How Group Miki handled 18,000+ weekly customer service interactions seamlessly with Hiver AI

About the company

Miki Travel Online Limited, established in 1967, specializes in hotel wholesale on a global scale. The company transforms the conventional tour operator model by focusing on emerging sectors. It drives this shift through proprietary in-house systems and advanced technologies that deliver services and solutions to business partners worldwide, including hotel suppliers and travel companies. Strong distribution and sourcing networks support these operations and strengthen Miki Travel Online’s position as a trusted partner in the travel industry.

At the center of this growth is Nelson Jay Dela Cruz, Deputy General Manager, who leads the global operations. Nelson oversees Manila Global Operations and has played a major role in modernizing workflows and introducing AI-driven processes that make daily operations faster and more effective.

Challenges

Group Miki’s Manila operations team faced mounting pressure to keep pace with high volumes of supplier and customer communication across multiple channels. Nelson explained that most queries came from customers confirming reservations or requesting modifications, but managing them at scale created ongoing challenges.

• Massive customer query volumes: The team managed between 18,000 and 20,000 weekly customer conversations, with spikes during global holidays and events. Chinese New Year, Ramadan, and summer brought the heaviest surges, requiring careful forecasting and resource planning.

• Manual, inefficient workflows: Before Hiver, the team depended entirely on Google Groups. Agents had to manually toggle between various email accounts, each one dedicated to a specific region or business line. For example, some inboxes handled reservations by geography — Asia-Pacific, Middle East and Africa, Europe, and the Americas — while others were set up for client-facing communication with partners in those same regions. Within each inbox, agents had to manually triage emails, create and update labels, and assign cases without automation. The lack of visibility across accounts meant leaders couldn’t easily track ownership and status of customer requests. This slowed response times, created inefficiencies, and left no room for process optimization.
  
• Long handling times: With no automation or AI to route, triage and respond to support requests, the average case handling time increased significantly, resulting in delayed responses.
  
• Case distribution bottlenecks: In the absence of automated routing, the team had to create manual guidelines for distributing customer queries every 10–15 minutes. This system was resource-heavy and prone to inconsistencies, especially given staff’s shifting schedules.

• High risk of duplicate work: In the absence of a ticketing system, multiple agents often worked on the same support requests simultaneously, which is quite inefficient.

These issues drained agent productivity and made it harder for leaders to track performance, maintain quality, and keep processes consistent across regions. Nelson realized the team needed a solution that could grow with them—one that would take care of routine tasks, highlight insights in real time, and free agents to focus on the interactions that truly mattered.

Implementation

Nelson began researching platforms to streamline customer service workflows. Most of the options that he explored were too costly, too complex to set up, or weren’t powerful enough to meet their growing needs..

Nelson stumbled upon Hiver, an AI-powered customer service platform that was incredibly powerful, yet easy enough for agents to adopt. During the trial, Nelson tested it himself and saw how quickly his team could move from scattered processes to a more organized, scalable system.

The rollout was smooth, with the team transitioning from shared email accounts to a unified interface in Hiver. This created the structure they needed to manage high volume of customer requests consistently and laid the foundation that allowed them to scale customer service through automations. 

Solution

After adopting Hiver, Group Miki transformed how its Manila Operations team managed customer service. Manual processes were replaced with more structured, AI workflows that allowed the team to handle a high volume of customer requests with greater speed and consistency.

• Leveraging Hiver AI to transform customer service

Hiver AI has been at the heart of Group Miki’s transition from manual processes to a scalable, proactive customer service operation. Nelson and his team were able to automate repetitive tasks and reshape how they manage quality and efficiency.

• AI Tagging for high-volume workflows

Booking confirmations and modifications account for most of Group Miki’s weekly conversations. With AI Tagging, these cases are automatically categorized and labeled the moment they arrive, eliminating the manual burden on agents. This allows them to focus directly on resolving issues. At a scale of 18,000–20,000 conversations per week, even small time savings per case compound into significant efficiency gains — reducing baseline handling time and ensuring that high-priority issues are surfaced immediately.

• AI Sentiment Analysis for real-time coaching

Hiver’s AI Sentiment Analysis scores every conversation for tone and automatically flags messages with negative sentiment — such as frustrated customers. This lets leaders:

1. Intervene in real time to coach agents while cases are still open.
2. Review responses before they reach customers.
3. Build targeted coaching programs around weak spots, instead of relying on samples.

This change shifted the Agent QA process from reactive to proactive, improving customer experience and agent performance.

• Centralized visibility to bring structure across the team

Nelson’s team consolidated multiple inboxes into Hiver. Each mailbox represented a specific region or function — Asia-Pacific, Europe, the Middle East, the Americas, reservations, and client-facing communication. By managing them through Hiver’s Shared Inboxes, the team brought structure and consistency to their workflows. Leaders can now easily monitor workloads by geography and function, something that was impossible in the old Google Groups setup.

• SLA tracking keeps the team accountable

The team created seven SLA policies tailored to different case types, priorities, and regions. For example, booking confirmations require a 30-minute first response, while more complex issues — such as billing disputes, hotel overbooking escalations, or multiple bookouts — have longer but still clearly defined timelines, like a 24-hour resolution window. With Hiver, Nelson and his leadership team get notified when a case risks breaching these targets. This lets them step in during busy periods, re-route cases, and keep service performance consistent across geographies and complexity levels.

