Retail businesses rely on data to maintain the flow of products. They must keep inventories running for the products more in demand. An organised insights and analysis of these data help retail businesses make informed decisions from supply to marketing campaigns.
Businesses can frame a well informed strategy with the help of retail analytics. However, retail analytics depends on historical data but with sensors and trackers retailers can monitor activities in real time. In this blog, let us learn more about retail analytics.
What is Retail Analytics?
Retail analytics is all about the complete analysis of retail data for finding trends, predicting results, and making related decisions. Retail analytics is one of the emerging analytics practices which include data from customers, employees, billing transactions, inventory level of stores, suppliers, etc.
Suppose a fashion brand wants to monitor its online reputation and visibility among its consumers and understand customer needs. With the help of retail analytics tools, brands can respond to negative feedback, capitalize on trends, and improve marketing strategies based on the customer’s behavior and needs.
Retail Analytics Key Takeaways
- Retail analytics analyses retail data to predict outcomes, and trends, and make better decisions.
- With the help of retail analytics businesses can measure the effectiveness of present marketing campaigns, track inventory levels, and more.
- Some of the popular retail analytics tools are Tableau, oracle Retail, QuilkSense, Walmart, etc.
Types of Retail Analytics
There are four major types of retail analytics mentioned below.
- Descriptive analytics
- Diagnostic analytics
- Predictive analytics
- Prescriptive analytics
1. Descriptive Analytics
Descriptive analytics is used to analyse historical data to understand and describe what has happened in the business in a defined period of time.
For example, a clothing brand analyses its historical data to find out which categories of clothes sell best and at which time of the year. This insight can help businesses frame their strategies and arrange effective stocks in the inventories.
2. Diagnostic Analytics
This retail analytics technique analyses investigates and diagnoses the data to determine the reason for the occurrence of a certain event. For example, dip in sales especially in the month of June to August.
Learn more about the Most popular Data Analytics Tools in 2024
3. Predictive Analytics
This analytic technique is used to predict future trends and behaviours by analysing historical retail data. Many businesses use retail analytics to perform sales forecasting, inventory needs projection, demand forecasting, etc.
4. Prescriptive Analytics
This analytic technique is used to recommend an action or optimised strategies based on the collective retail data insights. It can be very helpful for companies to make decision making based on the data collected from retail.
Apart from these four types of retail analytics, there are other types that are being followed such as customer analytics, inventory analytics, sales analytics, marketing analytics, price analytics, location analytics, etc.
Applications of Retail Analytics
Retail analytics is very useful for businesses to make informed decision making, future predictions, uncover trends, and more. Let us know some of the major use cases of retail analytics below.
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- Study Consumer behaviour: Retail analytics provide detailed information regarding the purchasing power of consumers, their preferences, and their past purchase records. This helps businesses in personalizing the recommendations for their consumers.
- Fraud Detection: With retail analytics, all retail data is under surveillance and watch. Any unauthorised input or fraud attempt can easily be identified and avoided.
- Sales Forecasting: Retail analytics help make sales forecasts based on product types and demand. This helps retailers manage stocks based on the insights.
- Demand Forecasting: With the help of retail analytics, retailers can develop an estimate of the future customer demand of a product or service. It includes a thorough analysis of seasonal demand variance, inventory level management, market trends and more.
- Supply chain management: With the help of retail analytics, various operations like the flow of goods and services from a company can be tracked and managed.
- Combo basket analysis: It is used to analyse customer retail data and provide them with the best offers.
- Keeping customers engaged: Retail analytic tools can help businesses arrange routine emails, messages, and wishes for customers.
Also, Check the Process of Business Analytics (step by step)
Benefits of Retail Analytics
The power of retail analytics helps businesses flourish and make the best use of available data. Check some of the best benefits of using retail analytics below.
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- Improved Customer experience: Giving personalized recommendations, and understanding customer preferences can help in increasing the satisfaction of customers.
- Inventory optimization: With detailed insights on the sales performances of products no more overstocking or understocking of inventories. This helps in better optimisation of sales techniques for retail businesses.
- Sales forecasting: With the help of predictive analytics, businesses can predict future sales and arrange inventories and strategies accordingly with higher efficiency.
- Price optimization: Retailers can make adjustments in product prices based on real time demand and competitor analysis. There are many factors that support dynamic pricing and are made possible with retail analytics.
- Identify market trends: With the help of detailed retail data companies can also analyse and uncover hidden trends.
- Competitor Analysis: Now with the help of retail analytic tools companies can monitor and track the performance of their competitors based on any location and adjust their strategies accordingly.
- Real time data analysis: Businesses also use real time data from mobile phones, sensors, or trackers.
Retail Analytics Tools
There are various retail analytics tools available online that help retailers collect retail data from customers, transactions, billing, etc. Let us find some of the useful retail analytics tools below.
- Tableau: Tableau is one of the best retail marketing tools with active AI powered insights that help in optimising data, accelerating data driven decisions, data visualization, and more.
- Oracle Retail: Oracle provides dedicated software for analysing customers and their needs. Get detailed insights and information at your end to increase your customer satisfaction level.
- Walmart: Integrate the Walmart software and get detailed retailer insights on your consumers. Analyse through smart voice assisted shopping, text to shop, and an AI voice assistant.
- Qlik Sense: The Quilk Sense software is used to provide insights on marketing and sales strategies in small to large sized companies. You can integrate this tool with AWS, Google, Snowflake, and more.
- Microstrategy: Microstrategy is an online retail analytics tool used to connect applicants and collect data. It can easily integrate with Google Cloud, AWS, POS, eCommerce and Supply Chain Systems, etc.
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Retail Analytics FAQs
Q1. What is Retail analytics?
Ans: Retail analytics is a process to collect, analyse, and interpret retail data from consumers, sales, suppliers, billing, and more to predict trends, forecast sales, and provide personalized recommendations to consumers.
Q2. What are the types of retail analytics?
Ans: The four major types of retail analytics are predictive analytics, descriptive analytics, diagnostic analytics, and prescriptive analytics.
Q3. What are the uses of retail analytics?
Ans: Retail analytics is used in providing personalised recommendations, sales forecasts, fraud detection, supply chain management, and much more.
Q4. What is retailer analysis?
Ans: Retailer analytics focuses on providing retail insights based on sales, inventory, customers, and other important metrics to help businesses make informed decisions.
