> ## Documentation Index > Fetch the complete documentation index at: https://docs.modulate.ai/llms.txt > Use this file to discover all available pages before exploring further. # Customer Retention > Surface churn signals, dissatisfaction, and save opportunities on every call. Surface the moments that decide whether a customer stays — cancellation intent, dissatisfaction, competitor mentions, and save opportunities — so teams can act before churn. Churn is just one lens. The same conversational signals surface upsell readiness, loyalty, and product feedback — so you can aim the package at whatever outcome your team owns. Use this package as a starting point — add or swap in any [preset from the catalog](/velma/behaviors/using-behaviors#listing-available-presets), or [define your own behaviors](/velma/behaviors/custom-behaviors) to fit your use case. **This package includes:** 6 conversation types · 5 participant roles · 23 behaviors. ## Use this package The config below is ready to use as-is — download or copy it and pass it as the `config` payload in a Velma Triage request. Its `behaviors` are [preset references](/velma/behaviors/using-behaviors): the API expands each `preset:` into its full definition at request time, so you don't need the criteria inline to run the package. Download customer-retention.json
```json theme={null} { "conversation_types": [ { "conversation_type_uuid": "11111111-1111-4111-8111-111111111003", "name": "Enterprise IT support", "short_description": "Any call to assist an employee in accessing or managing internal IT resources", "detailed_description": "business-operationsitprofessional" }, { "conversation_type_uuid": "11111111-1111-4111-8111-111111111008", "name": "General Customer Support Call", "short_description": "Any call where a customer is contacting a business to assist them with the use of a service or goods provided by the business", "detailed_description": "customer-supportprofessional" }, { "conversation_type_uuid": "11111111-1111-4111-8111-111111111027", "name": "Finance Account Support", "short_description": "Any call to support the configuration, management, or cancellation of financial accounts", "detailed_description": "customer-supportprofessional" }, { "conversation_type_uuid": "11111111-1111-4111-8111-111111111028", "name": "Consumer Technical Support", "short_description": "Any call to assist a customer in using a business's software or website", "detailed_description": "customer-supportitprofessional" }, { "conversation_type_uuid": "11111111-1111-4111-8111-111111111029", "name": "Insurance Policy or Claims Management", "short_description": "Any call to check on the status of, change, cancel, or check on the status of a claim for an insurance policy.", "detailed_description": "customer-supportinsuranceprofessional" }, { "conversation_type_uuid": "11111111-1111-4111-8111-111111111031", "name": "Retail Customer Support Call", "short_description": "A call to coordinate the delivery or transfer of goods to a consumer", "detailed_description": "customer-supportprofessionalretail" } ], "participant_roles": [ { "participant_role_uuid": "22222222-2222-4222-8222-222222222004", "name": "Support Specialist", "short_description": "Someone who works with employees or contractors to solve problems with things like IT, logistics, or communication", "detailed_description": "" }, { "participant_role_uuid": "22222222-2222-4222-8222-222222222005", "name": "Employee", "short_description": "someone who is employed by company and exists in professional settings", "detailed_description": "" }, { "participant_role_uuid": "22222222-2222-4222-8222-222222222012", "name": "Customer Service Representative", "short_description": "A representative from a business who is assisting a customer with an issue they are having", "detailed_description": "" }, { "participant_role_uuid": "22222222-2222-4222-8222-222222222014", "name": "Insurance Agent", "short_description": "A customer support representative from an insurance company", "detailed_description": "" }, { "participant_role_uuid": "22222222-2222-4222-8222-222222222017", "name": "Customer", "short_description": "The recipient of a service or good", "detailed_description": "" } ], "behaviors": [ "preset:complaints", "preset:vishing", "preset:account_impersonation", "preset:action_plan_created", "preset:cancelled_order", "preset:service_churn", "preset:off_topic_discussion", "preset:coercion_manipulation", "preset:return_fraud_attempt", "preset:feigned_ignorance", "preset:bargaining_manipulation", "preset:pre_established_professional_relationship", "preset:rapport_building", "preset:customer_gratitude", "preset:inclusive_practices", "preset:unaddressed_question", "preset:refund_or_credit_issued", "preset:issue_not_resolved", "preset:inappropriate_speech", "preset:discriminatory_practices", "preset:issue_resolved", "preset:threat_based_harassment", "preset:sexual_harassment" ] } ```
