Types Of Retail Analytics
There are four key types of retail analytics that each play an important role in providing today’s retailers with key insights into their business operations.
The four types of retail analytics are;
- Descriptive Analytics: Descriptive analytics uses historical data to provide an account of what has previously happened
- Diagnostic Analytics: Diagnostic analytics uses historical data to provide an explanation of why something has happened
- Predictive Analytics: Predictive analytics uses historical data to make predictions about possible future outcomes.
- Prescriptive Analytics: Prescriptive analytics uses data to provide specific recommendations on how to improve business performance
Descriptive Analytics In Retail
Descriptive analytics is a type of data analytics that looks at past data to give an account of what has happened. It works by bringing in raw data from multiple sources such as Google Analytics, POS terminals, inventory systems, OMS and ERPs to generate valuable insights into past performance.
Techniques used in descriptive analytics include data visualisation, summarisation statistics (such as mean, median, mode, and standard deviation), and correlation analysis. Results are typically presented in reports, dashboards, bar charts and other visualisations that are easily understood.
Descriptive analytics can help to identify previous issues within a business. However it doesn’t explain why those previous issues have occurred unless combined with other types of data analytics that can show patterns and correlations, e.g. predictive analytics or prescriptive analytics.
Diagnostic Analytics In Retail
Diagnostic analytics is a type of data analytics that looks at past data to give an account of why something has happened. Taking the same raw data used in descriptive analytics, diagnostic analytics uses statistical analysis, algorithms, and sometimes, machine learning, to drill deeper into the data and find correlations between data points.
Diagnostic analytics is particularly useful at highlighting anomalies and potential issues within a retail business that do not match pre-programmed benchmarks and business rules. This includes things such as technical issues and errors within an ecommerce website.
Historically this type of retail analysis used to be done manually but doing so can leave too much room for human error. This is why larger retailers use machine learning and AI to carry out diagnostic analytics.
Predictive Analytics In Retail
Predictive analytics is a type of data analytics that makes predictions about future outcomes based on historical data. Predictive analytics uses statistical algorithms and machine learning to analyse historical sales data and other information to identify patterns and make predictions about future sales.
Retailers use predictive analytics to forecast demand for products, optimise pricing, manage inventory and create personalised marketing campaigns. Insights into customer behaviour, purchasing patterns and preferences can be used to predict which customers are likely to leave, which products are likely to be popular, and how much stock to put behind each product.
Although this type of retail analysis can be carried out manually using excel, the data is quite complex and it would be incredibly difficult to account for all the possible factors that could influence future sales. This is why most retailers use predictive analytics, which relies on AI and advanced mathematics to more accurately forecast future trends.
Prescriptive Analytics In Retail
Prescriptive analytics is a type of data analytics that provides recommendations on how to improve business performance based on multiple predicted outcomes. Essentially, prescriptive analytics tells retailers what they should do next to get the best results.
Prescriptive analytics can be used to determine the best pricing strategy for a product, the most effective inventory management plan, or the optimal logistics plan for delivering goods to customers. It can also be used to optimise marketing campaigns, plan staffing schedules and assess potential investment opportunities.
The prescriptive analytics process typically involves a combination of optimisation techniques, simulation models and machine learning algorithms. The models will analyse historical data and present a range of potential solutions, ranked by potential performance, so that the decision maker can choose the most appropriate one.
Overall, prescriptive analytics provides actionable insights and recommendations that allows retailers to make better decisions and improve their operations.