How AI can transform Sales Pipeline Review

Usually, sales pipeline review in CRM is primarily focused on the deals that are on track and are on the closure stage, with no much attention is given to the early-stage deals or stalled ones.

Also, the pipeline review is mostly limited to sales forecasting that supplies the quota attainment data and post-mortem analytics derived from measurable sales factors such as deals won/lost based on various factors, revenue generation/loss and average conversion time. And the sales forecasting data is not that dependable, as it has been derived out of the inputs from the sales representatives, based on their personal reasoning & guess works.

But with the application of AI techniques and intelligence data capturing, sales pipeline review has been pushed to the next level. Let’s look at the significant improvements achieved leveraging artificial intelligence in sales CRM platform.

Focus on Risk Deals:

AI driven intelligence helps sales team to focus on stalled or stuck deals by

  • Isolating deals that spends more time on their pipeline stages, compared to average time tracked with machine learning the historical pipeline data.
  • Identifying deals that has been left idle with no engagements (from team/customer) for a longer time period compared to the usual engagement interval period derived from the data intelligence.
  • Pinpointing deals where the customer emails has not been responded within the best response time suggested with machine learning customer engagements of historical success deals.
Pipeline Tracking with Deadline Alerts:

If pipeline configured with defined stages & sequence of steps with a deadline specified, then the AI techniques with intelligent data capturing, will auto-drive the sales team on the next steps to be executed. In addition to reporting next steps, it also reports the deals with steps that have crossed the pre-determined deadline, thus always keeping the sales reps alert on their pipeline targets.

Intelligent Sales Forecasting:

The reliability of the sales forecasting depends on the accuracy of two factors – close date and the probability to close.  Leveraging AI, the close date is accurately derived through the stage duration analysis from the earlier won deals, and the probability to close is accurately guided with the stage outcome intelligence reported from the past won deals. Also with the stage duration analysis, even the forecasting can be derived for the early-stage deals too.

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