Customer-centric enterprises are increasing their investment in technologies that enable teams to measure and improve CX, while achieving key business outcomes such as maximizing customer lifetime value, reducing costs and more.
Over the past few years, CX leaders have adopted customer journey analytics to connect the dots between customer behavior and the KPIs by which businesses are measured. Leveraging these solutions enables you to aggregate, analyze and act on customer journey data to improve customer experiences and achieve business outcomes.
So, how are leading enterprises using customer journey analytics in the real world? We’ve segmented common customer journey analytics examples into four main categories:
- Increase operational efficiency and cost savings
- Measure and improve customer experience
- Grow revenue
- Drive customer loyalty
In this blog, we’ll explore how businesses across industries like financial services, telecommunications, utilities, hospitality, retail and more are leveraging customer journey analytics in a variety of ways, from reducing churn to improving customer satisfaction.
Let’s dive into the first category of these customer journey analytics examples.
Increase Operational Efficiency and Cost Savings
Efficiently delivering services and experiences to customers is a defining challenge for enterprises today. Streamlining operations and reducing the cost to serve are major priorities for many leading businesses.
For instance, McKinsey notes that organizations are optimizing self-help channels to triage common queries or issues, reduce costs and allow support or service employees to spend time on more complex requests. The key is to prioritize which improvements or new initiatives will impact customer satisfaction and your bottom line the most.
Journey analytics enables you to effectively manage and measure customer journeys, revealing opportunities to optimize experiences and reduce costs.
1. Increase Cost Savings by Leveraging Journey Analytics
Costs associated with overdue payments are a huge problem for the utilities industry, not only because of the lost revenue due to lack of payment, but also in truck rolls and service call costs.
The CX team at a national utility service provider notices the steep financial impact that grossly overdue payments and service disconnects have on the bottom line, totaling almost $850 million.
To address the problem, the team starts by analyzing the overdue payments to service disconnect journey. It starts with customers whose payments are overdue by 33 days. Next, a technician is sent to the residence to disconnect service, referred to as a truck roll. After service is disconnected, a high volume of customers contact the call center about the disconnect. Once payment is received, another truck roll occurs to restore the customer’s service.
By incorporating rich data from their call center platform, the team finds that a high percentage of customers that call the service center after their service is disconnected promise to pay their bill as soon as their next paycheck is deposited.
The team validates this by calculating that 22% of customers pay their bill within 10 days of the disconnect. They see potential cost savings if the disconnect truck roll is scheduled to occur after the date the customer intends to pay their bill, rather than a fixed 33 days after the disconnect notice.
The team implements a test to quantify the cost savings. Within the disconnect notice, the test group is provided with a link to provide the date that they intend to pay their bill. That date must fall within 14 days of the standard 33 day disconnect.
Using journey analytics, the team finds that in the test group, the need to disconnect (and subsequently reconnect) service is reduced by nearly 70%. They make the case that rolling out this option to all customers in all regions could lead to nearly a billion dollars in savings from unnecessary truck rolls.
2. Decrease Support Call Volume and Spend with Customer Journey Analytics
A retail bank uses customer journey analytics to uncover the cause of new client service calls and increase operational efficiency.
The team wants to understand the effectiveness of their self-service channels when customers have a problem making a mortgage payment. They start the journey with the ‘Payment Notice Sent’ Event, which shows that 12.5M customers were sent a Payment Request notice.
Next, they define separate paths for customers that went on to use each of their three self-service channels: mobile, IVR and web. And finally, they extend the journey to show customers who completed their payment after speaking with a call center agent.
Looking back from the Service Agent call, the team finds that the IVR is the least effective self service channel for customers with bill payment problems. The 81% leakage from the IVR channel is nearly two times higher than mobile and web. So why is the IVR so much less effective?
To find out, the team zooms in on the specific steps of the IVR micro-journey to better understand the leakage. They find that about half of customers who attempt to make their mortgage payment through the IVR successfully complete the payment while in the IVR system.
They also see that 60% of customers at the prior step abort the process and request to speak to a Service Agent without attempting to pay using the IVR system. The team acts on this information and analyzes the language at this specific point in the journey to better understand the issue.
After changing the language in this specific IVR node, more customers make their payment within the IVR, which leads to a reduction in IVR leakage, call volume and call center costs.
Measure and Improve CX
Customer experience, analytics and marketing professionals across industries report that measuring CX is one of the top five biggest challenges encountered today.
Assessing the success of your CX initiatives and quantifying ROI starts with the ability to understand the customer behavior that impacts metrics in a positive or negative way. By analyzing cross-channel journeys, you can easily measure results, identify opportunities to improve customer experience and quantify the impact of CX initiatives.
3. Measure the Impact of New CX Initiatives With Customer Journey Analytics
A leading cable, wireless and internet provider uses journey analytics to gauge the success of a new self-service appointment system intended to improve customer experience while reducing cost to serve.
