Customer, Product Data Drive Kohl's Merchandising Strategy
The growth of e-commerce has transformed merchandising, said Sarah Rasmussen, director, digital merchandising at Kohl's, to open her session yesterday at the Shop.org Digital Summit in Philadelphia. Excel spreadsheets and web analytics — once the primary tools for merchants — are dated and don't tell the full story. Data paired with listening to and tracking consumer signals is what leads to meeting demand with supply, Rasmussen noted.
But what does it take for merchants to evolve from product first, Excel jockeys to customer first, strategic thinkers? (Rasmussen joked that she got her first job as a merchant because she was proficient using Excel.) Rasmussen and Raj De Datta, CEO of BloomReach, a big data solutions provider, offered their thoughts during the half-hour session.
A Product is Just Data
While this is the truth, never tell this to a merchant, Rasmussen said, noting that she probably didn't get a job she was interviewing for when she made the bold declaration. Data helps you figure out what shoppers mean when they search for a product, she added. Rasmussen cited an example from last year's fourth quarter: shoppers were searching on kohls.com for “Christmas dresses,” and were being served an array of Christmas dresses for women, from formal to casual. What the retailer couldn't figure out was why the high-volume search term was converting at such a low rate. After digging into the data to see specifically who was searching — e.g., what devices they were searching from, when they were searching, how did they arrived on Kohl's website — Rasmussen and her team determined that it was mothers searching for Christmas dresses for their young daughters.
Armed with this new data, Kohl's made a change to its algorithm so that when “Christmas dresses” were searched, girl's dresses were at the top of the search results page, not women's dresses. The move resulted in an increase in conversion rate.
“Digital data makes merchandising more flexible,” Rasmussen said in regards to the Christmas dress example.
Another example came from the recently completed back-to-school shopping season. Rasmussen noted that the behavior of Kohl's online shoppers had completely changed from the previous year. A heat map revealed the categories and products and that were generating the most activity weren't what the retailer had planned for. As a result, in midseason Kohl's adjusted its back-to-school pages based on the new data, helping to optimize performance.
Tips for Better Merchandising Decisions
Rasmussen ended her presentation by offering some takeaways for the retailers in attendance:
1. Optimize your strategy based on shopping behavior in real time. For example, take behavioral data acquired over Black Friday and Cyber Monday and project it out through the rest of the holiday shopping season. You also want to look at last year's data, but do so cautiously, said Rasmussen, referring again to this year's back-to-school season. Of course you won't be able to identify a shopper with 100 percent certainty, but every piece of information can help.
“Right now we may have 10 signals, which is 90 percent more than we had at this time last year,” noted Rasmussen. “Ultimately, we want to know who exactly is searching and browsing, and not have to make inferences.”
2. Develop a merchant-centric data infrastructure. Furthermore, use this data infrastructure to transform your merchandising strategy from trial and error to one of continuous optimization, Rasmussen said.
3. Treat site browsers and searchers the same. “We decoupled browse and search for far too long,” Rasmussen admitted. “If you're decoupling these two segments, you're not listening.”