January 12, 2024

Transforming Sales Calls into Strategic Insights: AI in Marketing and Product Management

Ben Corbett

Transforming Sales Calls into Strategic Insights: AI in Marketing and Product Management

New mobile apps to keep an eye on

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What new social media mobile apps are available in 2022?

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Use new social media apps as marketing funnels

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Try out Twitter Spaces or Clubhouse on iPhone

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Try out Twitter Spaces or Clubhouse on iPhone

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Understanding the Value of Sales Calls

Understanding customer needs and market trends is crucial for success. Sales calls are rich sources of this vital information, providing direct insights into customer perspectives and behaviors. Traditionally, product and marketing teams have to rely on second hand information, requests and feedback from sales reps to get a picture of customer needs. 

This is where AI steps in as a transformative tool. By using AI to analyze sales calls, marketing, strategy, and product professionals can uncover hidden insights, eliminate bias from quota-driven sales reps and make better decisions about their customers and their business. This guide delves into how using AI when analyzing sales calls, can turn everyday interactions into strategic data goldmines.

Sales calls are more than transactional conversations; they're narratives filled with customer expectations, preferences, and objections. Although the primary purpose of a sales call is to close a deal,  each call is a potential learning opportunity that, if properly analyzed, can offer a wealth of knowledge about the market, the product and messaging. Traditional methods of analyzing these calls involve hours of sitting through long video or audio recordings, or listening to second hand information from sales. This leads to valuable insights being missed or misunderstood. This is particularly true in sectors where customer feedback directly influences product development, marketing strategy, and the overall strategy of the business.

AI-Powered Analysis 

AI brings a new dimension to the analysis of sales calls. AI can efficiently transcribe and analyze vast quantities of call data. Unlike manual analysis, AI algorithms can quickly identify patterns, sentiments, and key topics discussed during calls. This level of analysis goes beyond word recognition; it understands context, tone, and intent. For businesses, this means being able to systematically analyze every sales call, uncovering insights that would otherwise remain hidden in unstructured conversation data. The value of these calls also goes beyond analyzing data to improve sales performance. The insights you can uncover from a sales call can help product and marketing elevate the voice of the customer to all of their decision making. 

Benefits for Marketing, Strategy, and Product Teams 

How can different teams benefit from analyzing sales calls?
Product Teams:
  • Direct Feedback: AI analysis of sales calls offers product teams firsthand insights into how customers react to demos, new features or questions relevant to their experience.
  • Pain Points Identification: By analyzing customer conversations, product teams can pinpoint specific areas where customers face challenges.
  • Uncovering Opportunities: Trends, recurring requests or user needs can be identified through AI analysis. This can inspire new product features or entirely new product lines.
  • Customer Insights: AI tools provide marketing teams with a deeper understanding of customer needs, preferences, and behaviors.
  • Campaign Effectiveness: Analyzing sales calls helps in assessing the impact of current marketing strategies and adjusting campaigns for better resonance with the target audience.
  • Growth Opportunities: AI analysis can uncover emerging trends, enabling marketers to anticipate market shifts, understand competition and adapt their strategies proactively.
Customer Success:
  • Enhanced Customer Understanding: Sales calls can give customer success teams insight into common queries and concerns ahead of time, allowing for more effective onboarding and support.
  • Service Improvement: Identifying patterns in customer feedback, including conversations with sales, helps in refining customer service processes and policies.
  • Customer Retention: Insights gained can be used to develop strategies that improve customer satisfaction and loyalty, ultimately leading to higher retention rates.
Strategy Managers:
  • Competitive Landscape Analysis: AI provides a clear view of the competitive environment, highlighting strengths and weaknesses in comparison to market competitors.
  • Strategic Planning: With detailed customer and market insights, strategy managers can make more informed decisions regarding company direction and strategic initiatives.
  • Opportunity Identification: Sales call analysis can reveal unmet customer needs and potential new markets, offering avenues for strategic growth and expansion.

How It Works: How Orbit helps teams upload, transcribe and analyze sales calls. 

A tool like Orbit makes it easy to upload, transcribe and analyze sales calls. Here is an example of a B2B SaaS company using insights from sales calls into their product development workflow. 

Connect Orbit to Zoom, MS teams or whichever other video conferencing software your sales team uses. 

Import, sync and auto transcribe recordings directly to your workspace. This allows you to select meetings you want to analyze, or bulk analyze as a series. 

 Orbit uses the data from your customer calls to uncover opportunities for growth, reveal customer pain points, create personas and more.

Getting started: Integrating AI Analysis into Your Workflow 

Get Your Sales Team Onboard:

As a product manager or marketer, it is important to cultivate and grow a level of trust, and a strong working relationship with your sales team. Make sure you bring your sales team along the journey and explain why you are using this data, the value it brings and how it will ultimately help them in return. Start small by asking sales which calls would be most valuable for analysis. Building trust, sharing insights will allow you to eventually automate this process and eventually perform this type of analysis at scale.

Remember the Primary Goal is Sales:

Always keep in mind that the primary objective of sales calls is to close sales. Not every call will yield useful insights; it's often about finding quality over quantity. This is why AI can be so powerful when analyzing sales calls. Let AI sift through the data to highlight the most important insights, so you don't have to listen to every call.

Couple it with Other Forms of Data:

Sales calls are just one data source. Product feedback, structured customer interviews, sales notes, and support tickets are also vital sources of data to better understand customer needs. Diversifying your data sources ensures a more comprehensive understanding of customer needs and market trends.  

Understand the Bias:

Be aware of potential biases in sales conversations and customer feedback. Think about the last time you were sold something. How truthful or open were you about your needs? It's important to recognize that what is being said may not fully represent the customer's ultimate motives. While AI can assist in analyzing data, it doesn’t entirely eliminate these biases. 

Share Insights Widely:

The insights gleaned from sales calls can be valuable across various departments, not just within your team. Sharing these insights widely can help others (including the sales team). It also helps others understand the value of this work and contributes to more meaningful discussions around customer needs, and the decisions you make based on this analysis. 

No Silver Bullet:

Remember, analyzing sales calls is just one aspect of understanding your market and customers. It should be part of a broader strategy that includes direct customer interaction and other forms of feedback. 

Get started today

Discover how Orbit can transform your sales calls into strategic insights. Contact us for a demo or to learn more about integrating AI analysis into your business processes. Embrace the power of AI and step into a new era of data-driven decision-making.