• No more duplicate responses to avoid confusion during crisis

Before Hiver, agents often jumped into the same customer request and sent duplicate or conflicting replies. Collision Alerts now show when a teammate starts drafting a response, so others redirect their focus to different cases. When managers take over escalations, agents step back immediately. The team saves time, avoids confusion, and delivers one clear, professional response to every customer.

• Categorization to help prioritize requests

Although the team uses templates sparingly to avoid generic-sounding replies, they keep saved responses ready for recurring scenarios. They also sort cases using custom fields, internally called “case record types.” Each case falls into a complexity category — low, medium, or high — with a scale of one to five. This system helps agents focus on the right cases first and ensures leaders route the most complex issues to the most skilled staff.

• Analytics drive smarter decisions

Leaders use Hiver’s Analytics dashboards to track open and closed cases, SLA compliance, and how work spreads across issue types. Instead of guessing, they see which types of issues consume the most time. For example, analytics showed that booking confirmations—customers asking to verify that a reservation is confirmed and in order—and reservation modifications make up roughly half of weekly volume. That insight validated Nelson’s decision to prioritize AI triaging for these issue types first.

• Real time support for urgent requests

Group Miki embedded Hiver’s Live Chat into its two core booking systems — internally known as McKinnick Traffic and McKinnick Chain. The chat is designed to filter queries based on complexity:

1. Simple requests like checking a confirmation number or cancelling a booking are resolved automatically within the system. The Chatbot provides instructions or pulls up the relevant booking information, saving agents from handling repetitive questions.
   
   
2. Complex or urgent issues — such as payment problems, a hotel reporting no booking on file, or guests without a reservation are immediately escalated to a live agent. In these cases, the chat works like a real-time messenger, allowing customers to connect with a representative and get a resolution.

This layered setup allows the team to handle critical issues in real time without being overwhelmed by simpler requests that can be resolved automatically. It also ensures that customers in urgent situations such as same-day check-ins don’t have to wait in email queues or call lines.

Results and benefits

By adopting Hiver as their customer service platform, Group Miki achieved measurable improvements that reshaped how their team delivers support across email, chat, and calls.

• Ticket resolution time reduced by 67%

The biggest win came in case handling time. Average case handling time dropped from 12 to just 4 minutes, allowing agents to resolve issues faster and giving the team the bandwidth to manage higher volumes without expanding headcount.

• 300 Automations → 38 hours saved per week

Group Miki deployed over 300 automations to route and triage support requests, which eliminated repetitive manual actions and saved 38 hours every week. These efficiencies freed agents to focus on complex customer interactions that required human judgment.

• 168 hours saved overall

Using Hiver’s shared drafts and notes, teams across bookings, finance, and client coordination streamlined internal collaboration. By centralizing all communication in one interface, they achieved time savings of 168 hours per month.

• Consistent SLA compliance

Hiver’s SLA tracking ensured the team consistently met targets: first responses under 30 minutes and resolutions within 48 hours. These benchmarks became key indicators of efficiency for stakeholders and reinforced customer trust.

• Proactive quality management

AI Sentiment Analysis enabled leaders to coach agents in real time. Customer responses with a negative tone were flagged immediately, corrected on the spot, and later repurposed as targeted coaching material to help agents avoid similar mistakes in the future.

• No more duplicate work

Collision Alerts show the team exactly who is working on a case. When a teammate starts drafting a reply, others redirect their focus to different requests. During a Hilton incident with multiple related bookings, alerts allowed leaders to take over communications while agents shifted to other cases. The team avoided duplicate replies, saved time, and delivered one clear, professional response to customers.

• Ready to scale

With Hiver’s omnichannel ticketing, Live Chat, Analytics, and AI-driven workflows, Nelson’s team gained the structure to manage 18,000–20,000 weekly customer conversations across regions. Even during peak travel seasons, they now operate without overwhelming their staff.

Looking ahead

Group Miki’s customer service journey is far from finished. Nelson and his team plan to deepen their use of Hiver and unlock features that drive more efficiency. AI Copilot will help agents resolve routine cases faster and free their time for complex issues. Conversation merge will let the team combine related requests into one view, which reduces manual work and improves visibility. Hiver will also enhance round-robin routing, giving leaders more flexibility to distribute workloads fairly across a team with shifting schedules.

Beyond automation, Nelson is also preparing to launch a Knowledge Base with a custom domain, designed to give customers direct access to answers and reduce dependency on live support for simple queries. Together, these initiatives represent Group Miki’s commitment to building an AI-first customer service model that balances speed, scalability, and human empathy.

Conclusion

Group Miki’s experience with Hiver shows how a global hospitality business can rethink customer service by moving away from outdated processes to an AI-enabled platform. The change reduced inefficiencies, gave leaders real-time visibility, and equipped agents with tools that let them focus on meaningful conversations instead of repetitive tasks.

For Nelson, the real value lies in partnership. Hiver adapts quickly to Group Miki’s changing business needs and gives him confidence that their customer service will stay resilient, scalable, and future-ready.