Being a data-driven team isn’t out of reach. By overhauling how they collect and disseminate it, retail leaders can cultivate teams where data guides every decision.
In 2019, there’s a lot of emphasis on data, how much of it we create and whether it’s secure. For retail teams who want to remain competitive in a rapidly-evolving industry, data is especially crucial. Here’s why being data-driven should be your top priority—and how you can make sure data powers all of your decisions, across every team.
Customers demand data-driven experiences
It’s not news that the retail space is more competitive than ever before. Customers aren’t shy about their willingness to go elsewhere if they don’t feel like a retailer is giving them what they want.
“We’re in this age where the customer’s more empowered than ever before,” says James McCormick, a principal analyst at Forrester. “They’re actually more powerful than the brands when it comes to managing the narrative of the discussion and the engagement with the brands.”
But many retailers aren’t sure how to identify customer expectations — much less deliver on them consistently. In fact, almost two-thirds of retailers say that they struggle to define the types of conversations they ought to be having with customers, according to a Retail Systems Research report.
And so they’re stuck: Unsure of what customers want, unable to give it to them and missing out on revenue.
That’s where data comes in. The average retailer sits on top of a wealth of data that can provide key insights into what customers want, such as:
- Loyalty data from a CRM
- POS transactions
- In-store customer movement, product placement, and store design analysis
- Demographic information about consumers in different locations
- Social media and app feedback
- Mobile buying and browsing data
Using data like this to make better decisions has been the saving grace for many a retailer. Consulting firm McKinsey, for example, explains how one car rental company defined its ideal customer—and then looked for geographic pockets of them. By designing a tailored marketing campaign geared towards those consumers, they saw a 20% increase in revenue.
Okay, but how do you craft a data-driven retail team?
It’s not enough to simply make data-driven decisions at an executive level. You need to be data-driven at every level, from the C-Suite down to individual store locations.
That can sound like an insurmountable challenge; after all, not every store associate is a data scientist.
In reality, it’s mostly a cultural shift that starts by giving each stakeholder a seat at the proverbial table. Everyone should have some form of visibility into your different business intelligence (BI) platforms—or at least be able to review store performance reports.
Emphasize data at every meeting — big or small
To cultivate this data-driven ethos, leadership at every level should use data to guide team discussions. A great tip is to begin every team meeting and one-on-one by discussing store KPIs. This reinforces the importance of data in informing both strategic, long-term planning and day-to-day decisions like staffing.
Though it may seem like a no-brainer, it can be easy to let this form of accountability slide—especially if you’ve been working in a data-poor environment for a long time. As a leader, however, it’s your responsibility to consistently set a metrics-driven tone, even in the small things.
Not sure what KPIs to focus on? Foot traffic, conversion rate and cost of foot traffic are all good starting points. Learn more about using foot traffic data to guide decision-making >
Reevaluate your data toolkit
If you’re still struggling to be data-driven, you may need to reexamine the tools you’re using to collect, analyze, and distribute it. At Mercedes’ F1 racing team, for example, giving everyone access to data required overhauling their data storage system.
The same may be true for your team. You should regularly evaluate the tools you’re using to measure success in the first place. Are they capturing the data you need? Do they present it in an understandable format? Are they broadly usable by people without an advanced data analytics background? Are educational resources and training readily available?
If not, it might be time to switch tools or invest in additional solutions. After all, data is worthless if you can’t access it.