The utilities sector in Brazil still faces recurring challenges in customer service, especially when there are peaks in demand, multiple active channels, and pressure to reduce operating costs. In services such as energy, water, gas and sanitation, most contacts occur out of immediate necessity, raising the demand for quick screening and resolution.
When the operation does not have sufficient resources to absorb the volume of calls, waiting time, complaints on public channels and the perception of low service quality increase. In this context, the chatbot enters as an automation technology capable of absorbing repetitive demands, accelerating service and generating data for continuous improvement.
Sectorial surveys cited in studies of the bot ecosystem indicate an increase in the use of chatbots and an increase in message traffic in automated operations, reinforcing the consolidation of the conversational channel as a standard of contact in service.
Main challenges of servicing utilities
The service operation in utilities usually concentrates very specific difficulties, because of the critical nature of the service and consumer behavior in emergency situations.
Most common challenges:
- High volume of customers and large recurring service base (duplicate, invoice questions, request status).
- Seasonal peaks due to weather events and occurrences (heavy rains, falling trees, supply interruptions, sewer problems).
- New relationship patterns in the post-Covid period, with a greater preference for digital channels.
- Pressure for efficiency and cost reduction, maintaining quality indicators.
In utilities, contact usually occurs at times of friction. Therefore, the service must resolve quickly, register correctly and guide the next step with simple language.
What is a chatbot and how does it work in utilities
Chatbots are conversational assistants that interact via text or voice to identify customer intent and forward the appropriate solution. In mature operations, the bot combines business rules, knowledge base, and AI models to deal with variations in the language and context of the service.
In the utilities sector, the chatbot tends to operate in a flow of:
- Customer identification (CPF/CNPJ, installation number, SMS validation, or login to the app).
- Classification of the reason for the contact (invoice, interruption, reconnection, leak, registration change).
- Service execution (queries and requests integrated with internal systems) or routing for human care with full context.
The operational gain appears when automation reduces the volume of calls that require human agents and decreases screening time.
Use cases with the greatest impact on the utilities sector
Adoption tends to be more efficient when it starts with high volume and low complexity issues, with progressive integration for transactional services.
Typical use cases:
- Issuance of Duplicate invoice and debit consultation.
- Sending a barcode, Pix, and payment guidance.
- Negotiation and installment payment (when permitted by company policy).
- Consultation of power/supply outage status and estimated deadlines (when there is data from the occurrence system).
- Request for Religation, change of ownership and registration update.
- Registration of occurrences (leak, power outage, low pressure, sewage) with structured data collection.
- Scheduling of Survey, reading, and technical services (when the operation offers this model).
These flows reduce queues, increase first-contact resolution, and standardize call registration.
Chatbot benefits for utilities
In utilities, the chatbot usually generates results on three fronts: speed, operational efficiency, and data quality.
Operational and experience benefits:
- 24/7 service, useful for urgent requests and for off-office hours.
- Reduction in average service time (TMA) by automating the screening and execution of routines.
- Absorption of peak demand with simultaneous service to multiple clients.
- Standardization of information, reducing human error in guidelines and records.
- Reports and metrics to identify bottlenecks, contact reasons, and opportunities for improvement.
Some consultancy studies indicate significant gains when companies evolve their digital maturity in their relationship with the customer, with an impact on productivity and operating expenses. This type of evidence is usually more useful when the company measures its own indicators before and after implementation, with excerpts by channel and reason for contact.
Omnichannel in utilities: why the chatbot is an accelerator
Utilities often operate with multiple points of contact, such as WhatsApp, website, app, URA, and social networks. The experience deteriorates when the customer has to repeat data and context when switching channels.
The chatbot can work as a unified service layer when integrated with an omnichannel platform and a CRM, maintaining history, customer data, and request status. This architecture tends to improve the continuity of service and the quality of scaling for human agents.
How to implement chatbot in utilities with less risk and more results
A consistent implementation starts with process design and prioritized integrations, avoiding trying to automate everything in the first cycle.
Recommended steps:
- Map the main reasons for contact by volume, cost and critical nature (top 10 intents).
- Define success criteria by use case (deflection, FCR, CSAT, resolution time).
- Create flows with operational language, including data validation and contingency messages.
- Integrate essential systems (billing, registration, protocols, events/outages, scheduling) as appropriate.
- Design the handoff for human with the sending of context, history, and collected data.
- Implement governance and compliance (LGPD): data minimization, consent when necessary, audit trails, and adequate retention.
The efficiency gain appears with greater predictability when the bot solves transactional services and when the handoff reduces the agent's rework.
Metrics and KPIs to monitor in a chatbot for dealerships
Without instrumentation, the operation loses quality visibility and cost per contact. In utilities, these indicators are usually the most actionable:
- Containment rate/deflection rate (percentage of cases solved in the bot).
- FCR (First Contact Resolution) by reason of contact.
- Service time (time in the bot and total time until resolution).
- Transfer to human (fallback rate and causes).
- CSAT/NPS per channel (when there is research).
- Emerging contact reasons (to anticipate crises and seasonal demands).
These metrics help prioritize improvements in content, integration, and flows that generate the highest volume.
Frequently Asked Questions (FAQ)
Does Chatbot replace human service in dealerships?
The chatbot tends to assume repetitive and screening demands, while human agents are left with complex cases, exceptions, and sensitive negotiations.
What are the most common channels for chatbots in utilities?
WhatsApp usually has a high membership, followed by a website and an app. In some operations, URA with voice also enters the design.
What is the first recommended use case to start with?
A duplicate bill and debit consultation usually provide a quick return, due to volume and low complexity, provided that the integration with billing is stable.
How to avoid customer frustration in the bot?
Short flows, objective language, correct data validation, and a clear option to speak with an attendant when there are exceptions increase the success rate.
