Hi, I’m Nathan and I’m a sales guy.

Having spent my entire c. 20 year career in sales, I have seen a lot of change: techniques, frameworks, technology changes.  But, there are very few things that have made a truly disruptive and outsized impact on my career.  But, I can honestly say that using Conversation Intelligence is one of those things – arguably, the most impactful thing.

I first started using Conversation Intelligence (CI) in 2019 when my team started using Gong.  I have since used it as an individual contributor at multiple companies as well as a frontline sales manager.  I also spent a period as an Enterprise Account Executive at Gong where I got firsthand experience on how one of the leading companies in the CI space both thought about and used their own platform directly.

Disclaimer:  I consider myself a big Gong fan simply because it is all I have used – and frankly, I’ve never felt a need to try alternatives.  I recognize that there are multiple, respected players in the space.

What is Conversation Intelligence 

Conversation Intelligence tools have nuances that make them different but fundamentally they all do the following:

1. Record sales meetings and phone calls 
2. Transcribe those recordings and recognize different speakers
3. Analyze the conversations in multiple ways:  speaker dynamics (time spent talking, pauses etc); identify topics and themes discussed
4. Provide executive summaries of the conversations and allow playback for users to listen back to or watch the meetings
5. Surface key moments such as action items, objections, etc through alerts and the UI
6. Provide reporting at an aggregate level – for example, which rep talks the most, who talks the most about certain topics, how often do specific topics or phrases come up in conversation
7. Provide a searchable repository of calls
8. Analyze and integrate other seller-client interactions across a deal cycle – for example, emails

Why Conversation Intelligence in Sales is big business for Tech companies

As I said at the outset, I consider using Conversation Intelligence to be the biggest influence on my career – more valuable than most of the sales training I have gone through, more valuable than many of the sales managers I have worked for, more important than all of the other tools I have ever used.  If I could only pick one tool that I could use in a sales job (beyond a CRM), it would be a good CI platform.

Conversation Intelligence has had an outsized impact on my approach and experience of selling in numerous ways:

• Coaching:  let’s face it – having your sales manager sit in and participate in your meetings is not always ideal.  But, especially for younger and newer sellers, it is critical to get feedback.  Having been both a seller and a sales manager myself, I have firsthand seen (and been guilty of being) the sales manager who interjects or takes over a conversation.  One of the main ways that sellers improve their knowledge and skills is by doing – and yes, you have to occasionally mess up, to be asked tough questions and be forced to say “I’m afraid I don’t know”. 

• Self-guided improvement/reflection:  one of the best ways to improve is to see yourself and your performance in the cold light of day.  This is why we now see tablets and video devices routinely used on the sidelines of sports games.  Even in fast paced games like ice hockey, players will sit on the bench between shifts and look back at key plays.  The concept of watching game film footage to both learn and get an upper hand against an opponent, as well as using data, is hardly new in professional athletics and sports.  Sales – as a performance and largely skill based profession – is no different.  

• Focus on calls: I honestly don’t remember the last time I manually relied on notes in a meeting.  Knowing that I will be able to listen back to the call enables me as a seller to truly focus on the conversation.  Rather than needing to note down everything that the prospect/customer says so that I can later type it up in CRM, I am able to focus on listening and being in the moment with the customer, safe in the knowledge that everything I need to remember or share later will be captured for me.

• Knowledge sharing: in my sales career I have noticed a pattern – the best performing sellers operate in something of a vacuum.  They are often so “heads down” focused on the job that they don’t have the time or the inclination to share with others the secrets of their success.  The traditional “lone wolf” type seller is someone who also wants to protect their own success to the point where they will guard their ‘tricks of the trade’.  This poses a challenge for sales managers – how can they get their sellers to mirror the best in class tendencies and habits, without creating a pressure or frustration on their top sellers.  CI tools are a great way to do this.  Furthermore, sales is a team sport – marketing, product, engineering, support teams etc.  All cross-functional groups benefit at some time or another in hearing what the prospects and customers are saying.  Think about how it can help a product team’s new feature roadmap, or a marketing team’s messaging strategy, if they can hear directly what frontline prospects are saying.

• A perspective into the truth:  let’s face it.  Sales reps are fickle.  To be more precise, human beings are fickle.  Two people might hear a prospect talking and have completely interpretations of meaning.  The classic concept of “happy ears” is discussed a lot by sales manager (where sellers only hear the positive things that they’re hoping for and not the cold, stark reality of deal risks and objections).  Conversation Intelligence helps by enabling teams to reflect back on a call (“What was the client really saying at this point?”) and to reflect after the fact on a conversation.  Without a recording of the meeting – and some helpful data – trying to dissect a conversation after the fact is a bit like trying to solve a mystery with no clues.  Our memories are highly fallible and unreliable.  The problem is exacerbated when these questionable memories and interpretations are what is driving the data being entered into CRM.  This can lead to a situation where questionable opinions about deal strength, next steps, stakeholders, decision-making process etc are being used for forecasting and strategic decision-making as if it is gospel truth.   CI gives us firsthand evidence and useful data to guide us on what is actually happening and an omnipotent pair of eyes and ears to help catch what we didn’t miss – or didn’t want to hear.  

• MEDDIC & BANT – CI helps you tightly measure and track your use of tried and trusted sales methodologies like MEDDIC and BANT which help manage deal flow and conversion rates.

The challenges with Client Relationship teams adopting Conversation Intelligence.

It’s common for CI providers to want to get more seats (i.e. revenue) from a customer and an obvious choice is to attempt to get the post-sale Customer Success/Client Service team to adopt the solution as well. But there’s a challenge…

Optimising a sales process & team is fundamentally different to optimising a client journey.

