8 Factors that Affect Page Load Time & Website Optimization Strategies

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Published on Dec 5, 2019

Updated on Dec 13, 2019

A website’s page load time plays an important role in customer acquisition. Google states that if your website takes more than 3 seconds to load, over half of the visitors will leave it. Eventually, it leads to conversion and profits. Although there are online tools available to check your website loading time and performance (Lighthouse, for instance), it’s important to understand what affects your website’s page load time. You can then optimize your web page accordingly.

8 Factors that affect the page load time

#1 Web hosting

Today, no one would like to wait for a website to spin and load at its speed. Websites that load quickly perform more in user engagement, conversion rates, and user experience. Hence, it is very important to have a high-availability web hosting plans.

#2 Size of files

The page speed always depends on the size of the assets loaded on the browser. It is, therefore, good to have an optimum number of assets with the least possible file size. This will require lesser bandwidth.

#3 Number of HTTP requests

Greater the number of HTTP requests from a browser to server/server to server, the higher will be the bandwidth consumption. Therefore, keep the number of HTTP requests to the minimum possible.

#4 Absence of CDN

Using CDN will boost the performance of the web site. The absence of it will affect the load time. CDN is a content delivery/distribution network. It is a network of proxy servers and their data centres distributed across the globe to increase the performance and availability of services to the end-users.

#5 Mediocre coding

Bad coding will always affect the page performance and SEO ranking of the website. It is good to follow best practices starting from the initial stage of development.

#6 The number of redirections

The number of redirections impacts the DNS lookup time.

#7 Lack of Keep-Alive

If you’re using HTTP/1.0 protocol and have not configured Keep-Alive, then there’s a higher possibility that the browser to server connection will break. It will not load the page properly.

#8 Hotlinking

Sourcing page content from other sites might affect the load time and performance of your website.

You might also like to read about 11 proven techniques to optimize website performance.

Strategies and checklist for website optimization

You can implement either bottom-up or top-down strategy for website optimization (discussed later). However, website optimization is an iterative process and you can repeat the following loop after completing a cycle.

How to optimize the website - Infographic

Ideas: Prepare a checklist of all the possible strategies for the target website to optimize.
Prioritize: Prioritize the prepared checklist strategies and act on them.
Test: Test the applied strategies for enhanced performance.
Analyze: Analyze the impact and performance of the website and check if any further strategies are required.
Optimize: For further enhancement, perform the cycle again until you achieve the best.

#1 Bottom-up strategy

This strategy starts from planning to production (Proactive). It defines a set of rules and actions before/while starting the actual development.

Bottom up strategy for website optimization

The above infographic represents the lifecycle of Bottom-Up strategy in web page optimization.

#2 Top-down strategy

It is a reactive method, which analyses the existing process to find the issue/lag, then reworks on behavioural grounds to accomplish the target. It is a reverse engineering process to identify the performance-issue gap and methods to fix them.

You can identify the resources which are affecting in maximum page load by considering the following-

Resource size
Asset positioning
Render blockers
Uncompressed contents
Bad requests

Once you’ve identified the sources, lay down the process of optimizing the content and keep iterating to achieve the desired results.

Basic checklist for both bottom-up and top-down strategies

Layout performance principles
1. Page load time
2. Responsiveness
3. Minimizing the number of requests
4. Use Cache headers
5. Minify CSS and JS contents
6. Use CSS sprites
7. Encourage Lazy loading on contents wherever possible
8. Avoid iframes and redirects
Executive performance principles
1. During application design
2. During application development

Consider the following aspects during the design and development phase.

#1 Application design optimizations

Simple & lightweight: Include only key functionalities on load to keep it lightweight.
Client side components: Adopt client side validation to catch errors.
On demand data loading: Use on-demand data instead of pre-loaded data. (E.g. use paginations, pop-up contents on click instead of on load)
Asynchronous calls: Adopt implementation of AJAX calls from the presentation tier and the business tier.

#2 Application development optimizations

Include JS files at the bottom of the page (to avoid render blocking of page).
Combine multiple CSS files and optimize unwanted rules as per page requirements.
Avoid using external scripts at the beginning of the page.
Combine smaller images/icons to sprite & have optimi.
Use CSS rules/files in the head section of the document.
Reduce the number of requests to server.
Implement server/browser caching on possible sections.
Implement Mobile-specific sections to avoid overloading on small screen devices.

Below are few improvisation observations which are affected by optimizing the Webpage and it’s assets.

UI performance optimization and the performance gains - Infographic

We’re technology tinkerers, experimentalists, and experts in customer experience consulting. Get in touch with us at hello@mantralabsglobal.com to know more about our ventures in website design and experience consulting.

