Analysing a sales pipeline

How can you analyse your sales pipeline with AI to make better decisions?

1960 € per session, excl. VAT

In short

“Analysing a sales pipeline” is a private in-person session of a full day for sales managers and sales directors or sales ops and salespeople managing a large portfolio. It helps you prepare and clean a pipeline export for analysis. Groups of up to 12 participants, in French, with exercises on your own work cases.

Why this training?

Pipeline reviews are often discussions based on impressions: everyone comments on their deals, without an overall view or reliable figures. Stalled opportunities are identified too late. This day teaches you to use AI to structure and analyse a pipeline export, while checking calculations and keeping commercial interpretation with the team. Running case study: ‘Stratus Cloud’, a fictitious software vendor, prepares its quarterly review using an export of 120 fictitious opportunities containing duplicates, missing dates and inconsistent stages. Transfer to the workplace: The manager leads their next pipeline review using the summary and rules established, then adjusts the routine after two cycles.

Objectives of this training

  • Prepare and clean a pipeline export for analysis
  • Calculate key metrics with AI and check the calculations
  • Identify stalled or at-risk opportunities using explicit rules
  • Distinguish numerical findings from interpretations
  • Prepare a fact-based pipeline review and its questions
  • Define a regular analysis routine

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Sales managers and sales directors
  • Sales ops and salespeople managing a large portfolio

Prior skills

  • Regularly use a generative AI assistant and know how to structure a request (AI Prompting level or equivalent skills).
  • Have experience in B2B sales and know your company's sales process.
  • Be able to read a data table (sorting, filters).

What you will work on

  1. Pipeline metrics

    Objective: Choose what you want to measure. Volume, value, conversion by stage, cycle length, age, target coverage; limitations of each metric.

  2. Preparing the data

    Objective: Obtain a usable export. Duplicates, empty fields, inconsistent stages; ask AI to detect anomalies without modifying the original data.

  3. Calculating and checking

    Objective: Produce reliable figures. Calculations by AI or using a formula suggested by AI; verification by recalculating a sample.

  4. Identifying at-risk deals

    Objective: Identify where to act first. Explicit rules: time in stage, lack of activity, overdue signing date; list to review.

  5. Preparing the review

    Objective: Make the review fact-based and useful. One-page summary, simple charts, questions for each deal; separation of findings and interpretations.

  6. Review simulation and routine

    Objective: Test and embed the practice. Leading a 30-minute review; monthly routine; responsibilities.

What you take away

  • List of defined metrics
  • Cleaned export with a correction log
  • Table of verified metrics
  • Justified list of at-risk deals
  • Summary and questions for the pipeline review

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 an anonymised pipeline export (stages, amounts, dates). List the metrics currently tracked.

Observable criteria: Each metric has a written definition; At least three calculations are checked independently; Each flagged deal is flagged according to an explicit rule; The summary clearly separates findings from interpretations.

Frequently asked questions

Do you need to know Excel?

Knowing how to sort and filter is enough. AI can suggest formulae, which are always checked.

Can we analyse our actual pipeline?

Only in anonymised form and if the tool is authorised for this data; otherwise, the fictitious case study serves as the basis.

Does AI produce sales forecasts?

The session does not produce a guaranteed numerical forecast; it helps you understand the current situation and ask the right questions.

Which tools are needed?

The authorised AI assistant and a spreadsheet application; file analysis features depend on the tool and are checked during scoping.

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