Designing Superior Customer Experience through Analytics
Simply put, Customer Experience Analytics is the process of gathering and analyzing all kinds of data generated by customers such as contact center interactions, customer shopping, internet usage patterns, demographics, social media activity, reviews, etc.
Examination of such data generates a deep understanding of customer-stated and unstated needs and helps shape their journey. Machine learning, Natural Language Processing, data mining, and statistical modeling are some popular methods & technologies, used in data analytics to categorize customer data.
Predictive analytics, a sub-category of data analytics, has shown phenomenal results in forecasting events and suggesting the next best action. Organizations use predictive analytics to study current and historical data to detect trends and forecast occurrences that should occur at a specific time’, for ‘a defined sub-group, with a significant degree of precision.
Here are a few examples of how organizations can utilize analytics customer data. The hospitality industry can analyze variables such as ‘customer’s visit intent’, ‘number of travelers’, ‘travel period’ etc. to identify the most preferred alternatives to stay if the desired property is unavailable. Similarly, an e-commerce business can generate additional revenue by suggesting successful alternatives. Airlines often use predictive models to segment and target their customers and accurately predict sales patterns. the list is endless.
Another popular data analytics category delivering actionable insights and measurable benefits is Interaction analytics. Interaction analytics utilizes unstructured customer interactions such as voice calls, emails, chats, reviews on social media, etc., creating clever algorithms to categorize and help to identify and quantity trending topics and pain points.
The categorized data is then correlated with the available variables such as customer demographics, survey ratings, product name, etc., to deliver insights. A feature of Interaction analytics causing improved productivity and targeted coaching to contact center advisors is the Scorecard automation, which concentrates on automating the quality scorecard parameters and auditing almost 100% volumes regularly.
Popular Outcomes of Interaction Analytics
Trending Customer Sentiment & Journey Pain Points
Quality Scorecard Automation
Root Cause Analysis
Compliance & Script Adherence
Focused Agent Feedback & Coaching
While the CX analytics includes powerful methods and technologies, rudimentary CX systems, limited data, and a shortage of data scientists can be a stumbling block. However, with careful data capture, analysis, and adoption of CX analytics, most organizations can start building their capabilities and begin laying the groundwork to transform their CX programs and their customers’ experiences. Else, they will end up playing catch-up with their competitors in the years to come!