Revenue intelligence

Revenue intelligence builds upon the power of conversational intelligence and sales intelligence to create a wholly new kind of system. Revenue intelligence is a relatively new concept in the business world. It is a data-first process that is powered by AI and machine learning (ML) to better collect, manage, and analyze all of a company’s data that is related to revenue.

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Revenue intelligence

Business owners and C-suite executives know that generating revenue by creating a stellar or innovative product and marketing it to the right people is far from simple. Companies and teams need access to the right people, tools, strategies, and technology to take a product, sell it to the right prospects, and deliver excellent customer success that keeps clients coming back for more. Better yet, businesses need cross-departmental collaboration and access to actionable data and reports to execute winning sales and marketing campaigns.

Yet recent findings by Gallup indicate that only 30% of U.S. employees agree that in their company, they “openly share information, knowledge, and ideas” with one another. Despite that, 74% of executives report that social, collaborative tools are at least somewhat integrated into their employees’ work, according to McKinsey.

Revenue intelligence is the answer to this disconnection. It helps to break down data silos between teams with the use of modern, state-of-the-art technology. Additionally, it provides data-driven next and best steps to help teams hit their revenue targets.

This guide to revenue intelligence will explore what revenue intelligence is, the kinds of data it collects and analyzes, the benefits that companies get from it, and how to choose the right revenue intelligence platform.

Before exploring revenue intelligence any deeper, however, it is important to discuss conversational intelligence — the precursor to revenue intelligence — in addition to other related technologies, such as sales intelligence. This will provide greater context around what revenue intelligence is — and isn’t — and where it came from.

What is conversational intelligence?

Conversational intelligence is the process of analyzing sales communications and other conversational data to help sales teams better understand how to improve the customer experience, close deals more quickly and efficiently, and overall increase a company’s revenue. Conversational intelligence has been around longer than revenue intelligence. It could perhaps be considered an early version of revenue intelligence, in fact, because revenue intelligence draws on many of its features. While conversational intelligence has its roots in analyzing call recordings, today’s modern tools can take in other forms of digital communication, such as email and chat support, too.

What is conversational intelligence software?

Conversational intelligence software is the tool that analyzes the unstructured data of sales communications and provides recommended actions for users based on that data. With conversational intelligence software, call recordings, emails, and chat are consumed and analyzed by an artificial intelligence (AI) tool that uses machine learning and natural language processing to analyze speech and text. After it’s analyzed enough customer interactions, the software learns which kinds of communication, speech patterns, terms, and more lead to closes — and which don’t.

Because of its many uses, conversational intelligence software is now built into most revenue intelligence platforms to provide companies with a full suite of tools to improve their overall revenue.

What is sales intelligence?

A useful sales intelligence definition is that it is a term used to describe technologies and tools that help sales and marketing teams collect and analyze data on potential new customers. Essentially, sales intelligence could be considered one part of revenue intelligence — the part that provides things such as buyer personas — but some companies use sales intelligence tools in place of revenue intelligence.

B2B sales intelligence tools use data available online through social media, websites, and other sources to compile information about potential clients. This helps teams create better buyer personas, connect with target audiences, and market more effectively. Depending upon the individual tool, sales intelligence can provide users with lead generation lists, lead tracking and nurturing, data enrichment, sales enablement, and more. At Collective[i], for example, our products ingest huge data sets about the market and analyze buying and selling habits at scale to make relevant and valuable recommendations.

How do you use sales intelligence?

Sales intelligence uses AI technology and internet crawlers to search online for publicly available data (website visits, which videos people are watching, social media activities, etc.) to create a fuller picture of target audiences and buyer personas. Sales and marketing teams can use this data to better find prospects, create more targeted content, and even learn what stage of the buyer’s journey individuals may be in.

Like conversational intelligence, sales intelligence is built right into many revenue intelligence platforms to provide businesses with a one-stop-shop approach to software.

