AI-Driven Value Selling

Every big deal I’ve ever done was the product of value selling (VS). Back in the day at Oracle, we had teams of value engineers to formulate value hypotheses and develop boardroom business cases through formal customer engagement.  The results were profound: win rates were over 90% and the deal sizes were 4X higher than average. More recently at Qualtrics, we implemented similar approaches with similar results. However, the benefits need not be only for the big players who can afford expensive specialized resources. AI presents an opportunity to scale VS behaviors in organizations of every size. 

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Value Selling is more than just an ROI calculation 

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Let’s start with a definition, and the key to our definition is this: value selling is more than just an ROI calculation. It’s about understanding and addressing the unique needs, challenges, and value drivers of each customer throughout the customer lifecycle. Beyond selling features and benefits, VS is about presenting your value proposition to address the customer's strategic priorities, strategic initiatives, and business challenges. 

While VS includes the financial benefit of your solution, it’s a mistake to think of it as solely about ROI.  It’s a habit that happens throughout the customer lifecycle, from the first outreach to implementing the solution and realizing value. And it transforms sales professionals into trusted advisors who speak the customer’s language and align with their priorities and challenges in every interaction. 

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Value Selling requires you to really know your customer

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Perhaps the most critical element to VS is customer intelligence. VS demands a deep understanding of your customer's specific business challenges, strategic goals, and the industry context in which they operate. This includes knowing their pain points, what drives their decision-making, and how your solution can uniquely address their needs to deliver measurable improvements. The insights needed for VS are different from the kinds of insights you have traditionally focused on and are probably not in your CRM. But they can be found through a combination of effective research and discovery.

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Typical challenges to Value Selling

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Let’s unpack the challenges to value selling that exist in the current state. Those challenges are in 3 interrelated areas: leadership priority, sales team skill, and the systems and data that support them. 

1. Leadership Priority. Success starts with leadership priority, full stop. Leaders must prioritize value selling behaviors in their QBR’s, deal reviews, and coaching. For example, knowing a customer's strategic priorities, strategic initiatives, and business challenges should be an expectation on par with knowing the decision maker and budget authority. 

2. Sales Team Skills. As leaders prioritize value selling behaviors, they need to enable their teams with training and tools to evolve from traditional sales tactics focused on volume and persistence to those that prioritize the customer’s needs and desired outcomes.

3. Current Tools and Data. The challenge to value selling is compounded by the current state of CRM and sales enablement tools. While designed to streamline and enhance sales processes, they often lack  the deeper understanding and personalized approach required by today's market. Instead of enabling more strategic and customer-focused selling, they inadvertently reinforce outdated tactics: emphasizing quantity over quality; transactions over relationships; and internal data to the exclusion of external data. 

Here’s a simple exercise: if your customer could see all of the data in your CRM, how much of that would move them to action with you? Then ask yourself, why are your teams immersed in that data?

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The Role of AI in Value Selling

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Artificial Intelligence is a game-changer in enabling and automating many aspects of the value selling approach. Here are 5 ways AI can help scale VS for any organization:.

1. Automated Customer Research: AI can quickly gather and synthesize vast amounts of external data on your customers, their competitors, and their industry. This provides sales teams with deep insights into customer challenges and opportunities and enabling more targeted value propositions.

2. Dynamic Value Propositions: AI can dynamically adjust value propositions in real-time, based on new insights gathered about the customer or changes in the market, ensuring the sales pitch remains relevant and compelling.

3. Optimized Customer Engagement: Through natural language processing and sentiment analysis, AI can guide sales reps on the most effective communication strategies and timing for outreach, increasing the chances of engaging customers successfully.

4. Content Personalization at Scale: AI can automate the creation of personalized sales content, such as emails or presentations, that resonate with each customer's specific business context and value needs, enhancing the effectiveness of sales communications.

5. Value Selling “Muscle Memory”: An AI-assistant helps engrain the critical habits and behaviors of value selling. It’s always there to collaborate and provide real-time feedback and coaching to sales reps, identifying areas for improvement in their value selling approach.

AI not only streamlines the value selling process but also amplifies its effectiveness, enabling sales teams to deliver personalized, value-driven experiences at scale. By integrating AI into their sales strategies, organizations can navigate the complexities of modern B2B sales, achieving greater success through a deepened understanding of and alignment with customer needs.

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Conclusion

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The transition to value selling, supported by AI, represents a massive opportunity for sales organizations. By focusing on the value delivered to customers and leveraging AI to enhance personalization and efficiency, sales teams can unlock new levels of performance and productivity. In doing so, they not only meet the evolving expectations of their customers but also set new standards for excellence in the B2B sales landscape.

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