How to create great CX with customer analytics

Introduction

Did you know that a staggering ​86% of customers are willing to pay more​ for a great customer experience? While this clearly indicates that customer gratification pays off, putting your customers’ needs and goals first is impossible without the right customer experience analytics.  

Customer analytics is the process of discovering, collecting, and analyzing customer data to make informed and insightful decisions regarding your products or services. The data allows businesses to examine what works well and helps in identifying the problem areas. It also helps discover any setbacks that customers may be experiencing. Since a substantial amount of data is collected through customer analytics, it is important to recognize the metrics worth tracking. 

The following article discusses why it is important to measure and track customer experience, what metrics to track, and what tools to use to enable effective customer analytics.  

What is Customer Experience? 

As aptly put by Hubspot, “customer experience is the impression your customers have of your brand as a whole throughout all aspects of the buyer's journey. It results in their view of your brand and impacts factors related to your bottom line including revenue”. What’s important to keep in mind is that creating a remarkable customer experience isn’t a one-time effort but rather a continuous process. As the expectations of your customers evolve, so should your CX strategy.  

In order to maintain a good customer experience standard and strengthen brand loyalty, you must thus turn to tracking customer data analytics.

Why it is Important to Track Customer Data – The Benefits of Customer Experience Analytics  

Below we discuss the key benefits of tracking customer experience metrics:

• Identifying roadblocks and highlighting problem areas – in order to eliminate negative experiences before customers get frustrated and decide to churn.
• Improving customer satisfaction and encouraging loyalty. By reaching out to customers, companies can learn what their strong suits and faulty processes are. This gives them the opportunity to display that they value their customers’ opinions, which in turn may increase the user retention rate.
• Adjusting your website, email marketing, and onboarding sequence copy, to promote user retention and provide answers to customers’ most common questions.
• Increasing word-of-mouth recommendations from satisfied customers.

With this mind, let’s move on to discussing which metrics you should focus on to start learning more about your customers and boost their satisfaction levels. 

What Metrics and Data to Track to Create Good Customer Experience

There are five areas you should focus on when you embark on your CX tracking journey: customer service data, survey data, customer sentiment, conversion rate, and app returns.

Customer Service Primary Data 

This customer data category will likely be the best starting point for your business – most companies already have some basic information on their clients in the CRM or website analytics software. The data you should gather for your customer experience analysis includes the following:

• Marketing and customer service email open rates: It’s recommended to investigate the number of sent and opened emails to customers and/or prospects. For instance, let’s assume you have a new feature release and decide to inform all your customers (for example, 10,000 contacts) about the recent change. While you’ve reached out to 10,000 clients, you might notice that only a fraction of the recipients opened your email, and even fewer of them clicked-through to your website. One of the conclusions might be that your CX requires improvement, as you didn’t manage to evoke enough interest among customers to bring their attention to your new service.
• The number of open and closed tickets: You can find this data in your CRM software. Depending on your business and the volume of customer communication, you should track either the daily, weekly, monthly, quarterly, or even yearly statistics. Tracking the number of open and successfully resolved customer tickets provides a good overview of how effective your customer support is. However, it can also indicate an issue with your product – if unresolved tickets keep growing, it’s possible that customers are experiencing issues with your service. By keeping tabs on your ticket metrics, you can aim to resolve issues as soon as possible.
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• The number of complaints: Is your brand subject to a lot of customer complaints, do you address them regularly, and strive for improvement? If the number of complaints doesn’t go down or is growing, it will negatively impact your brand perception – especially if customer complaints become public. 

While the above data points will be useful for most businesses, it’s important to remember that each company has different needs and growth patterns. Therefore, we recommend that you regularly evaluate which customer service data is best to track to ensure that you’re not lacking any key customer information.

In the next section, we discuss several important customer satisfaction and loyalty metrics that can be tracked by surveying your audience.

Customer Survey Data 

Customer surveys are an effective method for verifying how satisfied your customers are with your website, product, customer service team, or other areas of your business. There are three primary channels you can use to start collecting user insights, namely:

• On-Site or In-App surveys
• Post-Service or Post-Purchase Surveys
• Email Surveys

For the purpose of this post, let’s focus on the top 3 customer experience metrics – NPS, CSAT, and CES, as these are the most common indicators of customer satisfaction. 

Customer Satisfaction (CSAT)

Among the three, CSAT is probably the most widespread, due to its ability to measure overall customer satisfaction with the company’s product, service or quality of a specific interaction. CSAT scores can be determined using different approaches in terms of used scales and calculation methods, such as the 5-point Likert scale, the 4-point modified Likert scale with no neutral point, or the binary scale, to name a few. 

As all of the above-mentioned scales have their advantages and disadvantages, it’s important that every company finds and adopts the most suitable approach. 

Besides the different “technical” considerations, it is equally important to understand the limitations of the CSAT, especially when it comes to using this indicator to predict future customer behaviour, engagement with the brand, and loyalty. 

This is where the NPS kicks in. 

Net Promoter Score (NPS)

Although the NPS has met with certain criticism throughout the years, including it in a customer survey is an effective method of understanding not only clients’ short-term satisfaction, but also their overall inclination towards continuing their relationship with a brand.

As a main consideration, the NPS is an instrument of segmenting customers into the promoters, passives, and detractors. 

