Decoding audience feedback

How can you turn scattered reactions into evidence-based communication decisions with AI?

1960 € per session, excl. VAT

In short

This seven-hour day, excluding breaks, provides a structured approach to using reactions to a communication: preparing feedback, building an analytical framework, using AI to assist classification, checking interpretations and prioritising adjustments. Each recommendation remains linked to documented observations and a monitoring indicator.

Why this training?

Visible comments do not necessarily represent the entire audience. Mixing questions, criticism and expressions of support leads to giving excessive weight to the most expressive reactions and sometimes correcting the wrong message. Generative AI can assist with grouping verbatim responses, but it can also smooth over disagreements or misinterpret irony. An explicit framework and human verification make its suggestions open to challenge, traceable and useful for communication. Continuing case study: The fictitious association Orbe Culture has just presented a new programme during a webinar. Participants use a fictitious corpus of comments, live questions and questionnaire responses to identify misunderstandings and prepare a clarifying communication. Application at work: For the next communication, reuse the validated framework, have a colleague review ambiguous reactions and compare the misunderstandings observed before and after clarification, documenting differences in data collection.

Objectives of this training

  • Define the scope of a corpus according to the communication decision it is intended to inform.
  • Classify reactions by theme, expressed tone and need for clarification.
  • Check the categories proposed by AI against a reviewed sample.
  • Distinguish observations, hypotheses and limitations in representativeness.
  • Prioritise message adjustments with monitoring indicators.

Who is it for and what are the prerequisites?

Roles and tasks concerned

  • Communication and internal communication managers using feedback after a campaign or event.
  • Content officers and community managers preparing editorial recommendations.

Prior skills

  • Be able to formulate and refine a request in a generative AI assistant.
  • Know the objectives, channels and audiences of a communication activity.
  • Be able to filter and count categories in a spreadsheet.

What you will work on

  1. Defining the decision and the signals

    Objective: Define what the reactions actually allow you to conclude. Message objective, intended audience, responding audience; the difference between engagement, understanding and support.

  2. Preparing verbatim responses

    Objective: Build a usable corpus without exposing individuals. Anonymisation, duplicates, response context, channels and exclusion rules; retaining technical identifiers for traceability.

  3. Building the analytical framework

    Objective: Separate the dimensions of a reaction. Theme, expressed tone, question, objection and suggestion; multiple categories, ambiguity and missing information.

  4. Classifying with assistance and checking

    Objective: Measure the reliability of the proposed classification. Structured instruction, justification using an excerpt, refusal to infer hidden emotions; comparison with a human review.

  5. Interpreting without overrepresentation

    Objective: Produce a synthesis proportionate to the data. Counts by channel, explicit denominators, participation bias and the distinction between frequency and importance.

  6. Prioritising clarifications

    Objective: Link each adjustment to an observed reaction. Prioritisation of misunderstandings; corrective message, channel, person responsible and subsequent measurement.

What you take away

  • Prepared corpus and exclusion log.
  • Coding framework and reusable analysis instruction.
  • Table of classified reactions with an audit of disagreements.
  • Sourced summary report and prioritised clarification plan.

Method and assessment

  • Short input sessions followed by immediate practice on the continuing 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: Choose a communication whose reactions could inform an editorial decision. Check which tools are authorised by the organisation; use only fictitious cases or anonymised data for the exercises.

Observable criteria: The corpus specifies its channels, period, exclusions and limitations. ; Each category has a definition and a verifiable example. ; Agreement between assisted classification and human review is calculated; disagreements are documented. ; Counts are recalculated and quotations linked to the verbatim responses. ; Each recommendation distinguishes findings from hypotheses, with an indicator and a monitoring deadline..

Frequently asked questions

Can the audience’s emotions be inferred?

The expressions present are described, without claiming to know the actual emotions or individual intentions.

Does a negative comment require a correction?

No. Its specificity, seriousness and context matter as much as its frequency.

Can public reactions containing names be imported?

Their public nature does not remove the need for anonymisation or compliance with internal rules.

Is a specialist tool required?

No. An authorised generative assistant and a spreadsheet are sufficient for the proposed exercises.

Last updated: