In a recent blog post, we shed light on the undeniable importance of prioritising web performance, especially for transactional websites like eCommerce. The response to our discussion was positive, but we also received questions from readers seeking more guidance on how to effectively address web performance.
Below is a list of 5 do’s and don’ts which can help you navigate the landscape of web performance, ensuring that your site not only loads faster, but also delivers an exceptional user experience that can significantly impact your success in the online marketplace.
Measure performance locally
Running website performance tests on your local machine is not recommended and can lead to inaccurate outcomes. It’s important to remember that your customers may not all have high-end devices or access to high-speed internet connections, which can significantly impact their real-world experiences. Test on slow hardware, regulary.
Assume performance testing is someone else’s job
For some teams, performance is often seen as the inherent responsibility of developers, as it aligns with their expected roles and responsibilities. In contrast, specific organisations have specialised QA teams tasked with performance testing. Conversely, there are those who assert that performance is primarily the domain of back-end API engineers and may not be a primary focus for front-end teams. Educate everyone.
Rely only on Google data
Although Google data is a valuable starting point and offers valuable insights into your performance relative to competitors, it exclusively assesses performance in Google browsers. Some teams may also primarily develop and test their alterations using Google browsers, potentially creating a misleading sense of confidence in performance. This approach overlooks crucial information about your site’s performance in diverse customer environments. Implement RUM.
Fix and Forget
Avoid turning performance improvement into an epic or an unending project, as it may never reach completion. While it’s essential to initiate a project, establish clear boundaries and specific objectives for improvement. Break it down into manageable phases by prioritising the most valuable performance enhancements. Update your ways of working.
Forget why you are doing this
Are you trying to retain your customers, increase the basket size or attract new customers? Understand the customer journey, their goals and optimise those paths for performance.
Examine the current performance of your website and its key pages. Publicly accessible data is readily available for the majority of websites and pages, offering insights into their performance and allowing you to make competitive comparisons.
Know your core customer journeys
Enhancing the speed of an underperforming page can be a relatively straightforward task, but does it align with your crucial customer journey elements that yield the greatest value? Gain insights into the routes your customers follow and optimise them for maximum returns.
Embed performance into your teams
Performance on the web is a collective responsibility. It’s crucial for every team member to recognize its significance and collaborate to enhance the application. This requires the active participation of developers, designers, testers, and product managers in the process of optimising performance.
Invest in RUM
Real User Monitoring (RUM) stands as the sole genuine method for gauging your website’s performance. It offers a real-world perspective on your actual customer experience, pinpointing areas that require your concentrated efforts.
To prevent any decline in site performance, continuous monitoring is essential. Once you have this in place, setting up alerts becomes a straightforward process to detect any changes that may lead to decreased performance. There’s no need to wait until your site reaches your desired performance level; measure it now and implement alerts immediately to promptly identify any future degradation.
If you’ve got any tips of your own or you are interested in how you can start improving your website performance, get in touch to find out how we can help.
There’s no denying that times are tough for e-commerce businesses. Faced with high inflation, dwindling budgets and a soaring cost of living, 2023 could be the worst year for the global economy in more than four decades.
Retailers need to make smart decisions to remain competitive, or risk falling prey to the economic downturn.
Investing modest amounts in web performance is an almost guaranteed way to incrementally improve e-commerce revenue making it a safe bet for e-commerce leaders in the current economic climate.
How do organisations typically compete?
To remain competitive, retailers must retain existing customers while enticing new shoppers. When faced with this challenge they can either:
- Spend money to increase the volume of customers entering the sales funnel, or
- Concentrate on new products and enhancements to retain existing customers
SEO and PPC
The most common technique for attracting new customers is investing in pay per click (PPC) advertising and search engine optimisation (SEO). A recent survey by Retail Week found that 86% of retailers are currently investing in PPC, while 84% are spending on SEO.
While these tactics can increase website traffic and visibility, they can be costly and don’t always result in meaningful sales conversions.
Building new product offerings and enhancements
Some retailers are investing in innovative technologies such as AI to be more competitive. Certainly, adding AI can deliver an enhanced customer experience but these investments can be extremely expensive, take a long time to deliver, and a lack of efficient market testing can make innovation a high-risk strategy with long timelines to deliver ROI.
The conversion challenge
Today the typical conversion rate (from entering the funnel post-acquisition to actually completing a purchase) for an ecommerce site ranges from 1% to 4%.
Rather than increasing spend on marketing, with the intention of increasing the number of people entering the funnel, it can be more cost effective to improve the conversion rate by addressing the web performance of the site.
A study conducted last year delved into the impact of web performance on conversion rates. It revealed that the highest e-commerce conversion rates were seen when page load times were between one and two seconds, decreasing significantly as page load times increased to three seconds and beyond.
Equal Experts recently studied 100 e-commerce sites, and found that average page load time was more than three seconds. Reducing this page load time would significantly increase conversion rates without needing to increase marketing spend.
Of course, page load times are just an illustrative metric – what really matters is the customer’s experience. Analysing and optimising the customer journey can increase both user satisfaction and engagement.
What is important is that e-commerce sites implement and prioritise effective strategies to improve web performance. This will increase sales conversion rates and enhance customer satisfaction.
How better web performance drives sales
Improving website performance doesn’t require a six month project or a dedicated team. You can make significant changes using existing resources and optimising key elements.
If you have concerns about web performance relating to any of your systems/products, get in touch to find out how we can help.
What do Data Science and User Experience have in common?
On the surface, you might expect very little as they appear to oppose one another. How about when attempting to understand human behaviour? Both UX and Data Science specialists try and solve these problems, but with different approaches. On a recent engagement, we found that combining techniques from both disciplines yielded powerful results.
Our client wanted to understand their users’ needs while using a job-posting website. User personas are a popular tool for communicating user needs off the back of conducting user research. On this engagement, we wanted to see if we could use some data science techniques to provide quantitative validation of the initial qualitative user research
The Tension Model
We worked in partnership with Koos Service Design. One of the techniques Koos use to develop personas is to investigate conflicting user needs, called “Tensions”. For example, a tension when applying for a job could be the conflict between ‘finding the perfect job’ and ‘finding a job quickly’. Initial research to capture user needs was conducted through in-depth interviews, surveys and exploratory data analysis of user logs.
From this small pool of data, an initial set of tensions was identified onto which personas (detailed below) are placed that encompass the different needs groups of users.
This approach was based on low-volumes of qualitative user research data. To enhance and refine the personas we would need to conduct further testing and experimentation with a much larger dataset.
With the information gathered during the initial user research, we developed a small survey asking True/False questions aimed at testing our hypotheses about the combination of needs people experienced.
This created an extremely large dataset on which we were able to use machine learning to group users together based on similarity.
The technique utilized was unsupervised k-means clustering. The aim of this is to group (or cluster) data that behaves similarly. An optimal number of 5 clusters was identified using the elbow method to minimise the error in the model without creating too many clusters. So the number of personas was revised to reflect this new information.
There was a lot of similarity between the initial personas and the final data-driven personas. The key divergence was the removal of one persona. However, there were sets of behaviours which persist between the initial and data-driven personas. For example, as the Survivor and the Quick Win, both have a desire to get a well-paid job quickly without any other preferences.
With these personas, the client was able to tailor individual user experiences based on their needs, ultimately improving customer satisfaction and engagement with the system.
This highlights how Data Science can bolster insights from UX design, leading to an end product more useful than using either technique in isolation.