Using In-Store Analytics to Combat Showrooming
By Tim Callan, firstname.lastname@example.org
The omni-channel shopper has arrived on the scene and is completely altering the retail landscape. He is equipped with a wealth of information to help him make buying decisions and is incredibly diverse in how and where he shops. The omni-channel shopper researches products for best choices; she checks social media channels for approval ratings; and, if she strolls into a brick-and-mortar store to browse, she will verify pricing options on her smartphone and perhaps even purchase online – a practice known as “showrooming.”
A challenging and costly trend for many brick and mortar retailers, showrooming has those with physical stores justifiably concerned. After all, brick-and-mortar stores have to maintain expensive assets like real estate, employees, and store inventory, and those investments only add up 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.
Want to combat showrooming? Get the facts.
Commentators are chock full of recommendations for how to combat this trend, suggesting retailers need more…
- Competitive pricing
- Premium experiences
- Employee training
- Unique products
And the list goes on and on. The trouble with these recommendations is that we don’t have objective measures of if they will actually help, or by how much. What retailers really need are the facts. They need more information 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 like in-store analytics, 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. And how individual products measure up based on these factors, including:
- What percentage of people who came to this section of the store stopped and engaged with the product? What percentage ended up purchasing?
- What was the average engagement time among those who shopped the product?
- 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?
Or it’s possible to go even deeper. Retailers can offer free Wi-Fi access in the store based on an opt-in agreement that allows the retailer to create aggregated counts of 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 most commonly 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?
This kind of information can isolate the specific products for which showrooming takes place and put some scale on the problem. Once a retailer understands what is happening in its stores, it can use that knowledge to create an environment that keeps shoppers in the door and ultimately sends them to the register with the goods they want.
Scenario: Abbie’s appliances
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 does not know how often or for what products or what to do about it.
1. Get the facts (with in-store analytics)
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 sites mobile users visit across the free Wi-Fi access she offers. It turns out that frequently visited sites sell washers of the same models that Abbie does. Now Abbie has confirmed that showrooming is a real threat to sales in at least one section of her stores.
2. Take informed action
Abbie’s two best selling washers are Washerator and Laundrymatic. Mobile searches and purchasing of Washerator is ten times that of Laundrymatic. Now Abbie knows where to put her attention. She could reduce the price of Washerator to match what she finds online. She could give the sales staff the freedom to discount more deeply to sell this washer. Or she could focus more of her outbound marketing on Laundrymatic to bring in customers who prefer that model (and who aren’t finding and purchasing it online nearly so often).
What Abbie doesn’t do is lower the price of Laundrymatic, a top seller in the category, for no reason. And she doesn’t put more marketing muscle behind Washerator 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.
Finally, as retailers make these changes, they can also learn what effects they’re having. Abbie can find out if her responses are actually mitigating the showrooming behavior and which products she is helping and which not. Using in-store analytics, retailers can use a test-and-measure methodology to zero in on their optimized responses. By fully understanding and combatting showrooming with rich analytics, retailers can sustain their competitive advantage and a loyal following among its customers.
Tim Callan is CMO of RetailNext, which provides real-time in-store monitoring and analytics. He has more than 15 years in marketing leadership roles for enterprise software and SaaS-based companies. You can read his blog posts at Retailnext.net/insights, follow him on Twitter @TimCallan. He can be contacted at email@example.com.