### Expand the full criteria To produce a self-contained config with every behavior's full criteria inlined — for review, customization, or pinning a snapshot — fetch the live [preset catalog](/velma/behaviors/using-behaviors#listing-available-presets) and merge it into the downloaded config. The catalog is the source of truth for detection criteria. ```bash curl + jq theme={null} curl -s https://platform.modulate.ai/api/velma-2-batch/list-presets \ -H "X-API-Key: $MODULATE_API_KEY" \ | jq --slurpfile cfg customer-retention.json ' [ $cfg[0].behaviors[] | ltrimstr("preset:") ] as $ids | { conversation_types: $cfg[0].conversation_types, participant_roles: $cfg[0].participant_roles, behaviors: [ .presets[] | select(.identifier as $i | $ids | index($i)) ] } ' > customer-retention.full.json ``` ```python Python theme={null} import os, json, requests cfg = json.load(open("customer-retention.json")) ids = {b.removeprefix("preset:") for b in cfg["behaviors"]} catalog = requests.get( "https://platform.modulate.ai/api/velma-2-batch/list-presets", headers={"X-API-Key": os.environ["MODULATE_API_KEY"]}, ).json()["presets"] cfg["behaviors"] = [p for p in catalog if p["identifier"] in ids] json.dump(cfg, open("customer-retention.full.json", "w"), indent=2) ``` `customer-retention.full.json` keeps the same `conversation_types` and `participant_roles` and replaces each preset reference with its full behavior definition — drop it into the `config` payload exactly like the preset version. ## Conversation types The interaction contexts this package expects to see. | Name | What it is | | ------------------------------------- | ----------------------------------------------------------------------------------------------------------------------------- | | Enterprise IT support | Any call to assist an employee in accessing or managing internal IT resources | | General Customer Support Call | Any call where a customer is contacting a business to assist them with the use of a service or goods provided by the business | | Finance Account Support | Any call to support the configuration, management, or cancellation of financial accounts | | Consumer Technical Support | Any call to assist a customer in using a business's software or website | | Insurance Policy or Claims Management | Any call to check on the status of, change, cancel, or check on the status of a claim for an insurance policy. | | Retail Customer Support Call | A call to coordinate the delivery or transfer of goods to a consumer | ## Participant roles The speaker roles the package distinguishes. | Name | What it is | | ------------------------------- | ------------------------------------------------------------------------------------------------------------------ | | Support Specialist | Someone who works with employees or contractors to solve problems with things like IT, logistics, or communication | | Employee | someone who is employed by company and exists in professional settings | | Customer Service Representative | A representative from a business who is assisting a customer with an issue they are having | | Insurance Agent | A customer support representative from an insurance company | | Customer | The recipient of a service or good | ## Behaviors The 23 signals this package detects. Each maps to a reusable `preset:` you can drop into the `behaviors` array of any [BatchConfig](/velma/behaviors/using-behaviors) — the config above already references them. Full detection criteria are not duplicated here. The live preset catalog is the source of truth — retrieve the exact criteria for any behavior by name from the [`list-presets` endpoint](/velma/behaviors/using-behaviors#listing-available-presets). | Behavior | What it detects | Preset | | ----------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------- | | Complaints | Customer expresses dissatisfaction or grievance. We detect this through elevated volume, sharp intonation, frustration markers, accelerated pacing, and emotional intensity. | `preset:complaints` | | Vishing | Attempts to elicit sensitive information through deceptive voice interactions. We detect vishing based on abnormal call pacing, probing question patterns, stress-induced pitch shifts, and background noise suggesting call centers or spoofed environments. | `preset:vishing` | | Account Impersonation | Fraudulent attempt to access another's account. We detect this through identity inconsistencies, rehearsed responses, stress-induced vocal shifts, and abnormal verification behavior. | `preset:account_impersonation` | | Action Plan