Customer Satisfaction Survey Fatigue? Predictive Customer Analytics offers a new method for measuring Customer Experience.
Most companies now understand the impact customer experience can have on their business. Recent studies have quantified the impact. According to Forrester Research, “customer experience (CX) is a more powerful customer loyalty driver than price-value perception and accounts for 55.1% of loyalty for banks and 46.5% for retailers.” (“You Cannot Price Your Way Out Of Bad Customer Experiences,” Forrester Research, Maxie Schmidt-Subramanian, May 1, 2013)
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Customer experience (CX) is often represented as a customer satisfaction (CSAT) score, customer effort score, or Net Promoter Score® (NPS), and relies heavily upon surveys to measure these importance metrics. This approach requires the customer to opt-in to a survey, resulting in several issues and challenges:
- Results that are not actionable, especially at the employee level
- Small sample sizes that are not statistically valid
- A survey respondent population that often doesn’t match the typical customer population
- Decreasing survey response rates due to over-surveying and customer survey fatigue
- The high cost of implementing surveying tools
Measuring and understanding important attributes, such as empathy, customer distress patterns, customer engagement, customer personality, positive sentiment, first call resolution, and rework are important. With such an understanding, we can act at a level of each customer or interaction, in order to improve customer experience and satisfaction. While companies increasingly understand the connection between customer experience and customer acquisition, loyalty, and repurchase rates, until now they have found that surveys lack actionable information to measurably improve these metrics.
Case Study: Predictive NPS
NPS is being used at a top 3 US property & casualty insurer to gauge customer satisfaction. Historically, this company used surveys but found the samples too small, not representative of the broader customer base and not actionable for employee-level analysis and performance improvement. NPS can be modeled and analyzed using solutions like Mattersight® Predictive Customer Analytics, which is exactly what this company did. By understanding the correlative factors occurring within customer interactions that impact NPS, they took action to improve scores and ultimately satisfaction, loyalty, and drove positive business value. The same process can be applied to internally developed CSAT and customer effort scores at other organizations. Using Predictive Customer Analytics, contact center and customer experience executives can bypass the challenges of surveys:
- 100% of interactions can be analyzed, avoiding small sample sizes that are not statistically valid and allowing for precise segmentation
- Actionable insight is gained based upon analysis of organizational drivers of NPS
- Employee level evaluation that links coachable behavioral factors to NPS results is possible
- The high cost of survey programs and negative impact of customer over-survey fatigue are avoided
Every interaction is captured, analyzed, and assigned a score that is highly correlative to actual customer surveys, without requiring a survey by the customer. The factors impacting satisfaction are made actionable for every customer and interaction, helping companies improve customer experience and customer satisfaction. Companies can act upon this new source of CX insight with training and coaching programs to improve employee performance characteristics that have correlative impact, or automatically pair and route callers to the best available employee who will provide the optimal customer experience for that specific customer.
Mattersight® Behavioral Analytics is offered via a SaaS+ delivery model, combining analytics with deep customer partnerships to improve customer experience, optimally pair customers and employees, reduce employee performance variability, and predict future outcomes. For more information, visit www.mattersight.com.