“It takes many, many years to gain credibility in your forecasting accuracy and your ability to deliver the number. It takes 90 days to lose all credibility with a single miss.” 

-Carl Eschenbach, Workday CEO and former Sequoia Capital Venture Partner

A 2020 Gartner State of Sales Operations Survey showed that fewer than half of all sales leaders considered themselves highly confident in their forecasts, while a 2021 study conducted by Forrester Research on behalf of Clari revealed that only 18% of forecasts land within 5% of actual revenue. 

What gives? 

“What’s your commit for the quarter?” 

While the exact process and philosophy can vary widely from company to company and leader to leader, the sales forecast generally serves two main purposes. 

The first is to create a sense of urgency within the revenue team. You might be a newly hired sales rep with no account list built out and shaky on your company’s value prop—but even so, you can’t commit nothing. Needing to call a number and chase after it is how sales teams drive activity, accountability, and revenue.

The second is to examine deals for warning signs—whether related to product, timing, political alignment, or other factors, and agree on the appropriate next steps to mitigate risks. 

These are both hugely important to the success of your company, but forecasting as it’s traditionally been done falls short because these two goals naturally conflict with one another. 

Problem #1: Unreliable narrators

As a sales leader, your landscape of data sources for the forecast probably looks like this: 

• Weekly forecast calls 
• Spreadsheets and other homework assignments that reps are asked to complete 
• Salesforce 
• Clari, Gong, and other revenue technologies

But that list relies exclusively on input from the sales team, and you don’t trust your reps. 

Seasoned sellers tend to be more trustworthy, but this isn’t always the case. A rep who isn’t performing might pump up their deals to save face. Someone who doesn’t want the stress of a higher commit number might do a bit of sandbagging. The homework assignments and spreadsheets you send out are filled with the bare minimum needed to satisfy your requirements. 

Maybe there’s a CRM score that seems to output believable numbers, but you’re not sure if you can fully trust it. The close dates in your CRM aren’t accurate, the dollar amounts aren’t accurate, and the opportunity summary was either written by one of your sellers in a hurry, or an AI program that doesn’t quite understand the specific terminology that you, your prospects, and customers use during meetings. 

Your team isn’t lying to you, but the high level of scrutiny that comes with B2B selling tends to discourage sales reps from being fully transparent about the state of their deals. They’re expected to execute flawlessly day in and day out while somehow predicting with certainty what’s going to happen—and highlighting negatives is generally frowned upon. 

Problem #2: Only looking at half the picture

What does the Sales Engineering team think about the deals they’ve been assigned to? SEs can be hugely beneficial to forecast accuracy because they’re incentivized to honestly capture technical concerns from customers and relay them back to Sales and Product. 

Furthermore, teams typically spend more time on technical validation than any other part of the sales cycle, and there’s a whole range of activities and deliverables that either lead to an opportunity stalling, or advancing to the technical win. 

But the work of overcoming technical objections or running an evaluation is usually captured just as a single stage in the sales process or under a generic term like “demo” or “test drive”. 

By paying close attention to the stages of the technical sales process, you can conduct far more detailed pipeline inspection—especially across the middle portion of your sales funnel. Examining PreSales stages and technical concerns alongside the close dates, forecast category, and sales stages on every opportunity is a great way to identify potential misalignment between Sales and PreSales. 

As a revenue leader, incorporating these sources of information into your existing forecasting process makes it easier to tell whether there’s actual risk on an opportunity, and whether your sellers have a good grasp of what’s happening in their deals. What if you had a way to easily capture these inputs from the Sales Engineering team to inform these kinds of predictions? 

AI-guided technical selling 

So you need a way to easily track and manage your team’s technical sales stages, and a less biased method of assessing deal health, accessible via your existing revenue technologies (whether it’s Salesforce, your organization’s own custom reporting, or something like Clari).

Vivun helps you solve both of these problems with two distinct capabilities in Journeys and the Hero Score, while also providing a real-time, bidirectional integration with Salesforce so that this data can be leveraged by anyone in the go-to-market organization. 

Journeys in Vivun allow you to capture your technical workflow with PreSales or technical sales Stages that are far more detailed and flexible than traditional sales stages in CRM. 

You can see where your technical selling team thinks a deal is and have multiple funnels that represent the technical sales process based on your team’s particular workflow—for example, a commercial/SMB PreSales team might have 3 PreSales stages, while their Enterprise counterparts use 6. Alternatively, your post-sales team may need to track their efforts for upgrades against Opportunities with a completely different workflow than is used for new business. 

These PreSales Stages are separate and distinct from your organization’s sales stages, enabling the PreSales team to track their progress from a technical perspective on related opportunities. By allowing the team to gain insights on what activities the team does in specific sales and PreSales Stages, you can see how these relate to trends towards Closed Won and Closed Lost deals, or highlight any mismatch between Sales and PreSales on open opportunities. 

The Hero Score is how Vivun enables technical sellers to add a data-driven perspective to deal outlook. 

As users use Vivun to log information on opportunities, such as where the team is in the technical selling process, how differentiated your product or solution is from the competition, and whether the product is missing a customer’s required features, the platform generates a score based on those inputs that clearly signals the quality of that opportunity and the overall technical fit of the deal.  

Each opportunity tracked in Vivun has a Hero Score that takes into account the PreSales stage it’s in, PreSales concerns (budget, timing, competitors) raised by the team, and product gaps (i.e. your product is missing specific features and functionality your buyers require). 

With these inputs, the Hero Score learns over time to provide a visual representation of how your deals are performing, compared to other opportunities at the same part of your sales cycle. 

Here, you’ll see a graph of the Opportunity’s Hero Score over time with an explanation, as well as the current score compared to your organization’s average score. This helps you to determine whether or not a deal is likely to close, and what risks (if any) there may be.

Run your best forecasts with Vivun 

At Vivun, we’re providing sales leaders with a more accurate view of where their deals stand in the forecast by combining data from the technical sales process with AI and machine learning models informed by decades of experience in B2B selling. 

On average, we’ve seen our customers increase their technical win rate by 35% after adopting Vivun, and drive more productive discussions between Sales and PreSales in the sales forecast. 

Interested in learning more? Reach out to us and request a demo.