All companies that provide CX solutions take a slightly different approach when it comes to measuring the effort that customers are putting forth when using products or services. In our case we created the Clarabridge Effort Score which evaluates effort in unstructured customer feedback. Some organisations include a structured effort question in a survey that asks the customer to rate how easy it was to do business with the organisation on a numeric scale. However, this approach limits analysis of customer effort to just those survey responses. We would argue that if the measuring of effort is based directly on customer feedback text it becomes a unifying metric that empowers analysis across all data sources.
To put this into context I will explain how our Effort Score works. It is automatically calculated when omni-channel data is ingested and processed through our Natural Language Processing engine. This AI-powered feature is built into our platform and automatically analyses effort, allowing businesses to begin measuring and deriving insights immediately. If desired, businesses can adjust the calculation and identification thresholds of the Effort Score to reflect how effort is measured in their industry. The Effort Score is calculated by a machine learning algorithm that evaluates individual sentences for significant words, phrases, and linguistic features that are commonly found in expressions of effort. The algorithm assigns a whole, non-zero value between -5 (very hard) and +5 (very easy) or null (when no effort indicators are expressed). This null value for effort is not included in averages so as not to dilute the other values; the numeric values are then aggregated in reporting across topics, attributes, and categories.
Measuring effort helps businesses quickly understand issues and design better solutions that ultimately make it easier for customers to interact with them. Examples of how companies can apply insights include: Finding points of high friction and customer confusion; discovering drivers of channel hopping; creating roadmaps to remove or alleviate drivers of high-effort experiences; determining product flaws, website issues, and opportunities for process improvements; developing more-intuitive products and user interfaces; identifying and marketing competitive advantages; integrating findings with sentiment analysis to identify emerging trends that inform the development of empathetic solutions; and combining results with emotion analysis to design solutions based on how they want customers to feel.
Effort is interesting in isolation but can be more valuable when analysed in conjunction with emotion, sentiment, satisfaction, and other KPIs. For example, effort analysis aids in discovering areas of customer friction efficiently while emotion analysis can then help explain how these difficulties made the customer feel. Together, they can be used to inform empathetic solution design and promote more customer-centric business decisions. Sentiment and satisfaction scores can be used to track trends over time and monitor the impact of customer experience initiatives, programs, or product changes.
In my final blog in this series I will look at the analysis of emotion.
Fabrice Martin is Chief Product Officer at Clarabridge. Fabrice brings to Clarabridge 20 years of experience in entrepreneurship, product management, marketing, and enterprise software sales, with specific domain expertise in SaaS/PaaS, data visualization/discovery, Business Intelligence, and analytics for marketing and contact center...