Make no mistake, revenue growth is directly impacted by the frequency, approach and quality of pipeline management.
A recent study conducted by the Sales Management Association found that sales teams at B2B enterprise companies who spend at least three hours per month talking with each sales rep about their pipeline had 11% greater growth.
The two main indicators of a successful pipeline review process – the ability to drive adequate pipeline coverage, and ensuring that deals are progressing at a healthy clip – are fundamentally important to driving revenue. Because this process was so central to the sale team, we wanted to know how happy sales people were with their current review process.
What we found out is this: the majority of sales teams have been using the same tools for 20+ years, and admit that with their current method, deals often slipped through the cracks.
What’s wrong with CRM reports and spreadsheets?
If you’re like most sales teams in B2B tech organizations, you export CRM data into a spreadsheet in preparation for your pipeline review meeting. First you sort deals by forecast category and then sub-sort them by deal size. Then, reps and managers sit down together, start at the top of the list and work their way to the bottom, reviewing each deal.
Plodding your way down a spreadsheet is problematic for two main reasons.
- Deals with large amounts and deals that fall into the best case and commit category are generally reviewed during typical deal review meetings. The top-down method employed by most teams focuses primarily on deals at the end of the sales process. Not enough time is spent reviewing the status of mid funnel deals or talking through new pipe generation.
- You can’t see what changed since last week. This lack of visibility makes it difficult to assess progress.
There is a better way, and it involves generating revenue through the use of AI.
Right about now most of you are thinking: Do I really need to apply AI to my pipeline reviews? The answer is yes. Here’s why.
High performing sales teams — those driving significant revenue growth — are applying AI to sales processes mostly closely connected to revenue generation, including pipeline reviews, forecasting, and deal reviews.
According to Salesforce’s Special Report titled “The AI Revolution,” there’s a clear relationship between early adoption of AI and future success. Sales teams who have significantly increased their YoY revenue growth are 3.1x more likely than their underperforming counterparts — those with negative YoY revenue growth — to use AI currently, or plan to do so within a year.
Why are sales teams adopting AI-driven technology? Because AI provides insights that move the needle in sales cycles and that are not available when using spreadsheets.
What information does AI-driven technology provide that I can’t get with CRM data and spreadsheets?
Glad you asked. In our previous asset, Disrupting Pipeline Reviews, Aviso’s domain experts provide insight into a new approach for conducting this process. Our latest guide goes one step further. It provides a step-by-step technical overview on how to conduct an AI-driven pipeline review, complete with screenshots.
To summarize what’s in the guide, here are four things you can do with AI-driven technology that you can not do with spreadsheets:
- View a comprehensive overview of rep performance with clicks – not by running reports. See snapshots of deal progression over time in a real-time report.
- Double click into a rep’s deals and see full detail along with smart selling AI insights.
- Use smart pipeline filtering to see what changed in your pipeline since last week. See deals that moved for the week, and double click on any bucket to see the list of supporting deals.
- View next quarter’s pipeline development and leverage AI-driven pacing. That’s right, you can see how much of what’s in your pipeline right now will close next quarter. And, you can see whether every rep, region, product line, or any other segment of your business, is on track to hit next quarter goals with our AI-driven pacing models.