HISTORY OF GROWTH AND DEVELOPMENT IN RETAIL ANALYTICS

What is Retail Analytics?

Retail analytics focuses on providing insights related to sales, inventory, customers, and other important aspects crucial for merchants’ decision-making process. The discipline encompasses several granular fields to create a broad picture of a retail business’ health, and sales alongside overall areas for improvement and reinforcement. Essentially, retail analytics is used to help make better choices, run businesses more efficiently, and deliver improved customer service analytics. The field of retail analysis goes beyond superficial data analysis, using techniques like data mining and data discovery to sanitize datasets to produce actionable BI insights that can be applied in the short-term.


 

1920s-1930s: The Rise of Consumer Culture

During the 1920s-1930s, the retail industry experienced significant changes influenced by socio-cultural shifts, technological advancements, and the adoption of retail analytics. Shopping arcades and department stores, such as Le Bon Marché in Paris, provided consumers with a more experiential shopping experience. Retailers began using basic data analysis techniques to understand customer preferences and optimize operations. Technological innovations like Ford automobiles, electric railway systems, and the invention of the bulldozer improved logistics within retail operations. The emergence of plastic bags and other technologies allowed retailers to cut costs and enhance product handling.

 

 

1940s-1960s: The Post-War Retail. Shopping Malls

In the post-war period, from the 1940s to the 1960s, retail analytics started gaining more prominence. Technological breakthroughs like transistors, colour TV, microwaves, and hard drives provided retailers with new product offerings and fuelled the growth of retail. Retailers utilized data analysis techniques to study sales patterns, inventory management, and customer demographics. Shopping malls became popular retail destinations, offering entertainment and convenience. Retailers leveraged analytics to better understand consumer behaviour and drive marketing strategies.

1970s-1980s: Gradual Automation of Retail

The 1970s and 1980s witnessed the gradual automation of retail operations and the increased use of retail analytics. The introduction of Electronic Data Interchange (EDI) in 1970 enabled electronic business transactions. The development of the Intel microprocessor revolutionized the economy and paved the way for the computerization of retail. Retailers started adopting more advanced analytics tools to analyse customer data, optimize supply chain operations, and enhance decision-making. The emergence of email and other communication technologies further facilitated business transactions and customer interactions.

1990s-2010s: Online Shopping Becomes a Reality

The 1990s marked a new era in retail with the introduction of the Internet and the widespread adoption of retail analytics. Secure online transactions became possible with the development of protocols like Secure Socket Layer (SSL). Ecommerce companies like Amazon and eBay emerged, leveraging analytics to understand consumer behaviour and personalize the online shopping experience. Online payment systems like PayPal facilitated secure online transactions. Retailers harnessed advanced analytics techniques to gain insights from large datasets, enabling dynamic pricing, demand forecasting, and personalized recommendations.

 

2010s and Beyond: Technological Advancements and Analytics

In the 2010s, technological advancements and the continuous evolution of retail analytics further transformed the retail industry. Cryptocurrency, such as Bitcoin, challenged traditional perceptions of money and introduced new payment options. Social media platforms like Instagram and Facebook became integral to online sales and advertising, allowing retailers to leverage analytics for targeted marketing campaigns. Mobile commerce empowered consumers with greater control over the purchasing process, while cloud-based solutions facilitated remote data management and collaboration. Advanced analytics techniques, including machine learning and predictive analytics, enabled retailers to gain actionable insights and deliver personalized experiences to customers. Throughout these eras, the evolution of retail analytics played a crucial role in understanding consumer behaviour, optimizing operations, and driving business growth. From basic data analysis techniques to sophisticated analytics tools, retailers have utilized insights from data to enhance decision-making, improve customer experiences, and stay competitive in the dynamic retail landscape.

to the aspects mentioned earlier, the 2010s and beyond have witnessed significant advancements in technology and analytics that have revolutionized the retail industry. Here are some further developments in retail analytics during this period:

1.     Big Data and Machine Learning: The proliferation of digital technologies, online transactions, and connected devices has generated massive amounts of data known as big data. Retailers have leveraged big data analytics to extract valuable insights from diverse data sources such as customer interactions, social media, website traffic, and more. Machine learning algorithms have played a vital role in analysing this data, enabling retailers to uncover patterns, predict customer behaviour, and make real-time personalized recommendations.

2.     Real-time Analytics: Traditional analytics focused on historical data, but real-time analytics has become increasingly important in the modern retail landscape. Real-time analytics provides immediate insights into customer behaviour, inventory levels, pricing dynamics, and market trends. This allows retailers to respond swiftly to changing conditions, optimize pricing in real-time, manage inventory more efficiently, and personalize the customer experience based on up-to-the-minute data.

3.     Sentiment Analysis: Sentiment analysis is a technique that involves analysing customer feedback, reviews, and social media conversations to gauge customer sentiment and opinions about products, brands, and shopping experiences. By applying natural language processing and text analytics, retailers can extract insights from unstructured data sources and identify areas for improvement, track brand reputation, and proactively address customer concerns.

4.     Location Analytics: Location analytics utilizes geographic data to gain insights into customer behaviour and optimize store operations. By analysing location data from mobile devices, retailers can understand foot traffic patterns, identify popular shopping areas, and optimize store layouts and product placements. Location analytics also enables retailers to deliver targeted location-based marketing campaigns, personalized offers, and location-specific recommendations to enhance the customer experience.

5.     Personalization and Recommendation Engines: Retailers have increasingly focused on personalization to meet the individual needs and preferences of customers. Advanced recommendation engines powered by machine learning algorithms analyze customer data to provide personalized product recommendations, cross-selling opportunities, and targeted promotions. These personalized experiences improve customer satisfaction, increase conversion rates, and foster customer loyalty.

6.     Visual Analytics: Visual analytics combines data visualization techniques with analytics to present complex data in a visually intuitive and interactive manner. Retailers use visual analytics tools to explore large datasets, discover patterns, and gain actionable insights. Visualizations such as charts, graphs, heatmaps, and dashboards enable retailers to quickly understand trends, identify outliers, and communicate insights effectively across the organization.

These advancements in technology and analytics have transformed retail analytics into a strategic asset for retailers. By harnessing the power of data, machine learning, and real-time insights, retailers can optimize operations, personalize customer experiences, drive sales growth, and stay ahead in a competitive market. Retail analytics continues to evolve with advancements in artificial intelligence, Internet of Things (IoT), and augmented reality, opening up new possibilities for retailers to innovate and deliver exceptional customer experiences.


CONCLUSION:

retail analytics is a vital tool for the retail industry, providing valuable insights for decision-making and improving customer experiences. It encompasses various techniques such as data mining, sentiment analysis, and location analytics. Advancements in technology and analytics have revolutionized the field, with big data, machine learning, real-time analytics, and personalization playing significant roles. Visual analytics tools help visualize complex data, and recommendation engines enhance customer satisfaction. retail analytics empowers retailers to make data-driven decisions, optimize operations, and innovate in the competitive retail landscape. As data analytics professionals, we have a pivotal role in shaping the future of retail through our expertise and skills in leveraging data and technology. I believe this field offers exciting opportunities, and staying updated with the latest advancements is crucial.

 

References: https://onlinelibrary.wiley.com/doi/full/10.1111/poms.13811

https://gepard.io/insights-trends/evolution-of-retail-over-one-hundred-years

 

ISME Student Doing internship with Hunnarvi Technologies Pvt Ltd under guidance of Nanobi data and analytics. Views are personal.

 

#RetailAnalytics #DataDrivenRetail #CustomerInsights #DataMining #RealTimeAnalytics #Personalization #MachineLearning

 

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