Amazon Prime Day — or “Black Friday in July” — offered several lessons for retailers to not only help them plan for this crucial shopping season, but also set themselves apart in the eyes of customers. The most critical to note: to level the playing field, and compete with e-Commerce giants like Amazon, location data and intelligence must to be at the forefront of their cross-channel strategies.
For context, it’s important to first look at how brick-and-mortar sales and foot traffic fared against Prime Day’s competitive e-Commerce deals. Gravy Analytics’ Prime Day Foot Traffic Analysis (measured consumer visits at 1,400 retail chains for the two weeks prior and following Prime Day 2018, and compared it to 2017 data), found that for some retail categories — particularly consumer electronics stores and cosmetic shops — in-store shopping is in fighting form. In-store traffic across higher-end consumer electronic stores picked up on Prime Day this year, adding 5% over the average daily attendance rates for the two weeks prior.
This kind of data, which reveals where people go and what they do there, provides valuable intelligence about consumer behaviors to help retailers plan, predict and take action ahead of the year’s pivotal holiday shopping season.
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A few lessons include:
1. In-store experiences are key
New data from Adobe Analytics predicts that Americans will be buying more big-ticket items this holiday season. When it comes to purchasing these items — whether it’s a 4K smart TV or an outdoor grill — shoppers (unsurprisingly) don’t want to just see the item…they want to experience it. The same can be said for cosmetics: customers often like to test out products before buying, “Am I sure this shade of foundation is a perfect match for my skin tone?” From jewelry to mattresses, there are other examples where browsing stock images and tapping the “buy now” button simply cannot compete with the sensory experience of in-store shopping.
Whether it’s taking a cue from Ulta Beauty’s salon services or experimenting with in-store tech like Zara, it’s essential for retailers to create an in-store shopping experience that isn’t easily replicable online.
2. Improving in-store operations
Understanding how foot traffic indexed across certain retail categories puts brick-and-mortar stores at a distinct advantage this holiday season. If the findings suggest that electronics and cosmetics bring consumers in-store, retailers must prioritize ways to elevate the in-store experience across these categories — from adding more display space to implementing demonstrational kiosks.
Additionally, partnering with brands in categories least affected by Prime Day is another strategy to consider to help increase dwell time, and minimize shoppers simply checking items off their list and heading for the door. Layering in location data, retailers also can make better tactical decisions, such as identifying which of their stores are the busiest. With this information, retailers could plan where to add more employees to help drive sales during key rush times…not to mention cut down on long lines, overcrowding and customer frustration.
3. Better understand the holiday shopper
Understanding why shoppers choose to buy certain items in-store leads to more valuable insights about the holiday shopper, such as behavior and motivation. For example: do customers only care about getting the best deal, or are other factors — like nearby dining options, a store’s location in relation to their home, or the proximity of other retail stores — guiding purchasing decisions?
When retailers have a holistic view of their consumers, they can better determine what motivates deal seekers to shop online vs. in-store, and identify any opportunities to highlight how shopping in-store is a resource, not an inconvenience. Adding sensory experiences, ensuring busy stores are well-staffed and having ample stock of the items that drive foot traffic all contribute to customer delight, which, online or offline, we know to be the most important driver of both sales and loyalty.
Jeff White is the founder and chief executive officer of Gravy Analytics. He is passionate about building disruptive technologies that have large applicability to change industries. Prior to founding Gravy Analytics, he founded several companies and led them to successful exits.
