Analytics in the Retail Industry

Retail is one of the prime industries in today’s economy. In 2025, the global retail sector is expected to generate USD 31.7 trillion in revenue (with approximately USD 1.32 trillion in sales in the USA alone). The retail industry accounts for a significant part of global GDP and employs billions of people.
Retail analytics is the collection of real-time and historical data that aids in the measurement of consumer behavior and sales performance.
However, it is not just about the raw data. A good retail analytics service cleans up inconsistencies in data sets and presents them in a single user-friendly retail dashboard/ report. Businesses benefit from a bird’s-eye view of their performance, more informed sales, and marketing decisions, and increased profit.
Retailers need in-depth insights to properly manage and predict future performance as the issues get more complex to answer. Retail analytics opens up various new avenues for business expansion. Retailers can receive in-depth insights on the following topics by working with the right retail analytics service providers and using the correct retail analytics tools:

– Understanding customer buying patterns and preferences
– Targeting customers with customized messages based on purchasing pattern
– Demand forecasting and inventory management
– Store layouts and market-basket analysis
– Price optimization


Retail scenarios: the power of data and its application

There are numerous potential situations for displaying an effective data + analytics solution, given the large amount of data accessible, the almost infinite variety of retail goods for sale, and the myriad business problems retail executives are trying to solve.
The potential and versatility of employing advanced analytics in retail operations, particularly when integrated with existing CRM technologies, can be seen in two scenarios: tailored promotions & experiences and demand forecasting.
In general, the adoption of Analytics technology components offers numerous advantages that can help firms maximize their profit potential and improve job management efficiency.

Leading retail industry players have had tremendous success applying retail analytics to many business areas as follows:

Store Layout Planning

Retail store layout planning (whether physical or digital) is the strategic utilization of the space influencing the customer experience. Retail Layout affects how customers interact with the merchandise and their buying behavior. It improves sales and customer satisfaction. With the help of analytics, retailers can find answers to questions such as:

• How customers browse through various aisles in the store
• Which aisles and segments attract maximum customers?
• How to improve the merchandise display?


Inventory optimization

All retailers wish to have the right product at the right place at the right time. Inventory replenishment, location optimization, and transportation cost reduction are all ways that retail analytics can help enhance inventory management. Retailers now have advanced analytics tools to forecast demand-supply changes, identify stock-out situations, and optimize supply chain efficiency.


Supply chain efficiency

Supply chains are also growing in complexity, with an influx of data and a demand for more – responsive relationships with customers who can choose to interact with the business across multiple sales channels. The use of analytics in the supply chain is also expanding to include collaborative systems that assimilate marketing and merchandising, and more closely connect retailers to their suppliers and distribution partners. This collaboration facilitates required product development, work-in-process, supply & demand synchronization, and enables opportunities to improve speed to market.


Sales Forecasting

By using analytics, Retailers can examine cross-effects between products, forecast new product sales, and account for lost sales to determine the net effect of promotion and pricing adjustments on whole categories. This helps retailers maintain their in-stocks and reduce out-of-stocks. With the use of analytics complex demand forecasting models can be created using factors like sales, figures, basic economic indicators, environmental, and economic conditions.
Techniques used in Retail Sales Forecasting –

• Naïve Approach
• Time-series methods
• Moving average
• Weighted moving average
• Kalman filtering
• Exponential smoothing
• Autoregressive moving average (ARMA)
• Autoregressive integrated moving average (ARIMA)
• Causal/ econometric forecasting methods
• Judgmental methods
• Composite forecasts
• Cooke’s method
• Delphi method
• Forecast by analogy
• Scenario building
• Statistical surveys


Customer Intelligence

In today’s market, retailers seek to understand which services, products, and discount offers are most attractive to customers, while customers continue to change their preferences and shopping behaviors. Customer Analytics is more focused on better targeting marketing messages to specific groups, the real opportunity lies in using customer insights to find sales and margin levels. It can be achieved through some core sets of analysis:
• Customer engagement assessment
• Customer churn prediction
• Share of wallet


RFM Analysis

The Theory RFM analysis is a model for segmenting customers based on three parameters that define their purchasing habits: recency (the date of the most recent purchase), frequency (how frequently the customer makes purchases), and monetary value (the total value of all purchases made by the customer). Analysts can use RFM segmentation to place their customers in a Cartesian system where the X, Y, and Z axes correspond to the RFM parameters.

Conducting an RFM analysis on your customer base and sending personalized campaigns to high-value targets has massive benefits for your eCommerce and physical store.
• Personalization: By creating effective customer segments, you can create relevant, personalized offers.
• Improve Conversion Rates: Personalized offers will yield higher conversion rates because your customers are engaging with products they care about.
• Improve unit economics
• Increase revenue and profits


Market Basket Analysis

It is a data mining technique used by retailers to understand customer purchasing patterns. Market Basket Analysis uses historical data, and it helps to find the products that are likely to be purchased together. It can help to increase sales and customer delight. Using data to determine the cross-sell patterns, retailers can optimize product placement, offers, and product bundling and store layout.


Price Optimization

Price Optimization techniques can help retailers to identify the price elasticity of their products and analyze their optimal price points. It helps retailers to make effective price decisions, forecast and gauge the impact of the price change, and decide on pricing strategies like markdown or skimming.


Product Portfolio Optimization

While customer preferences are one aspect of the retail sector, product reports are another that plays an important role in this industry. In light of this, Analytics can also be used to optimize product portfolios. As customer feedback reports are compiled using analytics, sales departments of various companies can optimize their product images and overall portfolios. This helps them understand what features customers want and what they don’t want in their products.


Identifying Quality Gaps

Aside from detecting anomalies in supply chain management, analytics can assist the retail sector in identifying quality defects in a company’s products.
Retail chains use analytics to mine data for quality flaws and assess a product’s market performance. However, analytics in the retail sector can be extremely beneficial to data scientists who use a variety of AI-powered procedures and mechanisms.
To compete with other such dealers and provide the best service possible, data scientists look for the best procedures to conduct retail operations and find the perfect match for their quality standards to boost quality improvement in retail businesses.


Risk and fraud detection

Fraud is another major challenge faced by retailers. Some instances of fraud include stolen credit card information or fraudulent returns, damaged goods, inventory inaccuracies, and several other issues. Retail companies can lose fortunes if service provider contracts are not monitored.

• Analytics can be used in collaboration with banks and retailers to identify fraudulent activities.
• Process-related errors can be identified by point-of-service data mining
• Analytics can be used with markdown systems, price optimization systems, BI reporting, and modeling deal prices to help control cash losses.
• Unusual patterns of products and inventory management can be identified.

The retail paradigm is rapidly changing with the development of new business models, channels, and pricing strategies to fulfill the requirements of the demanding customer base. Today retail analytics has been incorporated into the business function of retail chains of all sizes. Retail analytics can provide latent valuable insights with the help of data. When deciding to invest in retail analytics, it is important to understand the scope of its application, the type of data required, and what technology can help you in the longer run.
Virtue analytics can help to gain insights from customer purchase behavior and retail data to make smart retail merchandising decisions that boost growth.

Contact virtue analytics for a free consultation.