Here's an interesting case study highlighting how LiveCrew can help retail brands take better decisions, in Retail Operations, Merchandising, Marketing and Omnichannel Customer Journeys. Whilst we can't share the actual results, we can explain a great deal about what LiveCrew brought the brand.
LiveCrew helped a major apparel brand to understand what was happening in its stores, what the store visitors wanted, why they were buying and why 80% or so were leaving empty-handed. After three months of collecting data from stores throughout France, here are a few of the findings we can share with you.
First, we started by understanding what were the brand's key strategies. They provided us with their top fourinitiatives:
- Boosting the share of a category of products which they felt was a major opportunity
- Increasing sales towards a specific consumer group which were under-represented
- Boosting their loyalty program
- Improving the omnichannel journeys and removing frictions
In addition, they had two hypothesis which they wanted to verify:
- Segmentation or clustering of their stores. Like most retailers, they had put each store into one of three buckets which determined which product assortment each store would get and how much stock. We generally find most retailers use turnover as a basis for the clustering, occasionally adding qualitative information about the store's locations (high end / low end, tourists…). Most retailers struggle to find the right data and therefore stores get the same assortment despite the fact their visitors might be very different (ages, genders, weather, needs…).
- The wanted to reach a specific price point both for Men and Women customers, which was a frequent request from stores.
Once we had collected more than 20'000 LiveCrew surveys, our algorithms and visualisation tools allowed the brand to get unparalleled visibility into each of their strategies and business hypothesis. Here are a few of the key take-aways:
- How to push that important but underperforming product category: LiveCrew showed that different consumer personas had major different expectations when it came to that category and also product needs. If they were to be successful growing its share, it needed to rethink the product assortment and customer expectations based on who the collection was targeting. We identified differences on the basis of age, genders as well as major shifts when it came to gifting or buying for one-self.
- The underperforming consumer group was actually going to the stores a lot. Traffic wasn't the problem. The problem was conversion, due one key reason. A large subset of that group came after searching online and knew exactly what they wanted. Unfortunately, there were missed sales in the sizes and cuts that they were looking for, creating frustration. We quantified those missed sales due to stock or assortment, and then showed how many they missed per model and per size. That allowed them to get the right stock shipped to each store and plan the next orders more precisely. That last part is really important. Merchandisers place orders based on historical sales. They don't know how many times someone didn't find the size they wanted and left the store. So when they place the next order, they're replicating the same mistake, without even knowing it. LiveCrew solves this by allowing the merchandiser to see the hidden part of the iceberg.
- When it came to their loyalty program, LiveCrew provided the conversion rate for loyal customers versus those that weren't part of the program, as well as the effect on conversion you could expect from converting a non-loyal customer to the program. This validated the great work they had already done and allowed the Store Managers to explain with real numbers to their sales advisors how many more sales they could get by getting customers to join their program. It even allowed to see how older or younger visitors behaved and how best to convince some groups to join the program.
- Store clustering: you can do two things with LiveCrew:
First, you can understand if your current store clusters make sense from a visitor profile point of view and what the visitors of each of your clusters wants and why they don't buy. We showed the brand that their “youth” cluster wasn't actually much younger than the other clusters, however, it did show “youth” stores had very different expectations when it came to products and price points.
Secondly, LiveCrew has developed a clustering tool, to propose clusters of your stores, based on their turnover but also visitor profile and product needs. This allows you to offer the right product assortment to each store cluster and reduce missed sales significantly.
- Price point. It's always really difficult to decide to launch higher priced or lower priced collections. On the one hand, you want to trade your customers up to boost sales, but on the other side, if you launch an entry range collection, will the additional volume not cannibalise your other higher margin collections? LiveCrew showed precisely that there was a real need for a specific price point, but only in one cluster of stores and only for one gender. This allowed our customer to go after customer segments that have a real demand and minimise the risk of repositioning the price level of their assortment throughout their store network.
