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Top Customer Experience Metrics

Metrics are a fundamental tool to better understand users of products or services. However, there is a long list of metrics measurements so we thought it would useful to provide you with a selection of 10 key ones we feel are very useful to help you get started.

Customer On-boarding

Describes the process users go through when they start their journey as clients of a product or service, and it can be evaluated through a conversion rate. To utilize this indicator, the first step consists of defining what we mean by conversion. It could be a sale, user registration, download of content, trial of a product and so on. It has become almost habitual that in addition to calculating the conversion, companies evaluate the value of acquisition, for example, with tools that capture information about the effective use of a product after conversion.

Customer Satisfaction Score (CSAT)

CSAT is a survey through which clients are asked their level of satisfaction and it can be applied in a variety of cases: the last purchase, customer service, and helpfulness of assistance received. The objective is to identify the measurements that are necessary to introduce improvements to elevate the user experience. The CSAT is commonly used after there is an interaction between companies and their clients, and is typically made with questions such as “how would you evaluate the level of satisfaction with the attention received?”, and then offers a numeric scale of responses where for example, 0 indicates very unsatisfied and 5 very satisfied.

Customer Effort Score (CES)

CES is a survey that ask users questions such as “how much effort did you have to make to complete the task or action?”, and then offers a numeric scale like 0 to indicate very little effort and 5 a lot of effort. It is a very revealing measurement when it comes to the difficulty users are confronted with in order to complete their objective. As Mark Mollet, Manager of Helpling CX, said “no one wants to make more of an effort if they are giving the option not to”. CES is usually taken after the client has finished their customer journey, although it is becoming more commonplace for companies to also measure the effort required during the acquisition process.

Net Promoter Score (NPS)

NPS was developed in 2003 by Fred Reichheld, founder of Bain & Company, and is one of the most widely applied user experience monetization instruments today. It proposes questions along the lines of “what is the probability you would recommend us to a family member or friend?”, and offers a numeric response on a scale of 0 to 10, where 0 is very unlikely and 10 very likely. NPS groups those surveyed in to three categories: Promoters – those who responded with a score of 9 or 10; Passives – those that gave scores of 7 or 8; and Detractrors – those whose feedback was equal to or less than 6. The recommendation index is the subtraction of Promoters minus Detractors. The further this index is from 100, the more relevant improvements will be in relation to the issues companies must confront.

Product or Service Sales

The area of product and service sales is perhaps the most developed metric, as is evidenced by the large number of tools designed to collect information and analyze the monetization of this aspect of user experience. However, there is a set of indicators that are the most recurrent because they offer more valuable information about the sale of products and services. These are our Top 5:

  • Rate of Conversion: percentage of clients that are acquired by a company in relation to those that are not
  • Client Retention: number of new clients versus existing clients
  • Response Time: the time it takes for a company to respond to an opportunity
  • Cost of Sales / Proportion of Revenue: measures the efficiency of sales
  • Sales Drop-Out: detects the points in the sales process / customer journey where opportunities are lost

Cross-Selling Score

This measurement is used to collect information about cross-sales as an index of sales or the number of sales by ticket (UPT), whose main objective is to quantify the average number of products or services it takes to acquire each client. With the cross-selling score, we can obtain information about the quality of the sales on the basis that the larger the number of products or sales sold, the better is the quality of cross-selling actions.

Digital Engagement Time

It is generally understood as the time users dedicate to a company or brands’ product or service offer that has been launched in to the market. A diverse array of indicators exist to measure digital engagement time: session duration, number of clicks, pages visited, time users spent on each page during a session, number of content downloads, email open and click rates of emails sent, and bounce rate.

Customer Referral Score (CRR)

CRR is a metric that collects information similar to NPS; the relative way clients speak of our products and services in their environment, whether that be across digital or physical channels.

Churn Rate

The rate of abandonment is a tool implanted in digital media, although it originates from systematic use in traditional businesses. It deals with the percentage of clients that do not make a purchase or cancel a recurrent service. The way to calculate it is simple. We divide the total quantity of clients lost by the total quantity of active clients during a previously defined time period.

Client Complaints

To obtain information about client complaints, the metric that usually surfaces is called First Response Time (FCT). It measures the amount of time that passed since the client made their complaint until they received a response from the company or brand. The time they have defined as reasonable (from the point of view of users) is 15 minutes.

Conclusion

In measuring user experience, we must not forget key components such as the analysis of information, design of actions that produce improvements to the user experience, and following of the impact of those actions. Looking towards the horizon, are we capable of designing metrics that help us predict the intention of users, their habits and future preferences? The challenge is on the table.

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