Customer Satisfaction Word Cloud

Customer Satisfaction is defined as the level of content a customer reports after an interaction with a brand. This information is widely used for data driven decision making aiming to improve sales and earnings. 

Nowadays, businesses use a variety of tools to measure their brands’ customer satisfaction. One way to measure customer experience is using surveys aimed to measure the Customer Satisfaction Score (CSAT). 

CSAT surveys have become a common tool for measuring customer experience, and they work as long as the customer response rate is high enough to obtain a large sample size. However, the current response rates for customer satisfaction surveys are often very low. These low response rates endanger the accuracy of surveys, since non-response and self-selection bias alters the results and practically render them worthless.

Understand the voice of the customer

Back in 2003, Fred Reichheld asserted that by asking customers a single question, aimed towards determining their loyalty, service providers will be able to measure their customers’ attitudes towards their business. Answers given will be analysed using the Net Promoter Scoring (NPS) system. 

The Net Promoter Score is an index ranging from (-)100 to 100 which measures the willingness of customers to recommend a product or service to others. Customers are surveyed on one single question, for example — “On a scale of 0 to 10, how likely are you to recommend this company’s product or service to a friend or colleague?”. 

Based on their rating, customers are then classified in 3 categories:

  • ‘Detractors’ — customers who gave a score lower or equal to 6. They are not particularly enthusiastic about the product or service, and probably won’t purchase again from the company. They could potentially damage the company’s reputation through negative reviews.
  • ‘Passives’ — customers who gave a score of 7 or 8. They are satisfied but could easily switch to a different company. They probably wouldn’t spread negative word-of-mouth, but will not actually promote the product.
  • ‘Promoters’ — customers who gave a score of 9 or 10. They love the product and will recommend the company to other potential buyers.

The final NPS is determined by subtracting the percentage of customers who are detractors from the percentage who are promoters. What is generated is a score between (-)100 and 100.

Net promoter score alternatives

Using the Net Promoter Score index has some downfalls, such as not having an accurate method of calculation, and not qualitatively gauging customer dissatisfaction.

Alternatively, it is possible to use the Customer Effort Score (CES). Instead of measuring satisfaction, CES measures how much effort a customer had to put into their interactions with a brand. It uses questions such as “on a scale of ‘very easy’ to ‘very difficult’, how easy was it to interact with [company name].” The main principle behind this scoring model is that customers are more loyal to a product or service that is easier to use.

Customer segmentation

Customer segmentation is dividing a broad consumer market, normally consisting of existing and potential customers, into sub-groups based on shared characteristics like similar needs, interests, lifestyles or demographic profiles. 

The aim of customer segmentation is to identify the segments that are likely to be the most profitable or that have growth potential. It enables companies to target specific groups of customers and improve the allocation of marketing resources.

Identifying customer sub-groups prior to performing CSAT surveys can provide a more accurate score and help businesses have a better understanding of their customers.

The Problems With Customer Satisfaction Surveys

Although CSAT surveys are widely used and extremely popular with marketing professionals, they have a fundamental flaw built in, you only get answers to the questions you asked! 

CSAT surveys also tend to provide results for a relatively narrow area of customer experience in a set point in time. They also have a fundamental flaw to it, you only get answers to the questions you asked.

These surveys are unable to account for fake reviews and ratings, cannot pinpoint the cause of dissatisfaction or identify when it translates into market damage.

OK, So How Can We know What Customers Think?

In order to avoid the pitfalls of customer satisfaction surveys, companies can employ a customer experience analyzing platform which relies on Big Data.

Presented in the Cambridge Service Alliance paper, the Big Data model details the idea of basing your scale and score on several points of demographic, behavioral, and attitudinal data that is collected from past customers.

First, information is collected from a wide variety of sources such as online reviews, social media and more. The data is analysed and companies can profit from useful insights extracted from the collected information. By doing so, you get a much more accurate classification of customers (allegedly ~96% accurate, compared to 15-28% for NPS).

How Revuze Helps?

Revuze uses artificial intelligence technology, researching context, and identifying the attitude behind customer ratings, online reviews and other data sources like social media and call centers. Using Big Data analysis, it aggregates the information and analyzes the sentiment towards trends and topics. The end result is an innovative highly-granular customer feedback.

The extensive and professional data analysis Revuze provides can inform businesses about their market and brand. The analysis includes customer satisfaction levels, potential issues that need resolving, and more.

If you want to learn more about Customer Analytics and Sentiment Analysis, check out our Blog.

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