In the last six months we’ve seen a huge level of attention drawn to the concept of showrooming, which of course is the coinage for when shoppers visit brick-and-mortar stores to check out merchandise in person and then shop around online to purchase for less money. It’s hard to pick up a magazine or visit a site aimed at retailers without reading about how concerning this trend is to the world of bricks and mortar.
It makes sense that those with physical stores would feel this way. They have to maintain expensive assets like real estate, employees, and store inventory, and those investments only make sense if store visitors turn into purchasers at expected rates. The more that stores turn into showrooms, the harder it is to make the math work.
The retail-oriented media are also chock full of advice for how to combat this trend. You should focus on competitive pricing, or creating a premium experience, or employee training. Or maintaining unique products. Or extending store hours. Or maybe something else.
As we all know, the trouble with opinions is that everyone has one—and there are no objective measures to show us which will actually improve the bottom line and which will not. What retailers actually need are facts. Facts about what is happening inside their stores, how and when showrooming behavior is occurring, and how changes in the stores can lead to changes in this behavior.
Fortunately, facts like these are available. Using existing technology, retailers can understand much more than mere conversion rates on a store-by-store basis. They can measure where in the store shoppers go, where they stop, and how long they stay there. They can also measure conversion rates for individual products compared to these factors, including:
- What percentage of people who came to this section of the store stopped and engaged with the product?
- What percentage of people who came to this section purchased?
- What was the average engagement time among those who shopped the product?
- What percentage of people who stopped and engaged wound up buying in the end?
- How do these behaviors vary by geographic region, individual store, or time period? Is there daily or weekly cyclicality to these behaviors? Is it seasonal?
- What did these behaviors look like a year ago as compared to today?
But it’s possible to go even deeper than that. Retailers can offer free Wi-Fi access in the store depending on an opt-in agreement whereby they can monitor the sites used by mobile devices in the store. Now retailers can gain information on which sites the store’s guests visit, which items they shop, and even which items they add to baskets while on the store’s Wi-Fi. That gives the merchant insight into questions like:
- How often do shoppers use mobile devices to visit competitive sites while in the store?
- Which sites do they visit?
- How often do they use mobile devices to go to your own online store?
- Which products are they shopping for and which are they not?
- How do the most commonly found online prices compare to the prices in the store?
This kind of information can isolate the specific products for which showrooming takes place and put some scale on the problem. Let’s walk through a scenario for how that would happen.
Abbie’s Appliances sells a broad range of electronics in its stores. Abbie is confident that showrooming is taking place within her walls. But she doesn’t know how often or for what products or what to do about it.
Abbie starts by measuring traffic and dwelling activity. She measures the propensity for shoppers to stop and linger in different sections of the store and the average amount of time a “dwelling” shopper stays. Areas with long average dwell times and lower than average dwell-to-conversion ratios than the store as a whole are suspect areas for strong showrooming activity. By looking into these metrics, Abbie identifies that the washer/dryer section is suspicious as a showrooming focal point.
Next Abbie looks at the usage logs to see the set of sites mobile users visit across the free Wi-Fi access she offers. It turns out that frequently visited sites do indeed sell washers of the same models that Abbie does. Furthermore, she can find searches and purchases for the individual items on sale in the store. Now Abbie has confirmed that showrooming is a real threat to sales in at least one section of her stores.
Abbie also has the chance to identify an action plan that will reduce showrooming activity. Let’s refer to the store’s two best selling washers as Washer A and Washer B.
It could turn out that mobile searches and purchasing of Washer A is ten times that of Washer B. Now Abbie knows where to put her attention. Maybe she reduces the price of Washer A to match what she finds online. Maybe she gives the sales staff the freedom to discount more deeply to sell this washer. Or maybe on the other hand she focuses more of her outbound marketing on Washer B to bring in customers who prefer that model (and who aren’t finding and purchasing it online nearly so often).
What she doesn’t do is lower the price of Washer B, a top seller in the category, for no good reason. And she doesn’t put more marketing muscle behind Washer A without some other response in place to prevent her store from simply becoming a more popular showroom. By finding out what customers are doing inside her stores, Abbie is able to tailor her response to the real details of her challenge.
It could even be that Abbie discovers other popular washers that aren’t in her inventory. Maybe showrooming can become a useful tool for her to identify the inventory extensions that will succeed with her customers.
And of course, as retailers do make these changes, in-store analytics are how they can learn what effects they’re having. Are your responses mitigating the showrooming behavior? Are they irrelevant? Which products are they helping and which not? How do these impacts vary by store or by time period or by area of the store? Retailers can use a test-and-measure methodology to zero in on their optimized response, in just the same way that their online brethren have been doing for many years.
In a recent RIS News contribution RetailNext VP of Strategy John Crimmins explains how most people involved in traffic counting are considering performance using a misconception of accuracy.
Recognition extends as far as old-fashioned traffic counting providers.
This series continues with a description of the components that need to be in place to take full advantage of omnichannel analytics