How Retailers Find ROI with In-Store Analytics
As a retailer, you’ve got a much better chance of converting shoppers in-store than online. In-store analytics allows you to take charge of your in-store conversion. How? I’ll show you.
Now, the use of data to improve decision-making and ROI is, of course, not a new concept. What is new is that through Internet of Everything (IOE) technologies, retail data can finally be extracted in real-time and in ways that make it reliable and scalable for brick-and-mortar stores, providing real-time visibility at more granular levels such as department/category, aisle, store window or even merchandising and product.
Understandably many retailers are so far focusing on web-based metrics, POS sales data, second-/third-party data and/or yearly shopper research/focus groups. Such data are necessary for creating differentiation, as they offer insights into how and why shoppers make decisions along their path to purchase. But, these sources are not enough for retailers trying to optimize merchandising investments, SKU rationalization and sales per square foot. You must also understand conversion.
What do I mean by conversion? There are different definitions of conversion, but true conversion is the measure of transactions generated by a population of shoppers.
Conversion = # of transactions/# of shoppers
Let’s look at a business case using a typical store-to-store comparison.
$80,000/wk in sales…
$200 average transaction…
$40,000/wk in sales…
$160 average transaction…
Based on these POS numbers, Store A seems to be performing better.
However, once we take conversion into consideration and start focusing in at the aisle-/department-/category-/merchandising-level, we might find something different. Indeed, what if Store A actually had 4,000 shoppers passing through a particular category in the store that week and Store B actually had 840 shoppers passing through that same category?
Category Conversion in Store A = 400 transactions/4,000 shoppers = 10%
Category Conversion in Store B = 250 transactions/840 shoppers = 30%
As it turns out, that category is actually converting 20% more shoppers in Store B than it is in Store A. So, how much is Store A leaving on the table here?
If that category in Store A were seeing a conversion rate equal to Store B (20% more)…
20% conversion x 4,000 shoppers = 800 add’l transactions
800 add’l transactions x $200 avg. transaction = $160,000/wk
Or… $8,320,000 add’l sales/yr in store A alone
What if you could increase your category conversion rates by 20% across your stores?
This is a typical example of how lack of in-store metrics, like traffic to get true in-store conversion, can paint a false picture of store performance. Without really understanding conversion, it’s difficult to determine the real problem of lower revenue/transaction. Here we looked at conversion for one category, but as I mentioned before, with in-store analytics you can actually measure traffic and calculate conversion for a specific aisle, merchandising or even brand as well.
What could your stores gain from in-store analytics?
If in-store analytics is something you are interested in, I highly recommend:
Retail Analytics: Understanding the Options
Everything you need to know about in-store analytics to make the right choice for your stores.