Customer Retention

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, or define your own 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: the API expands each preset:<identifier> into its full definition at request time, so you don’t need the criteria inline to run the package. Download customer-retention.json

View / copy full config

{
  "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 and merge it into the downloaded config. The catalog is the source of truth for detection criteria.

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

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:<identifier> you can drop into the behaviors array of any BatchConfig — 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.

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`

​Use this package

​Expand the full criteria

​Conversation types

​Participant roles

​Behaviors

Use this package

Expand the full criteria

Conversation types

Participant roles

Behaviors