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Many sales leaders face the challenge of replicating success across their entire team. While top sales reps consistently close larger deals and do so faster, others struggle to achieve the same results. Even with modern CRM, forecasting, and conversation intelligence tools, revenue and enablement leaders have limited visibility into what makes high performers successful.
For instance, are top reps more skilled at "multi-threading," engaging with senior decision-makers more effectively, or sharing customer stories in a way that resonates? Are they quicker to respond to prospects? Are they better at handling objections? Are they just more "themselves" and more likable?
Without clear insights into these differences, even experienced leaders find it challenging to scale the behaviors of the top reps across the team. The usual methods—team meetings, training, call reviews, and one-on-one coaching—while valuable—don't provide the nuanced understanding needed to make impactful changes.
This is where AI and large language models (LLMs) make a difference by providing radical visibility into deal dynamics and winning tactics.
With LLMs and the right expertise, an organization can conduct granular analysis and extract insights from their sales activities or customer interactions (e.g., meetings recordings, emails, Slack messages, CRM records). Each organization can identify the winning tactics, behavior, and messages that drive success for top AEs. This article will discuss how these insights are generated in practice.
Once identified, the next step is to embed these deal-winning insights into the workflows of all AEs. Historically, sales enablement teams have documented best practices, conducted training, and incentivized the adoption of new techniques. However, AEs are often too busy to absorb and act on new insights effectively (and already feel overburdened by existing job requirements).
Suppose leaders want AEs to change behavior and use these deal-winning insights. In that case, we need to provide them with tools that integrate guidance and insights directly into their workflows, helping them apply learnings efficiently while reducing administrative burdens. The solution must be intuitive, minimizing disruption to the AE's daily tasks.
At Intersight, we're building a custom deal insights engine for every organization to help every AE operate at the highest level. Our platform equips teams with actionable intelligence while streamlining tasks to drive more efficient, higher-quality deals.
Based on my experience working with AEs for the past five years, I've seen that the best sales professionals consistently apply specific skills and strategies that make them stand out. Key characteristics of high-performing reps include:
High performers also do other things. For instance, they know how to prevent stalls by managing product evaluation and contracting workstreams in parallel, building rapport with buyers by finding common interests, and responding quickly (such as texting rather than emailing buyers). However, without a thorough analysis of all the sales activities, leaders lack a complete picture of these advanced and unconventional practices.
Intersight empowers sales teams to operate at their best by analyzing closed-won deals from each company, uncovering successful strategies, tactics, and messaging used by top reps, and offering actionable, data-driven guidance to their entire sales teams.
We believe that the qualities of top performers can be standardized, taught, and replicated across a sales team. Here's how Intersight's software platform and our data science team make this happen:
1. Aggregating and Analyzing Sales Data
Intersight connects directly with your CRM and other sales activity data sources, including Gong, Chorus, Zoom, Teams, and email. We compile, harmonize, and index your historical sales data. This consolidated dataset is then analyzed by a large language model and validated by humans with B2B sales knowledge. Our Applied AI Science Team crafts custom prompts to help the large language model identify winning tactics, messages, talking points, and strategies that can be scaled across the team. Our AI team implements rigorous evaluation methods to ensure AI-generated insights' truthfulness, accuracy, relevancy, and helpfulness.
2. Delivering Tailored Playbooks for Current Deals
Intersight generates tailored playbooks designed to guide deals intelligently based on your organization's data. These playbooks deliver insights drawn from similar closed-won deals. Whether it's advice on questions to ask to qualify a prospect, how to position your product, or guidance on which customer stories to share, these playbooks provide targeted recommendations that reps can trust.
3. Helping Sales Reps Create Content and Messages Efficiently
Writing documents tailored to a buyer's needs and populated with the buyer's own words is time-consuming. Intersight's AI capabilities and built-in templates help streamline document creation, providing first drafts for reps to customize. This allows reps to focus on creative writing (e.g., enhancing the AI-generated draft) and building relationships.
4. Improving Team Coordination
Complex enterprise deals often require collaboration across departments. Intersight helps streamline this process by generating tailored briefing documents, allowing new team members to quickly get up-to-speed on the deal and understand their role within it. This ensures that everyone involved understands the deal's objectives and can contribute effectively, creating a more cohesive and efficient approach to closing each opportunity.
To test an LLM’s capability in extracting winning tactics and strategies from historical sales activity data, we generated over 100 call transcripts simulating over 32 closed won deals from sales reps for a fictious company called Ultracheck.
We generated sales meetings transcripts for four sales stages: (1) Discovery, (2) Demo, (3) Proposal and (4) Closing. The simulated meetings represent conversations between sellers selling risk and compliance software and prospective buyers (GRC/compliance managers). Each of the simulated calls were manually reviewed and edited for realism. Each call transcript is at least a 20+ minute conversation.
We then indexed the calls into a vector database, defined the optimal chunking and retrieval configurations. We created multiple prompts and ran inference on the data. We tested how well LLM can do at retrieving the correct calls and how it performs in analyzing each deal individually to generate relevant insights. Once we collect the insights from individual deals, we then tell the LLM to look for patterns of insights across multiple deals and extract key insights to guide a rep on an active deal.
We used the O1 preview model from OpenAI. In this particular example, we’ve told the LLM we’re a sales rep working on a deal called ApexPoint. The LLM’s task: Generate a set of strategies other reps have used to close deals with similar accounts.
Below is a snippet of the results generated by our (proprietary) prompts. You can download the full results here. Notice how detailed it is and how LLM was able to cite specific examples and talking points from multiple deals.
Actionable Takeaway:
Relevant Examples:
Actionable Guidance:
Actionable Takeaway:
Relevant Examples:
Actionable Guidance:
Actionable Takeaway:
Relevant Examples:
Actionable Guidance:
By delivering intelligence that informs every interaction, sales teams are better equipped to:
With advanced LLMs, you can make winning insights and tactics accessible across your entire team. Intersight's revenue intelligence and orchestration platform enables sales leaders to create a blueprint for success by using an LLM to analyze closed-won deals and identify the tactics that contributed to the success. With this data-driven approach, sales leaders can finally answer the question: How do I make my entire team a team of top performers?
Sign up for a demo here.
