Using data analytics to inform decision-making is one of the most important reasons to ensure that your decisions are founded on the real truth (cold, hard figures) rather than just someone’s perspective of reality.

In terms of application, a retailer can utilize data analytics to:

  • Recognize the price and quantity of the average order’s sold goods.
  • Identify the goods that sell well, poorly, and in between
  • Learn about your genuine demand and previously missed opportunities for sales.
  • Identify the best-recommended order quantities and the recommended buy quantities and allocations.
  • Establish the best pricing for a particular commodity at a particular place.

  • Business Intelligence (BI)

Many firms use business intelligence solutions to manage and organize their data efficiently. BI tools are an illustration of descriptive analytics because they assist you in structuring and visualizing your data.

Many merchants do real business intelligence (BI) tasks by importing data straight into Microsoft Excel or using native ERP (Enterprise Resource Planning) system functionalities.

BI-specific software: will be used by slightly more sophisticated retailers.

  • Energy BI
  • Tableau
  • SAP
  • QlikView
  • Spark, Apache

These programmes support multiple data sources, attractive visualizations, and some degree of data modification.

Data scientists that employ programming languages (like Python) that provide them more freedom for data manipulation, data visualization, and data modeling are typically involved in the most sophisticated BI. A data analytics course will help you improve your programming, analytical and visualization skills which are essential for working as a data analyst. 

While useful, all of the examples above need a lot of human interaction and take a lot of time to manage. This is particularly true for medium-sized to large merchants who operate hundreds of locations or thousands of them (and tens or hundreds of thousands of products). Because of this, most departments in many retailers have dedicated teams of analysts who provide reports.

Most of the manual, repetitive procedures connected with conventional BI practices can frequently be automated by the sophistication of advanced analytics tools like Retalon.

  • Software for Sales Forecasting

Forecasting sales is another frequent use of data analytics in the retail industry.

Simply put, the process of using previous sales data to identify trends and project them into the future to forecast sales is known as sales forecasting.

This aids retailers in various tasks, including stock-taking, monitoring OTB budgets and creating ambitious financial goals for the business.

Sales forecasting is predictive in nature, as the name implies, and it is the most basic form of predictive analytics employed by retailers.

There are numerous methods for forecasting sales because firms have been doing it for centuries:

  • Estimating sales for this year based on data from last year
  • market analysis (surveys, observation, etc.)
  • Pundit projections
  • Excel statistical models
  • certain software

  • Software for Demand Forecasting

As was already established, merchants use a far more advanced form of predictive analytics called demand forecasting.

Demand forecasting determines the demand for each product at each retailer at certain time intervals rather than attempting to estimate sales based just on historical sales data. Demand forecasting is hence far more accurate than conventional sales forecasting.

  • a more precise forecast of the situation of the business in future
  • simulations or “what-if” scenarios, creation
  • the capacity to change course quickly as circumstances change both in flight and on the ground
  • combining essential retail functions (e.g. Promotions and inventory management)

As usual, there are various methods for predicting demand. Retailers can use, in descending order of sophistication:

  • using statistical models in MS Excel
  • Software for statistical modeling and analysis in general
  • AI-enhanced retail-specific analytics software

The two earlier solutions, albeit adequate for smaller merchants, become difficult to employ with huge data sets, if possible (like those found in medium to large retailers). 

This is so that demand forecasting is not solely based on sales information. An accurate demand estimate will also use information from:

  • Previous pricing
  • Historical database
  • Variety’s depth and breadth
  • Product families and clusters
  • Seasonality
  • Variability in the supply chain
  • Competitive behavior
  • Purchasing patterns

You can probably appreciate how challenging it would be to manually gather, examine, and model all of this data for the billions of different Store / SKU combinations.

The ideal method for retailers to use demand forecasting is to identify a retail predictive analytics software provider with experience dealing with businesses in their industry.

 

Advanced Retail Analytics in One Place

If used properly, this is the most potent type of analytics and can yield the highest ROI.

Unified advanced analytics, which falls under the category of prescriptive analytics, strives to bring together the advantages of business intelligence, strong diagnostics, and precise demand forecasts with intelligent automation that suggests the most lucrative actions across the business.

 A quality unified analytics programme should be able to:

  • Reporting and data visualization automated
  • Predict demand for every product in every store at precise times with accuracy.
  • Enable flexible simulations and “what-if” scenarios for introducing new products, creating new stores, etc.
  • Automatically suggest thousands, if not millions, of micro-optimizations for selection, distribution, pricing, and other factors.
  • Reconcile all alterations and modifications made across all divisions and data sources.

This type of advanced data analytics is offered by a number of solutions, including Retalon’s retail analytics platform, which uses extremely precise demand prediction and cutting-edge AI to provide hundreds of thousands or even millions of granular improvements that boost profitability.

Additionally, this kind of software is adaptable and may be set up to automatically accept some recommendations or require human permission for others to give the user more control. Besides that, Having knowledge with a Data Science course will help you in landing your dream job.