June 14, 2023

Transforming Retail with Artificial Intelligence and synthetic data

Transforming Retail with Artificial Intelligence and synthetic data

The shift toward AI/ML in the retail industry

The shift towards artificial intelligence (AI) and machine learning (ML) in the retail industry has been one of the most significant trends in recent years. A Deloitte study found that over 50% of organizations are planning on incorporating the use of AI and automation technologies in 2023, and industries such as retail are leading the way. With advancements in technology and the availability of vast amounts of data, retailers are utilizing the capabilities of artificial intelligence of AI and ML to gain a competitive edge and drive innovation in their operations. This transformation is revolutionizing the way retailers understand their customers, optimize supply chains, enhance marketing and sales strategies, and improve overall business performance.

As previously mentioned in our introductory article, there are several key areas where AI/ML is making an impact, for example, in customer analytics. Retailers are leveraging AI algorithms to analyze large volumes of customer data, including purchase history, browsing behavior, and even social media activity, to gain valuable insights into consumer preferences and behavior patterns. By understanding customers on a deeper level, retailers can optimize marketing campaigns, offer more relevant recommendations, and deliver better shopping experiences. This not only enhances customer satisfaction but also increases sales and customer loyalty.

Supply chain management is another area that is benefiting from the adoption of AI/ML technologies in retail. Research found that AI-enabled supply chains are over 65% more effective with lower risks, and overall costs. With the help of predictive analytics and machine learning algorithms, retailers can optimize inventory management, demand forecasting, and logistics planning. By analyzing historical data, market trends, and external factors such as economic conditions, AI-powered systems can make accurate predictions about demand fluctuations, allowing retailers to optimize their inventory levels, reduce waste, and improve overall operational efficiency. This can lead to cost savings, improved product availability, and better customer service. While retail business leaders expect that in the next two years, AI will have its biggest impact on the industry in customer intelligence (53%), inventory management (50%), and chatbots for customer service (49%), the majority of the retail industry has not yet embarked on this change.

Furthermore, AI-powered chatbots and virtual assistants are revolutionizing customer service in the retail industry. These intelligent systems can handle customer inquiries, provide product recommendations, and assist with purchases, offering a seamless and personalized shopping experience. By automating routine customer interactions, retailers can free up human resources to focus on more complex tasks, while still ensuring timely and efficient customer support.

The challenges associated with data in the retail industry

The Harvard Business Review published an article that discussed why retailers fail to adopt advanced data analytics within their operations. In their investigation, they engaged with a range of retail executives, including senior leaders from retailers, distributors, consulting firms, and analytics providers operating across the Americas, Europe, and Asia. They conducted interviews with a total of 24 business leaders, representing companies at different stages of their analytics journey. The “sticking points” that HBR identified focused on six key factors, namely

  • Culture
  • Organization
  • People 
  • Processes 
  • Systems 
  • Data 

Focusing on data, the retail industry faces a range of challenges when it comes to effectively collecting, managing, and utilizing data. Integrating data from different sources and breaking down data silos is a persistent challenge. Many retailers have fragmented data stored in various systems and departments, hindering a unified view of customers, inventory, and operations. Breaking down these data silos and integrating disparate data sources is crucial for gaining a comprehensive understanding of customer behavior, optimizing operations, and delivering a seamless omnichannel experience.

Processing data in real time is also a challenge for retailers, particularly in the context of rapidly changing customer demands and market trends. Retailers need to access and analyze data in real-time to make timely decisions and respond swiftly to market dynamics. This requires robust infrastructure and analytics capabilities that can handle high-velocity data streams and provide real-time insights.

The sheer volume and variety of data generated from multiple sources present a significant hurdle for retailers. Dealing with such a vast amount of data requires substantial resources and poses complexity in terms of storage, processing, and analysis. However, what exacerbates the issue is that some companies do not even collect the necessary data to gain a comprehensive understanding of their operations. This lack of data collection results in an incomplete picture and hinders the ability to draw meaningful insights.

These challenges highlight the need for retailers to develop comprehensive data strategies, and invest in modern technology infrastructure. By addressing these challenges effectively, retailers can utilize their data to enhance customer experiences, optimize operations, and stay competitive in the dynamic retail landscape.

The importance of a solid data strategy in the retail industry 

In today's highly competitive retail landscape, having a solid data strategy is crucial for organizations to thrive, stay competitive, maintain exceptional customer experiences, and reduce costs. Creating an effective data strategy for a retail organization involves several key steps, including:   

  • Defining objectives and priorities
  • Assessing data needs and sources
  • Ensuring data quality and integrity
  • Investing in data infrastructure and tools
  • Building a data analytics team
  • Ensuring data privacy and security
  • Fostering a data-driven culture

Once retailers have established proper data management practices and ensured compliance, they can capitalize on this valuable resource to actively engage with customers, cultivate loyalty, and drive revenue growth. However, it is important to note that maintaining compliance in the ever-changing landscape of data regulations presents an ongoing challenge for retailers. Existing privacy laws on consumer data impose strict requirements on the collection, storage, and use of customer data.

Failure to comply with these regulations can result in severe financial penalties and damage to a retailer's reputation, which can harm any competitive advantage. This is where synthetic data comes into play as a solution to address compliance challenges.

How has synthetic data emerged as a key enabler for the retail industry

Synthetic data has emerged as an innovative tool for the retail industry, providing a solution to some of the most pressing challenges surrounding data privacy, software testing, and innovation. As retailers strive to deliver optimized customer experiences, more efficient operations, and comply with stringent data regulations, the use of synthetic data has gained significant traction. 

