Reputational signals under control
How can you detect useful reputational signals without confusing noise, opinion and substantiated risk?
3500 € per session, excl. VAT
In short
Over two days, comprising 14 hours excluding breaks, you will design a reputation monitoring prototype combining assessed sources, documented search queries and AI-assisted analysis. You will test its relevance on a fictional corpus, define alert workflows and prepare a limited pilot with verifiable quality indicators.
Why this training?
An increase in negative mentions is not enough to establish a reputational threat. Namesakes, republished content and unverifiable accounts can divert attention from signals requiring prompt verification. Generative AI can assist with classifying and summarising mentions, but is neither a source nor evidence. The system must maintain traceability and provide for human validation before any escalation. Running case study: Orbeva Cycles, a fictional urban bicycle manufacturer, observes contradictory messages about a braking defect. A simulated corpus brings together reviews, articles, social media posts and duplicates; some messages concern a brand with the same name. Workplace application: Propose a pilot within an approved scope: authorised sources, a designated person responsible, a weekly review of errors and a decision on whether to continue based on the tested criteria.
Objectives of this training
- Define the issues, entities and sources to monitor
- Develop search queries and a reputation taxonomy
- Prototype assisted classification with associated evidence
- Measure false positives and missed alerts
- Formalise an escalation workflow and a pilot plan
Who is it for and what are the prerequisites?
Roles and tasks concerned
- Communication, press relations and reputation managers
- Monitoring leads and community managers coordinating alerts
Prior skills
- Have practical experience in communications and know the internal validation workflows.
- Be able to use a generative assistant and filter a table of mentions.
What you will work on
Define the risks to be monitored
Objective: Link monitoring to communication decisions. Issues, stakeholders, monitored entities and scope boundaries.
Assess the sources
Objective: Establish justifiable source coverage. Reliability, accessibility, blind spots, access rights, data minimisation and retention.
Write the search queries
Objective: Reduce irrelevant mentions. Brand variants, products, exclusions and syntax according to the resources available.
Define the categories
Objective: Distinguish sentiment from severity. Topic, reach, credibility, urgency, irony and an undetermined category.
Assemble the prototype
Objective: Produce traceable analyses. AI instructions, mandatory references, abstention and external content treated as untrusted data.
Organise alerts
Objective: Define proportionate escalation. Thresholds combining severity, credibility and developments; human validation.
Test detection
Objective: Measure the quality of the prototype. Held-out dataset, human reference assessment, precision, recall and corpus limitations.
Correct errors
Objective: Improve without concealing omissions. Trade-off between noise and detection; duplicate management.
Simulate an escalation
Objective: Communicate an actionable alert. Facts, uncertainties, sources and additional checks.
Prepare the pilot
Objective: Set a framework for a limited trial. Review workload, access, indicators and stopping conditions.
What you take away
- Monitoring charter and source register
- Query library and classification instructions
- Prototype monitoring table and alert form
- Test report and pilot plan
Method and assessment
- Short instructional inputs followed by immediate practice on the case study used throughout.
- Individual work followed by work in pairs, with participants reviewing each other’s outputs.
- Guided group discussion: what works, what needs correcting and what remains a human decision.
- Preparation: Identify reputational issues and the channels usually consulted, without personal data. Check which tools the organisation permits; the exercises use only fictitious or anonymised cases.
Observable criteria: Each selected mention has a reference, a date and a supporting extract. ; Namesakes and duplicates are handled according to explicit rules. ; Precision and recall are calculated on a human-annotated batch, with comments on errors. ; Each alert level specifies an approver, a recipient and a target timeframe. ; The pilot specifies scope, frequency, responsibilities and stopping conditions..
Frequently asked questions
Does the AI collect mentions automatically?
Not necessarily: the prototype works on a supplied corpus, without assuming any collection capabilities.
Can real mentions be used?
The exercises remain fictitious or anonymised; any real-world pilot requires approval of access and processing activities.
Is sentiment alone enough to trigger an alert?
No: severity, credibility and context are assessed by a responsible human.
Will the solution be operationalised at scale?
No: you leave with a tested prototype and a plan for a limited pilot.
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