to get a better picture of overall performance. Different people need to see different metrics based on their roles and responsibilities, which also change depending on the task they are trying to complete.

CoEFFICIENT uses data science to understand each location contextually, taking insights one step further and making them actionable. Interested in learning how our platform takes tools to the next level? Read more.

ABOUT THE AUTHOR:

Herschel is our “rockin’” product manager, quite literally. When he’s not rocking out at one of Austin’s live music venues, he’s chatting with customers and users. Herschel strives to be “customer obsessed,” and is passionate about building software that solves big problems and is a joy to use. Outside of Square Root, Herschel is a published translator and loves jogging on the trails of Austin. He holds a bachelor’s degree in computer engineering from the University of Texas at Austin.