How Distributors Use LLMs and RAG to Accelerate Product Selection and Cross-Selling

3 min read ● Silk Team

To improve efficiency for your sales team and shorten the sales cycle, you could implement a retrieval-augmented generation model. A retrieval-augmented generation model uses a large language model to generate an answer based on a set of documents it has been trained on. The documents provide the background knowledge the model needs to understand the question and provide a coherent response.

When used together, a retrieval-augmented generation model can help speed up the time your sales team takes to find the correct products and identify potential cross-sell opportunities by reducing the amount of time spent researching each product and providing them with more confidence in their ability to recommend the correct products.

A retrieval-augmented generation model works by first using a retrieval component to locate the documents relevant to the question being asked, and then uses those documents to train a generation component to produce a response.

Why Is Product Selection a Bottleneck?

In many cases, the reason for the bottleneck is due to the fact that product knowledge exists in several different systems within a distribution organization, including ERP product catalogs, manufacturer spec sheets and PDFs, internal pricing and discount rules, sales playbooks and historical deal notes.

Even with extensive experience, sales representatives will still have difficulty finding the correct information or may have to switch between systems to gather enough information to make a recommendation. This creates delays in response time, leads to errors in product selection, and in some cases leads to missed opportunities for the sale of additional products through the use of upsell/cross sell strategies.

How Do LLMs and RAG Work Together?

Large language models (LLMs) excel at understanding natural language and creating clear, readable responses; however, they require access to accurate, business specific data to do so effectively. That is where RAG comes in.

In a distribution sales workflow, RAG performs the following functions:

• Retrieves relevant information about products from internal product data, documentation, and sales content.
• Uses that retrieved information to create a concise, contextually aware response using the LLM.

This enables sales representatives to ask questions in plain language and receive answers that are based upon approved internal resources that reflect current product availability and pricing.

Faster Product Selection

Using LLMs and RAG in the sales workflow enables sales representatives to quickly find the products that match the customer’s requirements and make suggestions based upon the customer’s usage and budget.

Examples of common questions sales representatives ask include:

• “What products meet these technical requirements?”
• “Which is the best substitute product if the selected product is currently out-of-stock?”
• “Which product is best suited for this customer’s usage and budget?”

Rather than having to sift through a catalog of products and/or call product specialists, sales representatives are provided with instant access to all relevant information, such as product specifications and current product availability and pricing. This greatly accelerates the quote creation process and reduces the number of follow-up calls with customers.

Better Cross Sell and Up Sell Recommendations

Cross sell and up sell failures often occur due to sales representatives’ lack of time to research and identify potential opportunities rather than a lack of opportunity itself. RAG-powered systems greatly improve this function by accessing:

• Product compatibility and accessories relationships
• Internal bundling strategies and sales playbooks
• Historical purchasing patterns

As an example, once a sales representative identifies a primary product for the customer, the system provides recommendations for additional products that are complementary to the primary product, upgrades to the primary product, or replacement products that fit the customer’s context. Because the recommendations are relevant and timely, they appear as helpful to the customer, rather than as aggressive.

Benefits for Both New and Experienced Representatives

New sales representatives benefit from having a reliable resource to assist them in navigating complex product portfolios without experiencing lengthy ramp-up periods. Experienced representatives benefit from automating routine product lookups, which frees them to focus on developing strong customer relationships and negotiating deals.

In either case, the sales representatives become more confident in their decision-making capabilities due to the consistency, timeliness, and alignment of the recommended products with company policies and procedures.

Why This Solution Creates Confidence

Sales teams will not rely on tools that produce uncertain answers. RAG-based solutions establish credibility through:

• Only referencing verified internal product data.
• Keeping product information up-to-date.
• Eliminating manual guesses and interpretations.

Establishing credibility and trust is crucial when the suggested products affect customer satisfaction and order accuracy.

A Practical First Step

Distributors normally initiate with a focused launch of:

• Targeted product categories with high volume or margin
• Organizing and cleaning product documentation
• Pilot with a subset of the sales team
• Tracking key performance indicators such as response time, attachment rate, and average deal size.

Incremental successes drive momentum and acceptance.

Take Away

Large language models (LLMs) and retrieval augmented generation (RAG) convert the traditional manual and error prone process of selecting and recommending products into a rapid, informed, and intelligent process for distribution companies. This translates to faster responses, more targeted recommendations, and ultimately increased sales success for distributors.

By making product knowledge instantly available to sales teams, sales teams can spend less time searching and more time selling.