The main challenges I have seen in both trying to adopt and sell Conversation Intelligence to CS teams is that Conversation Intelligence tools are designed for sales teams and so the functionality and product design does not take into account the needs and different dynamics of CS teams.

As a very obvious example, Gong and tools like it have a large focus on tracking Deals and Opportunities.  This is critical for sales teams – it helps them identify risk in their pipeline, understand activity against a specific revenue opportunity, and reduces the need to manually log everything in CRM (which we all know sellers hate doing).

However, CS teams typically aren’t tracking Opportunities or Deal Cycles.  They are focused and measured on completely different metrics.  Where sales teams are primarily measured on Deal metrics (e.g revenue, win rate, deal cycle length and velocity etc)  CS teams are thinking about account health metrics (e.g sentiment, service delivery, churn risk, time to resolution, stakeholder mapping etc).  When I was a seller at Gong, I remember specifically presenting the platform to one prospect who loved the idea of how it could help her team – but the opportunity fell apart when I showed how all risk metrics were measured against an Opportunity (as defined in CRM).  This team – like many CS teams – did not even use Opportunities tied to existing client relationships.  The way they view a relationship is not transactional – but as an ongoing relationship that needs to be serviced.  Gong had no way of showing risk across the lifetime of an account, without an Opportunity being open in CRM, and utilising all the data points that contribute to client health and growth.

Another example of CI not being set up for the CS use case is that CS teams are generally having support and delivery related conversations in multiple forums outside of calls, emails etc.  Think about Chat (Slack, Teams), Project Management (Monday, ClickUp), Ticketing (Zendesk), and spreadsheets tracking NPS. None of these are priority integrations for sales teams, so CI tools have ignored them as low priority.  But without access to the conversations and interactions happening daily in platforms like this, CI tools would be missing many of the most important interactions that are occurring between a service team and their client.

Why Enterprise Relationship Management requires a different approach, and a different methodology! Welcome AI Coworkers for CS.

As someone who’s lived in the sales trenches, I can tell you that the fundamental difference between sales and client services comes down to the nature of the relationship. Sales is about closing deals – it’s transactional, even if we try to make it consultative. Client services and ongoing client relationship management in enterprises, on the other hand, is about nurturing long-term partnerships where success is measured in terms of client satisfaction, retention, and mutual growth.

AI has provided an opportunity to deliver what looks like CI and Revenue Intelligence solutions to the account management domain. When I first came across Kaizan and their unique approach combining a proprietary framework for client development (CARE) and the ability to harness the signals across CS and Ops I was intrigued. For the first time it looks like client delivery and service teams can manage the inputs that lead to outsized outputs for their team, their clients and their business.

Introducing the CARE Framework for AI-native Client Management

In today’s AI-driven world, we need a structured approach to client management. Kaizan developed the CARE framework for AI coworkers to be grounded in. This framework provides a comprehensive lens through which to view and manage client relationships:

C – Client Knowledge

• Deep understanding of the client’s business objectives and KPIs
• Industry context and competitive landscape
• Internal organizational structure and dynamics
• Strategic initiatives and roadmap
• Technical environment and constraints
• Budget cycles and decision-making processes

A – Activity with Stakeholders

• Engagement patterns across different channels
• Meeting frequency and quality
• Response times and communication preferences
• Project milestone tracking
• Support ticket patterns
• Training and enablement activities
• Importantly, executive engagement levels

R – Relationship Status

• Overall health score
• Stakeholder sentiment analysis
• Product adoption metrics
• Service delivery satisfaction
• ROI and upsell preparation 
• Risk factors and mitigation strategies
• Historical context and relationship evolution

E – Expansion Strategy

• Growth opportunity identification
• Cross-sell/upsell readiness assessment
• Value realisation tracking
• Stakeholder mapping for expansion
• Budget and timing alignment
• Success story documentation
• Champion development status

AI Coworkers built on top of CARE deliver value to teams that isn’t possible in a traditional SaaS CI platform:

Unified Intelligence:

1. AI aggregates and analyses data across all four CARE dimensions

2. Machine learning models identify patterns and correlations

3. Natural language processing extracts insights from unstructured data

4. Predictive analytics forecast relationship trajectories

Automated Insights:

1. Generate client health scores based on CARE metrics

2. Flags relationship risks and opportunities

3. Recommend next best actions

4. Surface relevant internal expertise

5. Track and measure success metrics

Proactive Account Management:

1. Anticipate client needs before they’re expressed

2. Identify expansion opportunities at the optimal moment

3. Intervene early in at-risk relationships

4. Scale best practices across the portfolio

Strategic Decision Support:

1. Allocate resources based on relationship potential

2. Prioritise initiatives for maximum impact

3. Build and execute targeted growth strategies

4. Measure and communicate value delivered

The Future of Client Relationship Management

In this AI-native world, the CARE framework transforms how we think about client relationships. Instead of reactive management based on lagging indicators, teams can take a proactive, data-driven approach to growing and nurturing client partnerships.

Think about it this way – traditional CI tools are like having game film for your sales calls. The CARE framework, powered by AI, is like having a strategic command center for your client relationships. It provides real-time visibility, predictive insights, and actionable recommendations across all four dimensions of the client relationship.

The companies that win in the future will be those that can systematically understand, measure, and optimize their client relationships using frameworks like CARE. It’s not just about having the data – it’s about having a structured approach to turning that data into actionable insights and measurable results.

For client services teams looking to adopt this framework, the key is to start with a clear understanding of where you are today across each dimension of CARE. From there, you can systematically build out your capabilities, leveraging AI to scale your ability to deliver exceptional client experiences.

In an AI-native world, we finally have the technology to make this comprehensive approach to client management possible. The CARE framework provides the structure needed to take full advantage of these capabilities and drive meaningful results for both your clients and your business. CI is finally ready for CS teams 🙂