What is revenue intelligence?

Revenue intelligence builds upon the power of conversational intelligence and sales intelligence to create a wholly new kind of system. Revenue intelligence is a relatively new concept in the business world. It is a data-first process that is powered by AI and machine learning (ML) to better collect, manage, and analyze all of a company’s data that is related to revenue. Revenue intelligence aims to create a more holistic view of a company’s revenue streams — in addition to the overall sales pipeline — by collecting and organizing data from the sales, marketing, and customer success teams into one platform.

There are several basic operating assumptions about data that underlies the concept of revenue intelligence, including:

Automation is superior to manual data entry as it cuts back on time and provides a more objective view of data by eliminating bias from logging. Therefore, revenue intelligence platforms aim to utilize AI, ML, and other innovative technologies to automate as much as possible. A holistic view of customer interactions, including data from all communications between a company and its customers, can inform future interactions for every team. Revenue intelligence aims to record as many interactions as possible to provide customer-facing teams with data to improve their processes. Data should be grounded in unbiased information, not on sellers’ opinions or past performance. Even the best sellers or customer success representatives occasionally include opinion in their CRM notes. Automating the process leads to more objective data. Companies benefit if all their revenue-generating departments operate with the same data. Gone are the days of teams duplicating work or not having the information they need to assist a customer. Using a singular system to give everyone the same data sets increases teams’ chance of success. Collective[i] offers an end-to-end digital solution to bring every member of the team into the same platform.

What are the goals of revenue intelligence?

While every company will ultimately have its own internal goals and reasons for adopting a revenue intelligence process, there are three basic goals that revenue intelligence aims to achieve.

Provide access to better data

Revenue intelligence collects a wealth of data from each team. Employees no longer have to input their own information into CRMs — a lengthy and often subjective process that relies too much on sellers entering their own interpretation of a deal, taking the time to add all information, and inputting the data accurately. Instead, revenue intelligence platforms gather data immediately and automatically about website traffic, prospects, customer interactions, sales pipeline progression, forecasting, and more.

Allow increased collaboration between teams

The very nature of each team using the same platform creates an environment for increased collaboration since everyone will have the same access to the same information. The best revenue intelligence tools will take it a step further, however, by providing next and best actions within and across teams to drive revenue for the entire company, as Collective[i] does.

Generate data-driven decision making

With accurate, unbiased, and up-to-date data available comes the ability to make data-informed decisions about next and best actions for sales, marketing, and customer success teams. What’s more, managers and executives will have the relevant information they need to course-correct to better meet revenue targets.

How does revenue intelligence connect to revenue operations?

Revenue intelligence is an essential component of a business process known as revenue operations (or RevOps). Revenue operations is a process that aligns sales, marketing, and customer success operations to keep all the departments working together as a cohesive revenue-generating unit. RevOps seeks to promote cross-collaboration, drive business growth, help teams better adapt to the changing sales environment, and ultimately grow revenue streams. To do so, revenue operations teams need revenue intelligence.

Revenue intelligence takes data from each of the three departments and analyzes it separately and collectively to provide recommendations and next and best steps for teams. In other words, revenue operations is the process that breaks down silos between people and teams, and revenue intelligence is the technology that makes it happen.

What is revenue intelligence software?

“Revenue intelligence software” is a term used to describe a class of AI-driven tools and platforms that are designed to help companies better collect, organize, store, and analyze their data. This data is generally about external sales and marketing information, internal efforts, and current and potential customers in the pipeline.

Revenue intelligence, along with conversational and sales intelligence, are examples of a category of technologies called intelligent applications. Intelligent applications move beyond AI-lite features such as data analysis or basic machine learning and instead make use of more advanced neural networks and deep learning to provide improved outcomes and dynamic features. These tools replace manual workflows and increase agility by providing up-to-date recommendations, suggestions, and instructions for users.

