What are the common biases to avoid when measuring customer support?

1 Confirmation bias

Confirmation bias is the tendency to seek, interpret, and remember information that confirms your existing beliefs or hypotheses, while ignoring or dismissing evidence that contradicts them. For example, if you believe that your customers are happy with your service, you might focus on the positive feedback and ratings, and overlook the negative ones or the complaints. This can prevent you from seeing the real issues and opportunities for improvement in your customer support. To avoid confirmation bias, you should use multiple sources and methods of data collection, such as surveys, reviews, interviews, and analytics. You should also challenge your assumptions and hypotheses, and seek feedback from different perspectives and stakeholders.

2 Selection bias

Selection bias is the tendency to select or sample data that is not representative of the whole population or group that you are trying to measure or understand. For example, if you only survey customers who have contacted your support team, you might miss out on the opinions and experiences of customers who have not reached out, or who have left your service without giving feedback. This can lead to a distorted or incomplete picture of your customer support performance and satisfaction. To avoid selection bias, you should use random and stratified sampling techniques, and ensure that your sample size and response rate are large and high enough to capture the diversity and variability of your customer base.

3 Recency bias

Recency bias is the tendency to give more weight and importance to the most recent or memorable events or experiences, while ignoring or forgetting the ones that happened earlier or less frequently. For example, if you receive a surge of positive or negative feedback in a short period of time, you might assume that this reflects the overall trend or quality of your customer support, and disregard the previous or average data. This can lead to overreacting or underestimating the impact of your customer support actions and strategies. To avoid recency bias, you should use long-term and consistent metrics and indicators, such as customer retention, loyalty, and lifetime value. You should also compare and contrast your data over different time periods and segments, and look for patterns and outliers.

4 Halo effect

Halo effect is the tendency to form a general impression of a person, product, or service based on one or a few positive or negative attributes, and to extend that impression to other unrelated aspects. For example, if you have a positive or negative experience with a customer support agent, you might assume that the whole support team or the company is equally good or bad, and rate them accordingly. This can lead to unfair or inaccurate evaluations of your customer support quality and professionalism. To avoid halo effect, you should use specific and objective criteria and standards to measure and rate your customer support performance, such as response time, resolution rate, and customer effort score. You should also solicit and provide feedback on multiple dimensions and factors, and avoid generalizing or stereotyping.

5 Anchoring bias

Anchoring bias is the tendency to rely too much on the first piece of information or data that you receive, and to use it as a reference point or benchmark for subsequent judgments or decisions. For example, if you see a high or low score or rating for your customer support, you might use it as a baseline or expectation for future or other scores or ratings, and adjust them accordingly. This can lead to overestimating or underestimating the significance or difference of your customer support results and outcomes. To avoid anchoring bias, you should use multiple and independent sources and methods of data collection and analysis, and avoid comparing or influencing your data with external or irrelevant factors. You should also use ranges and averages, and consider the context and variability of your data.

6 Attribution bias

Attribution bias is the tendency to attribute the causes and reasons of your own or others' behavior or performance to internal or external factors, depending on whether they are positive or negative. For example, if you receive a positive or negative feedback from a customer, you might attribute it to your own or your team's skills or efforts, or to the customer's mood or expectations, depending on whether it suits your self-image or goals. This can lead to biased or incomplete explanations and interpretations of your customer support feedback and data. To avoid attribution bias, you should use evidence-based and data-driven methods and tools to identify and understand the root causes and drivers of your customer support behavior and performance, such as customer journey mapping, sentiment analysis, and root cause analysis. You should also acknowledge and address the internal and external factors that influence your customer support, and seek feedback from multiple and diverse sources.

7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?