From a technical standpoint, the use of synthetic data accelerates development processes by granting access to a vast amount of high-quality and privacy-compliant data. This abundance of data allows for faster testing and validation, reducing the time required for product development and minimizing the presence of bugs. Load testing, for instance, becomes more efficient as synthetic data enables retailers to simulate real-world scenarios and assess system performance under various conditions, resulting in more robust and reliable applications.

In addition to the technical advantages, synthetic data brings several business benefits to the retail industry. Improved sales can result from the enhanced user experience achieved through personalization and optimized recommendations. By leveraging synthetic data, retailers can tailor their offerings and marketing strategies to accurately meet customer preferences, leading to increased customer engagement and sales. 

Additionally, synthetic data allows retailers to maximize the utilization of their data, resulting in opportunity cost savings. By generating synthetic data, retailers can overcome challenges associated with data integrity and distribution, where data is spread across multiple systems on different machines or exists in separate datasets that are not linked together. Synthetic data provides a unified and comprehensive dataset, enabling retailers to extract valuable insights and uncover hidden correlations that can drive business growth.

While synthetic data offers numerous benefits, there are also key drawbacks to consider. Ensuring the separation of synthetic and real data is essential to prevent any mix-up that could compromise the accuracy of analyses and decision-making. Furthermore, managing the additional infrastructure required to generate and store synthetic data poses a technical challenge that retailers must address. 

Use cases in the retail industry

Predictive analytics

The use of predictive analytics plays a crucial role in the retail industry by forecasting demand patterns, customer behavior, and market trends. Companies such as Walmart use predictive analytics to forecast sales demand, requiring an abundance of quality data. Synthetic data can enhance the accuracy and reliability of predictive models by augmenting existing datasets and enabling safe access to sensitive datasets. By generating synthetic data that captures the underlying distribution and correlations of real data, retailers can train predictive models to make more accurate forecasts, optimize inventory management, and anticipate customer preferences.

Improving recommendation systems 

Optimized recommendations are a key driver of customer engagement and sales in the retail industry. According to research from Salesforce, retail product recommendations generate 24% of orders and 26% of total revenue. Synthetic data can help enhance recommendation systems by enriching the available dataset and addressing issues related to data sparsity and privacy concerns. By generating synthetic user profiles and preferences, retailers can create more diverse and representative datasets, leading to improved recommendation accuracy and a better customer experience.

Data sharing 

Collaboration and data sharing among retailers, suppliers, and other stakeholders can unlock valuable insights and drive innovation. For example, UK grocer, Ocado, recently gave marketers direct access to its customer behavior data. Ocado said that all of the data supplied was consented to and shared to optimize their audiences and attribute campaigns through the global ad tech firm, The Trade Desk. However, sharing sensitive and proprietary data can pose significant challenges in terms of privacy, security, and legal restrictions. Synthetic data offers a solution by allowing organizations to share datasets that maintain data integrity while protecting the confidentiality of the original information. This enables collaborative analysis, benchmarking, and research without compromising sensitive information.

Data access

Access to real-world data can be limited or constrained due to privacy regulations, data availability, or data ownership. Synthetic data provides an alternative solution by generating representative datasets that contain the characteristics of real data. This enables retailers to create realistic testing environments, develop and refine algorithms and models, and perform various experiments without relying solely on restricted or limited real data.

Use case in focus 

Recommendation systems play a crucial role in retail operations, driving customer engagement and sales by suggesting products or content tailored to individual preferences and needs. However, when building effective recommendation systems companies face challenges such as data sparsity and privacy concerns. Synthetic data offers a solution by generating synthetic user-profiles and preferences that augment the available dataset and address privacy challenges.

Real customer data may be limited in terms of its variety and coverage, resulting in recommendations that are biased or incomplete. By generating synthetic versions of profiles that capture a broader range of preferences and behaviors, retailers can create a more comprehensive dataset for training recommendation models. This enables the system to offer a wider range of recommendations, ensuring customers are exposed to more diverse products.

Moreover, synthetic data can help to address privacy concerns associated with using real customer data. Retailers often face strict regulations when handling personal information, and this is evident from the €746 million fine issued to Amazon Europe (now the second largest GDPR fine after Meta) by Luxembourg’s government for non-compliance with general data processing principles. Synthetic data allows retailers to generate artificial user profiles that closely resemble real customers while preserving their privacy. This means that sensitive personal information is not exposed, reducing the risk of data breaches and privacy violations. Retailers can build recommendation models using synthetic data that maintain the accuracy and relevance of recommendations without compromising customer privacy.

By leveraging synthetic data to enhance recommendation systems, retailers can achieve improved recommendation accuracy and a better overall customer experience. With more diverse datasets, the recommendations become more personalized and relevant to individual customers' preferences. This leads to higher customer engagement, increased customer satisfaction, and ultimately, higher sales and loyalty.

Synthetic data allows for experimentation and testing without the constraints of limited or biased real-world data. Retailers can generate synthetic datasets that simulate different scenarios and customer behaviors, helping to identify the most effective recommendation algorithms and strategies. This iterative process of testing and refining recommendation systems based on synthetic data leads to continuous improvements and better performance over time. Retailers can leverage the power of synthetic data to unlock the full potential of their recommendation systems and gain a competitive edge in the dynamic retail landscape.


In an era where data is hailed as the new currency, the retail industry must stand at the forefront of leveraging artificial intelligence and machine learning to unlock the full potential of its companies. The emergence of synthetic data as a key enabler offers innovative solutions to the challenges faced by retailers in an increasingly data-driven world. Retailers can escape from the limitations of traditional data challenges and explore new frontiers of customer understanding and operational optimization with synthetic data. From improving recommendation systems to predicting customer behavior and enhancing supply chain management, synthetic data opens doors to uncharted territories of innovation and growth.