Auditing sales process performance
How can you audit your sales process with AI to identify the real levers for improvement?
3500 € per session, excl. VAT
In short
“Auditing sales process performance” is a private in-person session of two days for sales directors and sales team managers or sales ops, sales management controllers and internal consultants. It helps you map the actual sales process from end to end. Groups of up to 12 participants, in French, with exercises on your own work cases.
Why this training?
When sales results stagnate, explanations are often based on impressions: too few leads, prices too high, competition. Without a structured analysis of the process, corrective actions sometimes target the wrong problem. These two days apply an audit approach: describe the actual process, measure, analyse data and customer feedback with AI, then prioritise actions whose effects can be verified. Running case study: ‘Stratus Cloud’, a fictional software vendor, observes a decline in its conversion rate. Participants have a fictional pipeline export, 60 loss reasons and 30 verbatim customer comments to conduct the audit. Transfer to the workplace: The organisation launches the tests set out in the improvement plan and measures their effects before making any broader decision.
Objectives of this training
- Map the actual sales process from end to end
- Define performance indicators for each stage
- Analyse pipeline data and verbatim customer feedback with AI
- Identify bottlenecks and their probable causes
- Prioritise improvement actions according to impact and effort
- Build an improvement plan with measurable tests
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Sales directors and sales team managers
- Sales ops, sales management controllers and internal consultants
Prior skills
- Be proficient in the sales applications of generative AI (level 3 or validated equivalent prior learning).
- Have responsibility for sales organisation or tools (manager, sales ops, AI lead).
- Be able to read and manipulate a data table.
What you will work on
Official process and actual process
Objective: Describe what really happens. Step-by-step mapping, people involved, tools, time; gaps between the planned and actual process.
Indicators by stage
Objective: Measure each stage. Conversion, duration, volume, quality; definitions and sources; data limitations.
Pipeline data analysis
Objective: Identify where performance deteriorates. Comparison by segment, period and salesperson; anomalies; caution when drawing conclusions.
Analysis of verbatim feedback and loss reasons
Objective: Understand the customers’ perspective. Thematic classification with AI, verification on a sample, representative quotations.
Bottlenecks and causes
Objective: Link findings to probable causes. Cross-referencing analyses, cause tree, hypotheses to verify.
Prioritisation of actions
Objective: Choose where to act first. Impact / effort matrix, dependencies, risks, resources.
Testable improvement plan
Objective: Plan how to verify the effects of the actions. Action, indicator, baseline value, test duration, owner, success criterion.
Audit presentation
Objective: Present defensible conclusions. Summary report, presentation to management, responses to objections.
What you take away
- Map of the actual sales process
- Table of verified indicators
- Summary of quantified findings and verbatim feedback
- Action prioritisation matrix
- Testable improvement plan and audit report
Method and assessment
- Short inputs followed by immediate practice on the running case study.
- Individual work followed by work in pairs, with peer review of outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: If possible, prepare anonymised exports: pipeline, win and loss reasons, customer surveys. Describe the official sales process in a few lines.
Observable criteria: Each finding is supported by a data point or a sourced quotation; AI-generated classifications are checked on a sample; Each action in the plan has an indicator and a success criterion; The report distinguishes between findings, hypotheses and recommendations.
Frequently asked questions
Can the audit cover our real data?
Yes, if it is anonymised and the tool is authorised for this type of data; otherwise, the method is applied to the fictional case study.
Does AI identify the causes on its own?
No. It helps with classification and calculation; the causes are hypotheses developed and verified by the group.
Do we leave with guaranteed gains?
No. You leave with a plan of testable actions; their effects are measured during the tests.
Who should take part?
Ideally, a sales manager, someone from sales ops and someone able to extract the data.
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