Retail training with AI: strengthen confidence in retail partners

The impact of retail training with AI on 100,000 partners

As a personalized learning company in the store, Cinecraft Productions has always been committed to design 7 Best learning principles: authentic, opportune, accessible, relevant, engaging, fun and efficient. When a global retailer with nearly 6,000 stores and 100,000 store partners approached us to help modernize their retail training, we have seen an exciting opportunity to take advantage of artificial intelligence (AI) to meet their needs.

Learning strategy

The retailer used the same sales process for 15 years. The sales process was effective, but unfortunately, it was underused because it had too many steps that caused confusion. The retailer therefore condensed the sales process in three simple stages. In the end, this change would result in an increase in the average value of the basket.

To achieve their goal, we recommended a mixed approach including a behavioral modeling video, video simulations based on and recycling scenarios with a coach generated by AI which provides instant and authentic comments.

Since the new sales process is intended to guide partners rather than providing a script, we have recommended to use a dynamic approach and led by AI for recycling simulations. The partners write in their own answers to the customer rather than selecting a multiple choice option. A personalized language learning model (LLM) formed on the sales process feeds the comments of these simulations. Almost as a real training coach, the training of the LLM (also called model) allows it to provide specific comments according to what the partners type for their responses. This approach helps strengthen the confidence of the partners and allows them to obtain personalized comments.

Retail training with AI: How did we do it?

There were many factors to consider because we developed the AI ​​coach for recycling simulations. In addition to Our standard processHere are the steps we have followed to create an effective and secure solution.

Step 1: Determine customer needs

This required an in -depth analysis of their existing IT infrastructure as well as their legal and security requirements.

The customer did not have an existing AI platform but wanted to host the new AI solution in its existing infrastructure. This requirement required a robust and adaptable platform which could be integrated transparently into their current ecosystem while maintaining the complete control of the customer on the environment.

To meet these requirements, the AI ​​platform and all the associated data had to be solidly Sandinées to ensure that the customer maintained the ownership and governance of their data and workflows. In addition, we proposed to use an intermediate server to ensure data processing and minimize risks. This ensures that the learners' responses and AI comments remain secure and private.

Step 2: Determine the best technology

The next step was to select the right technology to integrate AI into the training of the retailer sales process. Like all effective learning solutions, precision and responsiveness are essential. The AI ​​model had to provide relevant and immediate comments to the partners to support a committing and dynamic training environment.

HAS Ensure quality standardsWe have tested several AI models to determine which have provided the most precise results and the fastest response times. This rigorous evaluation process has allowed us to select the model that is mostly aligned with customer needs in terms of efficiency and precision.

Although the integration of effective AI can be costly, it is influenced by two main factors: the amount of data input into the model and the number of requests or users accessing the service. To navigate these variables and find the most effective solution, we have created a detailed cost matrix. This matrix has evaluated various configurations and scenarios of use to determine the optimal balance of performance and the profitability of the specific use of the customer.

The solution that we have chosen has ensured affordability without compromising quality, providing an evolutionary solution that aligned with the customer's budget requirements and operational objectives.

Step 3: Determine the technical work flow

The equipment retailer wanted to use the Articulate 360 ​​scenario to build its course, which forced us to find a secure way for learners to interact with AI via the scenario interface. After research and in -depth discussions, we have implemented the following workflow:

• Type the answer in the scenario – The learner is watching a video of a customer entering the store or asking a question and typing his answer in a scenario course.
• Intermediate server treatment – The learner's response is securely sent to a server held and controlled by the customer for pre -treatment.
• AI platform treatment – The intermediate server sends data not sensitive to the AI ​​platform, which generates contextually relevant comments for the learner's response. Sensitive information is stored on the intermediate server and not transmitted to the AI ​​platform.
• Intermediate server treatment – AI comments are sent to the intermediate server, where it is refined and formatted for delivery to the scenario.
• Delivery of comments to history – The learner receives immediate and usable comments from the AI ​​coach directly in the scenario training module.

This process behind the scenes occurs whenever the learner answers a question, and it only takes seconds to finish!

Step 4: Form the model

We needed the AI ​​model to act as an ideal performance coach in store of the equipment retailer for partners. This means that we had to teach everything about the new customer sales process, as well as other behaviors expected for partners, the systems and resources they could use at work. It was a meticulous process. Instead of developing a personalized model, we used a basic model from our AI platform. We have provided a detailed educational context to align with the specific objectives of the retailer. This included the formation of the model to recognize the terminology specific to industry, current customer scenarios and retailer policies and procedures. This content has been described in a script grid and a complete storyboard similar to our regular training course process.

Step 5: Test the model

After providing all this information to the model, we had to make sure that it was formed effectively. If the AI ​​coach has provided answers to the customer's objectives, we have succeeded! Otherwise, we had to recycle the model by providing different information. The test process was first carried out by users who knew the training content but were not store partners. After refining the model, the simulations were launched as a pilot for a selected number of store partners to try. They provided their reflections on the conviviality, the relevance and the precision of the feedback of the training.

Step 6: Refine the model according to the comments

The test phase has revealed improvement areas. For example, we had to refine the model's responses to better match the retailer's communication style and ensure coherent tone and precision. After several iterations and adjustments, we reached the levels of performance satisfaction and desired learners.

Conclusion

The integration of AI into the training of detailed partners has proven to be a transformer for this world equipment retailer. By taking advantage of advanced technology alongside solid educational design principles, we have created an evolutionary solution that has increased partners' confidence, improved customer service and provided measurable commercial results. For professionals in learning and development by exploring AI, this case study highlights the importance of thoughtful implementation and a commitment to the principles of quality of the lines.