Here are some examples of data gathered by revenue intelligence software:

  • Sales: inbound and outbound calls and emails, meetings with prospects, customer contact information, customer engagement details, and deal wins and losses
  • Marketing: website visits, interactions, clicks, and downloads; marketing email open rates and clicks; chats on a website; and social media engagement
  • Customer success: customer emails and calls, customer meetings, support tickets, and customer feedback and survey results

How is revenue intelligence data used?

Once enough data is collected — both from internal sources explored in the previous section and from external sources, such as publicly available data — it is analyzed. Then revenue intelligence platforms can provide next and best actions for teams. These platforms continue to analyze new data and information to provide up-to-date, detailed information, reports, actionable steps, and more. Here are some examples of how the above data can be leveraged for sales, marketing, and customer success teams.

Sales

  • Insights from calls, emails, and meetings can be analyzed by revenue intelligence platforms so teams can better understand the projected interest of customers, what actions will help move prospects down the sales pipeline, and more.
  • Managers can glean coaching recommendations for sales staff to improve their sales tactics based on conversational intelligence recordings, deal progression predictions, and more.
  • Managers and executives can use dynamic forecasting predictions to alert teams about their sales goals, learn how those goals are changing in reaction to current market conditions, and strategize how to course-correct when needed.

Marketing

  • Marketing teams can analyze gaps in the sales pipeline, customer questions, and other clues to strategize about the types of content to create to resolve these issues.
  • Insights can be taken from revenue intelligence data that is similar to A/B testing — such as email open rates and click rates — about which messaging is resonating most with prospects. These insights can also be shared with sales teams to help them develop better pitches.
  • Marketing teams can use intelligence about how their company stacks up against the competition to develop content that details their product’s unique features and ROI.

Customer success

  • Insights from calls and emails can be analyzed by revenue intelligence platforms to identify which language patterns customer-facing team members should use to support a better overall customer experience.
  • Revenue intelligence platforms can collect, organize, and make recommendations based on insight about product glitches, repeat feature requests, and more that customer success teams hear about.
  • Managers can analyze data to provide coaching for success teams on when to upsell and how to improve their work with customers in general.

These are just a few examples of how revenue intelligence systems can take in large amounts of data, analyze it, and create many kinds of actionable steps for team members, managers, and executives alike.

What are the benefits of a revenue intelligence system?

Revenue intelligence is helping businesses break down silos to achieve better and actionable data about their revenue-generating teams. Here are some benefits companies realize after adopting revenue intelligence.

Objective customer interaction data

Without revenue intelligence, businesses are relying on their sales, marketing, and customer service teams to enter everything they can about a customer interaction into their CRM. Teams and managers are left to their own devices to analyze their own team’s data — in addition to the data from other revenue-generating teams — and it’s a tall order.

Not only do revenue intelligence software systems automate data collection about calls and emails with built-in conversation intelligence features, they also analyze the data using AI and ML to provide feedback on how to improve future communications.

Better access to data

The data that companies currently collect is often siloed by individual teams, which means that some teams miss out on learning from the data of others. For example, marketing teams could benefit from knowing common customer questions or stumbles that prospects have on the sales pipeline. Sellers could benefit from knowing a bit more about marketing efforts — such as which of their prospects open emails — to better guide their efforts.

Up-to-date information

It’s not unlikely for CRMs to contain errors. People change jobs, get promotions, and don’t update their LinkedIn profile, for example. Revenue intelligence takes the guesswork out of prospecting by automatically tracking and updating contact information that’s available via public data. Not only can this save teams time — but it means teams can trust their data.

Improve productivity

Revenue intelligence tools analyze data to better understand how a company’s prospects move through the sales pipeline. After enough information is gathered, these tools can provide next and best moves to sellers, customer success representatives, and marketers about how to move customers through the pipeline, how to upsell, and what kinds of communication to send to prospects. Rather than spending time analyzing their own data and making educated guesses, employees have tools to keep them engaged on their sales goals.