The CX team needs to understand why the current service repair micro-journey results in a decrease in NPS. When a customer calls the care center and schedules a repair visit, their baseline NPS is 14.7. Some customers are not at home when the service truck arrives. For these “No Shows,” NPS drops to 14.
Based on the high No Show rate, the team concludes that requiring customers to call the care center to reschedule service appointments contributes to a poorer experience, as quantified by a decrease in NPS.
In addition to a poor experience, they also calculate that service visits (called truck rolls) to customers that are not at home are costing the company $21M.
To improve this process, the company implements a self-service appointment system that is accessible via web, mobile and set-top box. The new system enables customers to reschedule existing appointments, add themselves on a waitlist with automated messaging when a slot opens up and receive automated messages that narrow down your time window as you get closer to the appointment. The team analyzes the impact of the test on customer experience so they can quantify the ROI of the investment and determine whether to roll it out to all customers.
The team adds the test of the new appointment system to their journey analysis, as shown in the large orange box below. They find that the experience of customers using the new self-service system is much better than the experience of those using the current approach, as quantified by a nearly 6-point increase in NPS.
4. Leverage Journey Analytics to Understand What’s Driving Customer Effort Scores
A health insurance company seeks to understand why their customer effort scores (CES) are so high for newly enrolled members trying to set up automated premium payments.
To get to the root of the problem, the CX team uses customer journey analytics to analyze the journeys for customers who set up automated payments via the website, mobile app and call center.
Their analysis shows that 21% of members who set up payments in the mobile app encountered an issue that leads to a service call, leading to higher CES. But members who begin payment setup over the phone or through the website are more successful and report a lower CES.
The CX team leverages this insight to prioritize a project to improve the user experience of their mobile app. In the meantime, the team can steer newly enrolled members into lower effort journeys to improve their experiences.
Acquiring high-value customers and upselling or cross-selling additional products and services to existing customers is an essential source of revenue growth for most enterprises. The key to success in both is to understand each customer’s unique journey context.
That requires accessible, connected data. But, too often, customer data is locked in silos. Today, customer-centric enterprises are adopting solutions like customer journey analytics to remove those barriers.
Journey analytics enables CX teams to visualize customer behavior across channels and time, define in-journey signals that indicate the likelihood of conversion and monitor performance towards end-of-journey success.
5. Discover Upsell Opportunities with Customer Journey Analytics
A luxury hotel and resort company uses journey analytics to identify customers to target with the right upsell offers and orchestrate a personalized, cross-channel campaign to increase upsell conversions.
First, the team wants to understand which upsell offers are most effective for getting guests to upgrade their rooms. Starting on the top left of the journey, we begin with customers who booked a standard room, and then analyze the effectiveness of three different upsell offers shown in the orange box: bonus loyalty points, a dining package and a spa package. Below each offer are the number and percentage of customers that upgraded their room by responding to each offer.
Looking back at the conversion rates, the team finds that while the most customers are offered extra loyalty points, the spa package converts at the same rate.
Next, the team determines the most effective channels and timing. This analysis compares the volume of upsell conversions across 5 separate channels: while booking on the website, over the phone, after clicking an email or social ad or at the front desk at check-in.
Sending the upgrade offer via email after the initial room has been booked is converting at the highest rate, while the largest number of upsells are generated using social ads.
Now that they have a better understanding of which channels and offers are most effective overall, the next question the team needs to answer is “which offers are more effective for upselling different customers?”
Their analysis shows that over 70% of guests that upgrade through a spa package offer are couples, whereas this offer has been far less effective for families, business travelers and other individual guests traveling alone.
In a similar analysis not shown here, they find that other offers were more effective than the spa package for some of these demographic segments. For example, business travelers and frequent guests are much more likely to convert on an offer for additional loyalty points, whereas families more commonly respond to the dining package.
Thanks to journey analytics identity matching capabilities, the team also is able to look-back and discover anonymous behaviors customers had across channels prior to booking a stay. They find that many of these customers that upgraded through the spa package had also visited the spa web page as an anonymous visitor before booking their standard room.
The team orchestrates a cross-channel spa upsell campaign targeting couples and spa page visitors. The highest converting segment by far consists of couples that ALSO had visited the spa page prior to booking.
In the orange box on the left, we see that this segment converted at 11% on the spa upsell offer, while the other two segments converted at only 6% and 2%.
By focusing spa upsell offers on this dynamic segment which combines both demographic and behavioral data from multiple sources and channels, the team can greatly increase the effectiveness and efficiency of marketing and advertising efforts to increase upsells.
Finally, they determine which channels are most effective for each audience and upsell offer. On the right side of the journey, we can see conversion rates for the campaign across 4 different channels; google and facebook advertising, email marketing and personalized call-to-actions on the website. This analysis allows them to quantify the overall impact of their cross-channel approach, and also optimize their campaign budget to further improve results and ROI.
Applying a similar campaign approach to their other customer segments and upsell offers like dining and loyalty points, the team is able to deliver personalization at scale and improve conversion rates across the board.
