60% of inquiries handled without an agent: How Rating Bookmakers built customer support with Flomni CS

Great customer support depends on more than response speed. The entire process needs to be reliable: inquiries must reach the team without disruption, routine questions should require minimal manual effort, and agents need to step in quickly when human assistance is needed.

For Rating Bookmakers, this was especially important. The team handles a high volume of inquiries across several channels, and the quality of support directly shapes the user experience. The company needed a solution that could automate routine interactions, streamline agent workflows, and maintain service quality without expanding the team.

The company already used a third-party platform to manage customer inquiries. Users could contact the team through the website's live chat, email, or phone, while agents handled incoming requests in their existing workspace.

Over time, the team became dissatisfied with the vendor's support. Following changes within the provider's team, response times increased and support specialists became less engaged. Whether staff needed to adjust settings, update bot logic, or troubleshoot a technical issue, they often had to find answers on their own.

The team particularly needed to be able to:

When timely responses and response-time KPIs matter, reliance on external technical support becomes an additional risk to day-to-day operations.

The high proportion of manual work added to the challenge. Before the new solution was introduced, every conversation had to be closed manually. A small agent team therefore handled the entire incoming volume without automated initial guidance.

The team needed a platform that would centralize inquiries, reduce manual handling of routine requests, and require fewer internal resources to configure workflows.

Rating Bookmakers evaluated several solutions, with five vendors participating in the tender. The decision was based on a combination of factors: licensing and implementation costs, support quality, vendor reliability, customizable reporting, and the platform's future development.

The team also assessed each vendor's commitment to product development and its ability to maintain support quality after implementation. Given the experience with the previous solution, this was a particularly important criterion.

Ultimately, the company chose Flomni CS, a platform for automating customer communications. The solution brought the required channels together, enabled button-based flows for common inquiries, and improved customer support without increasing headcount.

The Flomni CS team created a unified inquiry management workflow for Rating Bookmakers. The aim was to make it easier for users to find the information they needed while organizing the support team's work: the bot handles some inquiries, while those requiring human assistance are routed to agents in a single interface.

At the heart of the automation was a button-based chatbot built with the Flomni CS Chatbot Builder. Rating Bookmakers chose a straightforward rule-based flow: users open the widget, see a set of quick-action buttons, and select the appropriate option.

The chatbot provides quick access to key sections and common actions:

This approach works well for a service that receives many recurring inquiries. Users can find essential information or select a topic without waiting for an agent. When human assistance is necessary, the bot transfers the conversation to the support team.

Users can also continue their conversations in Telegram. The message history remains available in the messenger, so they can return to the discussion later without losing context.

The bot became the first line of customer support, resolving routine requests, helping users navigate to the right information, and reducing manual work for the team.

Inquiries requiring human assistance move to Dialogs. For Rating Bookmakers, this became a single workspace where agents handle messages from connected channels: website live chat, Telegram, and email.

Previously, some communications had to be managed manually across separate entry points. With Flomni CS, inquiries now reach a shared interface, where agents can see the conversation and its context and get involved more quickly.

In Dialogs, the team uses:

The team particularly values canned responses, which help agents reply faster while keeping communications consistent. Manual handling has become more organized: agents focus on cases that genuinely need human assistance instead of drafting the same replies from scratch.

After Flomni CS went live, the workload was split between the chatbot and the agent team. The button-based flow took on routine inquiries, allowing agents to focus on requests requiring manual handling and specialist attention.

This reduced the share of manual handling and made agent workflows more structured. Users reach the right scenario or specialist faster, while agents concentrate on inquiries that truly require human assistance.

The team also uses Flomni CS analytics to monitor workload, KPI performance, frequently selected buttons, inquiry topics, user ratings, and feedback. This provides visibility not only into inquiry volumes but also into which scenarios users choose most often and where customer support needs attention.

Together, the button-based chatbot, response templates, and inquiry routing have made agents' work more structured. The team can pick up inquiries faster, use prepared wording in recurring situations, and keep communications consistent.

Another important outcome has been platform stability and support quality. Following its previous experience, the team needed a system that would not introduce additional risks into daily customer support operations and a provider that would resolve emerging issues quickly and effectively.

Overall, Flomni CS helped Rating Bookmakers improve customer support without growing its team. The bot took over routine scenarios, Dialogs streamlined agent workflows, and analytics provided a way to monitor workload, KPIs, and user feedback on an ongoing basis.

*Note: All metrics and outcomes reflect the project's status at the time this case study was prepared.