A guide to customer feedback and data types
Based on the work of Anita Toth in her article: Ultimate Guide To Customer Feedback
For many businesses, customer feedback is a “nice-to-have” activity that they never get around to implementing. Others are not interested in asking the question, “What don’t you like about your experience/our product/how we served you?”
They might be fearful of the responses, are unwilling to adapt and change, or don’t value the importance of the customer experience and its impact on loyalty and revenue.
And that viewpoint continues to disappoint. Why? Because here at CustomerCount, we know the power of the data that customer feedback can provide. We have seen our clients take hold of that data, change their strategies, products, and services, generated loyalty, and retained their customers.
They have used the knowledge they gain from customer feedback to lower customer acquisition costs, increase customer lifetime value, and reduce churn.
In this mini-series Guide to Customer Feedback, we take a look at the different types of customer feedback data available to business, the issues facing those collecting customer data, and what data they should be collecting to increase profits and reduce churn.
According to Anita Toth*, most companies face three issues around their customer data:
- There’s a lot of it;
- They don’t always know which data is valuable from what is being collected; and
- They don’t still know which information is unimportant or even harmful.
So, it is critical for a business to understand the types of data being collected so that the decisions made on its analysis are correct.
Toth has identified three customer data categories:
All three categories provide data that is valuable and useful for business. However, they also generate data that can mislead or appear worthy when it’s not.
The opportunities for general data
Toth identifies general data as information that is “collected, viewed, and analyzed at the population level.” It is great for showing trends and is particularly useful for benchmarking and trend analysis.
Benchmarking is a useful process to measure your products and services against an industry standard. It allows you to see how your business performs relative to your competition and identify trends over time. This trend analysis can help the company make significant strategic decisions, such as retiring products.
Toth identifies three types of general data business typically collect:
These powerful tools look at different aspects of customer behavior, satisfaction, and loyalty over short-long term periods. These general data metrics provide useful information but can miss important issues.
1 Net Promoter Score and long-term loyalty:
The benefits of the NPS as a KPI are well-known. It is a useful measure of loyalty but has its limitations.
By asking a customer whether they would recommend the company to a family member or friend, NPS gives some insight into whether a customer will stay loyal. ‘Promoters’ (those who provide a ‘9 or 10’ response) are assumed to be the ‘most loyal’ given their high numerical response.
However, as Toth explains, using a single NPS measurement doesn’t factor that there are different types of loyalty:
- Retention loyalty – the best predictor of churn;
- Advocacy loyalty – a good predictor of new customer growth; and
- Purchasing loyalty – a great predictor of annual revenue per user (ARPU) growth.
As such, it is an incomplete measure of loyalty, and business is urged to look at other, more targeted metrics to yield better data and provide deeper insights.
2 Customer Effort Score and mid-term loyalty:
Developed over ten years ago, the CES uses a three, five- or seven-point scale (using numbers or emoticons) to measure how much effort a customer has to exert to get:
- An issue resolved;
- A request fulfilled;
- A product purchased; and
- A question answered.
Studies indicate that when customer effort is high, loyalty is challenged, and customers are more likely to churn. This is especially so in the customer service arena when customers have to contact a company multiple times, repeat information, and speak to different agents. It can cause frustration and the desire to find better service from a competitor.
The CES provides a snapshot of a customer’s most recent experience and can be useful to identify trends. The data can see if changes in procedures, personnel, or new initiatives improve the experience.
Like the NPS, the CES provides broad information about the customer experience, but Toth believes other measurements will offer a complete picture.
3 Customer Satisfaction and short-term satisfaction:
This metric uses a five-point scale to measure how satisfied customers are with a product or service. Similar to the CES, it focuses on the customer’s last interaction with the business or a critical moment.
The CSAT tool focuses on a feeling the customer has and, as such, is subjective. One customer might interpret their feelings of satisfaction differently to another. Or, they might answer differently depending on their mood.
As such, CSAT provides business with an indication of what customers are feeling at that time and can be quickly impacted by external influences.
As with the NPS and CES, the CSAT needs to be combined with other types of data to give the business a complete picture of the customer experience.
We will look into these options in the next blog in our mini-series – Guide to Customer Feedback and Data Types.
About Anita Toth
You can read Anita Toth’s full article here: https://anitatoth.ca/wp-content/uploads/2020/08/The-Ultimate-Guide-to-Customer-Feedback.pdf
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