GO Malta Chatbot Audit

A Comprehensive Chatbot Audit with a Future-Ready Framework for GenAI Integration

GO Malta (GO p.l.c) is the leading telecommunications company in Malta that offers a range of services, including mobile, broadband internet, TV, and fixed-line telephony. GO also provides business solutions, such as cloud services, data center hosting, and managed IT services. The company aims to deliver reliable and innovative communication solutions across Malta. In addition to their telecom services, GO is known for continuously exploring new technologies to enhance their customer experience, which is why they’ve created a chatbot to boost this metric.

The Challenge:

How did we help the client to find the areas of improvement and growth points of their existing conversational tool?

GO Malta wanted to expand the functionality of their web chatbot, transforming it into a more effective customer engagement tool. However, GO faced challenges like inconsistent responses, limited conversational design, and a lack of personalization that impacted user satisfaction and prevented efficient query resolution. As a result, they contacted Master of Code Global with clear objectives that included: increasing the chatbot’s success rate in addressing common queries; delivering a consistent, customer-centric experience to elevate satisfaction; establishing a future-ready framework for GenAI capabilities.

What we created:

Master of Code Global performed an in-depth chatbot audit and technical consultation to drive improvements for GO Malta’s chatbot.

The client’s goals were to streamline bot’s performance, improve user experience, and lay the groundwork for GenAI integration to create a more responsive and sophisticated AI-powered support. This is why our analysis had to cover every aspect of the chatbot performance, from analyzing how well it understands and responds to diverse inputs, to examining its technical capabilities and potential for GenAI enhancement. This in-depth audit aimed to ensure the solution meets the client’s needs and elevates user engagement.

Master of Code utilized a structured assessment framework to conduct an end-to-end evaluation of the chatbot experience. Working closely with GO’s team, we provided a clear roadmap to enhance the bot’s conversational quality, resilience, and alignment with GO’s brand. According to our report, the client now can transform their chatbot into a more efficient, scalable, and user-friendly solution. In addition, our recommendations provided a set of steps needed for integrating GenAI and for improving both customer journey and technical functionality.

Methodology

Our audit process was thorough and systematic, involving several key components:

Transcript Analysis

We meticulously reviewed a significant number of user transcripts to understand common queries, intent, and engagement patterns. This analysis helped us identify recurring issues, such as misunderstood queries or inadequate responses.

Flow Evaluation

We examined the chatbot’s conversation flows to evaluate their logic, coherence, and efficiency. This involved mapping out the funnel to identify any bottlenecks or points of confusion that could lead to customer frustration.

User Journey Analysis

Scored the chatbot against Master of Code Global’s Bot Analysis Framework to identify friction points in consumer interactions and streamline response flow.

NLP Performance Assessment

A detailed evaluation of the Natural Language Processing capabilities was conducted to assess the chatbot’s understanding of user input. We analyzed its ability to comprehend various intents, sentiment, and context, which is crucial for providing relevant responses.

Technical Implementation Review

We investigated the technical framework of the chatbot, including its architecture and integration with backend systems. This review allowed us to identify potential technical constraints or inefficiencies that could hinder performance.

Analytical Data Review

Leveraging analytical tools, we examined key performance metrics, such as engagement rates, drop-off points, and resolution rates. This data provided valuable insights into customer behavior and the overall effectiveness of the chatbot.

Error Management & Escalation Testing

We tested the bot’s ability to handle unexpected inputs and quickly direct users to live agents, optimizing for a seamless escalation process. This helps reduce redundant questions during escalation and provides suggestions for after-hours support.

Conversation Design Workshops

Our Conversational Designer conducted interactive sessions to refine the bot’s persona, tone, and conversational flow, ensuring it resonated with GO’s audience. We also included recommendations for a more user-friendly menu design, simplified response structure, and optimized flow that helps find answers quickly.

What Are the Outcomes?

Documented bot persona & tone of voice
Main flows for Conversation Design

Outlined main flows based on the analytical data
The detailed audit report

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