Promoters are those whose overall brand satisfaction has turned them not only into loyal customers but also active promoters of your brand. Passives are usually satisfied customers that do not, however, advocate for the brand. Detractors, respectively, are the customers who are decidedly dissatisfied with the company. 

The purpose of the NPS is to show whether a company has more promoters than detractors. Therefore, the NPS – usually measured on a 0-10 scale due to its “translation” of probability – is calculated by subtracting the percentage of detractors from the percentage of promoters. 

As a result, NPS values range from -100 to +100, with the two extremes meaning that a company has either detractors or promoters only. NPS values lower than 0 are considered to be bad results, values ranging between 0 and 30 as somewhat good, while values higher than 30 signify very high loyalty levels. 

To ensure actionable insight, companies can decide to complement CSAT and NPS questions with one, or more open-ended questions, allowing respondents to motivate their rating choices. 

Customer Effort Score (CES)

Going further, another indicator that conveys meaningful insight is the CES – or Customer Effort Score.

Asking customers to rate the difficulty – or ease, for that matter – with which they were able to solve a problem, get an answer, or interact with a company's products or services, offers a great deal of support when it comes to fostering continuous improvement. 

Some have stated that the CES is limited to showing potential roadblocks that have pushed customers to contact support, without shedding light on the problems customers have encountered elsewhere. However, this turns out to be true if the company decides to introduce this question in customer service surveys only. 

Introducing a CES question in other surveys has also proven effective. The answers unfold reasons why customers had a hard time performing certain actions, which help companies turn a negative experience into a positive one by removing or minimizing obstacles. 

Technically, CES questions can be measured using different approaches, however, many companies choose a 5-point scale and calculate CES scores as averages. 

CSAT, NPS, or CES?

An unanimous answer to this question does not exist, however, as it turns out, it’s probably for the better. Choosing to work with an indicator – or even all of them – should depend on the specifics of your business, what has to be found out, and the overall purpose. That being said, it should take into account all possible implications and limitations. 

Customer Sentiment Data 

Customer sentiment data is also known as opinion mining. It is the process of determining whether a customer’s language echoes positive, negative, or neutral sentiment for your product. This type of data is usually provided in the form of descriptive, qualitative feedback, and can serve as a great supplement to quantitative customer experience metrics such as the above mentioned NPS, CSAT, and CES scores. They are an invaluable source of the ‘why’ behind customer impressions and decisions. 

Let’s use chatbots as an example to understand the sentiment data. Chatbots are embedded in apps and web pages everywhere for different reasons. You could integrate a series of questions into the chatbots’ script to analyze the customer sentiment. One example would be asking “why didn’t you finalize your purchase?” upon a customer abandoning the cart. The answers provided will help you better understand the customer’s sentiment towards your product and supplement insights on user motivation. 

Conversion Rate 

Any interaction that your customer has with your brand that results in the achievement of a business goal is known as the conversion rate. Actions like clicking on a Call-To-Action button, making a purchase through a sales call, or signing up for an email newsletter are all conversion examples. As simple as it may sound, it is imperative to understand the basic formula for conversion rate calculation, as it gives you an accurate picture of where your brand stands. 

Basic formula: The formula for the conversion rate is the number of times a given action is taken (the number of outbound sales’ calls, for example) divided by the number of people who finalized the purchase. 

Example of a conversion rate formula for an email campaign: Below is a real-life example from JellyMetrics. To calculate your email campaign conversion rate, divide the number of people who completed the desired actions (signups, purchases, etc.) by the total number of successful deliveries. In the end, multiply that number by 100. 

App Return 

App return is the rate at which the customers return to your website or app. For instance, ​Yahoo​ calculates it’s return rate by measuring the percentage of users who return on a given day, week, or month. The value is then analyzed by looking at the cohort group when the users first started using their app.

Once you’ve collected all of the above customer data, it’s time to analyze it. The next section sheds some light on how to choose the right tool for your customer experience analysis.

Using the Right Tools for Customer Analytics 

A short search in Google will reveal a staggering number of customer data analytics software. So, what features should you look out for to choose the best-fitting solution? Given our expertise at Intersoft, we recommend that the option you choose checks the following features off your list:

• Allows access to customer insights for the entire team; not just management-level employees, but also customer support agents, who must be given the liberty of cross-referencing data on users whenever need be. By giving your entire team access to customer insights, you give them the tools needed to personalize their customer communication and, as a result, improve CX.
• Integrates all data sources to create accurate and consistent reports based on the above-mentioned metrics and other relevant customer data.
• Enables you to organize and/or reorganize your existing data so that it flows cohesively into your dashboarding application without the need to engage developers in the process of reconfiguration. 

Our Customer Insights solution at Intersoft provides you with all of these features, among many others. It is the fastest way to connect, consolidate, aggregate, visualize, monitor, and share all your data, enabling you to make your CX strategy an informed process. 

Summary

Customer support departments amass huge amounts of client data. However, only the most customer-oriented companies put in the effort to translate this important information into actionable insights and fuel their customer experience strategy. 

To make the most of your customer experience efforts, you need to not only track the right metrics but also choose a solution that lets you analyze and cross-reference your data with customer insights from your remaining data points.

If you’d like to learn more about the ins-and-outs of offering a good customer experience standard, don’t hesitate to reach out – we’ll be happy to recommend the best solution tailored to your needs.