Improving your Customer Experience with CX Analytics

Products and services aside, customer experience is your business—at least it should be. It’s no longer possible to compete on products and services alone. In fact, more than two-thirds of marketers surveyed by Gartner said their companies competed mostly or completely on customer experience (CX)—and that was in 2017 (Gartner).

During the pandemic, eCommerce sales have grown upwards of 27% (emarketer) and consumers have increasingly higher expectations for digital experiences, it’s pretty much a given that great CX and customer support are absolutely critical for your DTC brand to gain and/or maintain competitive advantage. 

How do you know if your customer experience strategy is working? Beyond response and resolution rates, do you know if interactions with your customer support team result in satisfied customers? CX analytics can help you more accurately and transparently quantify success and keep you on the right track. They also can help you identify areas for optimization and support your team in determining how to improve customer experiences.

What metrics should guide your CX analytics? 

Customer experience is (broadly) the perception a consumer has of a brand. This can be influenced by many things, but a powerful factor is customer support, including before, during, and after purchase.

CX metrics can help you monitor the performance of your customer support team, as well as the health of your business. To determine which CX metrics to track, it can be helpful to understand which metrics are essential to allow for both a big-picture view as well as a deeper dive for more nuanced insights. To help growing DTC brands get started on their CX optimization journey, we’ve highlighted key metrics and CX analytics approaches to consider.

Short-term CX metrics

If you’re not doing so already, short-term CX metrics are where to focus first, as they give you a timely picture of how well the customer support team is functioning and can help you proactively identify immediate issues. These customer support metrics typically include the following:  ‍

Tickets opened‍

Understanding the volume of tickets coming in shows how many customers have questions or need assistance; tracking this over time shows trends that can help you anticipate when or why customers might contact you. A high volume of tickets about specific issues also may indicate a need for providing customers with FAQs or information about topics like return policy and order tracking. It’s helpful to break out tickets by topic area—and we’ll get into that further down this blog post.‍

Tickets closed‍

Tracking the ratio of tickets open to tickets closed helps you understand how effective your team is in resolving customers’ issues. It also helps identify if further investigation is needed into any problems that may be preventing tickets from being closed in a timely manner.‍

Response times‍

More than 80% of customers say they expect an immediate response from a brand (Hubspot). Average response times can vary greatly by brand, size of company, and industry vertical, but in general, customer support should be able to respond to an inquiry within two hours, even if it’s just to let the customer know the inquiry has been received, and you’re working on their issue. Tracking median response time lets you know that the support team is able to efficiently manage the volume of tickets coming in.‍

Resolution times‍

Responding to or closing a ticket is not the same as resolving it, which means you’ve solved the customer’s issue. Again, average resolution times can vary, but a general benchmark is resolution of a customer issue within one day or less. Median resolution time is a very important indicator of the customer support team’s ability to provide satisfactory answers and information to address customer inquiries.

In addition to median resolution time, you may also want to look at “resolution by create date,” which can help you identify if there are any outstanding tickets that have yet to be resolved, how many days they have been unresolved, and consider how best to reach out to those customers who may be unhappy with your customer support.‍

Other CX metrics to track

While tickets opened/closed, response time, and resolution time are important for monitoring and optimizing your customer support team’s productivity, they’re not the only customer support metrics you should track to determine how to improve customer experience. Depending only on these metrics means that you will only have part of the CX story because they don’t provide insight into whether the customer was satisfied by the interaction with your brand. 

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A longer-term CX view

Performance metrics might be on target, but what good is timely, efficient customer support if customers never return to purchase again? The following are some key metrics that require longer periods of time to track but help you understand if your customer support strategies are effective in helping retain existing customers. ‍

Repurchase rates‍

When first-time customers return to buy again, it likely means they were happy with that first purchase, indicating a good customer experience. The repurchase rate tracks the percentage of customers in a cohort who placed another order within a period of time (e.g., 30-60-90-120 days from the first order). Specific to CX, you can track repurchase rates from ticket resolution dates. If customers purchase again, it indicates customer support did a good job solving the customer’s issue. Ideally, you look at the data from at least 6 to 12 months after the customer issue to track if they repurchase.

In addition to indicating satisfaction with CX, repurchase rates also can help you understand when customers may be ready to make another purchase—enabling you to proactively adjust your marketing and create additional great customer experiences that anticipate customer needs.‍

Customer lifetime value (LTV)‍

Customer lifetime value (LTV) is the average amount of money a consumer will spend on your products over the duration of your relationship (months, years). The longer you retain a customer, typically, the higher LTV they have. With about 65% of companies’ business coming from existing customers (SmallBizGenius) and customer acquisition costs (CAC) on the rise, increasing your average LTV and/or focusing on growing your number of loyal, long-term customers can have a big impact on revenue over time. One study showed that 92% of companies that improved CX saw an increase in customer loyalty, and 84% saw an increase in revenue (Dimension Data). 

Obviously, customer retention and LTV heavily depend on customer experiences with your brand. Using analytics to help you understand the impacts of customer support outcomes on retention and customer LTV is critical to optimizing CX to boost revenue. ‍

A common CX mistake to avoid‍

If you stop tracking customer behavior after a ticket is closed or resolved, you miss out on critical insights that can help you get a much more nuanced picture of the effectiveness of your customer support strategy, as well as the health of your business in terms of customer retention. ‍

A more granular CX analytics approach

Another useful way to break down customer support data is to segment tickets into topic categories to find insights and identify trends. For example, tag and then analyze tickets by issue, product, resolution rates, channel, and more. ‍

Identify problems ‍

If many customers are contacting customer support about specific issues like order status, product dissatisfaction, or subscription cancellations, it can be a red flag to investigate further. For example, you may find that the warehouse is experiencing fulfillment delays or a new product feature is causing customer confusion. Analyzing tickets by topic can give you clues about emerging issues in a timely manner so you can work to address them and improve that part of the customer experience.    

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Another common CX mistake to avoid‍

Don’t get stuck trying to make specific decisions from big picture metrics. There’s a place at the table for both high-level and more in-the-trenches insights when it comes to CX metrics. It’s important to leverage both approaches and apply them appropriately for different use cases. In other words, make sure that your high-level strategy is tied to relevant KPIs, and for tactical decisions, leverage specific metrics to understand what is going on.

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Beyond KPIs: customer surveys‍

Even if your customer response and resolution rates look good, do you know if your customers are satisfied with the outcomes? Will they come back to purchase again? An effective way to find out is to survey customers post-resolution and ask them to rate the customer support interaction. Some brands make it as simple as clicking on an emoji (Happy, Meh, Unhappy). Others do a Net-Promoter-Score-type survey and ask customers if they would recommend the brand to a friend or if they would purchase again. 

A survey helps you track customer satisfaction in the moment, as well as track over time if those customers who indicated “satisfied” show increased customer LTV. 

What do I do with these CX metrics? 

We’ve touched on how tracking and analyzing CX metrics helps you optimize customer support performance, identify customer issues to get ahead of potential business challenges, and improve customer satisfaction to increase customer LTV. You can also use your findings to inform new marketing campaigns. For example, you could test a targeted email campaign and offer discounts to customers who had issues with a certain product to encourage them to purchase again. 

In addition, at a higher level, your CX findings may cause you to adjust your business processes or products to improve customer experience or influence how you operate to mitigate issues coming through customer support. 

Making CX analytics a win for DTC brands 

To help DTC brands with their CX analytics, Daasity's CX Dashboard automatically aggregates data and provides clear visualizations for a user-friendly, intuitive way to understand CX health and optimize customer support performance.