How to Improve Sales With In-Store Analytics

It’s no secret that online retailers have almost universally adopted the practice of using analytics to improve sales. When you can track a customer from the moment they hit the landing page all the way through to the end of their purchase, why wouldn’t you?

Comparable versions of the sophisticated analytics used by online stores are also available to brick and mortar retailers. They’re just not used anywhere near as much as they are online.

If you knew you could improve sales through the use of in-store analytics, would you implement a few new tools to measure what’s going on in the store? Of course you would!

In case you need a little convincing, here are just a few of the ways in-store analytics can help increase sales.

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Measure Foot Traffic in the Store

If you asked many retailers how many people had been in their store yesterday, they’d check the register tape to see how many sales were made. That’s not the same thing!

Yes, it’s important to know how many transactions there were, but that doesn’t tell you how many shoppers left empty handed. It doesn’t tell you if the walk-outs peaked over the lunch hour.

People counting is the way forward for brick and mortar retailers. People counting is the best way to measure traffic in the store at any given time. It will allow you to compare the conversion numbers day by day or even hour by hour.

Without this important benchmark data, it’s difficult to plan effective marketing efforts for a few reasons:

  • You can’t evaluate strategies without enough quality data.
  • Without traffic numbers that can be compared to benchmarks for similar days, times, and seasons you cannot tell if your current promotions are drawing in customers.
  • You can’t track sales conversions without knowing how many customers are in the store.
  • It’s impossible to know if an in-store promotion is bringing in more prospects if you don’t know what constitutes an average day.
  • There’s no point in launching a marketing campaign to drive more people to the store if sales are down because the lines at the cash register are too long.

It’s easy to see why in-store analytics is a critical piece of the retail sales puzzle, and this brings us to our next important in-store data point: Wait times!

in-store analytics are a critical piece of the retail sales puzzle

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Use In-Store Analytics to Keep a Keen Eye on Wait Times

In many cases when sales are flat, it’s not your products that are the problem. It’s probably not your sales associates or how they make customers feel either. It’s how the existing resources in the store are used.

With the right in-store data, you can prepare “heat maps” that indicate where and when shoppers travel within the store. This knowledge allows optimization of the store layout as necessary. It also lends itself to appropriate upsell and cross-sell opportunities by placing the right products in the right spot.

Perhaps most importantly, accurate data can provide valuable information about the points where the sales process breaks down. Everybody knows that long queues to pay for an item are a significant obstacle to sales, especially for those shopping on the relatively short lunch period.

I’m a very impatient person, and standing in a slow-moving line is one of those very small, maddening aspects of life that drives me crazy. – Gretchen Rubin

But what about the fitting rooms for apparel stores? Long lines to try something on are just as off-putting to shoppers as long lines to pay. It’s a fact; people HATE waiting in line. If they also can’t easily get help from a sales associate while they’re finally in that fitting room, forget it! The sale is lost.

Worse still, that customer is probably never coming back!

Collecting detailed statistics on traffic flow and wait times is the first step to ensuring appropriate staffing levels at all times and in all areas of the store.

Maintain Appropriate Staffing Levels

There are fixed costs in every retail operation. Rent and utilities immediately come to mind, but the point is that there are some expenses that you can’t do much about. That’s why, when evaluating the bottom line, staffing levels are always under scrutiny.

With accurate in-store data, it’s possible to make sure staffing levels are appropriate in every area of the store. Staggered start times and break times are commonplace, but the decisions about when to start those shifts and lunch breaks have historically been pretty arbitrary.

Have you seen people walking out of the store without their planned purchases? Was it because they can’t get help finding the right size, or they simply can’t wait for the next available fitting room? Either way, you have a problem that isn’t necessarily reflected in the sales figures at the end of the day.

Detailed, accurate in-store analytics allows you to make informed, strategic decisions about staffing levels and intra-departmental organization. It all starts with having the right data.

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The Last Word on In-Store Analytics

There is a plethora of data available to all retailers, and there is no real reason brick and mortar retailers shouldn’t capitalize on that data in the same ways as their online counterparts. The modern retailer can and should be collect vital in-store data to adjust marketing tactics and manage store operations.

whitepaper download: in-store apparel retail analytics - beyond people counting