Created | Explicit agreement on next steps or follow-up. We detect this through structured enumeration, decisive tone, slowed pacing, confirmation cues, and reduced ambiguity in delivery. | `preset:action_plan_created` | | Cancelled Order | Customer cancels a previously placed order. We detect this using finality in tone, decisive pacing, administrative phrasing, and reduced emotional engagement after confirmation. | `preset:cancelled_order` | | Service Churn | Customer decides to cancel an ongoing service. We detect this through resignation tone, conclusive phrasing, disengaging cadence, and emotional withdrawal. | `preset:service_churn` | | Off-topic Discussion | Conversation largely unrelated to call purpose. We detect this using semantic drift paired with relaxed pacing, reduced task-oriented urgency, and tonal divergence from initial intent. | `preset:off_topic_discussion` | | Coercion Manipulation | Social engineering through intimidation or threats. We detect this using dominance-oriented tone, reduced empathy markers, pressure timing, and aggressive pacing. | `preset:coercion_manipulation` | | Return Fraud Attempt | Fraudulent product return behavior. We detect this using scripted explanations, defensive tone, timing irregularities, and emotional mismatch with stated circumstances. | `preset:return_fraud_attempt` | | Feigned Ignorance | Pretending towards ignorance to garner fraudulent sympathy. We detect this using inconsistent knowledge signals, exaggerated confusion tone, strategic pauses, and implausible vocal uncertainty. | `preset:feigned_ignorance` | | Bargaining Manipulation | Social engineering through cajoling and persuasion. We detect this through pressure-based tone, strategic silence, inconsistent emotional signaling, and coercive pacing changes. | `preset:bargaining_manipulation` | | Pre-established Professional Relationship | Evidence of prior interaction between speakers. We detect this through shorthand references, reduced formalities, synchronized turn-taking, familiarity in tone, and absence of introductory framing. | `preset:pre_established_professional_relationship` | | Rapport Building | Positive alignment forming a professional relationship. We detect this through reciprocal tone matching, affirming backchannels, relaxed pacing, and increasing conversational ease. | `preset:rapport_building` | | Customer Gratitude | Customer expresses satisfaction or appreciation. We detect this using positive emotional tone, softened volume, upward inflection, reduced tension markers, and closing politeness cues. | `preset:customer_gratitude` | | Inclusive Practices | Respectful language promoting inclusion and equity. We detect this through careful word choice reinforced by respectful tone, measured pacing, non-dismissive intonation, and calm emotional delivery. | `preset:inclusive_practices` | | Unaddressed Question | Failure to adequately respond to a posed question. We detect this through avoidance pauses, topic-shifting intonation, increased filler usage, and prosodic signals of deflection. | `preset:unaddressed_question` | | Refund or Credit Issued | Confirmation that financial remediation occurred. We detect this using transactional tone, formal cadence, confirmation phrasing, system-interaction pauses, and reduced customer tension. | `preset:refund_or_credit_issued` | | Issue Not Resolved | Customer's problem remains unresolved. We detect this through lingering frustration, repeated issue framing, unresolved tonal tension, and absence of closure cues. | `preset:issue_not_resolved` | | Inappropriate Speech | Unprofessional or unsuitable spoken content. We detect this using aggressive tone, boundary-crossing language, emotional volatility, and contextual mismatch with professional norms. | `preset:inappropriate_speech` | | Discriminatory Practices | Subtle prejudicial decision-making indicators. We detect this through biased framing, dismissive tone shifts, unequal politeness levels, and coded language delivered with emotional distance. | `preset:discriminatory_practices` | | Issue Resolved | Customer's problem successfully addressed. We detect this using relief markers, positive tonal shift, relaxed pacing, confirmation language, and conversational closure cues. | `preset:issue_resolved` | | Threat-based harassment | Targeted threats toward an individual in a professional context. We detect this using aggressive volume, hostile prosody, explicit threat markers, and sustained emotional intensity. | `preset:threat_based_harassment` | | Sexual Harassment | Unwanted sexualized speech or advances. We detect this through suggestive intonation, boundary-testing pauses, inappropriate familiarity, and discomfort responses from others. | `preset:sexual_harassment` |