Customer Analytics and Reporting | Qualtrics

Reporting and Customer Analytics

Improving customer and financial outcomes depends upon delivering the relevant, real-time insights to each team and function in your organization. Flexible role-based dashboards and dynamic reports allow every team and individual to optimize how they operate.

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Robust reporting gives users the ability to:

  • Track progress Set improvement goals for key customer and operational metric targets and assess how program changes impact progress toward these goals.
  • Understand and prioritize the drivers of satisfaction Identify, prioritize, and rank key customer satisfaction drivers to create strategic improvement priorities across the organization.
  • Identify friction points along customer journey Identify and characterize key underperforming touchpoints along with specific feedback on how to improve the touchpoint.
  • Coach teams with data and real-time metrics Create coaching opportunities to achieve goals and specific feedback on interaction optimization.
  • Manage and optimize the feedback process Continuously capture both tactical and strategic feedback to improve and innovate customer experience.
  • Predict customer behavior Create predictive models to identify how changes in key drivers will impact overall customer experience.
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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 organization. With relevant customer information, each team can optimize 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.

Role-Based Dashboards

Text Analytics

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 organizations 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 organizations the ability to prioritize actions and drive change. For example, key driver analysis can help you prioritize and automate improvement initiatives such as staff friendliness or call wait times.

Statistical Analysis

Statistical analysis tools allow users to move quickly from data visualization 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 recognize 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.

Operational Integration

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 Visualization

Data is only as good as it is understandable, which is why customer data visualization is critical for organizations trying to understand their customers. Organizations 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.