Voice of the customer: turn your conversations into better decisions
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Voice of the customer brings together surveys, conversations, reviews and social feedback to guide product, operations and experience decisions. Loonar designs, implements, trains teams and supports VoC programs that use AI to analyze available interactions, surface themes and sentiment, and put relevant evidence in the hands of people who can act.
In short
- Listen beyond survey responses.
- Give every finding an owner.
- Check whether changes address the problem.
How do you know you need voice of the customer?
Survey respondents tell only part of the story
NPS and CSAT offer useful signals, but participation shapes what you hear. Low response and selection bias can hide recurring friction. Without checking survey findings against everyday conversations, teams risk prioritizing issues from an incomplete picture of the customer experience.
Feedback lives in disconnected places
Tickets, chats, emails and spreadsheets hold valuable observations in different formats. Teams spend time assembling reports instead of understanding the underlying problems. Without shared categories and context, the same customer issue looks different to support, product and business leaders.
Product teams receive anecdotes, not evidence
Support hears where customers struggle, yet those observations often reach product planning without useful context. Individual requests compete for attention without showing recurrence or affected segments. Listening produces little change when nobody owns the handoff from customer evidence to product decisions.
Cancellation reasons miss the buildup
A cancellation can follow a series of unresolved difficulties rather than one decisive event. Looking only at the final reason misses earlier warning signs. Teams struggle to distinguish experience problems from product gaps or expectations set during the sales process.
How does it work?
Multichannel feedback collection
Bring together surveys, conversations, reviews and social feedback from approved sources. Retain channel, timing and journey context so teams can interpret each signal without treating every source as interchangeable evidence.
AI classification of themes and sentiment
Classify available conversations by theme, intent and sentiment. Review the categories with business specialists and resolve ambiguous examples before using the results to guide operational changes or product planning decisions.
Conversation experience scoring
Combine signals of effort, frustration and resolution into a conversation level view. Make the criteria inspectable and link back to evidence, while keeping inferred scores distinct from ratings customers explicitly provide.
Trend monitoring and anomaly alerts
Track shifts in contact drivers and dissatisfaction signals. Set alerts around relevant business contexts and use human review to separate emerging incidents from seasonal patterns or changes in data collection.
Closed loop action management
Turn findings into backlog items or operational actions with an owner and due date. Record decisions, follow progress and revisit conversations to see whether the original issue keeps appearing afterward.
Views by segment and journey
Explore feedback by product, customer segment and journey stage. Compare compatible groups, inspect examples and give support, product and leadership a shared reference, with access appropriate to each team's role.
How does Loonar deliver?
Discovery
Map feedback sources, permissions, data quality and the decisions the program should support. Agree on an initial scope, accountable teams and coverage limitations before setting expectations for what the analysis can reveal.
Program design
Loonar designs the taxonomy, implements agreed workflows, trains participating teams and supports ongoing improvement. This stage establishes sentiment criteria, human review requirements and clear routes for moving findings to decision makers.
Implementation and integrations
Connect approved sources and configure context, identification and access controls. Test classification against real examples and track ingestion gaps so missing data does not get mistaken for evidence that a problem has disappeared.
Team training
Prepare support, product and leadership to interpret themes, inspect evidence and challenge classifications. Practice turning a finding into an action, with clear boundaries around AI interpretation and human responsibility for decisions.
Operation and continuous improvement
Review coverage, classification quality and action progress together. Refine categories and alerts as products or journeys change, maintaining a practical cadence that connects listening, decisions and checks on the resulting customer experience.
What results do we measure?
- Feedback analysis coverage
- Shows how much available, authorized feedback actually enters the analysis.
- Time from insight to action
- Tracks the interval between identifying a finding and starting the corresponding action.
- NPS and CSAT
- Place customers' stated perceptions alongside signals observed in their conversations.
- Recurring contact drivers
- Shows whether the same issues keep generating contacts after teams make changes.
- Attributable churn
- Investigates cancellations associated with identified issues without treating correlation as proof of causation.
How does AI expand a voice of the customer program beyond sampling?
The distinction is both the breadth of available evidence and the process for acting on it.
| Aspect | Sample based VoC | AI VoC across available interactions |
|---|---|---|
| Coverage | Selected responses and reports | Connected, authorized interaction set |
| Analysis | Periodic manual consolidation | Ongoing classification with human review |
| Context | Views shaped by the sample | Themes across products and journeys |
| Action | Reports for discussion | Routing with an owner and due date |
Where do we apply it?
Financial services
Connect feedback about applications, charges and digital channels to spot friction across customer groups. Route supporting evidence to the responsible teams and check contact drivers after changes. Limit access to sensitive material and keep personal information out of broadly shared dashboards and examples.
Retail and e-commerce
Map comments about checkout, delivery and returns to the relevant journey stage. Distinguish isolated incidents from recurring issues and send each finding to operations or product. After a change, review new conversations to see whether customers still encounter the same difficulty in comparable situations.
SaaS
Read onboarding, support and renewal conversations together to distinguish usage questions, product gaps and mismatched expectations. Bring contextual examples into planning and track whether themes recur. Shared evidence helps customer success and product teams discuss priorities without assuming that the loudest request represents every customer.
Frequently asked questions
What is a voice of the customer program?
A voice of the customer program is an ongoing process for gathering feedback, understanding what it means and using it to guide decisions. It defines sources, analysis rules, ownership and follow through. Loonar designs, implements, trains teams and supports this process, connecting customer feedback analysis with the people responsible for product improvements, operational changes and customer experience.
Does VoC replace NPS surveys?
No. NPS captures a stated perception at a particular moment, while VoC brings together different kinds of experience evidence. Conversations help explain survey responses and include customers who never completed a questionnaire. A useful program combines those perspectives without flattening their differences. Sentiment inferred from a conversation should never be presented as a survey rating that the customer actually supplied.
How does AI recognize sentiment in Portuguese?
AI uses wording, context and relationships between passages to estimate sentiment. Regional expressions, irony and shifts in tone make this more complex than counting positive or negative words. We validate classifications against business examples, review ambiguous cases and refine the criteria. Sentiment analysis supports human interpretation; it should not independently determine issue priority or become the sole basis for evaluating an individual.
How do insights reach the product team?
Agree on a handoff before building dashboards. Each finding should include supporting evidence, relevant context, affected segments and a hypothesis about impact, plus an owner and a review date. Product teams retain responsibility for prioritization within their planning process. The VoC program records that decision and tracks the issue in later conversations, even when a proposed improvement is not immediately accepted.
What data do we need to get started?
Start with an approved source containing useful feedback and enough context, such as support conversations or open survey responses. Check availability, quality, the period covered and permission to use the data. Product, channel and journey identifiers help, but you do not need every field from day one. Discovery establishes a workable scope alongside privacy, retention and access requirements.
Shall we design the next version of your operation?
Tell us the challenge. One conversation with a specialist gives you a clear path, even if it isn't time to buy yet.