The massive shift to online shopping has resulted in unparalleled changes in how the retail industry operates. From focusing efforts on website development and online retail to requiring more timely shipping speeds, the retail industry is continuously evolving and will continue to grow. With all the changes to the retail ecosystem and the continuous move from traditional brick and mortar, one thing that is instrumental in their success is cognitive computing in retail.
What Exactly is Cognitive Computing?
Cognitive computing is essentially self-learning systems and algorithms that imitate the human brain’s thought process to look at big data extremely efficiently. So efficiently, that no human could analyze massive amounts of data and form conclusions at the same rate and accuracy. These algorithms become more intelligent as they are introduced to more data, similar to a human.
Cognitive computing is more than crunching numbers; it can also understand natural language, understand images, recognize patterns, and more. However, employees should not stress about cognitive computing replacing their jobs. Instead, employees should view it as a tool that will help them be more accurate, efficient, and knowledgeable in their line of work. Cognitive computing in retail facilitates decision-making.
One of cognitive computing’s key elements is the integration of self-learning systems that utilize natural language processing, data mining, and pattern recognition. The self-learning feature also means the system can learn from its experiences, thus becoming more intelligent and have more cognitive capabilities over time.
How Does This Apply to Retail?
Cognitive computing in retail is becoming essential as marketers can collect more data than ever. This data is then studied and utilized to make retailers more efficient and adaptive. In return, companies diving into digital transformation can generate more sales.
North Face is an example of a company that integrates cognitive computing into their e-commerce site to help select the best product for consumers. This is done by having potential customers enter the details they are looking for in a desired product. The tool then further inquires about when, where, and what activities the product will be used during. From there, the system begins to analyze multiple data sets such as customer reviews, weather for the geographical location the consumer provided, and their gender.
The end result is a customized list of recommended products based on the information the user provided, mixed with the other data points the system can access. Using cognitive computing allows North Face to identify customers’ wants and needs, accurately match consumers to products, have more satisfied customers, and ultimately drive sales.
Price optimization is a valuable feature of cognitive computing. Price optimization considers the demand for a particular product and looks to see if the product’s price should be lowered or raised. For example, price optimization will look at demand for all products in a store. If sales for a pair of jeans are not as high as predicted, price optimization will illuminate this and recommend an appropriate price for the product to help clear out the inventory, all while maximizing profits.
Price optimization is also used for competition benchmarking when it comes to pricing. The purpose is to shed insight into how a retailer’s prices compare to others in their space. Retailers then use the information collected to make informed decisions on optimizing their prices best.
Demand and Trend Forecasting
Demand forecasting is the science of predicting the number of sales certain products will garner during a specific period. Forecasting is imperative in retail because when retailers have excess inventory, they lose money due to not being able to sell all of it. On the other hand, if retailers do not buy enough inventory, they will miss out on sales.
Cognitive computing has made demand forecasting a much more accurate procedure than when humans spearheaded all calculations and analyses. This is a result of cognitive computing being able to examine substantially more data, illuminate patterns across seemingly unrelated information, and provide real-time adoption of information instead of focusing solely on historical data.
Fast-fashion retailer H&M, is a company utilizing demand forecasting to manage store inventories. The software analyzes customer receipts to determine the stock levels of products. This allows the stores to determine which products need greater promotion and which products need to have their stocks replenished. Patterns can also be found during this process. For example, H&M might discover they sell the most leather jackets on the east coast and may adjust store inventory accordingly.
Website User/Customer Experience Design
Companies can analyze the data of users interacting with a website. Whether it is investigating the step-by-step route of a customer’s journey upon entering the site until they make a purchase, identifying ways to improve customer service, improving social media engagement, or determining which pages experience the most visitors, companies can use cognitive computing to collect this data and adjust their interface accordingly. Therefore, the site can be modified to become more user-friendly and/or drive more conversions and mobile payments.
Cognitive computing in retail and cognitive technology has brought numerous advancements to the industry. Through demand forecasting, price optimization, and website design, cognitive computing has provided retailers with the tools to become more agile businesses.