Tracey Kaufman

Using Pipeline Analytics for Historical Analysis

Pipeline Velocity is a good framework to measure overall pipeline value and can also be used to get a general picture of the health of your business. Dig into each of the drivers to uncover potential issues and do more analysis on the exceptions to understand the root cause and implement course corrections as necessary.

Definition: Pipeline velocity is a dollars/day measure of your pipeline value. The formula for pipeline velocity is: (# of deals) x (win conversion rate) x (average deal size) /average selling time in a day.

Out of the total variables; number of deals, win conversion rate, average deal size and selling time, it’s best to focus on what you can control. Since Marketing owns the number of deals, and since both the quantity and average deal size are largely controlled by market factors, its best that Sales Managers focus on strategies for improving conversion rate and sales cycle days – how much you win & how long it takes you to do so.

Things to Focus On:
  • Understand the key velocity drivers for your business
  • Determine impact of improvement each on your business
  • Focus on improving the ones you can control
Measuring Sales Team Performance and Behavior
Use pipeline analytics to monitor to watch for sandbagging–resulting in poor pipeline quality metrics and most likely a behavior issue, over-committing–resulting in poor pipeline quality metrics, deal abandonment–which informs you about qualifying behavior, and setting benchmarks.
Indications of “Sandbagging”
  • High % of closed deals entered late into the quarter (or as post-quarter cleanup)
  • High % of deals entered at late stages
  • High % of won deals moved in from future quarters
  • Significant deal amount increases once closed
  • Deals progressing from early stage to closed without moving through the process
  • High percentage of deals closing that weren’t included in the forecast
Indications of “Over-Committing”
  • Low conversion rates and poor forecast accuracy
  • High % of deferred deals
  • Significant deal amount decrease once closed
  • High % of close dates clustered at of month/quarter
  • High % of deals with longer than average Opportunity or Stage Age
  • Deals deferred at the end of the quarter
  • Deal close dates clustered at the end of month/quarter
Indications of Deal Abandonment
  • Opportunity age, stage and stage age for closed-lost deals
  • Benchmarking
  • Sales rep productivity (closed revenue & # deals closed/period)
  • Ranking vs. other reps
  • Open Deal Aging
  • Average Close Cycle
  • Forecast Accuracy
  • Close Rate
  • Average # Deals Deferred
  • Average number of times a deal is deferred
  • Top 10 performers and stack ranking for quota achievement
Sales Forecasting
Pipeline analytics provide a historical context to help you analyze prior period results and identify patterns of rep behaviors. Information can be used to correct behaviors and interpret current results. Two things to consider around accuracy: achieving forecasted amount and closing committed deals.
Measuring forecast accuracy
  • Pipeline deal analysis – overall conversion measure & strength of pipeline: % won (dollar/unit) % lost (dollar/unit) % deferred (dollar/unit)
  • Committed deal analysis – measures rep’s ability to predict % won (dollar/unit) % lost (dollar/unit) % deferred (dollar/unit).
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