“What do customers want?” is a million-dollar question that organizations across the world are trying to answer. Organizations invest heavily in collecting and storing customer data – be it in the form of survey responses, support calls, chats with digital assistants, complaints and feedback, or reviews on social media. This data is immensely rich in insights but useless if not analyzed in time.
In today’s world, where even 5-year olds can ask questions and interact with digital interfaces in a conversational manner, traditional analytics is still restricting decision makers in conversing with data themselves. This is where conversational analytics comes to the rescue by simplifying analytics and making insights consumable.
What is Conversational Analytics?
Conversational analytics is the ability to analyze complex and unstructured data such as customer conversations and interactions using natural language processing (NLP), machine learning (ML) models, and artificial intelligence (AI) to extract insights on customer behavior and improve customer engagement.
Adding conversational capabilities to analytics workflows reduces complexities, simplifies interactions, and improves access to information. Providing this conversational experience for accessing and analyzing data can really help to extract valuable insights, especially from the wealth of customer interactions data organizations collect. With conversational analytics, every decision maker is empowered to understand customer needs and expectations in a self-service way. The actionable insights received from conversational analytics sets the foundation for customer success.
Components of Conversational Analytics
Conversational analytics, powered by AI analytics, leverages various AI-based technologies to decode data and simplify analytics.
- Natural Language Processing (NLP): NLP helps computers understand human language in terms of meaning, context, and intent. It plays a major role in conversational analytics by interpreting the questions asked by users in a simple language accurately. It converts the human-speak into machine-understandable SQLs for fetching the correct answer and relevant insights.
- Natural Language Generation (NLG): NLG helps generate meaningful responses to answer questions or continue conversations with users. NLG is also used to make insights understandable by presenting answers in the form of text summaries, audio narratives, and visualizations.
- Artificial Intelligence including Generative AI: AI continuously learns from all the user interactions and conversations to refine its ability in understanding human conversations and preferences better. It is instrumental in personalizing insights and creating targeted experiences for users based on their areas of interest, geography, demographics, and other characteristics. The content-generation abilities of Generative AI help customer support teams to guide customers and provide solutions to their queries faster.
- Machine Learning (ML) Models: Conversational analytics uses various ML models to perform sentiment analysis, analyze customer journeys, identify intent behind customer queries correctly, and monitor performances. These models can extract actionable insights such as anomalies, outliers, analogies, clusters, trends, predictions, root cause analysis, and influential business drivers from conversational data.
- Large Language Models (LLMs): With their recent popularity and advancements, LLMs have gained useful applications in analytics. While processing conversational data, LLMs are used to understand metadata, identify context, clean up unwanted terms and jargons, and make data consistent for conversational analytics.
How Conversational Analytics work?
Here’s how conversational analytics works
- Collecting data: For performing conversational analytics, it is essential to first identify the data sources from where useful data can be obtained. Collecting data involves documenting customer interactions, recording feedback and complaints, and mining conversations. Data can take various forms such as call logs with customer support teams, complaints and tickets raised, email exchanges, reviews on social media, responses to customer surveys and focused interviews, and conversations with digital assistants.
- Processing data: Conversational analytics platforms that are equipped with NLP capabilities, automated data catalogs, and data quality monitors can expedite the process of cleaning, categorizing, and organizing data, and also flag data quality issues upfront before data is released for analysis.
- Analyzing data: The cleaned and processed data is analyzed for identifying patterns, understanding sentiments, and predicting trends. Conversational analytics platforms generate valuable insights on customer buying behavior, preferences, opinions, areas of interest and improvement. These insights are generated in real time using the latest data available, thus ensuring their relevance and accuracy.
- Understanding user questions and presenting insights: Conversational analytics makes it easy and intuitive for non-technical business users to converse with data. Users can ask questions in a simple language, without undergoing extensive training or learning complex syntax. Insights are presented in the form of text summaries, interactive charts and visualizations, and audio-visual data stories. This truly makes insights consumable and improves the entire analytics experience.
