Rethinking NPS One Metric at a Time

OCTOBER 11, 2017

You can have all the data in the world at your fingertips, but it’s useless until you know what to do with it. For retailers, data can often be overly broad or too high-level, and difficult to tie to action. Every store has its own unique set of challenges and opportunities and pinpointing those opportunities, store by store can make those metrics useful and actionable to store employees.

Retailers should start by asking the question, “What change can we make in a store to impact the business?” and work backward.

Retailers should start by asking the question, “What change can we make in a store to impact the business?” and work backwards. What they should aim for is data they can show to their stores that will result in strategic business decisions and in-store change.

One example of how this works is NPS, or Net Promoter Score. NPS is a popular retail metric that measures customer satisfaction and predicts business growth. Many retailers use it as the core measurement for their customer experience programs. In general, NPS is calculated based on a single question: “How likely is it that you would recommend our company/products/services to a friend or colleague?” Answers are scored into three general groups: promoters, passives, and detractors. Companies try to use this to improve customer experience, but have a tough time identifying which store actions move the NPS metric.

Although an NPS score might predict how well the brand is doing as a whole, it rarely pinpoints why stores have customer satisfaction problems.

Although an NPS score might predict how well the brand is doing as a whole, it rarely pinpoints why stores have customer satisfaction problems. Within each store in each district, you have different problems (or advantages) that impact NPS (Store A has a stocking problem, but Store B struggles to greet customers). If you are only looking at the broad NPS score, you cannot see correlations in metrics that ARE actionable.

If a Store Manager sets the goal, “improve NPS” they will probably find the metric immovable. However, if they are able to look at correlating metrics, like wait times at the register or the time it takes to make a dinner reservation or the cleanliness of dressing rooms, they can identify actionable metrics to improve upon that will impact NPS.

These types of metrics can be hard to surface. At Square Root, we use Machine Learning to identify actionable metrics that will have the desired impact for each store, and can be tied to higher-level metrics used across the whole organization.

Every brand has a group of core metrics they track every day. While these might drive behavior and conversation, smart data software can pinpoint correlated metrics that are actionable. The metrics we deliver through CoEFFICIENT® include:

  • Focus Metrics, the top 3-5 metrics that dictate 80% of day-to-day decision making
  • Related Metrics, which help offer context to the Focus Metrics, providing simple root cause analysis
  • Index Metrics, which aggregate large amounts of information into a simple health check
  • Forecast Metrics that help put today’s performance into context based on historical data
  • Recommended Metrics that leverage artificial intelligence to learn patterns in your data and workflow in your employees, proactively finding hidden issues before they’re problems

CoEFFICIENT creates an environment where every person every department manager at every store gets a curated set of information that they can track every day. They can see what they should be doing for their stores, for their problems, and for their opportunities.

We understand every store is unique and managing them takes actionable, store-level data. This can be hard to surface. With CoEFFICIENT Metrics, District and Store Managers can take a complex set of metrics and simplify it to a few things where action can be taken immediately. Interested in learning about our CoEFFICIENT Metrics? Read more.

ABOUT THE AUTHOR:

Chris is our big vision maestro. He pushes the team to dig deep to understand our customers and innovate to make them better. He’s spent his career developing strategic partnerships in the automotive and technology sectors. His fundamental understanding of these industries helps us develop easy-to-use, problem-solving technology. Prior to starting Square Root, Chris held operational and strategic roles in several Internet and software companies, including: TrueCar, US Digital Gaming, Pricelock, CarOrder, Wayfare Interactive, Brighthouse, and Trilogy Software. Chris graduated Phi Beta Kappa from Carnegie Mellon University with degrees in Computer Science, Mathematics, and Psychology.