More accurate sales forecasts

While accuracy isn’t the only goal of sales forecasting, it’s certainly a goal for a lot of businesses. Revenue intelligence can improve sales forecasting by providing deep analysis of more and better data to inform actions toward prospects in the pipeline, seller performance, and goal setting. With revenue intelligence, sales forecasts will be more accurate and dynamic, adjusting to real-time tracking of sales projections, which means teams can course-correct.

What features should be included in a revenue intelligence platform?

Beyond the basic data collection and analysis, every piece of revenue intelligence software includes different tools and features to stand out from the crowd. Some features will be more compelling than others, however. We’ve compiled a list of tools that companies may find most beneficial.

Robust capabilities for capturing customer interaction

Revenue intelligence software should seamlessly interface with whatever CRM a company is using in order to automate data capture and compile it all into one place. Email communication and phone communication, for example, should be easily accessible in one centralized location so users can benefit from the whole picture. Collective[i]’s Intelligent WriteBackTM automates CRM data capture and cleanses and enriches data to boost productivity gains for users in each department.

Advanced analytics capabilities

Closing a deal is always a good thing. Closing several deals is even better. But knowing why exactly those deals closed is what can help teams replicate their success in the future. The best revenue intelligence software systems provide not only advanced analytics to help teams understand how and why deals are closing — or not — but also profiles about standard wins, buyer personas, and marketing effectiveness based on source and deal type. These types of advanced analytics make it easier for managers and executives to understand what is happening and coach their staff to repeat positive results. Collective[i]’s analyzes top sellers in the business to provide users with a host of recommendations, news, risk alerts, and more to help them more effectively move deals along.

True AI-powered features

So many platforms and tools out there today sell themselves as AI-enabled or -powered when they’re actually AI-lite systems that provide little more than data collection and low-level automation. Revenue intelligence platforms should make use of advanced AI features — machine learning, deep learning, and neural networks — to give companies a competitive edge. Collective[i]’s tools use all types of AI, in addition to Robotic Process Automation, to save teams time and offer crucial insights that drive productivity and revenue.

Meaningful forecasting

Sales forecasting is a powerful tool for businesses. The best sales forecasts are built with an understanding that they’re an ever-moving target to help customer-facing employees focus their efforts. The best revenue intelligence platforms automate forecasting and provide daily, dynamic updates to help users plan their day and course-correct. For example, Collective[i]’s Intelligent ForecastTM uses AI to remove bias, time, and human error from the forecasting process. It augments traditional sales signals with a vast network of data to provide a dynamic forecast for teams, managers, and executives to work from.

Dedicated space for cross-collaboration

Modern deals are exceedingly complex and require input from several internal and external stakeholders. That’s why revenue intelligence platforms should offer built-in space for cross-collaboration. For example, Collective[i]’s Virtual DealRoomsTM provide a dedicated space for communication about a deal. Buyers and sellers can talk. Marketers can support the sales journey of prospects. Legal can negotiate and review contracts. Everyone can stay informed about ongoing activities.

Enhanced social-selling tools

Revenue intelligence software should have built-in social-selling tools to better integrate all tools and processes into one main platform. Social-selling tools help sellers leverage their networks to build connections, create meaningful relationships, and share experiences with organizations and prospects. For revenue intelligence software systems, this may look like Collective[i]’s ConnectorsTM, which analyzes connections that users may have with prospects — including internal and external connections they may not even be aware of — to help capitalize on those existing relationships.

If you’re ready to harness the power of Collective[i] for your team, reach out to us today.

Work together, win together

Request an invitation to join IntelligenceTM, the world’s first global network of sales professionals.

On no, we ran into an issue submitting this form. Please ensure each field was filled out correctly and resubmit.

If this problem persists, please reach out to us and we will be more than happy to follow-up from there.

We’ll be in touch soon

In the meantime, explore Collective[i] and find answers to frequently asked questions.