Retail Data Explained: Descriptive, Predictive, and Prescriptive

Learn about:

• The Categories of Analytics

• How They Relate to Retail and Supply Chain

• What Suppliers Can Do With That Data

Categories of Retail Analytics 

There are different categories of retail analytics: descriptive, predictive, and prescriptive. Descriptive analytics is aimed at analyzing historical data, predictive analytics focuses on predicting future developments, and prescriptive analytics works to design a strategy for the predicted possibility. 

The right kind of data helps your company stay ahead of the competition. However, you cannot be sure of achieving this just by having the right kind of data. You must learn the various types available to make it easy to make the most of the available data.

It is important to know these three different types of retail analytics. When you know them well, it is easy to answer the big question: which can drive your business forward?

Descriptive Analytics

Descriptive analytics includes the examination of the preceding (or historic) data to figure out developments and estimate metrics over time. This is the simplest way to analyze data since it can be done with little or no coding whatsoever. There are many refined and already existent instruments for the management of descriptive analytics. 

A few of these comprise Tableau, QlikView, KISSMetrics, Google Analytics, and others. Using these instruments, data analysis can be done and displayed in a way that is easy to understand. In the method of descriptive analytics, data can only be presented in the form of tables and graphs. After the data is analyzed and displayed, it is up to the audience to draw knowledge from it.

A few usual instances of descriptive analysis are cash flow analysis, sales and revenue reports, performance analysis, and some others. As data has become a very integral part of our daily lives, nearly every business uses descriptive analytics. For instance, Google Analytics will be extremely important for you if you plan to launch a website. 

Even though these are basic uses of descriptive analytics, the complete analysis can only happen after including unorganized data (Big Data) into the frame.

Descriptive analytics is important for suppliers to judge how well their products are doing. Suppliers must review historical sales to create demand plans and predict sales forecasts. This type of analytics helps prevent out-of-stocks or overstocks by looking at how well products have done in the past. It also takes into account outstanding factors, such as climate conditions and viral marketing.

Predictive Analytics

Predictive analytics is a step into the next domain, ahead of descriptive analytics. Whereas descriptive analytics is restricted to historical data, predictive analytics is a fortuneteller of future developments. As a supplier, you must remember that predictive analysis only indicates future projections and that the predictions are not completely accurate.

Nevertheless, forecasting approaching prospects is simply an element of predictive analytics. It can also comprise forecasting the figures in the empty fields of a data set and possible consequences of shifts in future patterns.

Sentiment analysis and credit scores are superb instances of predictive analytics. Sentiment analysis is the research of content to assess the behavior expressed by it. The purpose to evaluate if the product elicits a positive, negative, or neutral reaction. Usually, this is generally evaluated by grading a part of the product between -1 to +1, with a positive rating expressing positive sentiment.

Credit score analysis involves examining historical economic behavior and increases in income of an individual, as well as economic trends, to forecast the chances of the person paying his debt. Despite that, neither of these analyses are completely accurate. In practice, few sophisticated systems also demonstrate the possibility of the accuracy of the analysis.

Suppliers also need good predictive analysis for their forecasts. Replenishment requires constant monitoring, and predictive analysis can help identify issues before they arise. Machine Learning is often used as a way of forecasting data, taking a diverse range of data into consideration, more so than simple historic sales.

Prescriptive Analytics

Prescriptive analytics is relatively a fresh field in data science. It goes the extra mile ahead of descriptive and predictive analytics. Prescriptive analytics exhibits rational solutions to a problem and the effect of considering that analysis - a solution on likely trends. It is regarded as the purpose of any data analysis venture.

Though prescriptive analysis is still transforming, this process has restrictive usage in business. Google's self-driven car is an example of prescriptive analytics.

This method analyzes the surrounding area and determines the course to be taken, as per the data. It selects whether to expedite or delay, switch channels or not, get on to a longer route to stay clear of traffic, opt for a shortcut, etc. It analyzes data in much the same ways humans do.

Prescriptive analytics also relies on artificial intelligence and machine learning to create models without the intervention of humans. Suppliers, who know the right questions to ask ("How will this new modular affect my sales? How will customers react to this price change?"), can use prescriptive analysis to educate their marketing strategies. Walmart even has a tool in Retail Link that allows its suppliers to view cost change scenarios using prescriptive analysis.

To conclude, descriptive analytics considers historical data to come up with a great explanation for the happenings and the reasons behind those happenings. Predictive analytics and prescriptive analytics use historical data to predict future happenings and what are the ways that can be taken to impact those results. 

Future business visionaries blend different analytics to come up with the best choices to boost their businesses.

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