In one of the most competitive industries out there, retailers are always on the hunt for that small edge on that could make their business great. With better tools being developed constantly, data science could be that decisive factor retailers have been looking for.
Understanding the characteristics and behavior of their customers has always been a holy grail for retailers trying to get the most out of their business. This has created a new need in the jobs market, a new type of specialist – the data scientist. Their job, not an easy one, is to get down to brass tacks and find that gold nugget buried deep under tons of data clutter. Retailers might choose to either hire a data scientist or to externalize this task, but the tools and processes used remain the same, as do the essential use cases. In anticipation of the re:Focus on Retail and Logistics event, Business Review has put together a Top 5 data science use cases for the retail industry.
- Customer sentiment. Analyzing the customer sentiment is now easier than ever thanks to data received from social networks and online platforms, which virtually rendered focus groups and customer polls obsolete. With automated algorithms, through which the process became less expensive and time-consuming, an analyst can determine positive or negative feedback for a product, service, or brand.
- Smart customer experience. Consumers want companies to engage them in real-time and have a positive interaction. In response, by using data science, companies have developed tools such as chatbots to be at the forefront of providing personalized interaction in the form of customer support, shopping assistance, or even interviewing candidates.
- Recommendation engines. Amazon reported in 2018 that 35% of its sales are generated by a referral engine. What does this mean? Based on customers’ transaction history, their viewing history, shopping cart inventory, or social media likes, an engine will generate recommendations for similar products. This can lead to effective sales strategies that target consumers based on specific online behavior.
- Price optimization. Setting prices for their products is one of the most important tasks for retailers. The price tag can make or break a product, so it is extremely important to know what competitors are charging for the same, or a similar, product. By collecting data from across the internet, algorithms can provide detailed information, helping retailers set the perfect price on their products for both the customer and themselves.
- Inventory optimization. Data science can also be used to predict product life cycles and better understand supply chains, providing optimal distribution. By analyzing macroeconomic conditions, climate data and social data, algorithms determine which products to keep and for how long, increasing profitability.
Enjoyed this article and interested in retail? Find out more at BR’s re:Focus on Retail and Logistics coming up on January 29th.