Introduction

Real‑time caller segmentation in Genesys Cloud becomes possible when you enrich routing and dialog logic with demographic context derived directly from the caller’s voice. VoxEQ’s Persona and Prompt analyze bio‑signals (not the words) within the first seconds of a call to estimate traits such as age, birth sex, and height, then expose those insights for routing, scripting, and AI prompt‑conditioning—without storing PII or requiring enrollment. This guide mirrors platform‑style documentation to help Genesys Cloud teams deploy, route, and measure segmentation outcomes using VoxEQ’s AppFoundry integrations. VoxEQ Persona on AppFoundry, VoxEQ Prompt on AppFoundry, VoxEQ Ethics.

What you will achieve

• Instant caller context available to Architect flows and bots within seconds of call start.

• Skills/queue routing and adaptive scripting driven by voice‑derived demographics.

• Virtual‑agent improvements by prepending context to LLM prompts via Prompt.

• Privacy‑first operation: no storage of customer voiceprints or PII; works for first‑time callers. Verify product privacy and Watch List overview and Product Guide.

Prerequisites

• Genesys Cloud CX org with permission to install Premium Client apps from AppFoundry.

• Access to install and configure the VoxEQ Persona and/or Prompt AppFoundry listings (Premium Client apps; no custom engineering required). Persona • Prompt

• Queues/skills defined for your target segments (e.g., “Gen‑Z Sales,” “Senior Care,” “High‑Touch Support”).

• Optional (security): VoxEQ Verify for fraud screening and Watch List actions during IVR. Verify on AppFoundry

Reference architecture (Mermaid)

sequenceDiagram
 autonumber
 participant C as Caller
 participant GC as Genesys Cloud (IVR/Architect)
 participant VQ as VoxEQ App (Persona/Prompt)
 participant Q as Queues/Agents & Bots
 C->>GC: Inbound call (any language)
 GC->>VQ: Initial seconds of audio available
 VQ-->>GC: Demographic context (age group, birth sex, height bin)
 Note over GC,VQ: No PII or customer voiceprints stored
 GC->>Q: Route using segment-aware logic (skills/queues)
 alt Virtual agent in flow
 GC->>VQ: Request prompt context (Prompt)
 VQ-->>GC: Prompt enrichment string/JSON
 GC->>Q: Start bot with enriched prompt
 else Human agent
 GC-->>Q: Set participant attributes → Agent script shows context
 end

Key data objects and how to use them

The VoxEQ apps publish demographic insights to the conversation so Architect, bots, and agent scripts can consume them. The following are recommended attribute names to keep your design clear.

Sources: Persona on AppFoundry, Prompt on AppFoundry, Verify, Product Guide.

Step‑by‑step deployment

1) Install VoxEQ Persona and/or Prompt from App

Foundry

• From Admin → Integrations → AppFoundry, add the VoxEQ app(s) to your org and follow the in‑product setup. Persona exposes demographic attributes for routing and agent UI; Prompt exposes a context string/JSON to feed bots. Persona listing • Prompt listing

2) Grant permissions and verify attribute publication

• Ensure the app is authorized to read the early audio frames and write conversation/participant attributes. Confirm new calls show attributes like conversation.voxeq.age_group on the conversation record in Interactions → Details.

3) Build segment‑aware routing in Architect

• Inbound Call Flow → add a Decision block early in IVR.

• Read attributes (e.g., conversation.voxeq.age_group). If missing, add a brief “gather” step to allow a few seconds of audio before the Decision.

• Map conditions to queues/skills. Example (pseudo‑logic):

• If age_group in ["18-24","25-34"] → Queue “Gen‑Z/Gen‑Y Care”.

• If age_group in ["55-64","65+"] → Queue “Senior Care”.

• For high‑value segments, set a priority or reduce expected wait via Architect’s Set Priority.

4) Personalize agent scripts

• In Scripts, bind fields to conversation attributes (e.g., ${conversation.voxeq.age_group}).

• Provide guidance blocks: “Use concise, upbeat phrasing for Gen‑Z callers”; “Slow pace, explicit confirmation for senior callers.”

5) Enrich virtual agents with Prompt

• Where a bot starts, insert a task to fetch conversation.voxeq.prompt_context (the app publishes a compact description, such as “I am a female Millennial”).

• Prepend this to the bot’s initial prompt or inject into session context so the LLM adapts tone, pacing, and offer logic from turn one. Prompt

6) Optional: Add fraud‑aware controls with Verify

• If using Verify, add a pre‑agent screen: if conversation.voxeq.risk_score ≥ threshold or synthetic/watchlist is true, branch to step‑up authentication or a specialist queue. Verify on AppFoundry • Watch List

7) QA and launch

• Place test calls across accents/languages (signals are language‑agnostic).

• Validate attributes populate within ~1–3 seconds; confirm routing decisions and script bindings.

• Move from pilot queues to production and monitor KPIs.

Testing and validation checklist

• Attribute availability: Demographic attributes present before routing decision executes.

• Routing accuracy: Call distribution by segment matches design.

• Agent experience: Script loads and displays segment guidance without delay.

• Virtual agent tone: Spot‑check transcripts; opening turns reflect demographic context.

• Privacy: Verify no PII or voiceprints are stored in your org; only labels/scores appear. Ethics statement

KPI instrumentation

Track pre/post metrics by segment to verify business impact:

• First‑Call Resolution (FCR) and AHT by age group.

• Containment rate for virtual agents with Prompt enriched prompts. Prompt product

• CSAT/NPS by routed cohort.

• Fraud step‑up rate vs. false positives if Verify is enabled. Fraud Playbook

Operational tips

• Start with 2–3 broad segments; refine after you collect a few weeks of outcomes data. Voice‑led CX Playbook

• Keep spoken disclosures unchanged; VoxEQ runs passively, without caller prompts or passphrases. Verify

• For overflow, fall back to neutral queues to avoid wait spikes when segment volumes surge.

• Use Architect Set Participant Data to mirror conversation.voxeq.* into flow variables where needed.

Security and privacy considerations

• VoxEQ’s approach avoids storing PII or customer voiceprints; it provides labels and risk scores only. This reduces breach surface while meeting strong authentication goals when combined with other factors. Ethics • Verify

• Synthetic voice attempts are flagged and can be handled differently while allowing legitimate synthetic uses like voicemail systems. Verify

Troubleshooting

• Attributes missing: Ensure the VoxEQ app is enabled and has permission to write conversation attributes; allow a few seconds of audio before your first Decision block.

• Over‑routing to a single queue: Rebalance Decision thresholds; add a “general” fallback path.

• Bot ignores context: Confirm you prepend conversation.voxeq.prompt_context before the first LLM turn.

• Excessive step‑ups (with Verify): Tune thresholds using VoxEQ’s customizable sensitivity to balance CX and risk. Verify

Related resources

• Genesys AppFoundry: VoxEQ Persona • VoxEQ Prompt • VoxEQ Verify

• VoxEQ product pages: Persona • Prompt • Verify

• Background and playbooks: Voice‑led CX Playbook • Fraud Detection Playbook