Using AI for Troubleshooting Customer Questions in Technical Sales

In specialty chemicals, technical sales representatives wear many hats. They have to constantly acquire and manage new leads, stay up to date on the latest industry regulations, interact with customers, and quickly compare multiple complex products. 

In addition to all of this expertise, technical sales reps must also provide accurate, timely solutions to complex customer queries during long and often complicated sales cycles. 

Effective troubleshooting is not just a skill in technical sales — it's a crucial responsibility that significantly impacts customer satisfaction. Successful troubleshooting can land high-value clients; ineffective troubleshooting can lose important leads. 

Because sales reps have to troubleshoot for customers at various stages of the sales cycle, teams must stay prepared to address challenging questions and complaints. To do this, many companies are using artificial intelligence (AI). 

Keep reading to learn about AI for troubleshooting in chemicals and how it’s empowering sales teams to deliver exceptional service throughout the customer journey.

Three Phases of Troubleshooting in Technical Sales

Effective troubleshooting spans the entire customer lifecycle, which puts a lot of pressure on already busy sales teams. Here are some of the main phases of troubleshooting and the challenges associated with each.

1. Initial Sales Cycle

During initial sales calls, technical sales reps must diagnose customer needs and recommend suitable products. This requires balancing technical knowledge with consultative selling skills. However, challenges this stage of troubleshooting occurs when sales or services reps encounter obstacles such as:

• Insufficient Training — Sales and service reps often lack the in-depth technical knowledge or training on each product’s unique selling points (USPs) to effectively sell and educate their customers on why their products are superior to the competition. 
• Lack of Centralized Information — Without a unified system, reps struggle to quickly access and deliver accurate data or insights to customers who have highly specific technical questions.
• Inconsistent Processes — Many teams lack a systematic approach to product selection and long-term relationship building with clients.
• Limited Insights for Diverse Industry Applications — Chemical products often serve multiple verticals, requiring an in-depth understanding of various applications and the needs of customers in different industries. While product data may be available, sales reps often lack industry-specific knowledge.

Generative AI solutions address all of these obstacles, which we’ll see below.

2. Testing Phase

Once a potential solution is identified in the sales cycle, technical sales teams work with procurement teams to support R&D professionals conducting product tests. Building trust with procurement and R&D teams becomes crucial at this stage.

The typical process involves:

1. Selling to procurement teams
2. Procurement departments requesting testing samples
3. Samples being shared with R&D teams for evaluation
4. Procurement sharing information back and forth between their R&D and the product’s sales rep as the customer evaluates the product. 

The most experienced technical sales reps know there’s a better way built on earning their customer’s trust.

Here’s the ideal scenario. Sales teams work directly with R&D to ensure successful testing and application. Gaining this direct access requires reps to prove their deep technical knowledge and their ability to provide rapid, accurate responses to complex queries. 

Accelerating this point of the sales cycle is critical. In the absence of deep technical expertise, Generative AI gives reps the fast, accurate information they need to do this successfully. Where sales reps often rely on back and forth with product experts to get the answers they need, GenAI helps experts and sales reps work faster together to get their customers the product insights they need at this critical evaluation point in the sales cycle. 

3. Post-Purchase Support

After testing and the actual sale, the customer-sales relationship continues. Being able to provide swift, knowledgeable support during product application and any challenges that arise is essential for maintaining long-term partnerships and driving future growth — especially in crowded, competitive markets.

After purchase and the deal is closed, the ongoing support and service process involves:

• Quickly addressing any issues that arise during product implementation
• Proactively identifying potential challenges before they become problems
• Continuously educating customers on best practices and new applications
• Gathering insights on customer usage to inform future product development and sales strategies
• Rapid response with alternative products as customer’s face supply chain challenges or regulatory requirements drive formulation changes

At every stage of technical sales and services, reps need all the accurate answers they can get. They need to be their customer’s trusted product expert. This is where AI in sales and customer support comes in.

How AI Boosts Technical Troubleshooting & Performance

AI brings powerful solutions to technical sales teams’ troubleshooting challenges.