6. Customer Journey Analytics Helps a Major Retail Bank Increase Credit Card Conversions
The credit card team at a major retail bank is tasked with improving credit card opening rates among millennials. To understand the role that different channels play in credit card offers and their respective efficiencies, the bank uses customer journey analytics.
They uncover a variety of customer journeys across online and offline channels – such as branch visits, website browsing, mobile data, email data and in-app interactions—that lead customers to view a credit card offer.
Within minutes, they discover how many customers go on to apply for a card online versus how many reject or ignore the offer. With one click, they are able to see how many customers move forward at each step, how many drop out and how many are still present at that step.
Using journey analytics, the bank determines that the offer converts better for people who see it as an email than as a text message or within the bank’s mobile app. Based on this information, they decide to send a personalized email offer to those who view the credit card offer and then abandon their journey.
Drive Customer Loyalty
Your organization’s future revenue and profitability depend heavily on retaining your customer base. For that reason, customer churn rate is a key business metric for enterprises across industries.
By understanding the drivers of customer behavior, CX leaders can reveal friction points across customer journeys, as well as identify behavioral indicators that lead to churn.
Customer-centric organizations use customer journey analytics to manage journeys and uncover actionable insights that allow them to make better decisions about how to prevent churn and maximize customer retention.
7. Identify High-Impact CX Issues that Lead to Churn with Customer Journey Analytics
A leading telecom provider uses customer journey analytics to understand the drivers of churn so they can identify and prioritize CX initiatives that will have the largest impact on reducing churn.
First, the team wants to discover the most common CX issues customers encounter. Customer interactions related to CX issues can occur across many different channels, which ordinarily is difficult and time consuming to analyze. Using journey analytics, the team can easily connect all cross-channel interactions to see which CX issues are most common overall.
On the left are all customer interactions across 5 different support channels: the help forum, web chat, web support articles, customer care calls and mobile app support. The pie chart on the right shows a breakdown by support issue category, which indicates that TV and billing related issues are by far the most prevalent issues customers experience.
The team also wants to analyze the impact these issues have on NPS and determine which problems lead to the most NPS detractors.
Below, you can see that TV and billing problems lead to a high number of detractors and that internet related issues have an equally negative impact.
However, only a small percentage of customers actually participated in the NPS survey. So, the team decides to continue their analysis before making their final conclusion.
Next, they use Pointillist to analyze the direct impact of different CX issues on customer churn. They find that while TV and billing were the most prevalent, internet difficulties actually drive the largest number of account closures and lost revenue.
Customers who experience these issues are the most likely to churn, and churn in the shortest average time frame after having a problem.
With hundreds to thousands of possible internet-related issues, knowing the category of the issue isn’t enough. So, the team uses Pointillist to uncover two specific internet issues that have the highest impact on churn and are responsible for the most lost revenue.
Armed with these insights, the CX team prioritizes and launches an initiative to address these issues, which they anticipate will reduce their overall churn rate by 2% in the first 12 months after implementing a solution.
8. Identify Customers at Risk of Soft Churn with Customer Journey Analytics
A retail bank uses customer journey analytics to understand customer behaviors that are predictive of soft churn and identify at-risk customers in order to support their retention efforts.
In the bank’s case, soft churn refers to customers whose activity decreases over time and suddenly, they stop adding money to their accounts. First, the team creates a rolling 12-month baseline of customer behavior that may be predictive of soft churn, including mobile app sessions, direct deposits, bill pay activity and branch visits. These customer interactions across channels are included in the journey image, including a date filter applied to narrow it to a rolling 12-month window.
Next, they assess the same behaviors over the last 30 days to compare each customer’s most recent behavior with their behavior over the preceding 12 months.
Not surprisingly, each behavior is fairly constant at an aggregate level across the entire customer base. But what the bank really wants to find are the individual customers whose recent activity has reduced significantly when compared to their previous behavior.
The team exports a list of all individual customers with significant changes in behavior over the past 30 days, as compared with their behavior over the preceding 12 months.
Moving forward, when a customer shows signs of potential soft churn, the bank can immediately identify the customer and quickly notify the appropriate internal teams and employees.
In this way, they put these insights into the hands of employees who can take action in time to prevent customers from soft churning and prevent substantial lost revenue.
Applying Customer Journey Analytics to Your World
In order to determine if customer journey analytics software can enable your organization to manage, measure and improve CX, start by defining the goals that matter most to your customers, such as setting up an account, paying a bill or resolving a technical issue. The goal at the end of each customer journey should also map to a specific business outcome that you use to measure CX success, such as acquisition, cost savings and so on.
Customer journey analytics supports your ability to manage those crucial customer journeys, as well as measure performance and reveal opportunities for improvement. Ultimately, implementing journey analytics in your organization empowers every team across your enterprise to connect customer behavior and experiences to the KPIs your enterprise is measured by.