Understanding Customer Service Behavioral Analytics

Interaction Customer Service Analytics

Interaction analytics tell the story of a single interaction. From the moment your customer reaches out to you, to the moment that interaction ends, the customer is generating information. These metrics let you understand and improve your communications on the micro-level, interaction.

Examples of interaction analytics include:

• Customer Satisfaction (CSAT) – CSAT is measured by asking the customer one question: “how satisfied are you with your experience?” From here, customers respond on a scale from ‘very satisfied’ to ‘very dissatisfied’. The scale can be from 1-5 or 1-10, giving you a score between 0 and 100.

• First Contact Resolution (FCR) – FCR is one of the most significant metrics of contact center performance. It measures the number of interactions resolved on the first attempt – a failure to achieve a high FCR is indicative of inefficient customer support, or perhaps a problem your agents can’t resolve.

• Customer Effort Score (CES) – CES tracks the level of effort the customer had to expend to reach a resolution. This metric is a good indicator of how accessible your organization is, and how easily your customers can navigate through your customer contact ecosystem.

Each of these metrics allows you to pick out the pain points in your customer contact. This is incredibly useful at the micro-level, but to build a broader CX strategy, you need a higher level of abstraction.

Customer Journey Analytics

The customer journey doesn’t stop after a single interaction (or certainly, it shouldn’t). To get meaningful insight into the success or failure of your CX, you need a view that spans multiple interactions. Customer journey behavioral analytics include:

• AI-Powered Sentiment Analysis – Sentiment analysis is an AI-backed technique that assigns and emotional value to every word in a transcript. The values of those words are then totaled up, to reach an overall sentiment score for that interaction.

• Comprehensive transcription – Sentiment analysis, however, needs a transcript to work with. Natural Language Processing (NLP) is an AI-powered technology that allows for word-perfect transcription of every voice interaction. These transcriptions can be tagged to an associated customer record, linking together every interaction, for a complete interaction history.

• Predictive analytics – Armed with a complete record of a customer’s interactions, and an overall sentiment score, you can make predictions on their next steps. A low sentiment score can indicate that they’re likely to drop you. It’s time to make proactive contact before that happens.

By building a complete picture of the customer journey, spanning multiple interactions, you can predict what they’re likely to do next. But your business is bigger than one customer, and you need a plan for the long-term.

Lifetime Customer Service Behavioral Analytics

Medium-term analysis can only take you so far; you need a long-term perspective, and that requires a deeper level of analysis. Lifetime analytics are a sub-category of customer service behavioral analytics that let you assess the long-term value of a customer, over the lifetime of your business.

• Measuring Customer Retention – Customer Retention Rate and Customer Churn Rate measure the same phenomenon from different perspectives; that being, how many customers stick with your organization. Poor retention means it’s time to rethink your CX strategy.

• Customer Lifetime Value – All customers are not created equal. Some will offer greater lifetime value to your business than others. Customer Lifetime Value (CLV) is calculated by an algorithmic process, factoring in customer acquisition costs, average purchase value, purchase frequency, and other factors.

• Planning for the long-term – Efficient CX is about managing limited time and resources. By calculating the lifetime value of your customers, you can craft a strategy that prioritizes the most valuable customers, tags them within your systems of record, and routes them to a priority line.

Calculating Customer Lifetime Value isn’t easy. Bringing all these customer service behavioral analytics into a single interface is even harder. You need a powerful, flexible CX analytics solution.