Ever-changing consumer tastes, the proliferation of competition, and the turbulent supply chain environment create significant obstacles for retail managers preventing them from operating their businesses successfully. Retail managers can overcome such challenges by getting business insights to guide their decision-making process better. Using the right analytics tools on the vast amounts of data available today, managers can achieve operational excellence and generate valuable insights about product demand and customers’ preferences. 

For example, a manager can predict consumer demand and measure the importance of various attributes such as brand name, product category, shape, size, or color. They can provide the most value to the customer using predictive analytics, generate higher customer demand, and increase product popularity. Understanding how product features impact the market also influences service levels (inventory, stockouts, variety), which affects the bottom line. Thus, predictive analytics is also crucial for conducting better inventory planning.

A manager can also evaluate the effectiveness of past promotions (e.g., advertisements, discounts, endcap displays, price reductions). Some products sell well without a promotion, whereas others need that extra promotional boost to be noticed by customers. Thus, having an advertisement for products that technically don’t need it carves into retailers’ profitability. 

Another critical way predictive analytics can be used in managerial decision-making is by evaluating substitution and cannibalization effects among products. This happens when products share similar features, so the customer can easily replace the functionality of the preferred outcome with an alternative. The absence of a highly substitutable product in the assortment will not do as much damage as the absence of a product that customers find challenging to substitute. In the former case, customers will quickly find a product that will do a similar job, but in the latter case, customers can turn away and buy this product from a competitor. Measuring this effect requires a deep understanding of the dynamic relationship among products’ features.

Transactions data is generated every time a purchase is made and contains detailed information about the features of the actual transaction, such as product features (brand name, shape, size, color), marketing mix activity (price, promotion, discount), store characteristics (location, size, type), method of sale (online/offline), timing (season, day of the week, time of the day). Different types of data can be used for the analysis. Additional information can be obtained through own or third-party sources can also include:

• Online product reviews.
• Customers’ level of education/occupation.
• Social media posts.
• Scraping competitors’ website information. 

We help our clients utilize all available information to make the most profitable business decisions using advanced analytics tools.