Intro

Forecasting is essential for business success. It allows companies to plan, make informed decisions, and stay ahead of the competition. Revenue Intelligence includes forecasting, but also provides comprehensive insights into revenue streams, identifies key trends and patterns, and makes data-driven forecasts and sales process improvements. 

This blog post explores the three generations of revenue intelligence platforms and how they can help you rev up your forecasting game.

Revenue Intelligence Beyond Forecasting 

Revenue intelligence is an advanced data analytics tool for sales. It provides real-time insights into the

most profitable actions and strategies, so you can adjust your approach on-the-fly. Unlike traditional forecasting, revenue intelligence combines CRM data, sales activities, and market trends to provide a comprehensive picture of your sales landscape.

Revenue intelligence answers critical business questions, such as:

– Which deals are at risk?

– What are our most successful sales representatives doing differently?

– Why did we lose this deal?

– Which aspects of our strategy are working and which are not?

– How can we replicate their success across the team?

Revenue intelligence provides a clearer picture of the buyer’s journey and uncovers the key factors that influence purchasing decisions and deal success. This empowers CROs with actionable insights to not just anticipate, but shape the future of their revenue streams.

In an increasingly competitive market, these insights can be the game-changer, separating the frontrunners from the rest. For CROs looking to maximize their revenue potential, mastering revenue intelligence is a strategic imperative.

The First Generation – Seller Driven Forecasting

First-generation revenue intelligence platforms relied on seller-driven forecasting. This method was subjective and often resulted in imprecise forecasts.

Seller-driven forecasting was based on sales representatives’ input on the likelihood of closing each deal in their pipeline. They would rank their opportunities based on their judgment, predicting win rates, and estimated deal values. This method was limited by the individual sales representative’s perspective, experience, and bias.

As a result, seller-driven forecasting often fell short in delivering reliable and accurate forecasts. It was unable to account for external factors affecting sales performance, such as market dynamics, customer behavior, and broader economic trends. It also lacked the capacity to aggregate and analyze vast amounts of data in real-time.

Despite its limitations, seller-driven forecasting set the stage for the development of more sophisticated and accurate revenue intelligence platforms. It demonstrated the importance of utilizing data for sales forecasts and highlighted the need for a more objective, data-driven approach to revenue intelligence.

A good example of such a platform are the forecasting features that are a part of most modern CRM systems, such as Salesforce. 

The Second Generation – Historical Data Analysis and Engagement Metadata 

Second-generation revenue intelligence platforms used historical data and engagement metadata to provide more accurate and reliable sales forecasts. A good example of such a solution would be Clari – which is, in my mind, a BI platform for revenue data. Historical data included past sales figures, win-loss ratios, conversion rates, and other key performance indicators (KPIs).

Engagement metadata captured the interactions between sales teams and customers across various touch points – emails, phone calls, meetings, and social media engagements.

Together, these data sources enabled CROs to base their decisions on hard evidence rather than gut feeling, significantly improving the accuracy of sales forecasts.

The integration of AI and machine learning further bolstered the analytical prowess of these platforms.

These technologies enabled the platforms to sift through vast amounts of data, identify patterns, and generate forecasts with a level of speed and precision beyond human capabilities.

They automated the tedious process of data collection and analysis, freeing up valuable time for CROs and their teams to focus on strategic decision-making and customer engagement.

Despite their advancements, second-generation platforms were not without their limitations.

While they were able to aggregate and analyze vast amounts of data, they struggled with understanding the content itself.

They could tell you how many times a prospect opened your email or how long they spent on your website, but they couldn’t tell you why.

They could measure engagement, but they couldn’t decode the sentiments or intentions behind it.

This gap in understanding marked the need for the third generation of revenue intelligence platforms.

The Third Generation – Leveraging AI to Understand What’s Being Said and Written

Third-generation revenue intelligence platforms use AI to understand the content of sales interactions. This provides deeper insights into the underlying reasons, motives, and sentiments behind every sales interaction.

For example, the platform can pick up on signs of hesitation, confusion, or excitement in a customer’s email response, or detect underlying issues in a sales rep’s call that might be hindering deal closure. It also identifies key negotiation phases (for instance, if competitors or pricing were mentioned). This level of understanding goes beyond just measuring engagement and provides a deeper understanding of the customer’s needs, concerns, and motivations, as well as deal maturity. 

AI-powered revenue intelligence platforms are also able to analyze large amounts of data from various sources such as emails, chat transcripts, CRM notes, social media interactions, 3rd and 2nd party intent platforms and more. As a result, it is possible to gain a comprehensive understanding of the customer journey and deal process and identify patterns or trends in customer behavior that could influence the success of a deal. 

The use of AI in revenue intelligence not only saves time by automating manual tasks but also provides valuable insights that humans may miss. With these insights, CROs can make informed adjustments to their strategies. For example, if the platform detects a recurring concern among prospects about a product feature, the CRO can quickly address this in the sales narrative or relay the feedback to the product team.

These platforms also offer predictive capabilities, using AI to anticipate future outcomes based on the analyzed content. This means you can get real-time predictions of deal closures, potential churns, or upsell opportunities.

Crucially, these third-generation platforms facilitate alignment across sales, marketing, and customer success teams. They provide a shared, transparent view of the entire revenue process, fostering cross-functional collaboration.

The benefits of third-generation revenue intelligence extend beyond the sales department. The granular insights provided can be utilized across the organization, informing product development, marketing strategies, customer service, and even talent management.

An example of such a platform is Gong.

The Future of Revenue Intelligence

Revenue Intelligence Platforms will be essential for stack consolidation and revenue efficiency in 2024.

These two market needs are becoming increasingly important in the current economic climate. The insights that second- and third-generation revenue intelligence platforms provide are invaluable for achieving this goal.

PMG has partnered with the leading revenue intelligence vendors—Gong, Clari, and Ebsta—to support its clients in this journey.