Guest Blog By Ralph Crabtree

Thanks to clickstream data, SMS records, social media chatter, and other “digital breadcrumbs,” today’s retailers understand a lot about the behavior of their online and mobile customers. How many people visited my website? What items did shoppers view but not buy? Which ads did they respond to? How long did they browse? Answers to questions such as these help businesses improve their marketing, pricing, promotional and customer service strategies.


But in a multichannel world where a typical consumer might view shoes online, research prices on a mobile device and buy at the mall, many retailers have a huge analytic blind spot: they don’t know much about how shoppers behave when they are in an actual store. Retailers that leave out brick and mortar behavioral intelligence do so at their own peril.

According to a Forrester Research analyst recently quoted in The New York Times, “well over 90 percent of sales still happen in physical stores.” Closing the “insight gap” that exists between their brick and mortar and digital sales channels is a business imperative, but where to start? First, retailers must identify what types of in-store data need to be captured so that they can ask (and answer) the kinds of questions that are already part of routine analysis in online and mobile environments. For example: How many people came into my store? What are they doing while they shop? What products do they look at? What products do they buy?

Here are four data categories absolutely critical to gaining more detailed and accurate in-store intelligence:

  • Transactional Data (Online parallel: Ecommerce Data) – In order to track sales, returns, and inventory levels, most brick and mortar retailers already have point of sale (POS) data capture systems installed at registers. These systems provide vital information about what items customers are buying, which stores are bringing in the most revenue, and more. POS data alone is not enough, however, as the picture that it delivers of a customer’s shopping experience is oftentimes incomplete. For example, POS data can tell a retailer that customer X purchased a specific style, size and brand of shoes using a credit card. It cannot reveal that the customer also looked at the Ralph Lauren display for 10 minutes and wanted to buy a pair of jeans, but didn’t because her size was out of stock. (Or that customer Y didn’t buy anything at all.)
  • Traffic Data (Online parallel: Site visits) – Keeping an accurate, ongoing, real-time count of in store traffic, also known as people counting, is essential in brick and mortar environments, for several reasons: 1) Traffic counts help retailers measure sales conversions. How many store visitors turn into buyers? 20 percent? 30 percent? How much did they buy? This is valuable insight. For example, a low conversion rate could signal that a location’s product mix, service levels or pricing need to be adjusted. 2) People counts also help retailers manage their workforce more effectively in real time. Based on the number of visitors walking through the door and average shopping time, technology can predict how many checkouts will be needed. The store manager can then beef up staff at registers or send more associates to the sales floor. 3) Finally, by analyzing store traffic against conversions over time, retailers can begin to see important patterns both within and across locations. For example: Why is the conversion rate higher in some stores as opposed to others? How are seasonal events and marketing promotions changing the patterns? Why have conversion rates gone down instead of up over the last 12 months?
  • Queue Data (Online parallel: Order Processing Data) – How many people are waiting in line at registers? How long are they waiting? How fast are sales staff processing transactions?  Today’s busy consumers hate to wait, and data related to queue times, as well as customer actions in the queue is critically important for retailers that want to optimize service (i.e., all retailers). Research studies have shown that the amount of time a customer waits in line has a lasting impact on their positive or negative perceptions of an in store shopping experience. By tracking queue data, stores can adjust staffing and speed wait times in real time, better forecast workforce needs over time, and even communicate wait time estimates to customers, who appreciate being told what to expect.
  • In-Store Behavior Data (Online parallel: Clickstream Data) – Data about what customers actually do once they pass through the front door is the Holy Grail, but it’s also the trickiest to capture in a brick and mortar environment. Retailers want to know things like how shoppers move through their store, what displays they stop at, and what items they touch and try. Online channels have an advantage here, because in the digital world, retailers can track “clicks,” collecting highly granular data about what products consumers look at, put in and then discard from their shopping carts, check back on, etc. This kind of data enables highly personal targeting—as anyone who has had an online ad for a handbag that they recently viewed on a retail site “follow” them around the Web knows. While tracking behavior in real world locations is a bit more challenging, with new technologies, it can be done. There’s no excuse for being blind when it comes to the retail world’s biggest and most profitable channel.

Ralph Crabtree is CTO of Brickstream


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