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What to offer and where, that is the question – and it’s one of the most basic problems in retail chain management.
The root of the problem is that retailers today are stuck with using historical sales data as the basis for making decisions around store assortments.
In order to truly optimize assortments to match your customers’ needs and maximize sales, you need data on how shoppers engage with merchandise in each store.
Valuable in-store data that goes beyond sales to track the customer journey enables algorithm-based systems to deliver localized insights, optimizing store assortments and driving growth.
Getting Store Assortment Optimization Right
Store assortment optimization is the process of selecting the optimal mix of products to display on store shelves. In-store assortment and allocation can affect margins, sales, financials, and customer satisfaction. It is a continuous and complex process that is key to retail organizations – but it’s nearly impossible to achieve with traditional systems and methods.
Today, new technologies powered by real-time, real-life data analytics drive innovative solutions to optimize merchandise assortments and in-store operations, such as identifying which sizes are taken to the fitting room most often and adding more of those sizes to the store, moving inventory based on localized needs, and achieving in-store shelf optimization.
6 Benefits of Store Assortment Optimization
- Generating Demand – Improved store assortment better fulfills customer needs, resulting in increased engagement and demand.
- Customer Satisfaction – The experience of a store that has what you want and need improves customer in-store experiences and drives loyalty.
- Sales Growth – More options that fit your customers result in sustainable growth.
- Improved Financial Results – Due to more sales, improved profitability, and less capital tied up in stock.
- Stock Optimization – Less inventory and warehousing costs, less markdowns and better inventory turnover ratio.
- Reduce Inventory Costs – Less excess and dead stock.
What Goes Into Assortment Planning
Store assortment optimization starts with planning. It’s the process retailers use to decide which products to display and where and when they’ll be available. Assortment planning is the result of a complex attempt to forecast customer demand to drive the most sales – aligned to a retailer’s business strategy and goals on brand issues such as variety, quality, and price.
Store assortment planning includes multiple product attributes, such as:
- Style
- Size
- Color
- Brand
- Price
- Inventory levels
When planning assortment, retailers consider more than just the product attributes. There’s seasonality, for example, that calls to prioritize bathing suits in summer. And location, with a Florida-based store that may carry bathing suits year-round. There are also many other considerations, such as price points, categories, trends, customer expectations, and more.
Store assortment planning based on data-driven insights can be much more precise and effective, reflecting sales opportunities and optimally addressing customer needs.
The Store Assortment Problem
Fashion retail executives in aim to create the right mix of products. To best achieve that, they need to constantly make decisions regarding which items to sell and where – thousands of products and numerous stores, often across diverse geographical, cultural, and demographic locations. The problem is that they don’t have the in-store data they need to systematically sync assortment decisions with actual demand and expectations.
Merchandisers and planning teams try to achieve store assortment optimization through a mix of research, sales data, reports and tools that are all static, and not based on real time data.
For example, to better understand consumer decision-making and buying habits in a category, retailers often use consumer decision trees. Although this graphical tool may be helpful, decision trees cannot solve the retail assortment dilemma. Consumer expectations and behaviors vary, and the trees can give a partial and inaccurate picture.
The In-Store Data Void
When it comes to in-store hard data, what retail executives don’t have is the data they need beyond sales.
Excels and historical category and style data. Looking at past sales data is not enough to optimize assortment. After all, it doesn’t reveal the underlying reasons for customer decisions, such as why some products don’t sell (style, fit, broken sizes, price, etc.), which items had the potential to sell more had they been displayed on another shelf, or when sales don’t reflect demand because the store needs more inventory.
They have no information on what happens from the moment a customer enters the store until he leaves, except anecdotal data from store staff that does not guarantee accuracy.
What brands need is granular and accurate data on in-store buyers shopping behavior and purchasing decisions so they can derive insights and make informed decisions on store assortments down to the individual store – and fast!
Fast Paced Environment
Adding to the challenges of assortment planning and optimization is the rapid pace of inventory changes. The fierce competition in the industry pushed retailers to a model of fast fashion and even ultra-fast fashion, requiring them to continuously introduce new collections.
Displaying new products at a higher pace engages customers and boosts sales: it drives customers to acquire more stuff – and more often, and allows them to follow hot trends shortly after they are seen on fashion weeks’ catwalks. Zara is known for its ultra-fast fashion and customer engagement, with loyal customers who visit about six times per year, whereas the norm for other retailers is often two to three times per year.
The international fashion brand also has a high turnover of products. As a result, it grows sales while saving on warehousing and inventory costs at scale.
