Learn how Inside Sales’ Predictive Pipeline can help solve challenges related to sales forecasting in businesses in this post.

In this article:

  1. The Pipeline Management and Sales Forecasting
  2. Artificial Intelligence on Sales
  3. Inside Sales’ Predictive Pipeline
  4. Solving the Pipeline Management Equation
  5. Identify Sales Superstars from Average Performers
  6. Spot a Good Deal from the Bad
  7. Capture and Understand Pipeline Changes
  8. Increase Sales Forecasting Accuracy by 30%

Predictive Pipeline for Sales Forecasting


Predictive Pipeline Definition: A pipeline management and forecasting tool offered by InsideSales.com that aims to help managers gain confidence in their forecasts by showing which reps and deals to focus on. Click here to learn more about it.

The Pipeline Management and Sales Forecasting

As analysts and brokers are clamoring to predict the next economic downturn, business leaders are permanently trying to get ahead of the curve and create predictable and sustainable growth, allowing stakeholders to know what’s coming down the road. However, given how many variables they encounter on sales pipeline management and sales forecasting, it’s no wonder some fail.

Data show that 79% of sales teams and organizations miss their sales forecast by 10% or more.

Artificial Intelligence on Sales

Artificial intelligence (AI) is one technology that has proven power in predicting transactional outcomes.

In some cases, AI has been able to increase revenue by up to 30% for companies just through these methods:

  • Providing increased productivity
  • Bringing more insights into customer behavior
  • Suggesting actions to close business faster

Now, more than ever, sales leaders are realizing that AI can also help them produce accurate revenue projections by offering insights into their pipeline, rep behavior, and future outcomes.

Inside Sales’ Predictive Pipeline

InsideSales.com, the sales acceleration leader, has recently launched Predictive Pipeline – an AI sales software for managers and leaders looking to gain insight into the health of their pipeline and coach their reps towards more accurate sales forecasts.

Solving the Pipeline Management Equation

The pipeline management equation starts with people management. People are the most important variable in this equation, as they still run the sales process, which includes forecasting data.

Some might think AI is taking over the world, but that’s not happening just yet.

Here are a few things that AI can do for you when it comes to pipeline management:

sales effectiveness quadrant - defining your top performing sales reps - evaluating sales performance concept | Predictive pipeline quadrant | Solves Sales Forecasting Challenges With Predictive Pipeline

The Sales Effectiveness Quadrant from InsideSales.com shows a sales rep performance and the AI engine makes suggestions on how they can improve their skills.

Identify Sales Superstars from Average Performers

Sales managers need to cull bad sales rep behaviors quickly when it comes to pipeline management and forecasting. The end-of-the-quarter rush, opportunity sandbagging, and over or under-estimating deal sizes are all bad rep behaviors that affect pipeline management.

‘Zombie’ deals get carried from one month to the next, from one quarter to the next, because sales reps keep thinking they will close. Other times, they might not report good deals moving through sales stages so they can have data to report in the next quarter.

All these lead to inflated pipelines and difficulty forecasting revenue.

Accurate information is the key to removing bad rep behavior and rewarding good behavior. Here are the areas where Predictive Pipeline can help you achieve this in your organization:

  1. Segment your reps into performance quadrants.
  2. Identify the behaviors that separate one group from the next.
  3. Establish remedy plans and provide coaching to improve sales effectiveness.
Reps deals dashboard | Solves Sales Forecasting Challenges With Predictive Pipeline | predictive pipeline deals dashboard - insidesales.com

Predictive Pipeline shows if sales reps are on track to hit their quota and the probability to close deals, based on AI opportunity scoring.

RELATED: The State of Sales Development: What Does the SDR Prospecting Pipeline Look Like?

Spot a Good Deal from the Bad

A sales rep will be happy when they’ve come across an opportunity. However, not all sales deals are created equal. There are key characteristics of a good and a bad deal.

These are fairly evident by studying what has happened in the past. There are a few cues you can look at:

  • Is your sales cycle 30 to 90 days, or shorter, and what has happened to deals going over the sales cycle?
  • In which region does your product-market fit best, and what’s the outcome for other regions?
  • How many touches does a customer need, on average, to close a deal? Have you had success outside this average range?
  • What happens with your deals if sales reps forget to follow up? What if they go too long in answering inbound leads?

InsideSales.com research shows that leads called on 5 minutes after coming in are 100 times more likely to connect and qualify.

It shouldn’t be hard to spot patterns like this, but the good news is you don’t have to comb through your operational data in spreadsheets to do it. Intelligent sales forecasting software can work it out faster than you doing a manual analysis.

Once you figure out your sales patterns, you can weed out deals that never seem to die or highlight deals that are at risk, for further evaluation.

Predictive pipeline dashboard | Solves Sales Forecasting Challenges With Predictive Pipeline | Predictive Pipeline dashboard InsideSales.com

AI can perform change analysis on your pipeline, so you’re not caught off guard by a sudden shift in sales outcomes.

Capture and Understand Pipeline Changes

Changes in the pipeline from one month to the next can help identify what to predict and whether or not a deal will close. Most sales managers initiate pipeline review calls on a weekly basis with their sales agents.

However, they can waste too much time just by assembling data for analysis in Excel. This is no longer acceptable — sales managers need to spend more time analyzing patterns and understanding pipeline changes, rather than scrambling to capture data.

Predictive sales forecasting software allows sales reps to save time, with features like the following:

  1. Automatic and continual snapshotting of all pipeline changes over any period of time (close date, pricing, or probability)
  2. AI to inform predictive lead scoring. Combined with sales reps’ notes, this gives an accurate idea of how the pipeline has been adjusted dynamically.
  3. Pivot views, so you can see your results by product, region, or business unit at all times

Increase Sales Forecasting Accuracy by 30%

Predictive Pipeline can increase forecasting accuracy by up to 30%, compared to just using an Excel spreadsheet or other sales forecasting systems.

To learn more about how top sales organizations use artificial intelligence for pipeline management and improved sales forecasting, join the InsideSales.com webinar “Artificial Intelligence vs. Sales Development”

During the webinar, author and speaker Victor Antonio, and Gabe Larsen, VP of Growth at InsideSales.com, will demonstrate how AI technology can solve your pipeline management challenges and help you create sales visualization.

Reserve your spot now!


With Inside Sales’ Predictive Pipeline, sales forecasting is a lot easier for salespeople. Sales managers can train their sales agents to determine quickly which deals are bad and good with this AI technology. Consider utilizing our Predictive Pipeline and don’t hesitate to reach out to us if you want to increase your sales forecasting accuracy.

What are your thoughts on Inside Sales’ Predictive Pipeline’s use in helping solve and manage a sales forecasting problem? Share what you think in the comments section.

Up Next: Sales Effectiveness Metrics for Evaluating Your Team


Editor’s Note: This post was originally published on July 18, 2018, and has been updated for quality and relevancy.

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