The level of understanding a small mom and pop store has about its customers is very impressive. They know exactly what their customers want to buy, in what quantity, and at what time. But how to achieve the same level of understanding when scaling to a retail giant like Walmart? This is where data analytics comes to the rescue. Data analytics can help retailers understand customer behavior by analyzing massive data sets regarding their information and transaction history. Big data allows retailers to focus on improving customer experience in terms of providing them with what they want at the right time. Retail analytics also enables retailers to introduce dynamic pricing and customize promotions and discounts to each customer. Emerging technologies such as RFID tags, beacons, QR codes, and NFC technology enable retailers to effectively guide the customers in their journey from the store to make a purchase. Retail analytics offers endless possibilities to retailers in terms of improving their operational efficiency.
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Types of Retail Analytics
Descriptive analytics collects data from transactional history, promotional success, and inventory changes to give a detailed summary of such business activities. The retailers have used this type of analytics for a long time. For instance, retailers use measurement metrics such as response rates, cost per lead, and conversion rates to ascertain the success of their direct mail campaigns. However, with the advent of big data, descriptive analytics has started encompassing multiple data points, including social media data, time on page, link clicks, conversions, and engagement. Descriptive analytics is an elementary form of retail analytics, which provides a visual summary of past performances.
Diagnostic analytics is an augmented form of descriptive analytics, which compares the relationship between two variables and outcomes to discover ongoing trends. Descriptive analytics provides information on the lines of what happened in the past, whereas diagnostic analytics gives insights into the ‘why’ aspect of the outcome. For instance, diagnostic analytics compares the data set of two different promotional campaigns to ascertain why one campaign succeeded while the other failed. By establishing a correlation between multiple variables, a retailer can determine what factors can be changed to achieve the desired result.
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Descriptive and diagnostic analytics both employ a reactive measure for strategic planning as it provides analysis only after certain events have occurred. Predictive analytics is a level above the other two as it can predict future trends by analyzing historical relationships between multiple variables. Predictive retail analytics uses complex statistical tools and emerging technologies such as machine learning and data mining to forecast future trends. It allows retailers to predict customer behavior and estimate what kind of products will become popular in the upcoming season so that they can plan and strategize beforehand. For instance, predictive models can determine which customers are unhappy with the brand and are likely to defect. Based on such insights, retailers can then provide offers and incentives to retain customers.
Prescriptive analytics is the final phase of retail analytics. It goes far beyond the forecasts made by predictive analytics models to prescribe the best course of action to maximize the company’s ROI. This type of retail analytics can anticipate changes in demand, consumer sentiment, and supply shocks so that the retailers can make necessary adjustments. For instance, it can suggest retailers the appropriate quantity of a particular product to stock and its selling price. Additionally, it also enables online retailers to implement dynamic pricing to maximize revenues from each customer.