Using Big Data to Improve Retail Assortment Planning

Product assortment planning is the process by which retail stores determine which products to offer to customers in different locations, at different times, and in what quantities to store them. There are many factors involved in making these decisions. To make accurate predictions, retailers must take into account both internal and external data.

So much data and there is no good way to use it?

With advances in communication, the Internet, the mobile platform, and the instant exchange of information, there is so much information available that businesses can use to their advantage. In the retail context, data on the competition, market trends, etc. it can be captured and analyzed to make better decisions in various departments such as marketing, sales, supply chain, etc.

New sources of information

Many retailers now use motion sensors, WiFi, and Beacon technologies to capture data on customers’ movement, browsing, and purchasing patterns within their stores. These help the retailer to better understand their customers’ preferences, tailor their stock and product placements according to demand, and provide personalized service to customers.

In addition to this, there are now several sources to collect data on customer opinions, expectations, and buying patterns. Most of the retailers have an online presence and most of them allow customers to leave comments, reviews, etc. There are also reviews, discussions and ratings on third party sites like consumer review websites, social media, etc.

Can all these diverse sources of customer feedback and behavior be captured and processed?

Big Data and the Retail Industry

Many factors affect retail sales and store performance on a day-to-day basis. A sudden change in product trends, a successful competitor’s sales strategy, the weather (if it’s raining, or if it’s too hot or too cold, customers don’t venture out to shop), and peer feedback can affect sales in each store. on your chain.

There is now an urgent need to access rich and varied sources of external data. You need to collect data on the sales and strategies of your competitors, the sales strategies of the online giants, data on the products offered, the promotional strategies used by the local competitors, etc. You also need a way to collect and use customer-generated data from various external sources.

However, these cannot be collected and processed using traditional database and analytics tools. This is where Big Data comes in.

Big Data provides the methodologies necessary to collect and organize disparate information from very different sources, and the tools to analyze them. These advanced data analysis and data processing tools provide broader and deeper insights on various factors. These help retailers make more accurate decisions about different aspects of their business, including product assortment planning.

However, most retailers have not been too quick to tap into these sources. About 92% of retailers, according to a recent survey, do not have a complete understanding of their customer base.

Product assortment planning and Big Data

All businesses are becoming more and more customer-centric and this is especially important in retail. One of the great benefits that Big Data offers is its ability to collect and organize customer-related information from various sources. This customer-generated data helps retailers stay alert and agile. Now they can respond quickly to customer reviews and preferences.

They can make better assortment decisions for multiple stores, tailoring stock to local preferences and the strategies of competitors in the neighborhood. This will help them provide what the customer wants and eliminate products that are not in demand there. Therefore, they can free up space and make better use of it, stocking high-demand stock-keeping units (SKUs).

Using the data provided by analytical tools, individual stores can design product placement and even adjacencies. Adjacencies refer to the placement of products with each other. With a deeper insight into customer preferences, stores can decide whether one product will perform better when placed next to another.

Analyzing the purchasing patterns of customers in a locality could also help determine the type of products to store. For example, if the majority of shoppers in a particular store are price sensitive, that store might focus on offering good products at affordable prices. For the segment of its customers who prefer exclusivity and are not bothered by the price, the store can create small sections that show products such as gourmet foods, expensive cosmetics, etc.

There are other ways to use the information collected through Big Data tools. It can also help retailers design an inventory and sales strategy that ensures a consistent experience across multiple channels. In the end, if the customer is happy, it translates into more sales for the stores, and Big Data technologies can make this happen.