Sales Performance, Analytics & Artificial Intelligence

Evaluate sales activities based on customer outcome, profitability, forecasting reliability, and sustainable performance.

Direct sales performance should not be measured solely by sales volume. Lead conversion, opportunity speed, offer success, margin, customer acquisition, forecast accuracy, and post-sales results should all be evaluated together.

Analytical systems should show managers the overall results and sales staff their priorities and areas for improvement.


Sales Performance, Analytics & Artificial Intelligence

Performance areas

  • Lead and opportunity conversion
  • Sales pipeline and forecast
  • Proposal and contract success
  • Net sales and gross contribution
  • Customer acquisition and growth
  • Sales cycle time
  • Activity Event
  • Reasons for cancellation, return, and loss.

Management dimensions

  • Company and sales organization
  • Region and team
  • Sales representative
  • Customer and segment
  • Product and solution
  • Campaign and lead generation
  • Period and sales phase

From analytics to action

Reports should not only describe the past; they should establish responsible and traceable actions for missed opportunities, risk of loss, capacity issues, and customer opportunities.

Artificial intelligence governance

Scoring, prediction, and recommendation models should be regularly monitored for accuracy, bias, explainability, data source, and human control.

Measure sales performance not to generate more activity, but to understand and improve behaviors that deliver real value to the customer and the company.

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