The Three Dimensions of Monitoring and Database Optimization Digital of Customer Experience
As businesses shift more of their customer engagements to the web and mobile devices, the digital customer experience is becoming the primary method they connect with consumers and prospects. To adequately realise the significance of creating a great digital customer experience, you must first understand what the phrase means, what dimensions make up a digital customer experience, and what KPIs to track to assess the Quality of Service you are giving.
What exactly does the term “digital customer experience” (DCX) imply?
Customers leave an impression every time they connect with your website, mobile app, or other digital touchpoint. You get a mosaic that represents your whole digital client experience when you put all of these impressions together.

Whether you’re ready or not, many clients’ primary relationship with your company is now digital. Your websites and mobile apps are now essential income generators, marketing/service channels, and brand experiences. The impact of DCX difficulties on revenue, customer happiness, and brand reputation can be significant. Negative digital experiences, such as when your website or mobile app goes down, impact not just the engineering teams that manage your digital channels but also the overall success of the company.
- Is it reachable and operational?
- In terms of functionality, is it working correctly?
- Is it operating at a fast enough pace?
This sequence aids in the triage of any issues you may encounter. Functionality and speed are meaningless if your page isn’t up and running. Rate isn’t as vital if your page is prone to mistakes and is unusable. However, persistent slowness degrades the user experience and produces irritation even when the site is up and running.
Applying DCX’s three dimensions
Let’s apply these dimensions to several sample situations to investigate the service-level quality of different digital experiences and pick the most relevant KPIs to your organisation and the digital customer experience you’re providing for a better understanding.
A desktop website with video streaming:
- Is the website up and running? Is it on the verge of collapsing? Are the video delivery services on the backend operational?
- Did the website crash when the user attempted to log in to see the video? Is the player up to date? Is the video starting to load? Are the advertisements loading?
- Is the video stuttering? Is it true that the bitrate has dropped? How long did it take for the advertisements to appear?
For in-store pickup, a native mobile food-ordering app:
- Is the software available for download? How frequently does the application crash? Is the printer at the store operational?
- Was it possible for the user to make a purchase? Is it possible that the order was created incorrectly? Was it correctly routed through the printer?
- Speed—Did the order confirmation on the mobile app go smoothly?
A business technology firm that provides a rich web application as a service:
- Is the web application up and running? Is the contractual uptime SLA being met?
- Do you have the right approach to database optimization
- Is it possible for users to log in? Is it possible for them to access the main dashboard? Will they be able to perform their vital tasks?
- Is the web app fast and responsive? Does it become slower for VIP clients who use a lot of data? Are users bouncing too soon because they’re frustrated?
Different programs for different purposes, predictably, have various vital factors. Starting with these users- and business-oriented KPIs will help you better understand the digital experience your technology teams produce.

Developing the foundational Quality of Service metrics
While these dimensions assist in answering high-level business concerns, we also want to dig deeper into the underlying technical data that feed these dimensions. These lower-level indicators focus on specific technical layers on which teams can work. Here are several examples:
Availability
- All services are available at all times around the world
- The front’s uptime
- The uptime of the backend
- Uptime of the backend
- The launch of a mobile app
- Crash rates for mobile apps
- API availability
- Third-party server uptime
- Server availability
Functionality
- On the front end, error rates are high.
- On the backend, error rates are high.
- APIs’ error rates
- Rates of third-party inaccuracy
- Errors in user transactions that are critical
- Mistakes in container management
- Errors in infrastructure
Speed
- The amount of time it takes the front end to reply.
- The length of time it takes the backend to respond.
- The time it takes for an API answer to arrive.
- Transaction times for applications
- The time it takes for JavaScript to run
- The time it would take to query a database
- Utilisation of resources
- RAM, CPU, and network latency of containers
- Response times for third-party services
The increased sophistication of modern application architectures and their critical technical mix is reflected in the wide range of these health measures. The degradation of your digital customer experience can be caused by various underlying reasons that originate from many layers of your technology stack, ranging from the frontend to the backend/database to the supporting infrastructure
- Unnecessary AJAX requests in a single-page application as an example on the front. Multiple, redundant AJAX calls to a backend service are triggered by single user interaction in a single-page application, lengthening load times for desktop website users.
- Example of a back-end/database: A poor database query executes a complete select statement. This slows down the backend API service, making it unresponsive to data requests from native mobile apps. The request times out, causing the mobile app to crash on occasion.
- As an example of infrastructure: Infrastructure services are under-provisioned due to incorrect container service configuration. Requests cannot be handled quickly due to a lack of resources, overloading the load balancer, and bringing the system down—the entire stack falls.
Managing the avalanche of information
Throughout the entire technological stack, the problem with KPIs is determining which ones are the most essential. We can choose the most meaningful underlying indicators to separate the signal from the noise, similar to the availability, functionality, and performance hierarchy.
The closest indicators to the user and the business are the most accurate representations of what is happening. It’s a more definitive test of your technology stack’s final product. Any good indicators are superseded if something is broken for a consumer. To understand how an app is performing in the field frequently means starting with actual page load speeds, mobile app launches, or automated user-action monitors.
Metrics further down the technology stack, on the other hand, can act as early warning indicators. They’re louder and less explicit, but they can give you a heads-up on possible problems before they reach your customers. Furthermore, these more detailed measurements can aid in diagnosing issues that hurt customers. Utilise them to identify bottlenecking micro-services and misconfigured infrastructure resources before they affect your consumers.
Steps to take next
Technology teams must increasingly take responsibility for how their work affects customers and the business, rather than merely developing and managing their part of the stack. Monitoring your digital customer experience can assist development and operations teams to locate and resolve issues no matter where they are.
Now that you’ve figured out what dimensions and KPIs you want to track, it’s time to get to work on actually following them. Best Practices for Monitoring Digital Customer Experience is a technical reference that will help you optimise your whole technology stack and integrate DCX monitoring into your operations. Then you’ll demonstrate how these advancements have benefited the company as a whole.
About Enteros
Enteros offers a patented database performance management SaaS platform. It proactively identifies root causes of complex business-impacting database scalability and performance issues across a growing number of RDBMS, NoSQL, and machine learning database platforms.
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