In 1989, a missing $0.50 bolt led to the mid-air explosion of United Airlines Flight 232. The smallest oversight in manufacturing can set off a chain reaction of failures. Now, imagine a factory floor where thousands of components must function flawlessly—what happens if one critical part is about to fail but goes unnoticed? Predictive analytics in manufacturing ensures these unseen risks don’t turn into catastrophic failures by providing foresight into potential breakdowns, supply chain risk analytics, and demand fluctuations—allowing manufacturers to act before issues escalate into costly problems.

Industrial predictive analytics involves using data analysis and machine learning in manufacturing to identify patterns and predict future events related to production processes. By combining historical data, machine learning, and statistical models, manufacturers can derive valuable insights that help them take proactive measures before problems arise.

Beyond just improving efficiency, predictive maintenance in manufacturing is the foundation of proactive risk management, helping manufacturers prevent costly downtime, safety hazards, and supply chain disruptions. By leveraging vast amounts of data, predictive analytics enables manufacturers to anticipate machine failures, optimize production schedules, and enhance overall operational resilience.

But here’s the catch, models that predict failures today might not be necessarily effective tomorrow. And that’s where the real challenge begins.

Why Predictive Analytics Models Need Retraining?

Predictive analytics in manufacturing relies on historical data and machine learning to foresee potential failures. However, manufacturing environments are dynamic, machines degrade, processes evolve, supply chains shift, and external forces such as weather and geopolitics play a bigger role than ever before.

Without continuous model retraining, predictive models lose their accuracy. A recent study found that 91% of data-driven manufacturing models degrade over time due to data drift, requiring periodic updates to remain effective. Manufacturers relying on outdated models risk making decisions based on obsolete insights, potentially leading to catastrophic failures.

The key is in retraining models with the right data, data that reflects not just what has happened but what could happen next. This is where integrating external data sources becomes crucial.

Is Integrating External Data Sources Crucial?

Traditional smart manufacturing solutions primarily analyze in-house data: machine performance metrics, maintenance logs, and operational statistics. While valuable, this approach is limited. The real breakthroughs happen when manufacturers incorporate external data sources into their predictive models:

Weather Patterns: Extreme weather conditions have caused billions in manufacturing risk management losses. For example, the 2021 Texas power crisis disrupted semiconductor production globally. By integrating weather data, manufacturers can anticipate environmental impacts and adjust operations accordingly.
Market Trends: Consumer demand fluctuations impact inventory and supply chains. By leveraging market data, manufacturers can avoid overproduction or stock shortages, optimizing costs and efficiency.
Geopolitical Insights: Trade wars, regulatory shifts, and regional conflicts directly impact supply chains. Supply chain risk analytics combined with geopolitical intelligence helps manufacturers foresee disruptions and diversify sourcing strategies proactively.

One such instance is how Mantra Labs helped a telecom company optimize its network by integrating both external and internal data sources. By leveraging external data such as radio site conditions and traffic patterns along with internal performance reports, the company was able to predict future traffic growth and ensure seamless network performance.

The Role of Edge Computing and Real-Time AI

Having the right data is one thing; acting on it in real-time is another. Edge computing in manufacturing processes, data at the source, within the factory floor, eliminating delays and enabling instant decision-making. This is particularly critical for:

Hazardous Material Monitoring: Factories dealing with volatile chemicals can detect leaks instantly, preventing disasters.
Supply Chain Optimization: Real-time AI can reroute shipments based on live geopolitical updates, avoiding costly delays.
Energy Efficiency: Smart grids can dynamically adjust power consumption based on market demand, reducing waste.

Conclusion:

As crucial as predictive analytics is in manufacturing, its true power lies in continuous evolution. A model that predicts failures today might be outdated tomorrow. To stay ahead, manufacturers must adopt a dynamic approach—refining predictive models, integrating external intelligence, and leveraging real-time AI to anticipate and prevent risks before they escalate.

The future of smart manufacturing solutions isn’t just about using predictive analytics—it’s about continuously evolving it. The real question isn’t whether predictive models can help, but whether manufacturers are adapting fast enough to outpace risks in an unpredictable world.

At Mantra Labs, we specialize in building intelligent predictive models that help businesses optimize operations and mitigate risks effectively. From enhancing efficiency to driving innovation, our solutions empower manufacturers to stay ahead of uncertainties. Ready to future-proof your factory? Let’s talk.

In the manufacturing industry, predictive analytics plays an important role, providing predictions on what will happen and how to do things. But then the question is, are these predictions accurate? And if they are, how accurate are these predictions? Does it consider all the factors, or is it obsolete?

8 Factors that Affect Page Load Time & Website Optimization Strategies