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 stock them. There are many factors that go into making these decisions. To make accurate predictions, retailers must consider internal and external data.

So much data and no good way to use it?

With advances in communication, the Internet, the mobile platform, and the instant exchange of information, there is a lot of information available that businesses can use to their advantage. In the retail context, data on competition, market trends, etc. they 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, Wi-Fi, and Beacon technologies to capture data about customer movement, browsing, and purchasing patterns within their stores. These help the retailer better understand their customers’ preferences, tailor their stock and product locations based on demand, and provide personalized service to customers.

In addition to this, there are now various 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, such as consumer review websites, social networks, etc.

Can all these diverse sources of customer sentiment 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 sales strategy from competitors, the weather (if it’s raining, or if it’s too hot or too cold, customers won’t venture out to buy), and the opinion of customers. colleagues can affect sales in each store. in your chain.

There is now a compelling need to access rich and varied sources of external data. You need to collect data on your competitors’ sales and strategies, online giants’ sales strategies, data on products offered, promotional strategies used by 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 by traditional database and analytic tools. This is where Big Data comes in.

Big Data provides the necessary methodologies to collect and organize disparate information from very different sources and the tools to analyze it. These advanced data analysis and data processing tools provide broader and deeper information 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 quick enough to tap into these sources. Some 92% of retailers, according to a recent survey, do not have a complete understanding of their customer base.

Big Data and Product Assortment Planning

Every business is now becoming more customer-centric and this is especially important in retail. One of the great advantages 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 quickly respond to customer feedback and preferences.

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

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

Analyzing the buying patterns of customers in a locality could also help determine the type of products to stock. For example, if the majority of shoppers at a particular store are price sensitive, that store might focus on offering good products that are available at affordable prices. For the segment of its customers who prefer exclusivity and are not concerned about price, the store can create small sections that display 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.