Book Demo
Revenue Optimisation
Revenue Predictability
Sales Process
Revenue Intelligence

Enhance your CRM Data with RevOps

Saahil Dhaka
Saahil Dhaka,CEO at Clientell
6 mins read
Last updated:
Copy link

Sales leaders spend hours analyzing funnel data from the CRM weekly, and salespeople spend even more time filling in this data. But with 40% of the organizations, even after meticulous processes, regular cadences and 6 hours spent every week the data is still not entirely accurate.

Revenue Leaders have a simple objective regarding optimizing their funnel- Finding areas to improve and prioritizing opportunities to go after.

Most leaders understand that CRMs are not user-friendly, and reps want to focus on selling rather than data entry. This leads to multiple silos across google sheets, marketing tools, emails, calendars, and task planners. Imagine a scenario where a company that rejected you due to a lack of budget raises funding in the following months. Are you confident that your reps will reach out to them again?

Having a Revenue Intelligence system in place can help you capture critical contact and activity data and go even further in sealing the cracks in your sales process and improving your team's productivity.

What is Revenue Intelligence?

Revenue intelligence is the process of deriving actionable insights to drive revenue growth. A simple investigation into your CRM and sales funnels can help gauge areas where revenue might leak.

For instance, understanding the risk associated with every deal based on engagement via calls, emails, and meetings can give you a comprehensive understanding of your coaching efforts.

Revenue intelligence, as the name suggests, integrates Artificial Intelligence with your tech stack across sales, marketing, and customer success. AI allows the automated capture of critical contact, activity, and other first-party data in a single source of truth.

Revenue-generating teams can use this data to coach effectively, identify buying signals from prospects, understand the risk in the funnel, and forecast with over 95% accuracy.

What’s the data that’s missing?

The increasing number of GTM tools has created a disconnect within teams and silos of data. From CRM to conversational intelligence to marketing attribution data, each needs to be entered into or integrated into the CRM separately to get a complete picture of the RevOps funnel. Even basic things like contact numbers from email signatures must be updated separately in the CRM.

For example, suppose you have a qualified opportunity where you’ve had a few interactions. Suppose a decision maker is added to the email chain but not to your CRM. You could close the deal faster if you include the decision maker in the next few meetings and update emails. An AI-powered platform seamlessly identifies decision makers, includes them in your plan of action, and even surfaces deals where there’s a lack of one, even at the later stages.

The previous example is a nuanced yet critical insight into closing deals faster. AI can also, in one place, organize and structure your calls, meetings, and emails and combine this with behavioral intelligence data such as website visits. This creates a knowledge cloud around every opportunity to sell, highlighting areas where there was perceived to be none.

Such automation saves up to 30% of your reps’ time which can be used to plan strategies, research, and close deals. It also takes care of situations where a rep suddenly leaves your company and forgets to log in their opportunities or cases where the lead may convert to an opportunity in the future.

Revenue Intelligence beyond “Data Ingestion”

Data ingestion is just the beginning of Revenue Intelligence. The next immediate step is to derive actionable insights from the data. Things like engagement risk is a good measure of your engagement with your prospect. It factors in things like how many times you’re competitor was mentioned, or what key objections came up. Engagement risk often includes behavioral insights like when your prospect is most likely to respond or who amongst the buying team is the decision maker.

Engagement may be better than expected, but that does not indicate whether the deal will close. External factors such as buyer intent, duration in a particular CRM stage, or even the company's budget can hamper the conversion of a deal. Leveraging AI by integrating with multiple such data sources can improve the accuracy of your forecasts to 95% and above.

Imagine having the data confidence to say where you’ll land at the end of the quarter, with 95% accuracy. That would enable you to make data-driven decisions about forecasting, opportunity qualification, and even which areas of the funnel need improvement.

Traditional forecasting is simply sales leaders submitting their numbers based on basic excel calculations or intuition-driven assumptions. Indicators such as conversion rates and assumptions on industry benchmarks are the backbone of such processes. This process is inaccurate as external market and competitive scenarios evolve. Still, it’s also not inclusive, as the front-line salespeople who know what’s happening at the deal level are not included.

This is where Revenue Intelligence adds the most value as AI can, in real-time, calibrate forecasts based on over 400 leading indicators. A combination of such AI-powered and bottom-up manual forecasts can build the data trust required for sustainable and inclusive growth.

How do you get started?

You don’t need to start with full-blown Revenue Intelligence software!

You can start by first understanding the various types of data each of your existing tools provide and how they can be integrated within your CRM. Start with meeting with each team to understand their decisions and ask whether they are data-backed. If the answer is yes, then understand what more data would make this decision faster.

The best process is no process
-Elon Musk

The number of processes and data can be overwhelming across teams. So start with eliminating manual work and see if automation can be done. Your team will thank you for this; you won’t need to hire expensive data scientists and bear high costs.

Once you have a breakdown of all the processes and data available across multiple tools and have eliminated unnecessary processes, work on building data stories. Data stories are simple dashboards that sequentially break down various metrics and KPIs.

For example, if you want to understand which channel to spend on in demand generation, start with understanding the ratio of qualified leads to closed won just for that channel. Break it down by geography, business unit, or teams. The channel which is the cheapest may not be the best for a particular team, say in North America, but it may be the one for APAC. The more granular you go, the more answers you find.

Once you’ve done this for a few weeks, the gaps in your GTM will surface, and you’ll notice areas where your team members view data as a task, not a habit. This is the time to bring in your Whatson, as every Sherlock needs one. Invest in a Revenue Intelligence solution and make sure it integrates within your workflows seamlessly. The last thing you want is another tool to solve the problem caused by multiple tools. The choice of tool needs to have customizable forecasting and automated workflows. This combination will give you the data confidence to focus on execution over planning.

If you want to know how Clientell can help you with your CRM data, you should definately see how we did it for Pixis.