How to Drive Predictable Sales Revenue with Kevin Knieriem, #162

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One of the most common struggles for sales leaders is forecasting, understanding what is going on with the business and how to get predictable revenue.

In this episode of the Modern Selling Podcast, my guest, Kevin Knieriem, CRO at Clari, talks about using Artificial Intelligence for sales forecasting. Clari’s Revenue Operations Platform automatically gathers data from across your emails, meetings, marketing campaigns, and CRM data, and then uses its AI to create dashboards and execution insights.

Kevin brings more than 20 years of experience driving revenue growth and building successful sales teams for leading enterprise giants and high-growth startups. Most recently, he spent more than four years at Oracle where he held several leadership positions, including CRO at DataScience.com (acquired by Oracle in June 2018).

In this role, Kevin led DataScience.com’s demand generation, field marketing, sales, and customer success initiatives from pre-revenue through acquisition by Oracle. During this time, DataScience.com helped define and then lead the Forrester Wave for Predictive Analytics & Machine Learning Platforms. Prior to that, he spent over a decade at SAP where he led regional and national organizations.

Don’t miss this exciting conversation to learn how to use AI to predict revenue.

Why is predicting revenue a challenge for sales leaders?

The market has changed significantly in the last few years, where buyers don’t engage with reps at the top of the funnel, but later in the sales cycle.

Modern buyers are consuming content and finding information on their own about the solutions they are seeking. And although there are many touchpoints in this process, CRMs don’t gather data from them, but only from those interactions between the buyer and the sales rep.

The customer journey is not linear, anymore, especially in B2B enterprise sales, where sellers deal with multiple buyer personas within the same company in long sales cycles.

Furthermore, most sales reps are not trained to gather and interpret prospects’ buying signals correctly.

That’s why sales leaders need data to understand everything that is happening in the sales process to identify risks and opportunities, so they don’t make mistakes that prevent sales from growing.

Traditional B2B sales forecasting is often inaccurate and leads to missing sales targets, but many sales AI-based predictive analytics tools are now available to help sales organizations obtain predictive revenue.

AI-based forecasting methods are the most accurate, such as Clari’s technology.

The Impact of Missing your Sales Forecasts

Kevin says that the single most important number to any company is their ability to forecast. The forecast is a direct input into the operating plan of the company.

If you know what you are able to overachieve, you can make investments to accelerate the business; and if you can predict where you will underachieve, you can forego some investments and figure out how to close that gap so you can hit the numbers.

Data Needed to Forecast Revenue

Every seller is different and every manager is different. That is why sales don’t have a consistent way to be measured or a consistent process.

In traditional forecasting, the sales manager interrogates every sales rep in the team, enters some data into a spreadsheet, and forecasts future sales from that data.

Kevins says that sellers should be savvier when analyzing data to get a clear view of their opportunities. For example, they should ask themselves if the customer is actually engaging with them, replying to emails, and opening documents. By the way, the best way to achieve engagement is with the right sales methodology.

“All that is a signal of where you’re going,” Kevin says. “With that data available, both the manager and the rep will know where they are and what to do to change the narrative and assess whether it is worth pursuing a deal or not. We need a process to tell us what a healthy opportunity looks like as it goes through the funnel. With years worth of history, AI can make predictions of what a good opportunity is.”

Instead of relying on intuition or the sales pipeline stages, sellers need to focus on engagement and activities that show that the deal is moving forward. For example, are they engaged with the executive buyer? Do they know where the budget is coming from? Where are they in the paperwork process? In the security process?

Kevin says that companies need to establish what a good deal looks like, what a bad deal looks like, and what one in between looks like.

Data is vital in a sales forecast. The right data can help sellers know where they are and what they need to do to close the deal, a process called sales enablement.

“You need the ability to look forward,” Kevin says, “to look at the pipeline and know if you are building the right kind of pipeline with the right persona moving through the sales stages the way they should. In other words, having visibility up the funnel, see how it moves, and use that information to predict if you have enough pipeline to hit your target for the quarter.”

Good sellers will know how to use this data to their advantage, to figure out where they are; and managers and leaders will lead differently, knowing where to invest their time.

According to Kevin, a sales forecast must have these three inputs:

• Current quarter opportunities: What does a healthy opportunity look like? Collect the data in your CRM and non-CRM systems to get a view of healthy opportunities.
• Renewals business: Most SaaS companies have renewals as part of their business model. Collect data for healthy customers, customers impacted by COVID or customers booming and categorize them.
• Activity across the board: Review if sellers are building the pipeline they need to reach the forecast.

“If you have the data in real-time,” Kevin says, “you can know which deals are going to slip and not wait until the end of the quarter to find out. Then you can take action immediately.”

Predicting revenue is an art and you must get good at it and have consistent wins. Forecasting well starts the quarter before.

You should be able to forecast the quota of Q4 while still in Q3. The earlier and more accurate the forecast, the more valuable it is to the company to make decisions.