Types of Retail Analytics

1. Types of Retail Analytics https://www.manthan.com/blogs/category/retail-analytics/

2. About • Today, to remain competitive, even small mom-and-pop stores must be on their toes and know precisely what their customers want to purchase, at what time, and in what quantity. But they cannot stop customers and talk to them in the middle of their purchasing process to find out how they are feeling about their shopping experience and what they are thinking. • With retail analytics, retailers can offer tailor-made promotions and discounts to each customer and introduce dynamic pricing. Retail analytics offers infinite possibilities in terms of enhancing operational efficiency.

3. Here are four major types of retail analytics that show how deep you must go into data to look for fact based insights: • Descriptive Analytics • Diagnostic Analytics • Predictive Analytics • Prescriptive Analytics

4. Descriptive Analytics • With Descriptive Analytics, retailers will get a brief description of the performance of the major actions such as inventory changes, transactional history, promotional success, and more. Retailers in the past have used it to study mail campaigns to find out costs per lead, response rates, and conversion rates. • However, with Big Data, retailers can find out the number of users who visited a site, the number of pages, the time they spent on each page, the links they clicked, the links that led to acquisitions, and more.

5. Diagnostic Analytics • Diagnostic analytics is an enhanced version of descriptive analytics that compares the association between two outcomes and variables to find out current trends. Descriptive analytics offers data on what happened in the past while diagnostic analytics offers insights into the ‘why’ aspect of the result. • For example, diagnostic analytics equates the data set of two separate advertising campaigns to find out why one campaign failed while the other succeeded. By setting a correlation between several variables, retailers can establish the factors that can be changed to accomplish the desired result.

6. Predictive Analytics • Predictive analytics in retail gives you an indication of what may happen in the future. It uses the results of diagnostic and descriptive analytics to spot exceptions, clusters, and tendencies, and predict future trends. However, predictive analytics is essentially forecasting, and it is just an estimate. The accuracy of predictive analytics depends on the stability of the situation and data quality, so it requires constant optimization and careful treatment.

7. Prescriptive Analytics • Prescriptive analytics is the concluding phase of retail analytics. With prescriptive analytics, you can anticipate changes in consumer sentiment, demand, and supply shock so that retailers can make • required adjustments. For instance, it can recommend retailers the right quantity of a specific product to stock and its selling price. In addition, it also allows online retailers to enforce dynamic pricing to enhance revenues from each customer.

8. Conclusion • With the availability of these different types of analytics, retailers must decide how deep they need to dive in data analysis to fulfil their commercial needs. While diagnostic and descriptive analytics offers a reactive approach, predictive and prescriptive analytics help users make proactive decisions. The insights that retail analytics offers, support substantial sales growth without amplified advertising cost. Manthan offers newer ways to seamlessly align analytics and customer to create prospects for businesses.