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8 Factors that Affect Page Load Time & Website Optimization Strategies

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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
  1. Ideas: Prepare a checklist of all the possible strategies for the target website to optimize.
  2. Prioritize: Prioritize the prepared checklist strategies and act on them.
  3. Test: Test the applied strategies for enhanced performance.
  4. Analyze: Analyze the impact and performance of the website and check if any further strategies are required.
  5. 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 

  1. 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
  2. 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

  1. Simple & lightweight: Include only key functionalities on load to keep it lightweight.
  2. Client side components: Adopt client side validation to catch errors.
  3. 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)
  4. Asynchronous calls: Adopt implementation of AJAX calls from the presentation tier and the business tier.

#2 Application development optimizations

  1. Include JS files at the bottom of the page (to avoid render blocking of page).
  2. Combine multiple CSS files and optimize unwanted rules as per page requirements.
  3. Avoid using external scripts at the beginning of the page.
  4. Combine smaller images/icons to sprite & have optimi.
  5. Use CSS rules/files in the head section of the document.
  6. Reduce the number of requests to server.
  7. Implement server/browser caching on possible sections.
  8. 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. 

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Across the Insurance ecosystem, a special fraction within the industry is noteworthy for its adoption of new technologies ahead of others. However slow but sure, uberization of insurance has conventionally demonstrated a greater inclination towards digitization. Insurers now more than ever, need big data-driven insights to assess risk, reduce claims, and create value for their customers. 

92% of the C-Level Executives are increasing their pace of investment in big data and AI.

NewVantage Partners Executive Survey 2019 

Artificial Intelligence has brought about revolutionary benefits in the Insurance industry.

AI enriched solutions can remove the ceiling caps on collaboration, removes manual dependencies and report errors.

However, organizations today are facing a lot of challenges in reaping the actual benefits of AI.

5 Challenges for AI implementation for Insurers

5 AI Implementation Challenges in Insurance

Lack of Quality training data

AI can improve productivity and help in decision making through training datasets. According to the survey of the Dataconomy, nearly 81% of 225 data scientists found the process of AI training more difficult than expected even with the data they had. Around 76% were struggling to label and interpret the training data.

Clean vision, Process, and Support from Executive Leadership

AI is not a one time process. Maximum benefits can be reaped out of AI through clear vision, dedicated time, patience and guided leadership from industry experts and AI thought leaders.

Data in-silos

Organizational silos are ill-advised and are proven constrictive barriers to operational productivity & efficiency. Most businesses that have data kept in silos face challenges in collaboration, execution, and measurement of their bigger picture goals. 

Technology & Vendor selection

AI has grown sharp enough to penetrate through the organizations. As AI success stories are becoming numerous investment in AI is also getting higher. However big the hype is, does AI implementation suits your business process or not – is the biggest question. The insurtech industries have continued its growth trajectory in 2019; reaching a funding of $6B. With the help of these insurtech service firms, Insurance organizations have made progress, tackling the age-old insurance ills with AI-powered innovations.

People, Expertise and Technical competency

‘Skills and talent’ in the field of AI is the main barrier for AI transformation in their business.

Still playing catch-up to the US, China, and Japan — India has doubled its AI  workforce over the past few years to nearly 72,000 skilled professionals in 2019. 

Are you facing challenges with your Insurance process but have no idea where the disconnect is? Is your Insurance business process ripe for AI in the year 2020?

What is the right approach?

Join our Webinar — AI for Data-driven Insurers: Challenges, Opportunities & the Way Forward hosted by our CEO, Parag Sharma as he addresses Insurance business leaders on the 13th of February, 2020.

Register for the live webinar by Parag Sharma (AI Thought Leader & CEO Mantra Labs). 

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Ratemaking, or insurance pricing, is the process of fixing the rates or premiums that insurers charge for their policies. In insurance parlance, a unit of insurance represents a certain monetary value of coverage. Insurance companies usually base these on risk factors such as gender, age, etc. The Rate is simply the price per ‘unit of insurance’ for each unit exposed to liability. 

