Conversational Analytics in Customer Service: Benefits & Use cases

The key to understanding customers – their preferences, the reasons behind their choices, and their perception of different brands – is often provided by the customers themselves. The communication and interactions they have at different touch points throughout their customer journey is a rich information source that often remains untapped. Analyzing this conversational data can yield invaluable insights.

What is Conversational Analytics?

Customer conversation analytics, encompassing various sources such as feedback and surveys, online reviews, social media interactions, discussion forums, customer support interactions like phone calls and tickets, messages and emails, and chatbot logs, constitutes the process of conversational analytics. This method involves extracting insights from customer conversations to aid organizations in enhancing their offerings and refining their messaging strategies targeted towards customers. Advanced AI techniques like Natural Language Processing and Machine Learning models are utilized to analyze each conversation comprehensively.

How is Conversational Analytics used for Higher Customer Engagement?

Conversational analytics in customer service provides invaluable insights that can enhance customer engagement at every stage of the customer journey.

Identifying needs

Conversational analytics can help businesses identify the gap between what is available in the market and what customers actually need. By understanding this need, businesses can channelize their resources better and gain the first movers advantage in introducing new products and services.

Improving offerings

Customers often provide feedback on products or services in terms of quality, features, and usefulness. By analyzing conversations with support teams and other feedback channels, product development teams can get insights on usage patterns, most preferred features, redundant or less useful features, and areas of improvements. Such insights can guide them in improving their offerings.

Personalizing communications

One of the crucial aspects that influences customer experience is communication. Throughout the day, customers are bombarded with thousands of messages through different channels. If not personalized, these messages can be easily ignored as noise, reducing their impact and failing their purpose. Conversational analytics can help identify the channels and messaging preferred by customers, helping organizations focus their resources optimally.

Creating targeted messaging

Conversational analytics can help customer service teams learn what the customers are discussing and which topics or events are trending. Using these insights, marketers can design targeted campaigns that resonate with customers and generate positive impacts.

Measuring customer response

Once a product or service is launched, organizations are eager to learn how customers perceive it, what they like or dislike, and how it has fared in the market in comparison to its competitors. Conversational analytics has become an important tool to conduct user acceptance testing and measure customer response.

Benefits of Conversational Analytics

Organizations that understand the importance of conversational data can gain the following benefits from conversational analytics.

Understand customer behavior

Customer behavior and preferences are impacted by a lot of factors such as availability of choices, change in income and employment, inflation, environmental concerns, geopolitical situations, uncertain economic conditions, and so on. Conversational analytics can provide insights on business drivers and influencing factors. This helps organizations prepare better for facing upcoming challenges and converting opportunities.

Reduce customer churn

Conversational analytics provides important insights to understand customer concerns, track various customer success metrics, analyze their choices, and predict their response. Armed with these insights, customer service teams can create strategies to reduce customer churn, retain existing customers, and target new customers.

Empower customer support teams

With conversational analytics, customer support teams can analyze customer problems better and ensure faster resolution of queries. With direct access to insights, they can track recurring requests, identify areas of improvement, and prevent escalations. This way, conversational analytics can improve productivity and reduce support costs.

Expedite decision making

Conversational analytics democratizes access to insights on customer feedback, sentiments, preferences, and market trends for every decision maker. With ready access to accurate insights, decision makers across teams can make informed data-driven decisions quickly, without any delay or dependency.

Use Cases of Conversational Analytics

Conversational Analytics in Customer Support equips customer service teams with the tools needed to effectively handle various tasks involved in assisting customers.

Intent Recognition

By analyzing customer conversations, complaints, and feedback, customer support teams can recognize the intent behind customer queries and perform root cause analysis to identify the areas from where the issues are arising. Support teams can promptly highlight such potential red flags to the management, so that speedy decisions can be taken to prevent any customer frustrations.

Customer Journey Analysis

Customer experience teams can leverage conversational analytics in customer engagement to map out diverse touchpoints within a customer’s journey, pinpointing areas for enhancing engagement and bolstering retention at these crucial junctures. Through refining customer interactions, they can guarantee a smooth and seamless customer journey experience.

Sentiment Analysis

Customer service teams can gauge customer satisfaction levels by analyzing customer sentiments using conversational analytics. By analyzing social media conversations, product reviews, survey feedback, discussion forums, and interactions through support channels, customer service teams can identify pain points, positive sentiments, favorite features, and unfulfilled requirements. This helps them determine customer response and identify the hits and misses.

Personalization and Upselling

With insights on customer behavior and preferences at their fingertips, customer service teams can identify exact needs, personalize their communication, and position offerings better. Conversational analytics can reveal upselling and cross-selling opportunities, thereby increasing sales and keeping customers satisfied.

Product Roadmap Planning

Customer support teams can pass on the insights they receive on gaps in expectations, broken features, and most recurring issues to product development teams. This can serve as the basis for planning future product roadmaps, reviewing pricing strategy, and identifying opportunities to improve the product.

Turn Conversation Data into Insights with MachEye

MachEye’s AI-powered data analytics platform simplifies analytics by empowering customer support teams and customer service teams to perform advanced analysis in a self-service manner, without any delay or dependency. Conversation analytics including sentiment analysis, root cause or why analysis, anomaly detection, predictive analysis, and trends forecasting is made as easy as asking questions in simple English to conversational data. MachEye analyzes complex data such as customer conversations and interactions using NLP, ML and AI to extract insights. With its intelligent search, actionable insights, and interactive audio-visual data stories, MachEye helps organizations unlock the power of conversations, improve customer engagement, and empower customer service teams.