The Analytics reality of the Retail Industry 

6 December 22

The Analytics reality of the Retail Industry

The pandemic disrupted online and offline retailers on various fronts. Data reveals shoppers’ preference for an in-store shopping experience despite the reduction in footfall due to restrictions. According to a PwC survey, 40% of consumers visit a physical store at least once weekly for their purchases. Almost 65% of shoppers went for in-store shopping to avoid delivery fees, and 60% preferred immediate delivery. A whopping 61% of shoppers like in-store shopping as they want to feel the products while shopping. 

It is undoubtedly not the apocalyptic scenario predicted for brick-and-mortar retailers, transformation in the experiences is here to stay. Sixty-two percent of Baby Boomers and 58% of Gen Zer’s who prefer in-store shopping would also like the conveniences of online. 

Retailers can resort to analytics to make the most of this emergent demand. It helps them to understand their customers and devise hyper-personalization strategies toward their needs. 

There is also a possibility of physical stores leveling up by implementing a data-driven approach. That is because they have at least a clear advantage over pure-play retailers- physical availability. 

It results in a 13% relatively higher conversion rate of brick-and-mortar retailers over their online counterparts, which lags at only 3 %. 

How do we negotiate curveballs?

1. Recommendation engines 

It is a system that filters information and predicts users’ preferences while browsing the Internet—proving to be an excellent tool for retailers to expect customers’ behavior. Customers can find trends and increase their sales and revenue by supplying recommendations. 

These engines manage themselves and adjust to the selections made by customers. There are three main recommendation techniques: 

  • Collaborative filtering 

This system predicts what one likes based on several other users’ preferences. It assumes that if A likes Samsung and B likes Samsung and Motorola, then A might want Motorola too.  

  • Content-based filtering 

This system focuses on the products, not other users, and recommends products with similar attributes or characteristics. 

  • Hybrid recommendation system 

It is a system that uses the above two methods, and their results are combined. 

2. Machine learning and detection of frauds

Deep Neural Networks, a Machine Learning technique, and Data Science are used to detect business transaction fraud.  

The rapid growth of online transactions, banking, filing insurance claims, shopping, etc., has sparked fraud, and it has become a significant problem for these companies, and they are investing many resources to recognize and prevent fraud.  

The traditional approach to fraud detection is rule-based, which is just a race between criminals to find ways and sellers’ fraud detection systems. The conventional approach is not flexible; our current policy uses vast amounts of data collected from online transactions and predicts fraudulent transactions. 

3. Real time price optimization

The optimization mechanisms bring a significant advantage by having the right price for both the retailer and the customer. Some price optimization tolls include online tricks and customer approaches (carried out secretly).  

Data generated from multichannel sources undergo analysis. That helps to define price flexibility, customer location, buying attitude of the customer, the season of purchase, and the competitors’ pricing.  

Retailers can use a real-time optimization model to attract customers, keep attention, and fulfill personal pricing schemes.  

This way, it helps retailers give shoppers prices the view as fair on the products they care about, boosting consumer pricing feeling and retailer profitability. 

4. Hyper personalization

Retailers use personalized marketing to integrate customized recommendations based on their user’s browsing history, likes, past purchases, and dislikes. Further, retailers create highly targeted campaigns that increase ROI.  

It is only possible if retailers have data and can extract meaningful insights from it. Data Science comes to the rescue here. Customer data is used from various data platforms, and predictions about what customers will do next can be made. Marketers can now suggest products according to the preferences and concerns of customers. 

5. Intelligent cross-selling and upselling 

All companies practice cross-selling and upselling in the retail industry to improve their revenue. Cross-selling is the practice of recommending complementary products to customers for their purchase. While upselling is the practice of giving customers a choice to buy a high-end product that is superior to the product considered.   

Data Science in retail can enhance profits by not even running A/B tests. Higher personalization is now possible for different customer segments, resulting in more gain. 

6. Natural Language Processing and trend spotting

Through the medium of social media, people can express everything nowadays. For a retailer, there is precious information stacked in social media that will help find trends. Social media forms unstructured data, many videos, images, and texts. NLP, or Natural Language Processing, is used to unravel information from social media channels. This data is used then to find trends and predict what customers want to buy. 

7. Improving commercial real estate with data sciences 

Large retailers can perfect their real estate management spending with the help of data science. They analyze the maintenance data of various equipment in a building that will prevent disasters from happening. This way, retailers save money when efficiently deploying the expenditure by thoroughly analyzing historical data and predicting maintenance parts. With the help of data science in this realm, a budget is set up, and how properties like shopping malls can be improved is investigated. 

8. Customer sentiment analysis 

It is a brand-new data science tool for the retail industry.  

Retailers traditionally used customer polls and focused groups on deciding the customer’s experience with the product. It used to take up a lot of time and was expensive.  

Using data, customer sentiment analysis is done from social media networks and feedback from online services. These are readily available sources that are fast and free of cost.  

Retailers perform analysis based on natural language processing and text analysis to find positive, neutral, or negative sentiments. The output that comes out is customer ratings and reviews. Retailers can then supply better customer services in the future. 

9. Augmented Reality in retail

Retailers have been experimenting with modern technologies that help them to implement augmented reality in the shopping experience.  

Now customers can select clothes and find out how they look without wearing them. Decisions are thus made faster, and it also saves effort and time for customers.  

One of the largest global retail furniture majors recently rolled out augmented reality and image recognition in their 2013 catalog presentation for the first time. Now their customers can scan items in the catalog and virtually place them in their homes to check how they look.  

Customers can prioritize colors and sizes to find out what suits them the best, so they can avoid going out to buy products. 

10. Supply chain and demand management

Retailers are always trying to decide the quantity of a specific product or service customers want to buy during a certain period. This demand forecasting helps them stock goods to use in a crisis. The crux of the business in retail is to provide its customers with a superior product in proper condition at the perfect time and place. With several machine learning algorithms and data analysis platforms, retailers find and detect patterns and correlations within supply chains. It helps to find inventory strategies and the best stock. Patterns appear and, when placed, relate to sales trends and best strategies for delivering goods and managing stores. 

11. Customer lifetime value prediction 

 Customer lifetime value is the total profits a customer can bring to the company over the entire customer-business relationship.  

Revenues calculated by customers’ past purchases, purchase gaps, and the number of repeat orders receive significant attention. 

CLV collects, filters, and cleans the data associated with customers’ preferences, purchases, expenses, and behavior toward a specific product or price and structures them into the input. Retailers get an idea of the possible value of existing and potential customers after carefully processing the data.  

Machine learning algorithms and data science statistical methodologies help retailers understand their customers and the need for improvements in products or services.  

Embark on a journey with Datalysys   

With analytics empowering your retail business with higher growth rates, there is a pressing need to work with the right partner.  

Datalysys can be your Partner of Choice, where they can successfully unravel the insights from your data and help you make prudent decisions for your business. Visit