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Glossary · Category

Agentic VoC.

Agentic VoC is voice-of-customer practice run by named software agents — not human analysts staring at dashboards.

Definition

Agentic VoC is a voice-of-customer practice in which named AI agents continuously read feedback across retail, support, survey, and social channels, then produce decisions and actions — theme detection, anomaly alerts, SKU scorecards, draft responses, defect escalations — rather than only populating dashboards for humans to interpret. It is the operating model Indellia built its platform around.

Definition

Agentic VoC describes a voice-of-customer practice organized around software agents that do the reading, tagging, routing, and recommending that analysts used to do by hand. Each agent has a narrow job. The Theme Agent clusters emerging issues. The Anomaly Agent watches review velocity and sentiment for statistically significant shifts by SKU. The SKU Agent maintains a per-product scorecard across every channel the product sells on. The Search Agent (indelliaGPT™) answers natural-language questions against the full corpus. The Defect Agent (Beta) correlates field reports with serial, batch, and ship-date metadata. The Response Agent (Beta) drafts replies back to the channel of origin.

The term is Indellia's category coinage. It is meant to name a difference in kind, not a marketing adjective. Dashboards display; workflows route; agents act. A page of charts and a Slack notifier do not constitute agentic VoC — the test is whether the system would produce something useful with no human watching it.

Why it matters

Most feedback platforms are passive — they ingest, score, and chart. A human still reads the chart, forms a hypothesis, files a ticket, and drafts the response. For a consumer brand with thousands of active SKUs spread across Amazon, Walmart, Best Buy, Costco, Lowe's, Target, and Bazaarvoice-syndicated retailers, that human is the bottleneck. Themes surface late. Defects surface later. Responses do not go out at all.

Agentic VoC moves the reading, sorting, and first-draft decision work to software that runs continuously. People stay in the loop for the judgment calls — approving a defect escalation, editing a response, deciding whether to pull a SKU. They stop doing the triage. The consequence is that the program scales with catalog size rather than with headcount, and signals surface in hours instead of weeks.

Example

A sporting-goods brand ships a new bike helmet across Amazon, Target, and Dick's. Three weeks in, the Anomaly Agent flags that one specific ASIN has a sentiment drop on the theme "chin strap buckle" versus the prior two weeks. The Theme Agent shows the cluster is 34 reviews, all referencing a plastic cam lock. The SKU Agent links the ASIN to the matching Target TCIN and the internal Model#. The Defect Agent (Beta) cross-references the plant-of-origin field and surfaces that 89% of the affected reviews match one production batch.

A QA lead reads the output in 12 minutes, confirms, and opens a factory ticket. No one built a dashboard. No one ran a weekly report. The agents did the reading; the human made the call. The same pattern repeats across the rest of the catalog the next morning — on a different SKU, a different theme, and a different retailer — without anyone being assigned to watch it.

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