Filling in the White Spaces: Best Practices in Bank Customer Experience Measurement Design – Social Listening
Increasingly social media is “the media”; prospective customers assign far more weight to conversations on social media then any external messaging. A social listening system that analyzes social media conversations, while still a little immature, are increasingly becoming a valuable source of customer comments.
Social media analytics software collect data across multiple sources (Facebook, Twitter, Google+, etc) using text analytics in an effort to reveal patterns, identify trends and detect potential business problems from what people are saying in these online forums.
While these analytical tools are still a little immature, sentiment analysis technology has become more capable in recent years. Among the common features of these tools is sentiment tracking of conversations, determining if the sentiment is positive or negative and tracking a ratio of sentiment over time. Additionally, these tools typically mine text for specific key words. Beyond automated analytics, we’ve had success using this unstructured social feedback and reducing it into quantifiable themes through a manual process of coding, where comments are read and grouped by theme. While a manual process, we’ve found taking a sampling of social conversations and manually reviewing them, provides valuable context not available through pure automated analytics.
Like comment monitoring, social listening is not a standalone research tool. It is not a survey, nor is the data collected from a representative sampling of customers, as such, it is not statistically valid. Social listening, however, fills in the white spaces between other research tools. Its value lies in correlating social data with other data sources.
Research without call to action elements may be interesting, but not very useful. As with all research tools, call to action elements should be built into a social listening program. Any time there is negative criticism, it presents an opportunity for process improvement. Among the ways managers can act of social listening are trend identification, finding chronic customer complaints, and identifying and correcting root causes of customer complaints.
Additionally, managers should construct processes to identify and respond to social conversations where appropriate. Customers who have had a problem fixed are famous for becoming vocal advocates of a company. The flip-side is that customers who have had a positive experience can be thanked for their feedback, which encourages customer loyalty. Try to respond to each review (positive or negative), thank the client for their feedback constructively and professionally, address the issue, and offer solutions to correct the issue and leave it at that.
Finally, the unsolicited nature of social conversations offer a unique opportunity to feed themes identified in these conversations back into customer survey design, allowing managers to determine if issues uncovered are broadly present across all customers.
For more posts in this series, click on the following links:
- Introduction: Best Practices in Bank Customer Experience Measurement Design
- Customer Surveys: Best Practices in Bank Customer Experience Measurement Design
- Mystery Shopping: Best Practices in Bank Customer Experience Measurement Design
- Leverage Unrecognized Experts in the Customer Experience: Best Practices in Bank Customer Experience Measurement Design – Employee Surveys
- A New Look at Comment Cards: Best Practices in Bank Customer Experience Measurement Design – Customer Comments & Feedback
- Customer Experience Measurement Implications of Changing Branch Networks
The customer value equation is an on-going process by which the customer keeps a running total of all the benefits of a product or service (both tangible and intangible) and subtracts the sum of all the costs associated with the product or service (tangible and intangible). If the product of this equation is positive they will start or maintain a relationship with the provider.
But is this a continuous process? Or do many customers travel through the customer journey in a state of inertia until they reach critical points in the customer journey where they feed knowledge gained, at these critical points, into the customer value equation?
The fact of the matter is not all points along the customer journey are equal. In every customer journey there are specific of “moments of truth” where customers form or change their opinion of the provider, either positively or negatively, based on their experience. Moments of truth can be quite varied and occur in a skilled sales presentation, when a shop owner stays open late help dad buy the perfect gift, or when a hold time is particularly long.
In designing tools to monitor the customer experience, managers must be aware of potential moments of truth and design tools to monitor these critical points in the customer journey. Some of these tools include:
Mystery Shopping: Mystery shopping allows managers to test their service experience in a controlled manner. Do you have a concern about how your employees respond to specific customer complaints or problems? – Send in a mystery shopper with that specific problem and evaluate the response. Are you concerned about cross-sell skills? – Send in a mystery shopper with an obvious cross-sell need and evaluate how it is handled. With mystery shoppers managers can design controlled tests to evaluate how employees react when presented with specific moments of truth.
Customer Comments: Historically, comment tools have taken the form of cards; however, increasingly these tools are migrating onto online and mobile platforms. The self-administered nature of comment tools make them very poor solutions for a customer survey, as we tend to hear from an unrepresentative sample of customers who are either extremely happy or extremely unhappy.
However, this highly self-administered nature of comment tools makes them perfect to monitor moments of truth. Customers on the extreme end of either scale probably are at a moment of truth in the journey. In designing comment tools, be sure to limit the amount of categorized questions and rating scales; rather give the customer plenty of “white space” to tell you exactly what is on their mind. Over time, an analysis of these comments will give you insight into the nature and causes of moments of truth.
Social Media: Similar to collecting comments from customers, social media can be an excellent tool for identifying common causes of moments of truth. Customers who take to social media to mention a product or service are likely to be highly motivated – again, at the extreme ends of the satisfaction spectrum.
Survey Tracking: Finally, ongoing satisfaction tracking of all customers can be a source of intelligence regarding moments of truth. To turn a satisfaction tracking study into a moment of truth monitor, focus your attention on the bottom of the satisfaction curve. If a customer assigns a satisfaction rating of “1” or “2” on a 5-point scale, drill into these customers’ responses on a case by case basis to determine what caused the low rating – this will most likely reveal a moment of truth.
Here are four ideas to identify and monitor moments of truth.
How do you monitor your moments of truth?
- Implications of CX Consistency for Researchers – Part 3 – Common Cause v Special Cause Variation
- Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency
- Implications of CX Consistency for Researchers – Part 1 – Inter-Channel Consistency
- Business Case and Implications for Consistency – Part 7 – Disparate Treatment of Protected Classes
- Business Case and Implications for Consistency – Part 6 – Intra-Channel Consistency
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- Implications of CX Consistency for Researchers – Part 3 – Common Cause v Special Cause Variation… twitter.com/i/web/status/1… 1 year ago
- Implications of CX Consistency for Researchers – Part 2 – Intra-Channel Consistency blog.kinesis-cem.com/2018/06/06/imp… https://t.co/HROwALOZJ5 1 year ago
- Implications of CX Consistency for Researchers – Part 1 – Inter-Channel Consistency blog.kinesis-cem.com/2018/04/26/imp… 1 year ago