In the era of customer-centric merchandising, retailers need to use their test stores as a lens into how customers engage with merchandise for smarter decision-making across the chain.
If you’re a merchandiser or retailer launching a new product line, chances are you start by rolling out the merchandise in a test store. You might display the newest merchandise near the entrance or on a mannequin.
But when you decide to put it there, is that decision based on intuition or actual data?
Today, test stores are critical for figuring out what to sell and how to sell it: testing targeted assortments, understanding price points, deciding visual merchandising strategies and defining allocation across the chain..
But for the most part, merchandisers and planning teams don’t have any data on which to base these decisions beyond historical category and style data and sales excels that come in every six weeks.
And that data doesn’t tell you why some products don’t sell, or which items had the potential to sell more, had they been displayed differently.
Today, new technologies can allow retailers to see why in-store shoppers abandon merchandise before checkout, the same way digital retail teams do on their e-commerce platform, and use that data to make decisions about display merchandising, assortments, localized allocation, and pricing accordingly.
It’s time to rethink how you use your test stores. And it starts with your customer behavior data and analytics.
Why we need test store analytics
Today, retailers have data on how products in test stores are sold, but not how customers engaged with them. Did they try anything on before purchasing it?
How long did they spend looking before they found the products? Everything that happens from the moment a shopper enters the store, through the checkout line, is a black box.
Whatever data retailers do have is anecdotal and collected manually from store staff, with no guarantee of accuracy.
In order for test stores to accurately forecast sales for merchandise, they need to be able to answer the following questions:
- WHY isn’t something selling as expected – could it have sold more?
- WHERE is the optimal location for each category and brand?
- WHICH styles are best suited for store mannequins
- WHAT visual merchandise approaches will drive sales?
New AI-based customer behavior analytics solutions can help you answer those questions in your test stores, so you have the insights you need to drive more sales across your store chains.
Item-level customer behavior data
To understand merchandise performance, it’s not enough to know the bottom line of sales – you need item-level data from every single piece of merchandise to tell the story of how customers behave with it – understanding where it is in the store, where it moved to if it was left in the fitting room or purchased after being tried on.
When you can see how shoppers engage with merchandise in real-time, and can turn decisions like where to display merchandise into data-driven decisions – based on predictive analytics, for example – it can directly and deeply impact sell-through rates and turnover time.
When the right customer behavior analytics solutions are used, you can implement that solution in test stores and gain insights that will help you optimize across your chain.
The insights you gain can then be applied across the chain to amplify the results.
That data will help you understand why some styles are underselling, and help you optimize each style for better conversion and create localized assortments.
For example, if a shirt is in a prime location on the sales floor, gets lots of engagement, and is even taken to the fitting room, but no one ends up buying it, it might be a pricing issue. But it could be a localization issue of bad performance with customers in a specific area, and need to be transferred out to a store where it is already selling well to avoid overstock.
But to get those valuable insights, you need to ensure a steady flow of data from each and every item in your test store. You need tools to measure merchandise performance. And you need actions to apply your insights to merchandise on a large scale.
Connected merchandise refers to merchandise that can transmit data on merchandise behavior in real-time. This data will take merchandisers from gut-based decisions to algorithm-based decisions about pricing, assortments, and allocation.
Today, NanoBT (Nano Bluetooth) solutions are poised to replace RFID in stores because, for similar price points, they go beyond inventory count to offer limitless new use cases for merchandisers, buyers, store operations, and omnichannel sales.
For merchandisers planning a new product launch, using algorithm-based decision-making for planning pricing, assortments, and allocation will soon become the norm.
Using AI algorithms in test stores to optimize pricing and promotions
Today, when an item doesn’t sell, retailers’ usual resort is to turn to markdowns, which hurt their bottom line. Deciding the exact price is also guesswork – it’s impossible to know if that item would still have sold with a higher price point.
Most of the time, you don’t have alternatives to markdowns. But using connected merchandise coupled with machine learning models, you can measure merchandise sensitivity and apply discounts accordingly – and even identify where other actions will result in a sale, so you can avoid applying markdowns entirely.
This works by capturing metrics on how customers engage with the merchandise, including:
- Whether the item is on the sales floor
- Shopper taking merchandise off the shelf or rack and looking at it
- High or low intent to purchase, based on whether someone took the outfit to the dressing room
- When the item is purchased, or abandoned prior to purchase
A machine learning model measures all of these metrics to identify when to apply high, low, or medium discounts, and when to take other actions entirely.
For example, merchandise that has a high inventory count, sells OK, but is not seen by enough shoppers, doesn’t necessarily need a discount to sell more – it can increase in sell-through rate without impacting profit margins if moved to a more prominent location in the store, or put on a mannequin.
The same methods can be applied to make more informed decisions around allocations, assortments, and other merchandising strategies, but prior to and after launching new styles or categories.
4 Steps to Actionable Insights from Test Stores
Injecting intelligent automation in test stores is just the first step to optimizing merchandise across your chain.
Deploy – Choose your test stores and deploy connected merchandise technology with minimal impact on store operations.
Understand – Treat different types of customer engagement as KPIs, then use those KPIs to gain actionable insights.
Optimize – Optimize merchandise decisions in the test stores based on customer-centric merchandising decisions.
Scale – Apply insights to your merchandise across the chain to sell more.
Test Stores - The Bottom Line
Retail is primed for a transformation in how decisions are made. For merchandisers, going from a linear, product-centric merchandising strategy to a circular, customer-centric one will start with smarter test stores that give real-time feedback into how customers are engaging with merchandise.
Using Connected Merchandise and applying intelligent automation and AI algorithms to your test stores will provide new levels of insights that will help increase turnover and reduce the need for high markdowns.