• Rapid Information Access: AI platforms like Nesh provide sales reps with instant access to detailed product specifications, application notes, expert technical knowledge, and industry insights. This single source of truth enables technical sales reps to quickly find the information they need, even when dealing with complex technical queries.
• Query Standardization: AI-powered knowledge bases with question and answering deliver consistent, accurate responses across the organization. This not only improves the quality of customer interactions but also reduces the investigative burden on technical specialists or product experts.
• Contextual Understanding: AI systems trained on industry data as well as company information provide reports beyond basic product specifications. Specialized workflows like Chemical Product SWOT or Account Research are tailored to the chemical industry. These AI-powred processes empower sales reps to have more meaningful conversations about industry trends, challenges, and solutions when troubleshooting customer questions. 
• Real-Time Problem Solving: When faced with unexpected questions or concerns during customer interactions, GenAI delivers immediate suggestions and solutions. This could include raw material alternatives, specialized product recommendations, or troubleshooting steps for specific applications.
• Relationship Intelligence: AI can analyze previous customer interactions to inform future sales strategies and deepen relationships. Leveraging insights from call transcripts or customer histories (often buried in a CRM) helps identify patterns in customer behavior, predict potential issues, and suggest proactive outreach opportunities so that sales reps can do their best work.

Examples of AI for Troubleshooting in Chemical Sales

Wondering how generative AI could actually support a troubleshooting scenario? Consider these instances where AI makes a significant impact.

Overcome Formulation Challenges

A customer reports an issue with a product's performance after making a formulation change. Using AI, technical services can quickly input the new specifications, compare them against previous applications, and suggest adjustments or alternative products that meet the new requirements.

Accelerate On-Site Problem Solving

During a visit to a high-priority customer, unexpected challenges are identified. With AI-powered tools, the technical service rep can instantly access relevant data, make comparisons, and provide insights in real time. AI enhances the quality of on-site conversations and potentially leads the service rep to a better solution for customers or new long-term upsell opportunities.

Cross-Industry Applications

When selling a product into a new industry, AI provides rapid insights into how that specific product has been successfully applied in similar contexts. This helps sales reps tailor their approach and overcome industry-specific objections — no matter their experience level.

Predictive Maintenance

By analyzing historical data and usage patterns, AI helps technical services teams anticipate their customer’s potential issues before they occur, enabling proactive outreach and cementing customer relationships.

Embracing AI in Technical Sales & Services 

In an industry as competitive as chemicals, AI for troubleshooting is essential — especially for companies that want to stay ahead. The right chemical sales AI empowers your team to:

• Significantly reduce time spent searching for accurate information
• Provide more reliable and comprehensive answers to customer queries
• Build deeper trust with both procurement teams and R&D professionals
• Identify potential issues proactively, enabling preemptive support
• Accelerate the sales cycle by delivering value at every interaction
• Continuously enhance industry knowledge and technical expertise

Additionally, AI tools become necessary to help sales and technical service teams align their positioning and communication strategies. AI also augments human expertise, enabling teams to focus on building relationships, solving complex problems, and driving innovation.

Nesh Sales AI for Technical Troubleshooting in Chemicals 

The Nesh Sales AI platform is specifically developed for the complexities of advanced manufacturing and sales of specialty chemicals. Nesh enhances troubleshooting capabilities with:

• Smart Product Recommendations: By inputting customer specifications, Nesh suggests the most relevant products from your entire portfolio. This streamlines the initial sales process and ensures customers are matched with the best solutions.
• Comparative Analysis: Quickly benchmark your offerings against competitors, strengthening your value proposition. This feature is particularly valuable during the testing phase where you need to clearly articulate your product's advantages.
• Application Support: Access industry-specific knowledge to solve customer challenges in real time. This could include formulation adjustments, process optimization tips, or alternative application methods.
• Knowledge Preservation: Capture and retain expertise from subject matter experts, ensuring continuity as team members leave the field. This institutional knowledge becomes a powerful asset for training new reps and maintaining consistent customer support.

With Nesh, technical sales and services teams overcome common challenges, provide superior customer support, and drive growth through effective troubleshooting at every stage of the customer journey. From the initial sales cycle to post-purchase support, Nesh empowers reps to deliver high-value insights that strengthen customer relationships and drive success.

Ready to enhance your technical sales capabilities that involve troubleshooting? Reach out to Nesh for a demo and unlock your competitive advantage in technical sales.

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‍Want to learn more about AI for the chemicals industry? Check out our Ultimate Guide to AI in Chemicals.