Conversational Analytics for Customer Success
Conversational analytics has become instrumental in providing business intelligence for customer success and improving customer engagements.
- Customer support teams can identify common issues faced by customers and provide better resolutions. By having vital insights at the tip of their fingers, they can handle customer calls and requests better, improve response times, and improve the overall service experience.
- Sales teams can track highest selling products and preferred services, identify high-worth customer segments, identify potential cross-sell and upsell opportunities, and compare sales across various regions in real time.
- Marketing teams can measure campaign performance and popularity of discount schemes and promotion codes. They can identify the top channels, monitor touchpoints with highest traffic, and optimize marketing messages to create targeted campaigns.
- Security and Compliance teams can monitor customer interactions for suspicious activities, compliance breaches, and adherence to regulations.
- Product development teams can gain insights for developing innovative products, introducing new features, identifying areas of improvements, and keeping track of the competition’s offering.
Benefits of Conversational Analytics
Conversational Analytics benefits organizations in the following ways.
- Empower teams: With conversational analytics, even non-technical business users can access data easily, perform their own analysis, and gain personalized, actionable insights, without any dependency or extra training. This empowers teams across the organizations to understand their customers better, identify trends, spot potential roadblocks, optimize their work processes, and make data-driven decisions quickly and confidently.
- Get early signals: Conversational analytics serves as an early warning system to uncover hidden insights on potential escalations, revenue losses, performance gaps, urgent issues, and other threats that may not be evident. This helps organizations get a head start and prepare better to face future challenges.
- Improve customer experience: With conversational analytics, organizations can understand sentiments, spot areas of improvement, identify preferences, measure performance of campaigns, and track market trends effectively. This helps them align their strategies and efforts to service customer requirements and ensure a better customer experience.
- Plan product enhancements: By analyzing what customers are saying about products and services, organizations can understand their expectations, track changing needs, and gain insights into missing functionalities and unused features. These inputs can be vital for product development teams for introducing new features, improving existing features, and removing blockers to ensure a smooth customer journey.
- Lower customer churn: With conversational analytics, organizations can track various customer success metrics such as customer retention rate, churn rate, renewal rate, net dollar retention, annual and monthly recurring revenue, average revenue per user, customer lifetime value, and customer satisfaction score easily. These scores can be measured in real time to make better decisions on improving customer engagements and lowering customer churn.
- Personalize campaigns and communication: Conversational analytics provides valuable insights that help build stronger relationships with customers. Campaigns, recommendations, offerings, and rewards can be personalized. Communication can be targeted better to gain attention and positive response from customers.
Challenges in Conversational Analytics
The following are some key challenges encountered in conversational analytics.
- Incoherent and ambiguous data: Incomplete conversations, colloquial words, mixed language content, and non-standard grammar can impact the way conversational analytics interprets conversations and data.
- Low data quality: The quality of insights directly depends on the quality of data that is being analyzed. Low quality conversational data can impact the accuracy of insights.
- Misinterpretations: Failure to identify the correct context and biases in training data can lead to misinterpretations while analyzing data using conversational analytics.
- Continuous learning: The AI-based technologies used in conversational analytics need to continuously learn from new data and improve their outcomes to interpret changing behaviors correctly.
- Data security and privacy: Concerns about data privacy, security, and compliance regulations need to be examined to ensure personal data is safeguarded from frauds and breaches.
Modern business intelligence and data analytics platforms like MachEye have the potential of becoming true copilots on the journey to understand customers through conversational analytics. Analytics platforms that offer intuitive and conversational search interfaces, actionable insights, and interactive audio-visual data stories help every decision maker converse with business data in their natural language and consume insights in an easily understandable manner.
By unlocking the power of conversations, conversational analytics can empower everyone to gain insights for understanding customer behaviors and buying patterns, improving customer engagements, and creating meaningful experiences.
