Qualifying and scoring leads

How can you qualify and prioritise your leads with AI using explicit criteria?

1960 € per session, excl. VAT

In short

“Qualifying and scoring leads” is a private in-person session of a full day for salespeople, inside sales and business developers or sales managers and sales ops. It helps you define four to six qualification criteria suited to your market, build a transparent, weighted scoring matrix. Groups of up to 12 participants, in French, with exercises on your own work cases.

Why this training?

Sales teams receive more leads than they can handle. Without shared criteria, everyone prioritises in their own way and serious opportunities go unanswered. This day helps you formalise explicit criteria, use AI to apply them consistently to a batch of leads, and keep decisions on borderline cases in human hands. Running case study: ‘Thermo Pro’, a fictional manufacturer of heat pumps for non-residential buildings, receives 40 inbound enquiries in one month. Participants qualify and prioritise this fictional batch using their matrix. Transfer to the workplace: The team applies the matrix to its real leads for one month, compares the priorities with the results achieved and revises the matrix.

Objectives of this training

  • Define four to six qualification criteria suited to your market
  • Build a transparent, weighted scoring matrix
  • Apply the matrix with AI to a batch of leads and justify each score
  • Identify errors, biases and borderline cases in the assessment
  • Define rules for prioritising and following up leads
  • Measure scoring consistency across several people

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Salespeople, inside sales and business developers
  • Sales managers and sales ops

Prior skills

  • Regularly use a generative AI assistant and know how to structure a request (AI Prompting level or equivalent knowledge and skills).
  • Work in B2B sales and know your company's sales process.

What you will work on

  1. What a qualified lead means to us

    Objective: Share a common definition. Common qualification methods (budget, authority, need, timeline and variations); signals of interest; a definition tailored to the company.

  2. Building the scoring matrix

    Objective: Formalise measurable criteria. Choice of criteria, scales, weighting, priority thresholds; criteria to exclude (discriminatory or irrelevant).

  3. Applying the matrix with AI

    Objective: Obtain consistent, justified scoring. Structured instruction: matrix, definitions, output format with justification for each criterion; batch processing.

  4. Checking and correcting

    Objective: Identify errors and borderline cases. Comparison with human scoring; discrepancies, missing information, possible biases; rules for borderline cases.

  5. Prioritising and organising follow-up

    Objective: Turn scoring into an action plan. Priority levels, response times, actions by level, information to collect.

  6. Ensuring reliability over time

    Objective: Prepare for actual use of the matrix. Measuring consistency between people, quarterly revision, feeding sales results back into the matrix.

What you take away

  • Shared definition of a qualified lead
  • Weighted scoring matrix
  • Batch of 40 leads with justified scores
  • Analysis of discrepancies and rules for borderline cases
  • User guide and revision plan for the matrix

Method and assessment

  • Short teaching 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: Bring your current qualification criteria, even if informal. If possible, prepare an anonymised export of around twenty leads (without names or contact details).

Observable criteria: Each criterion in the matrix is defined and measurable; each score awarded is justified by criterion; discrepancies between AI and human assessments are analysed and explained; borderline cases follow a written rule with a human decision.

Frequently asked questions

Does AI decide which leads to handle?

No. It applies a matrix defined by the team and justifies its scores; the decision on priority remains with people, particularly for borderline cases.

Is a CRM required?

No. The session uses a table. Integration into a CRM is covered in a level 4 training course.

Can we use our real leads?

Only in anonymised form and if your internal rules allow it; otherwise, the fictional batch is sufficient to build the method.

Does the scoring comply with data protection requirements?

The session avoids personal data and discriminatory criteria; compliance in actual use still needs to be validated with your data lead.

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