The size of the retail AI market is expected to grow from USD 7.30 billion in 2023 to USD 29.45 billion by 2028, a CAGR of 32.17% over the forecast period (2023-2028)*. This exponential growth highlights the impact of natural language processing AI, which is redefining the retail landscape by optimizing operations, improving the customer experience and boosting innovation.
In this article, we will explore the specific applications and tangible impacts of these advanced technologies in the retail sector.
*Source: https://www.mordorintelligence.com/fr/industry-reports/artificial-intelligence-in-retail-market
The importance of analytical AI over generative AI.
Analytics AI generates structured data from a large volume of structured and unstructured data. Unlike generative AI, which generates unstructured data from a large volume of unstructured data. The uses are therefore different, and sometimes complementary. For example, the former excels in extracting information from email streams to simplify the handling of carriers' complaints, while the latter could produce email responses by bringing creativity in the formulation.
The natural language processing is also crucial for analyzing customer reviews and feedback, providing a thorough understanding of consumer preferences. Generative AI focuses more on creation and innovation, while analytical AI focuses on interpreting and exploiting unstructured data (or excelling at) to improve business processes and strategic decisions.
Therefore, in the retail sector where decisions often need to be made quickly and are based on large volumes of variable data, analytical AI offers a powerful approach to improve sales performance and customer experience, remaining otherwise totally explainable for those with a symbolic approach, which allows:
- avoid data processing errors
- avoid bias and hallucinations
- Improve IAs configuration in an agile way
We invite you to watch our Minute Ai&Society Ep.5 : “Do we know what happens in the head of an AI” to better understand the importance of AI explainability.
Accelerating retail performance with analytics AI
Supplier relationship monitoring
Analytical AI can be used to monitor supplier relationships, as it optimizes inventory management. For example, A fashion retailer faced with inventory management issues implemented AI-driven demand forecasting. This has led to a significant reduction in stock outs, improving customer satisfaction and increasing cash flow. The results include a 15% reduction in out-of-stock and a 30% reduction in storage costs through optimization of inventory levels* .
Moreover, analytics AI can connect to your CRM (Customer Relationship Management) and therefore relieve you of simple and tedious tasks by directly reporting the necessary information in your CRM to optimally process your supplier relationships and your freight tracking. In addition, a direct connection with a carrier’s tool allows real-time tracking of a delivery, to be shared with the customer.
*(Source: Retail Analytics Case Study, 2022)
Follow-up of incoming requests
Analytical AI can be a major ally in customer relationship monitoring, as it allows the management of incoming requests. Indeed, let’s take the example of claims management, analytical AI is essential to instantly detect situations requiring urgent intervention and direct them to the appropriate person. This approach significantly improves the efficiency of complaint processing, thereby consolidating the relationship between the company and its customers, up to a 10-fold reduction in the time taken to process incoming messages thanks to InboxCare
Analysis of satisfaction surveys
Satisfaction surveys often lead to answers in the form of free text. Therefore, analytical AI is crucial to extract information from the received text, identify relevant data and provide a detailed analysis of it.
These use cases clearly demonstrate how analytical AI can be a powerful tool to accelerate performance in the retail sector, offering significant benefits in terms of supplier relationships, monitoring incoming requests and analysing satisfaction surveys.
The Manutan example
The Impact of Analytical AI in the Collaboration between Manutan and Golem.ai
Manutan, an European leader in business supplies and services, has taken up a major challenge by collaborating with Golem.ai to optimize the processing of more than one million emails received annually. The goal was to make email processing more efficient, especially for orders, inquiries and complaints.
The use of analytical AI by Manutan
Golem.ai has set up InboxCare, an analytical AI solution, to automate the sorting of 3,000 to 4,000 daily emails. This technology can detect multiple intentions in a single message, such as requests for quotes or address changes, and connects to the CRM to access all the necessary information.
Results and benefits
Thanks to this collaboration, Manutan was able to increase its efficiency and turnover, while saving time for its teams. Analytics AI enabled 85% of emails to be routed to the right service, while processing requests in French, English and Dutch, demonstrating theeffectiveness of AI in managing multilingual communications.
This initiative is a perfect example of how analytical AI can transform business processes, improve operational efficiency and deliver an optimal customer experience.
In conclusion, the use of artificial intelligence analytics is a major asset in the Retail sector. More specifically, language processing is proving to be a powerful tool to accelerate the performance of Retail players, in cases of uses involving texts to be analyzed. And this, thanks to applications such as optimization of supplier relationship monitoring, monitoring of incoming requests, and analysis of satisfaction surveys. In short, analytical AI proves to be a powerful asset when it comes to extracting information and transforming unstructured data into structured data. However, if analytical AI proves to be a key growth factor for the Retail market, generative AI also proves to be an interesting tool when generating marketing campaigns, or content generation in general, it all depends on which use the AI is used. Ultimately, the adoption of AI in retail is no longer just a competitive advantage, but a necessity to remain relevant and prosperous in today’s dynamic market.
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