How Brands in Retail Find ROI with In-Store Analytics
If you’re a brand on the retail shelf, 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 not just for retailers, but also for brands merchandising inside these stores as well—giving them direct, real-time visibility in-store at the product- or even display-level.
Understandably many brands 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 the decisions along their path to purchase. But, these sources are not enough for brands trying to optimize their in-store performance and shopper marketing/POP display investments. 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 retailer-to-retailer comparison.
$40,000/wk in sales for brand…
$200 average transaction…
$20,000/wk in sales for brand…
$160 average transaction…
Based on these POS numbers, the brand/products appear to be performing better in Retailer A.
However, once we take conversion into consideration, we might find something different. Indeed, what if Retailer A actually had 2,000 shoppers passing by the brand’s display that week and Retailer B actually had 410 shoppers passing by the brand’s display that week?
Conversion in Store A = 200 transactions / 2,000 shoppers = 10%
Conversion in Store B = 125 transactions / 410 shoppers = 30%
As, it turns out this brand is actually most successful at converting shoppers in Retailer B, which is getting a conversion rate of 20% more than Retailer A. But, what’s going on with Retailer A? How much is this brand leaving on the table here?
If Retailer A were seeing a conversion rate equal to Retailer B (20% more)…
20% conversion x 2,000 shoppers = 400 add’l transactions
400 add’l transactions x $200 avg. transaction = $80,000/wk
Or… $4,160,000 add’l sales/yr in store A alone
What if your brand was missing out on a 20% conversion rate increase across just 5 of your retailers?
This is a typical example of how lack of in-store metrics, like traffic passing by your brand to get true in-store conversion, can paint a false picture of performance for a brand. Without really understanding conversion, it’s difficult to determine the real problem.
Fortunately, thanks to powerful in-store analytics technologies—brands now have ways to detect, track and measure these critical metrics before any damage is caused. These metrics can be gathered using a wide variety of sensors and devices, including Bluetooth beacons, smartphones, anonymous facial detection cameras, weight and motion sensors and counting systems. All have various pros and cons, but the most advanced analytics systems are real-time, providing brands with valuable conversion comparisons at the product- or even display-level.
What could your brand 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 brand.