Finding the right customer service representative can range from frustrating to infuriating for many consumers. Being forced to repeat their problems and questions multiple times as they get passed off to different departments sends a message that customer experience isn’t a top priority and neither is meeting the basic needs of those customers. The exchanges can feel cold and impersonal, souring relationships with businesses, sometimes before they even start.

These experiences are not an anomaly: More than 60% of consumers in 2023 felt that most companies treated them like a number. And B2B customers weren’t feeling much better. About 60% of business buyers said sales representatives weren’t taking the time to get to know them, while more than 70% said that their interactions felt transactional.

That is risky behavior for companies in today’s marketplace. More and more, consumers are showing a willingness to switch brands, so relying on customer loyalty isn’t a safe bet.

To help achieve better consumer satisfaction, many companies are turning to conversation intelligence platforms that combine voice of customer data and AI analysis to better understand their customers’ needs and experiences and produce better engagement in the long term.

The Power of Personalization in Customer Service

For businesses that want to build strong relationships with their customers or take advantage of shifting marketplace dynamics to attract new customers, personalization is key. That doesn’t just mean sending discount codes to buyers on their birthdays.

Companies must be able to accurately assess and anticipate how their customers’ preferences are changing and tailor their experiences to meet those needs. Consumers want timely assistance from humans, and service should be consistent no matter which representative a customer interacts with.

A comprehensive strategy to create personalized customer service and customer experiences can not only help companies build their customer base but also create advocates that lead to new business opportunities. However, teams collecting a lot of data aren’t always sure if they’re using data effectively. For example, marketers have a range of tools at their disposal, including marketing analytics and measurement tools, customer relationship management platforms, and customer data platforms, but two-thirds are unsatisfied with how they are using customer data to create relevant experiences for their buyers, leaving significant room for improvement.

Putting the Voice of the Customer First

Businesses need to think more critically about the types of data that can support the development of personalized experiences.

While business intelligence teams can make insightful observations by analyzing how customers interact with sites and what journeys they take to make a purchase, Voice of the Customer (VOC) programs can also play a crucial role in learning about customer preferences, needs, and problems.

Within VOC programs, surveys and questionnaires are common tools, providing structured data on customer satisfaction and preferences. In-depth interviews and focus groups offer qualitative insights, allowing businesses to understand the nuances of customer expectations. Social media monitoring and sentiment analysis can also capture real-time feedback and broader public opinion.

However, customers are giving less feedback to companies, particularly if they are dissatisfied with a service or product. Instead, they may drop the product or service altogether, completely disengaging and leaving companies with little to no feedback on what went wrong. Capturing customers’ voices will therefore require more creative new listening tools.

Conversation intelligence platforms are integrating Speech AI to help companies solve these data management problems, unlock voice of customer insights, and, ultimately, drive more intelligent customer interactions.

Speech AI models can capture conversations, swiftly transcribe them into text, and perform sophisticated analysis on this conversational data.

Speech AI models (including Speech-to-Text AI and LLMs for speech) can perform tasks such as:

• Transcription
• Speaker diarization
• Profanity filtering
• Automatic language detection
• Summarization
• Sentiment analysis
• Topic detection
• PII redaction

Companies can use these AI models to build powerful conversation intelligence tools and platforms designed to support VOC programs that inform sales, customer support, and other teams across the company. Instead of relying on time-consuming or costly surveys and focus groups, each customer service or sales interaction instantly becomes a valuable source of data that can drive informed decision-making and strategic improvements.

Using Speech AI models, Echo AI developed conversation intelligence tools that showcase what this visibility can mean for business leaders and managers. With Echo AI’s tools, users can summarize customer conversations, flag key terms like “cancel my subscription” and identify the overall sentiment of both participants in sales calls or customer support interactions. This data can be used to monitor individual accounts and address specific service needs, or be pooled into larger data sets that allow companies to answer questions like, “What are the main causes of customer churn this quarter?”

Conversation intelligence data and insights can also be integrated into platforms that house other forms of customer and sales data, enabling leaders to generate a holistic view of their business and to more effectively share insights across teams and departments. Every touch point can then be tailored to the unique experience of a consumer.

Tailoring Customer Experiences with AI and Human Touch

When it comes to customer interaction, hearing a human voice that recognizes and empathizes with a customer’s challenges makes a difference. Speech AI and conversation intelligence tools can augment companies to help their customer service representatives better understand customers’ needs and pain points and create personalized experiences that keep customers satisfied in the long run.

Explore AssemblyAI Speech AI models

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