Creating and nurturing a great digital customer experience is not as easy as simply deploying the right digital intelligence solution. You also have to create processes to support widespread data sharing.
The four-step methodology presented here includes lessons learned from New Relic customers and best practices proven in real-world implementations. It’s designed to help empower the entire digital customer experience team to deliver the best possible experience across all channels, from mobile apps to websites and even new platforms like set-top boxes:
- Step 1: Collect the data
- Step 2: Identify and fix problems: troubleshooting and more
- Step 3: Optimize and iterate Step 4: Share the data and analysis
Step 1: Collect the data
The first step is all about collecting customer experience data with a modern digital intelligence solution, preferably one that makes it easy and fast to get started. (Software-as-a-Service, or SaaS, solutions can help speed deployments, beginning to provide data almost immediately.)
Most organizations begin tracking their apps and websites using Application Performance Management (APM) metrics: Is your site broken? How long does it take pages to load? These metrics are critical, but to focus on customer experience you also need to track related user and business-oriented metrics across all your various platforms and touchpoints. These metrics tell you such things as:
- Is your store open and serving the expected number of users?
- Are users abandoning transactions mid-stream?
- Are your customers able to check out smoothly and without glitches?
- Which features are getting the most engagement?
- Are conversion rates holding steady, rising, or falling?
- Are users in some geographic regions using your services differently than in others?
These and similar metrics help establish the current state of your digital business, customer experience, and application performance.
To make sure your data-collection efforts deliver their full potential, you’ll want a digital intelligence solution that delivers end-to-end visibility across the full stack, easy-to-use dashboards, and real-time as well as historical data. Your solution should also enable you to create a monitoring and alert strategy that automatically notifies relevant stakeholders when specified customer experience thresholds are reached. It’s critical to know when customers like Olivia are experiencing issues without having to wait until droves of unhappy users start abandoning your site.
Step 2: Identify and fix problems: troubleshooting and more
In Step 1, you began collecting a common set of customer experience and application performance data. Next, it’s time to start using that data to improve the digital customer experience.
Tracking application performance, usage, and customer experience lets you see what’s happening with your software and its impact on users and your business. Typically, you’ll also start identifying issues you need to fix. A particular page may be loading too slowly. A certain step in the transaction process might be throwing errors. A mobile issue could be impacting conversions. To help you troubleshoot more efficiently, your digital intelligence solution should provide code-level visibility for faster, easier identification of the root cause of the problem.
- Lack of user engagement with relevant content • Performance and availability of third-party services and APIs, for functions such as payment processing
- Broken check-out processes that add friction to completing purchases
- Disparities in experiences across different geographical regions
- Differences between usage patterns of VIP and lower-value customers
- Seasonal changes in traffic and conversions
- Features and products that aren’t working as planned or attracting as many users as expected.
Critically, that focus should extend across all your digital channels, from desktop websites to mobile websites and native mobile apps all the way to set-top boxes.
This additional layer of visibility helps identify performance and customer experience issues, and make the connection between performance issues and their effect on the customer experience. The idea is to know that the 50% slowdown the app experienced led to a 20% increase in users leaving the website and abandoning their shopping carts, for example.