The advantages of data science in retail cannot be underestimated for gaining insights into consumer behaviour for implementing dynamic, robust and responsive strategies
The retail industry contributes significantly to the global economy. In 2023, the US economy alone recorded a revenue of $7 trillion from retail; the figure is $ 2 trillion for China and $ 1.4 trillion for India (Sabanoglu, 2024). Whereas the number of visitors flocking the offline stores is increasing, in the online mode, consumers are shopping by the minute, thanks to an expanding e-commerce.
Quick commerce (Q-commerce), which promises superfast deliveries, is the latest frenzy in India’s retail revolution. A survey conducted in by Rakuten insights on consumers’ online shopping behaviour in India mentions that 24% of Indian consumers shop online several times a week. This data is a blessing in disguise for retail firms, for it could be churned into meaningful insights, which the firms leverage for making better, informed and data-based decisions, courtesy, data science.
The Data Science Edge
According to world-renowned data science expert Dr Anthony Kilili from Kroger Precision Marketing, USA, personalization is perhaps the biggest advantage that data science has to offer to the retail industry.
Source: Statista, 2024.
Data science, which finds wide applications in retail, utilizes a plethora of quantitative as well as qualitative consumer data ranging from contact details, PIN codes, shopping frequency, purchase amount, types of products purchased to number of website visits, time of visit, cart composition, app download, social media interactions, and online rating and reviews to churn meaningful insights.
Predictive analytics, machine learning models and neural networks have made it possible for companies to customize their offerings in line with consumers’ preferences. Today, the recommendation systems in online retail work on sophisticated algorithms, personalizing shopping experience of the consumers in unthinkable ways. This in turn helps the companies strengthen their relationship with customers.
Data has helped companies offer tailored ‘loyalty programs’ to their consumers. The theory of ‘one size fits all’ has been shunned and the current approach is to offer different types of rewards to different types of customers to motivate them to make repeat purchases. Data science has helped companies create consumer profiles and decide the most suitable loyalty programs for each profile to maximize consumer engagement. This, in turn, has had a positive impact on consumer lifetime value (CLV).
Platforms like Facebook, Twitter, YouTube and Instagram carry huge chunks of textual and other kinds of data (e.g. emoji), where customers post their views and interact without hesitation, thus offering rich and unbiased qualitative data to the companies, which, by conducting social media ‘sentiment analysis’ obtain useful and relevant information.
Role of new-age tech
Technologies like Augmented Reality (AR) and Virtual Reality (VR) can elevate the online shopping experience to a new level. Realising this, companies are increasing their investments in such technologies. It is estimated that by the end of 2024, global retail sector investment in new-age technologies will reach $12 billion as per Statista. Working in tandem with data science, AR/VR would further push the ‘richness’ of consumer data, ready to be captured by the retailers for ‘hyper-personalization’.
AI and Data Science
The knowledge generated by AI technology while analysing (descriptive, predictive, cognitive) data reveals hidden patterns and predicts future trends. This enables better decision-making at all levels—strategic, tactical and operational management for organizations operating in retail—thereby enhancing customer satisfaction and loyalty. It also helps gain insights into consumer behaviour (Timofeeva, 2019).
The potential value of data science and AI has galvanised organisations into re-working their approach towards business because it is not the customer alone which is centre of this technology. The data science enables efficient management of supply chain, inventory controls, dynamic pricing, competition and much more as the data comes from various sources, which is analysed for overall efficiency and profitability.
But with the growing use of real-time data, there is risk of fraud, cyber-attacks, malware, bias, data theft and data privacy (McKinsey Report, 2024). To counter the challenge of data accuracy and data privacy, the use of synthetic data (mimics real data) has been explored by Xia et al., (2024).
Be it real-time or synthetic data, the advantages of data science in retail cannot be underestimated for gaining insights into consumer behaviour for implementing dynamic, robust and responsive strategies. The future trends can be analysed keeping in view the changing patterns of consumer preferences.