Finding the right balance between assortment changes and sales depends on the brand, its financials, and the competition. But no matter the fashion brand, data is the key to gaining clear visibility to quickly and continuously optimize your brand decisions.
Store Localization
Retailers strive to tailor merchandise allocation to individual stores for categories, styles, sizes, color assortment, and quantities so they can fulfill in-store demand as well as e-commerce sales and hybrid fulfillment customers have come to expect.
Localized assortment plans are particularly significant for chain stores that cover a variety of geographical locations and local preferences. Retail chains usually attempt to localize assortment to a cluster of stores, as clustering helps to create assortment plans and meet demand. However, clustering is the product of the parameters chosen to determine the grouping. For example, retailers can group stores based on similarities in sales performance, demographics or store size.
Post clustering, some merchandise selections aim to meet the perceived preferences in the cluster, such as a more price-sensitive cluster where retail businesses want to make sure they have a greater variety of economy products to drive sales and customer loyalty. It’s a broad, non-specific decision that calls for additional analysis of factors such as size, color, and style preferences in the cluster, likely to influence demand or store performance metrics.
For refined localization, retailers can rely on real-time in-store customer behavior data. For example, shoppers in a specific cluster may not be interested in an item that sells well elsewhere – calling to transfer it out of the store to avoid overstock and into the location where it can drive sales. If, on the other hand, the customers engage with the item, try it on and then abandon it, it might be a fit or pricing issue, and headquarters can swiftly respond.
Today, retailers work with outdated tools at the chain, cluster, and individual levels. To create data-driven localized assortments and get those performance-driven insights, you need the merchandise engagement data from every item and the tools to measure, compare, review, and apply them at scale.
In-Store Operations Optimization
Store retail operations include core functions like in-store inventory control, store layout and shelf optimization, customer service, and order fulfillment.
To optimize inventory selection and stock levels, retailers need to decide which products to bring in and how to allocate them in stores. But that’s not where assortment and allocation decisions end. Once the products are delivered and displayed, there are ways to further optimize inventory through product swaps and changes aimed to increase the turnover ratio, reduce unplanned markdowns, and avoid deadstock.
Product swaps based on real-life results of underperforming merchandise may be complex and costly. It’s more than the logistics of moving products from place to place, it requires new store layouts and taking products out of inventory and back in. The bottom line: It’s better to get the localized assortment right and then work to optimize further.
Fashion chains also need to quickly decide which items to keep on shelves and which to change and make room for new merchandise.
With quantitative and behavioral insights, they can better select the products to rotate vs. the products they choose to keep on the sales floor, such as items and brands that are overperforming the category or styles customers are loyal to.
In-Store Shelves Optimization
For brick-and-mortar, assortment selection is inseparable from space allocation. To sell, products must be seen, and to be seen they must be out there, on shelves and racks.
Floor space is crucial because it encompasses variety and quantities. But floor space is not created equal, and naturally, some shelves and displays get more engagement than others. Planograms are tools often used to plan a store layout, focusing on product placement and displays and point-of-sale that eventually aid with in-store inventory control. Heat mapping focuses on providing store managers with insights into store layout and shopper behavior.
For better shelf optimization, new technology can track which shelves and items best catch a customer’s attention and which are overlooked. Moreover, it can show the actual situation on shelves, alert store staff when they need replenishment, track store compliance with planograms, group items that go together and place them next to each other, and get valuable insights based on real-life data.
Tech Optimization Solutions
NextGen store assortment technological solutions offer a new set of capabilities to overcome the obstacles created by the in-store data void and integrate assortment into in-store operations.
When new technology collects item-level data to a cloud-based platform, it can do things like analyze a product’s performance in comparison to the category, compare individual stores or clusters of stores, and take into account issues of sizes and availability and merchandise display, as well as customer trends and localized recommendations.
Retail executives can then efficiently apply the data to store assortment optimization, localized allocation, shelves optimization, and pricing.
Assortment Optimization - What’s Next
The highly competitive fashion environment and customer-centric approach call for knowing your customers, delivering the items they need, tailoring to local preferences, creating engaging experiences and increasing customer satisfaction.
Innovative solutions like Nexite’s Connected Merchandise solution make it possible to achieve.
Patented NanoBT tags stream data on merchandise engagement in connected stores, giving retailers new visibility into the customer journey and real-time in-store operations.
Valuable, accurate data is gathered on a cloud-based platform, offering advanced analytics and actionable insights that enable retailers to create the optimal assortments per store and region, and increase sales accordingly.