All You Need to Know About Revenue Intelligence

With almost every business today navigating rough waters due to the current economic climate, business efficiency is the talk of the town. There’s a growing need to connect products to key indicators like ROI, revenue, and efficiency. But do all leaders, especially CS executives, actually see themselves as revenue leaders? This post will show you how AI-powered revenue intelligence can turn all executives into true revenue leaders in 2023. Hint: Your 2022 plan probably won’t work.

What is Revenue Intelligence?

Revenue intelligence is an approach that combines siloed data from multiple channels to create actionable insights and drive revenue growth. This process of collecting and analyzing customer data (along with sales and product analytics) helps decision makers and business executives to make better decisions about their sales, customer success, marketing, and product strategies.

Revenue intelligence is largely based on data from these channels:

• Lead data: Organizations can identify customer behavior patterns and new leads by analyzing revenue intelligence data.
• Sales data: Revenue intelligence can be used to understand which products or features are selling better to help create a better overall strategy.
• Customer success data: AI-based revenue intelligence can help identify frustrated customers, churn risks, and growth opps. More on this later.
• Marketing data: Revenue intelligence data can help businesses break down customers interactions and gauge marketing channel effectiveness
• Revenue leakage data: By defining and monitoring data related to revenue, businesses can use revenue intelligence to identify losses and issues

Revenue intelligence places a lot of emphasis on customers and identifying new expansion opportunities. A lot of people immediately think about onboarding new logos when asked about revenue growth. But the truth is that you’re 75% more likely to sell to your existing customers. And that’s not all. With the right revenue intelligence data about your customers, that number can go up even more. 

2023 and 2024 are going to be extremely challenging for businesses worldwide. Revenue intelligence is the call of the hour and it’s no surprise that GVE is predicting a 10% YoY growth for the revenue intelligence industry between 2020 and 2027.

Related: Optimizing CS Team Structures

Key Principles of Revenue Intelligence

Revenue intelligence isn’t rocket science, but there are some core principles that need to be adhered to. Not doing so can result in incomplete or inaccurate data, something that can soon prove to be counterproductive.

Here are the key principles of revenue intelligence:

1. Collect information from ALL sources, including customer data

Harvesting ALL siloed data is key to having a robust and accurate revenue intelligence model. This includes product usage trends, marketing insights, and sales information, with the latter usually being inputted into some kind of CRM solution. But the missing link is often customer data, with only 1% of which actually makes it into the CRM. You need to make sure you capture all customer data.

2. Data cleansing and consolidation

Once you have collected all siloed data, you also need to make sure that it’s ready for processing, This means that you have to eliminate duplications, standardize formats, and cleanse everything for the analysis process. As you can imagine, there’s a lot of data and doing all of this manually is extremely time consuming. 

3. Analyzing and Crunching the Data 

Once your data is ready, the revenue intelligence model should be able to process it fast (outdated data doesn’t help in the grand scheme of things) and generate accurate insights that can be used to boost revenue streams. These insights are key to making data-driven decisions. Most modern revenue intelligence models also perform granular customer segmentation and ongoing CLV analysis.

What Challenges Does Revenue Intelligence Solve?

Revenue intelligence can help any kind of business today, regardless of its size, industry, or geolocation. Efficient selling is the name of the game. Businesses must know where to focus because they simply can’t afford to spray and pray anymore.

There are multiple challenges that revenue intelligence solves. Here are some:

• Blind spots and missed signals caused by data gaps
• Missed upselling and cross-selling opportunities
• Instinct-based decision making and sluggish growth
• Churn prediction, prevention and analysis
• Productivity issues caused by misaligned teams
• Lack of clarity about underperforming products or features
• Bad revenue forecasts and inability to plan ahead

Let’s zoom in on three main challenges:

1. Too Much Information (TMI)

This challenge simply can’t be understated. Revenue Intelligence models need data. This data needs to be harvested and fed into the analysis loop as soon as possible. Unfortunately, The amount of information that needs to be collected is growing exponentially. For example, you have emails, chats, social media channels, review websites, guest posts, product usage data, and support information.

2. Outdated and Incomplete Data

Collecting information is another major challenge customer success and sales teams are facing today, especially when it comes to customer data. Manual collection creates frustration, increases blind spots, and is also a slow process. Outdated or incomplete data often lead to inaccurate insights that can confuse customer success teams and ruin the implementation of playbooks.

3. Dependence on CRM Systems

Most companies today are using CRM systems to record and document customer-centric information, which is often changing on a daily basis. This can be information about a stakeholder change or some piece of data that’s related to customer expansion. With so much information falling between the cracks, cross-department visibility is iffy at best. This is a big revenue roadblock.

Revenue Intelligence Powered by AI: The Future is Here

Revenue intelligence solves a wide range of operational and strategic issues, but the common issue with all of them is the same – too much information. TMI is becoming a huge challenge for businesses of all sizes from literally all industries. We came up with similar findings in our recent CS survey. For example, 62% of the participants admitted that their playbooks are either incorrect or irrelevant.

This is exactly why more and more businesses are turning to AI-based solutions today.  But how can AI turn leaders into revenue leaders? What problems does it solve? Is the hype around AI-powered revenue intelligence justified? 

AI-powered revenue intelligence is taking customer success to the next level.

• Consuming and learning from data – AI is helping companies overcome the TMI issue by automating the data collecting and processing steps, even relationship information and account history that are had to document in CRMs. Furthermore, nothing slips between the cracks, with CS results now being connected to key KPIs that impact business growth. It’s that easy.
  
• Making CS playbooks relevant again – Our recent survey showed that CS professionals still believe in playbooks, 82% of them to be exact, despite 62% claiming that they are hard to implement. AI-based revenue intelligence is helping put the focus on the customers again with accurate data and actionable insights that are eliminating flawed strategy executions.
  
• Boosting productivity and alignment – CSMs are being asked to handle more and more accounts. With their daily tasks taking up so much valuable time, communication and analysis is suffering. Many tasks are often irrelevant by the time they are performed with playbooks becoming an afterthought. AI is helping boost productivity and visibility by automating all mundane tasks. 

The easiest revenue, customer expansion, is the easiest to miss. But AI-powered revenue intelligence is helping change this by unlocking customer insights and freeing up CSMs to focus on what matters most – communicating with customers at the right time with the right strategy. This is a true gamechanger and a mini-revolution, even within the relatively new revenue intelligence space. 

Staircase AI: Pioneering AI Revenue Intelligence 

Staircase AI is helping transform the revenue intelligence space with its AI-powered platform that allows automated customer communication data collection with  end-to-end coverage. Furthermore, all customer data is processed in real-time with a unique property algorithm to create actionable insights in real-time, with signals presented intuitively via a user-friendly centralized dashboard.

On the strategic side, besides helping CS teams become leaner and meaner, revenue intelligence solutions can help CS leaders can now base their decisions and strategies on historic trends, accurate real-time insights, and red-hot growth opportunities. Generative AI, fueled by AI-powered Customer analytics, can read, digest, understand and summarize incredible amounts of customer data. 

Staircase AI users also benefit on the operational side of things, especially when it comes to long email conversations with customers. Generative AI saves 50% of time in writing content. CS platforms that will learn the customer and populate a relevant response will help customer-facing professionals achieve double their customer outreach, with every word being relevant and in the right context.

Summing things up. Staircase AI’s revenue intelligence and customer analytics platform helps customer-facing professionals accurate their tasks and playbooks by cutting the noise, minimizing manual processes, and improving visibility. Learning about your prospects never hurts, but being in sync with the voice of your customers is what will really transform CS leaders into revenue leaders in 2023 and beyond.