Product assortment planning is the process through which retail stores determine what products to offer to customers in different localities, at different times, and in what quantities to stock them. There are many factors involved in making these decisions. To make accurate predictions, retailers have to consider both internal and external data.
So Much Data and No Good Way to Use Them?
With the advances in communication, the Internet, the Mobile Platform, and instant information sharing, there is so much information available that businesses can use to their advantage. In the retail context, data about the competition, market trends, etc. can be captured and analyzed for better decisions in various departments like marketing, sales, supply chain, etc.
New Sources of Information
Many retailers now use movement sensors, WiFi, and Beacon technologies to capture data about customer movement, browsing and buying patterns inside their stores. These help the retailer in better understanding their customer preferences, tailoring their stocks and product placements according to demand, and in providing personalized service to customers.
Besides this, there are now varied sources to collect data about customer opinions, expectations and buying patterns. Most retailers have an online presence and most of them enable customers to leave feedback, reviews etc. There are also reviews, discussions, and ratings in third-party sites like consumer review websites, social media etc.
Can all these diverse sources of customer opinions and behaviour be captured and processed?
Big Data and The Retail Industry
So many factors affect retail sales and store performance from day-to-day. Sudden shift in product trends, a competitors successful sales strategy, the weather (if it is raining, or if it is too hot or too cold, customers do not venture outside to shop), and peer opinion can all affect the sales in each store in your chain.
There is now an imperative need to access rich and varied sources of external data. You need to gather data about your competitors sales and strategies, the sales strategies of online giants, data about the products offered, the promotional strategies used by local competitors and so on. 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 analytical tools. This is where Big Data comes in.
Big Data provides the methodologies required to collect and organize disparate information from widely differing sources, and the tools to analyze them. These data processing and advanced data analytics tools provide broader and deeper insights into various factors. These help retailers make more precise decisions about the different aspects of their business, including product assortment planning.
However, most retailers haven’t been quick enough to take advantage of these sources. Around 92% of retailers, according to a recent survey, do not have a comprehensive 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 big advantages Big Data provides is its ability collect and organise customer related information from diverse sources. This customer generated data helps retailers stay alert and nimble. Now they can respond quickly to customer views and preferences.
They can make better decisions about assortments for various stores, tailoring the 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 in that locality. So, they can free up space and make better use of it, stocking high demand stock keeping unit(SKUs).
Using data provided by the analytical tools, individual stores can design product placing and even Adjacencies. Adjacencies refer to product placement in relation to one another. With a deeper perception of customer preferences, stores can decide if one product will do better when placed next to another.
Analyzing customer buying patterns in a locality could also help determine the type of products to stock. For instance, if the majority of shoppers at a particular store are price-sensitive, that store could focus on making available good products that are available at economical prices. For the segment of their customers who prefer exclusivity and are not bothered about the price, the store can create small sections that display goods like gourmet foods, expensive cosmetics etc.
There are other ways to utilize information gathered through Big Data tools. It can also help the retailers design an inventory and sales strategy that ensures a uniform 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.