Why Are Customers Calling and What It Means for Your Business?

In our last blog post in the series of Performance Management Solution for Contact Centers, we discussed quality forms and their pivotal role in driving business outcomes. We explored how these often-overlooked tools can significantly impact customer satisfaction, compliance, and agent efficiencies. We also touched on the exciting potential of AI in revolutionizing quality assessment processes.

Today, we're taking the next step in our journey through Performance Management Solution for Contact Centers by exploring a fundamental question: Why are customers contacting us?

Understanding why customers reach out is more than just a matter of curiosity. It's a mine of insights that can transform customer experience strategy. So, let's dive in and explore how we can uncover these valuable insights and, more importantly, what we can do with this information to drive meaningful improvements in our contact centers.

The Traditional Approach: A Trip Down Memory Lane

Before jumping into the horde of AI-powered insights, let's take a moment to reflect on how companies have traditionally captured contact reasons. These methods, while well-intentioned, often fall short of providing a complete and accurate picture of customer needs.

1. IVR Call Disposition Codes: Many companies use speech-enabled Interactive Voice Response (IVR) systems to capture call reasons. While this method can be effective, it comes with several challenges:
   
   
   1. Single-word limitations: Complex issues often can't be captured in a single word or phrase.
   2. Lack of context: A customer might say "payment," but are they making a payment or investigating a missing one? The context is crucial.
   3. Single reason capture: If a customer has multiple reasons for calling, traditional IVR systems often can't capture this complexity.
2. Manual Capture by Agents: This method relies on agents to categorize calls, which can lead to inconsistencies due to loose definitions and overlapping categories.
3. Traditional Speech Analytics: While more advanced than the previous methods, traditional speech analytics often rely on keyword searches, which can lead to false positives and miss important context.

Advent of the AI Revolution: A New Way to Understand Customer Contact Reasons

Now, let's explore how AI is transforming the way we capture and analyze customer contact reasons. By leveraging the power of Large Language Models (LLMs), we can overcome several limitations of traditional methods and gain deeper, more nuanced insights into why customers are reaching out.

Here's how an AI-powered approach works:

1. Comprehensive Disposition Code List: Start by creating a detailed list of disposition codes, organized into two levels. The first level consists of broad categories (e.g., New Bookings, General Inquiries), while the second level provides more specific subcategories.
2. AI Analysis of Customer Interactions: Release the AI engine on your customer interactions, allowing it to analyze conversations in their entirety.
3. Context-Aware Categorization: The AI comprehends the full context of each interaction, accurately categorizing the reason(s) for contact and providing commentary on why it assigned specific tags.
4. Multiple Reason Capture: Unlike traditional methods, AI can identify and categorize multiple reasons for a single interaction, providing a more complete picture of customer needs.
5. Issue and Resolution Summaries: In addition to categorization, the AI can generate concise summaries of both the issue and its resolution, offering valuable insights for training and process improvement.

The results of this approach can be truly eye-opening. Let's look at some real-world examples from an online travel agency (OTA) that implemented this AI-powered system:

The data shows a breakdown of contact reasons into broad categories like Booking Inquiry, Cancellation, and Payment Queries. Each of these is further divided into more specific subcategories. For instance, under Booking Inquiry, we see detailed dispositions such as "Airline and Flight Booking," "Booking/Reservation," and "Post-booking Inquiry."

What's particularly impressive is the level of detail in the "Reasons" column. For each disposition, the AI provides a clear explanation of why it assigned that category, demonstrating its ability to comprehend complex contexts and nuances in customer conversations.

Key Learnings from AI-Powered Contact Reason Analysis

Implementing an AI-powered system for analyzing contact reasons can be a game-changer, but it requires careful planning and execution. Here are some key takeaways from our experience:

1. Invest time in defining your disposition codes: Involve your supervisors and quality team in creating a comprehensive list of parent and child categories. This foundational work pays dividends in the accuracy and usefulness of your insights.
2. Avoid over-generalization: Resist the temptation to lump unclear cases into a catch-all "Other" category. Push yourself to define specific categories – you'll be glad you did when analyzing the results.
3. Embrace anomalies: The AI will likely uncover unexpected patterns or categories you hadn't considered. Use these anomalies as opportunities to refine your categorization and gain deeper insights into your customers' needs.
4. Trust the process: In many cases, the AI proves more accurate than human categorization. While it's important to verify and calibrate the results, don't be surprised if the AI consistently outperforms manual methods.
5. Use insights to optimize knowledge management: The detailed categorization of contact reasons provides an excellent foundation for organizing and prioritizing your knowledge base and agent training materials.

Turning Insights into Action: The Power of Contact Reason Data

Now that we have this wealth of detailed information about why customers are contacting us, what do we do with it? Here are some powerful ways to leverage this data:

1. Create a Transformation Roadmap:
   Cluster your contact reasons into broad categories such as Information-based, Account Maintenance, Problem Resolution, and Advisory. Combine this with volume data to create a prioritization matrix for process improvements and digital transformation initiatives.
2. Analyze Trends and Identify Root Causes:
   By plotting contact reason data over time, you can spot emerging trends and potential issues. For example, a sudden increase in payment-related calls might indicate a problem with your digital payment channels.
3. Cross-Pollinate with Other Metrics:
   Combine your contact reason data with other performance metrics like Customer Satisfaction scores, Repeat Calls, and Escalations. This allows you to identify which types of interactions are driving key performance indicators, both positively and negatively.
4. Tailor Training and Quality Programs:
   Use the insights to develop targeted training programs and refine your quality assessment criteria. Focus on the skills and knowledge agents need to handle the most common and impactful types of interactions.
5. Enhance Self-Service Options:
   Identify frequently asked questions or common processes that could be automated or addressed through improved self-service options, reducing call volume and improving customer experience.
6. Inform Product and Service Development:
   Share insights with product teams to inform future developments. Recurring issues or frequent requests can highlight areas for product improvement or new feature development.
7. Personalize Customer Interactions:
   Use the data to anticipate why a customer might be calling and route them to the most appropriate agent or provide relevant information proactively.

Embracing the Future of Customer Insights

Understanding why customers contact us is no longer a guessing game or a matter of broad generalizations. With AI-powered analysis, we can gain unprecedented insights into customer needs, pain points, and preferences. This level of understanding empowers us to make data-driven decisions that enhance customer experience, improve operational efficiency, and drive business growth.

As we continue our journey through the evolving contact center innovation, remember that the key to success lies not just in collecting data, but in turning that data into actionable insights. By embracing AI-powered tools and approaches, we can unlock the full potential of every customer interaction, transforming our contact centers from cost centers into strategic assets that drive customer satisfaction and business success.

Stay tuned for our next installment, where we'll explore how to identify and correct common mistakes in customer interactions, further enhancing our ability to deliver exceptional customer experiences. The future of customer service is here, and it's driven by insights – are you ready to take the leap?