As retailers and e-commerce platforms evolve in a competitive digital landscape, delivering a personalised customer experience has become more crucial than ever. Shoppers expect brands to know their preferences, anticipate their needs, and provide seamless interactions across multiple channels. The good news? Large Language Models (LLMs) and generative AI are set to transform how businesses meet these expectations. This shift towards hyper-personalisation can enhance the customer journey and drive business outcomes like never before.

The Rise of Personalisation through LLMs

With the rise of AI, personalisation is no longer confined to simple recommendation engines based on past purchases or demographic data. LLMs, which power advanced conversational tools and AI-driven decision-making, can harness vast amounts of structured and unstructured data. These models can process everything from purchase histories and real-time customer interactions to reviews, social media posts, and external market trends, providing a more complete and dynamic understanding of individual customers.

By offering personalised product recommendations, refining marketing messages, and customising user interfaces in real-time, LLMs enable retailers to create a unique and tailored shopping experience for every customer. For example, AI-powered chatbots equipped with natural language processing (NLP) capabilities can engage in sophisticated, human-like conversations, helping customers discover new products or suggesting the perfect item based on their preferences and past behaviours.

How Personalisation Drives Value

Retailers who have successfully integrated generative AI into their operations are already seeing the benefits. According to a recent study (McKinsey), generative AI has the potential to unlock between $240 billion and $390 billion in economic value for the retail sector. This is largely driven by improvements in customer engagement, conversion rates, and overall shopping satisfaction through tailored experiences.

One notable use case is within e-commerce, where LLMs can automate the generation of product descriptions, create smart search functionalities, and personalise the shopping experience in real-time. For instance, AI-driven tools can create custom shopping lists for customers based on their preferences and past purchases, or even provide recommendations for a specific occasion, such as a dinner party. These AI systems not only make shopping more efficient for customers but also reduce operational costs by automating content creation and decision-making.

Overcoming Implementation Challenges

While the potential is enormous, scaling these capabilities across the entire organisation requires strategic planning. Retailers must focus on data quality, integration, and talent development. Many companies are still in the early stages of experimenting with LLMs, often hindered by challenges like privacy concerns, lack of resources, and the complexity of integrating AI solutions into existing systems. However, businesses that make the leap to operationalising LLMs at scale—through a combination of third-party tools and proprietary data—will lead the charge in delivering cutting-edge personalisation.

For retail and e-commerce leaders considering the adoption of LLMs and AI into their technology stack, several key factors should be prioritised:

Building the Business Case for Investment and ROI

Leaders must carefully evaluate the potential return on investment. AI and LLM implementations can drive significant value, McKinsey estimates suggest brand could unlock up to $390 billion in value for the retail sector. As it is crucial to build a solid business case that considers:

• Potential basket uplift (typically 2-4% can justify LLM costs)
• Impact on Customer Acquisition costs
• Increased purchase frequency for existing customers
• Implementation and ongoing operational costs

Prioritise Use Cases to Get the Highest Value From your Investment

Identify and prioritise specific use cases that align with your core value proposition and the business goals you are aiming at achieving with AI. In Retail the most common high-impact areas include:

• Personalized product recommendations
• Intelligent search and discovery
• Customer service automation - Watch this space as it is evolving rapidly
• Inventory management and demand forecasting
• Dynamic pricing optimisation

Look for instance at the value proposition on how Salesforce AI autonomous assistants, that we are implementing on retail brands, can deliver value in the retail space:

Technical Considerations - Data Integration and Quality

Ensure to work closely with your eCommerce tech team so your AI systems can access and utilise high-quality data from various sources, including:

• Product catalogs
• Customer data (purchase history, preferences)
• Order and fulfillment records
• Promotional and pricing information

In our experience working on AI projects so far we find that this is one of the most challenging to achieve, as Data Quality and Data Integrity tend to be areas of complexity that require deeper collaboration. Integrating this data effectively is crucial for powering personalised experiences and accurate decision-making.

Customisation and Fine-Tuning Takes Time

Off-the-shelf AI solutions may not fully meet the specific needs of your business. Consider adopting a "shaper" approach by customising existing LLM tools with your own code and proprietary data to create more tailored and effective solutions.

Implementing strong LLM observability and evaluation practices can help set the benchmarks and KPI monitoring to improve ROI:

• Monitor performance and accuracy
• Detect and mitigate potential biases or errors
• Continuously refine and improve AI models based on real-world performance

Building Customer Trust and Loyalty Through Better Experiences

Ensure AI-powered features enhance rather than disrupt the customer experience requires focus and cross functional collaboration. This includes creating natural, conversational interfaces for customer interactions and integrating AI recommendations seamlessly into the shopping journey. The pre-requisite for this is understanding the knowledge that underpins human operations so AI bots can access this information, so it is not just about data, it is also about information and having the know-how ready for bots to consume and execute.

This is very interesting, we see how this is being implemented in the tech tools we implement, Salesforce Agentforce for instance, or similarly Zendesk and Ada assistants. They all have the common concept of managing knowledge, holding the know-how on how things work and how this can be used to execute actions across the digital ecosystem.

That is how you create autonomy.

Be transparent about the use of AI in customer interactions and ensure ethical use of customer data. Implement strong privacy protections and give customers control over their data and AI-driven experiences.

Humans and AI Assistants Should Collaborate as a Team

View AI as a complement to human employees rather than a replacement. Develop strategies for effective collaboration between AI systems and human staff, particularly in customer service roles where the impact on the customer experience is greater and can yield more tangible measurable results.

What I am finding is that key team members from customer services teams hold a lot of knowledge that is not written anywhere, and that these non-explicit policies are the base to better train the AI models and the Assistants. As a result, in order to create collaborating teams, members from the service team need to learn to create the knowledge base for AI, so this implies 1) to implement the technology frameworks for knowledge management, and 2) to change the functions of those roles that hold that knowledge.

These new circumstance require you to invest in ongoing training and education for your team to stay current with AI advancements and best practices in the retail and e-commerce sectors. By focusing on these key areas, retail and e-commerce leaders can effectively leverage LLMs and AI to drive innovation, improve operational efficiency, and deliver superior customer experiences in an increasingly competitive digital marketplace.

Building the Future of Retail with AI

As AI technologies continue to evolve, the retailers who embrace LLMs and generative AI early on will stay ahead of the curve. The ability to provide deeper, more meaningful customer interactions across the full customer journey will not only enhance loyalty but also lead to higher sales and profitability. By developing capabilities that allow for personalised and dynamic customer engagement, the future of retail is set to be more customer-centric than ever before.

Learn more about us at redk, we are working with brands in different sectors to help you create the digital capabilities that are required to either built the foundation or implement AI into your technology framework and help drive that next level competitive advantage.

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