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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:
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
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
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:
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.
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.
Once your data is ready, the revenue intelligence model should be able to process it fast (outdated data doesn’t help in the grand scheme of things) and generate accurate insights that can be used to boost revenue streams.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.
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:
Let’s zoom in on three main challenges:
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.
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.
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 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.
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 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.
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