Bridging the Gap: Enhancing Customer Experience Through Generative Machine Learning

The Hidden Cost of Customer Frustration

Moving Beyond Guesswork: How We’re Using GML to Close the Gap

What Exactly Is GML?

Hands-On: How GML Works in Practice

What Held the Solution Together

What Changed as a Result

Making Real-Time Action Possible

Why GML Isn’t Just About AI, It’s About Intent

Frequently Asked Questions

Bridging the Gap in Customer Experience: The Power of Generative Machine Learning

In today’s fast-paced business world, the way a company manages customer experience can make or break its brand perception. One misstep—a missed delivery or an unhelpful customer service agent—can lead to irreplaceable damage. Industry statistics reveal that nearly 32% of consumers abandon a brand after just one bad experience, highlighting the significant risk businesses face. Unfortunately, many companies still find themselves struggling to deliver on customer expectations.

The Hidden Cost of Customer Frustration

Consider the experience of Bob, a loyal customer who orders a $1,000 dishwasher. After a frustrating wait, the appliance arrives damaged. Bob contacts customer support, only to be placed on hold, transferred, and ultimately informed that his issue will undergo further investigation. In this scenario, Bob’s frustration goes unnoticed, and he is perilously close to canceling his order.

Now, scale this situation across hundreds of thousands of customers—this disconnect between operational data and customer sentiment can have far-reaching consequences. Operational systems may show a shipment delay, but they often lack insight into the emotions of customers like Bob. Support transcripts and chat logs hold valuable clues, yet current systems fail to connect these insights quickly enough to make a difference.

This disconnect often translates into higher costs for companies, as each callback or escalation can add $8 to $15, and issues that remain unresolved further erode trust.

Moving Beyond Guesswork: Enter Generative Machine Learning (GML)

At Dentsu Global Services (DGS), we recognized the urgent need for an innovative solution. While many companies are experimenting with Generative AI (GenAI) in isolation, we asked: what if we developed a system that not only comprehends individual customer needs but also learns from their behavior on a larger scale?

This led us to Generative Machine Learning (GML). Unlike traditional methods, GML synergizes the language capabilities of GenAI with the predictive power of machine learning to create a comprehensive customer experience framework focused on understanding and empathy.

What Exactly Is GML?

In essence, GML marries the strengths of GenAI and machine learning. While GenAI excels at processing language and identifying customer emotions, machine learning is adept at spotting patterns and predicting potential outcomes based on historical data. Together, GML becomes a robust system that can:

• Analyze everything from shipping data to chat transcripts
• Identify emerging issues before they escalate into crises
• Prioritize immediate attention on high-risk cases
• Initiate personalized, timely responses

GML in Action: A Real-World Example

Let’s take a look at how GML transforms customer service:

1. Early Detection of At-Risk Orders: Unlike traditional systems, which only react once a customer complains, GML continually analyzes operational and conversational data. It can flag the top 10% of orders most likely to become problematic.

2. Deep Conversation Analysis: By using large language models, GML sifts through chat logs and call transcripts to identify subtle cues of frustration or explicit escalation threats.

3. Risk Scoring: Operational signals combined with conversational insights generate a risk score for each order, which helps prioritize agents’ efforts effectively.

4. Real-Time Response: If Bob’s order shows signs of both delay and customer frustration, the system prompts support agents to take immediate action, whether it’s issuing a goodwill discount or arranging a replacement.

5. Scalability: The automated nature of GML means it can handle millions of interactions per year, allowing support teams to focus on resolving complex issues rather than sifting through cases.

Achieving Remarkable Results

After implementing GML, the results were significant within just weeks:

• Customer satisfaction increased by 22%
• Resolution times were reduced by 80%
• DGS saved $6 million by preventing churn
• Operational costs decreased by $500,000 due to lower call volumes

These metrics reflect an adaptable system that prioritizes customer needs and simplifies agents’ jobs.

The Infrastructure that Makes GML Work

The success of GML doesn’t solely depend on its technology; it also hinges on robust infrastructure:

• Real-time data integration is essential for timely insights.
• Seamless communication between systems ensures agility.
• Swift response protocols are vital, minimizing delays in customer care.

Too often, great ideas fail due to inadequate infrastructure, but GML demands readiness across all fronts to ensure proactive engagement.

Intent Over Technology

It’s crucial to note that GML isn’t just about utilizing advanced AI—it’s about aligning true intent with exceptional service. Instead of merely analyzing problems after they arise, GML fosters a proactive mindset, equipping teams to anticipate issues and address them before they escalate.

This approach allows us to prioritize our customers as individuals, recognizing their stories, frustrations, and expectations.

Furthermore, Dentsu Global Services prides itself on cultivating a future-focused environment where top-tier talent and groundbreaking technology converge. With over 5,600 experts specializing in various fields, DGS is poised to deliver innovative, scalable solutions that meet the evolving needs of clients across the globe.

Conclusion

In a world where customer loyalty is hard-won, the implications of the experiences brands create cannot be overstated. By embracing generative machine learning, businesses can bridge the gap between their intentions and customer realities, ensuring they don’t just survive but thrive in today’s competitive landscape.

This article was enriched by insights from Pavak Biswal, Senior Manager at Dentsu Global Services.

If you want to delve deeper into how GML can revolutionize your customer experience strategies, stay tuned for further insights!