Performance analysis tools like Google’s PageSpeed Insights provide a valuable service by highlighting potential bottlenecks, but they often fail to account for the “real-world” variables of running a business. While these tools offer a checklist for improvement, they rarely weigh the implementation costs against the actual effectiveness of the changes. For many merchants, the quest for a perfect score leads to a rabbit hole of technical debt where the investment becomes entirely disproportionate to the benefit.

One of the benefits of modern SaaS platforms like Shopify is that the architecture is already highly optimized with built-in CDNs, compression, caching, and automated image optimization. Because the foundation is already fast, the “fine-tuning” suggested by Google often demands significant development resources for what amounts to a marginal improvement.

Discovery the hidden costs of optimizing for metrics instead of real impact

In e-commerce, a product without an image is essentially invisible. For distributors managing massive catalogs of industrial parts, hardware, or specialized components, obtaining unique photography for every SKU isn’t just difficult—it’s a logistical nightmare.

When products lack images, or use low-quality “Image Coming Soon” placeholders, you hit two major roadblocks:

1. Consumer Distrust: Customers perceive a lack of images as a sign of an unmaintained or unreliable site.
2. Ad Rejections: Major ad platforms—like Google Merchant Center—will flag or suspend products that don’t meet their image requirements.

Automate the creation of better product image placeholders for your catalog

Recently, I’ve had multiple clients and Project Managers suggest some version of: “We should just hardcode this because we need it done by the end of the week.”

While understandable under deadline pressure, this idea reflects a fundamental misunderstanding of software development. Hardcoding is often assumed to be faster because it sounds simpler, but you might be surprised to learn that it isn’t.

Learn why 'quick fixes' are actually slowing you down

There is a common misconception that the goal of Quality Assurance is simply to verify that code matches documentation. But the name “Quality Assurance” implies something much deeper. Assuring quality is about far more than satisfying a set of requirements created in a vacuum; it is about how that experience is realized in a real-world context.

In the “vacuum” of the mockup phase, everything is perfect. The data is clean, the screen sizes are standard, and the user always follows the intended path. But “Minimum Quality” does not exist in a vacuum. It is defined by the user’s lived experience with the final implementation.

If a feature meets every written requirement but feels clunky, looks broken on an unusual screen size, or creates friction during a non-standard interaction, it has failed the quality test. True quality isn’t found in the plan; it’s found in the implementation.

Learn how to improve the quality of your Quality Assurance

When teams look for ways to automate workflows or improve business processes, a common question arises: Should we write a script or build an app? Both can interact with the same APIs, solve similar problems, and save time—but choosing the wrong approach can create extra maintenance work or make a solution harder for teams to use effectively.

This post explains the difference between scripts and apps, when each makes sense, and why a working script doesn’t automatically simplify app development.

Understand when to write a script and when to build an app