Typically, a unit of insurance (both in life and non-life) is equal to $1,000 worth of liability coverage. By that token, for 200 units of insurance purchased the liability coverage is $200,000. This value is the insurance ‘premium’. (This example is only to demonstrate the logic behind units of exposure, and is not an exact method for calculating premium value)

The cost of providing insurance coverage is actually unknown, which is why insurance rates are based on the predictions of future risk.  

Actuaries work wherever risk is present

Actuarial skills help measure the probability and risk of future events by understanding the past. They accomplish this by using probability theory, statistical analysis, and financial mathematics to predict future financial scenarios. 

Insurers rely on them, among other reasons, to determine the ‘gross premium’ value to collect from the customer that includes the premium amount (described earlier), a charge for covering losses and expenses (a fixture of any business) and a small margin of profit (to stay competitive). But insurers are also subject to regulations that limit how much they can actually charge customers. Being highly skilled in maths and statistics the actuary’s role is to determine the lowest possible premium that satisfies both the business and regulatory objectives.

Risk-Uncertainty Continuum

Source: Sam Gutterman, IAA Risk Book

Actuaries are essentially experts at managing risk, and owing to the fact that there are fewer actuaries in the World than most other professions — they are highly in demand. They lend their expertise to insurance, reinsurance, actuarial consultancies, investment, banking, regulatory bodies, rating agencies and government agencies. They are often attributed to the middle office, although it is not uncommon to find active roles in both the ‘front and middle’ office. 

Recently, they have also found greater roles in fast growing Internet startups and Big-Tech companies that are entering the insurance space. Take Gus Fuldner for instance, head of insurance at Uber and a highly sought after risk expert, who has a four-member actuarial team that is helping the company address new risks that are shaping their digital agenda. In fact, Uber believes in using actuaries with data science and predictive modelling skills to identify solutions for location tracking, driver monitoring, safety features, price determination, selfie-test for drivers to discourage account sharing, etc., among others.

Also read – Are Predictive Journeys moving beyond the hype?

Within the General Actuarial practice of Insurance there are 3 main disciplines — Pricing, Reserving and Capital. Pricing is prospective in nature, and it requires using statistical modelling to predict certain outcomes such as how much claims the insurer will have to pay. Reserving is perhaps more retrospective in nature, and involves applying statistical techniques for identifying how much money should be set aside for certain liabilities like claims. Capital actuaries, on the other hand, assess the valuation, solvency and future capital requirements of the insurance business.

New Product Development in Insurance

Insurance companies often respond to a growing market need or a potential technological disruptor when deciding new products/ tweaking old ones. They may be trying to address a certain business problem or planning new revenue streams for the organization. Typically, new products are built with the customer in mind. The more ‘benefit-rich’ it is, the easier it is to push on to the customer.

Normally, a group of business owners will first identify a broader business objective, let’s say — providing fire insurance protection for sub-urban, residential homeowners in North California. This may be a class of products that the insurer wants to open. In order to create this new product, they may want to study the market more carefully to understand what the risks involved are; if the product is beneficial to the target demographic, is profitable to the insurer, what is the expected value of claims, what insurance premium to collect, etc.

There are many forces external to the insurance company — economic trends, the agendas of independent agents, the activities of competitors, and the expectations and price sensitivity of the insurance market — which directly affect the premium volume and profitability of the product.

Dynamic Factors Influencing New Product Development in Insurance

Source: Deloitte Insights

To determine insurance rate levels and equitable rating plans, ratemaking becomes essential. Statistical & forecasting models are created to analyze historical premiums, claims, demographic changes, property valuations, zonal structuring, and regulatory forces. Generalized linear models, clustering, classification, and regression trees are some examples of modeling techniques used to study high volumes of past data. 

Based on these models, an actuary can predict loss ratios on a sample population that represents the insurer’s target audience. With this information, cash flows can be projected on the product. The insurance rate can also be calculated that will cover all future loss costs, contingency loads, and profits required to sustain an insurance product. Ultimately, the actuary will try to build a high level of confidence in the likelihood of a loss occurring. 

This blog is a two-part series on new product development in insurance. In the next part, we will take a more focused view of the product development actuary’s role in creating new insurance products.

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