Reporting and Customer Analytics
Improving customer and financial outcomes depends upon delivering the relevant, real-time insights to each team and function in your organisation. Flexible role-based dashboards and dynamic reports allow every team and individual to optimise how they operate.
Key components of customer analytics
Customer analytics and insights come in many various formats but the following components are critical to creating a strong customer experience:
Flexible Role-Based Dashboards
Flexible role-based dashboards ensure relevant real-time insights and operational metrics are provided to each pertinent team and individual in your organisation. With relevant customer information, each team can optimise how they operate in ways that improve customer and business outcomes. For example, executives can have access to high-level business and operational metrics and key drivers at a geography and business level. Managers can view how their team is performing and provide data-based coaching, while operators can see account-level detail and take action based on automated alerts and real-time feedback.
Oftentimes the richest insights come from open text responses. Open text data is often used to supplement traditional quantitative metrics. For example, qualitative data could be used as a more granular level of feedback that accompanies traditional quantitative customer experience metrics, like NPS or CSAT.
With text analytics tools, customer experience practitioners can uncover valuable qualitative, free-form text insights. Additionally, text analytics help organisations better understand trends, group feedback categories, and get to the heart of what their customers are saying.
Key Driver Analysis
Key driver analysis allows you to instantly identify which areas of improvement offer the greatest impact to improve your customer satisfaction. Key drivers also help uncover the elements of the customer experience that are most important to your customers. By understanding the root cause behind a wide variety of customer experience elements—be they internal processes or external factors—key driver analysis gives organisations the ability to prioritise actions and drive change. For example, key driver analysis can help you prioritise and automate improvement initiatives such as staff friendliness or call wait times.
Statistical analysis tools allow users to move quickly from data visualisation to understanding. In the context of customer experience, statistical analysis helps practitioners quantify the relative importance of each customer touchpoint as well as the importance of the experience that touchpoint provided. Additionally, statistical analysis can isolate customer interactions by certain segments, locations, or accounts, providing a deeper and more meaningful set of insights.
While many customer experience and market research professionals are familiar with tools such as SPSS, other technology providers offer tools that immediately recognise variables, outliers, and other data elements and automatically provide the summaries and views. Additionally, the ability to conduct regression analysis, correlation measurement, dual and multivariate analysis, and other advanced techniques is critical to developing a mature customer analytics program.
Seeing operational data side-by-side with customer data helps build a complete view of your business and demonstrate true ROI. Further, full operational integration means operators are empowered to take action in ways that fit in existing processes. For example, embedding customer feedback into CRM systems means customer accounts automatically update and reflect changes for all account owners, while actions can be automated directly from the CRM system itself. Other typical integrations include financial systems, ticketing systems, and communication platforms.
Customer Data Visualisation
Data is only as good as it is understandable, which is why customer data visualisation is critical for organisations trying to understand their customers. Organisations looking to present customer data should not only look for the ability to display insights through standard views — bar charts, tables, line graphs and gauges—they should also focus on the ability to control and view the data however they desire. With more advanced dashboarding capabilities and available views, including geographical and heat maps, users can adjust design elements to match their brand and make instant changes as they collect new information, adjust their focus, or expand their objectives.