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How does great user experience play into your business strategy? Is it ‘nice to have’ or a ‘must-have’? No matter how big is your business, building an intuitive and frictionless user experience can make a huge difference in your yearly reports. In this article, we will explore what makes web pages or mobile apps great user experiences (UX) and what a typical user experience analysis looks like – and then show you five ways to use AI to make your life easier while doing it. Let’s get started!
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UX is a term that describes how your customers experience your website, mobile application, or product. This includes all aspects of the interaction, such as interface design, usability, accessibility, performance, and user satisfaction. UX design aims to create experiences that are intuitive and enjoyable – such that spark emotion beyond the transactional use of the product. This intricate process involves user research, analysis, and usability testing to understand the target audience’s needs, behaviors, and goals – and to create designs that meet those needs.
To begin with, UX designers talk about two methods they use: quantitative and qualitative analysis methods.
Quantitative data is the type that can be measured numerically (think of metrics used in conversion rate optimization, Net Promoter Score, etc.). This type of data can be used to pinpoint the most important problems with your product and give you a clue as to the severity of each problem. Quantitative data can be gathered using a product analytics tool that offers funnels, diagrammatic representations of error occurrences, user behavior surveys of the Likert type, heatmaps and click maps, and more. The insights from these tools build a ‘skeleton’ of data to support your assumptions about possible improvements.
Qualitative data, on the other hand, is a more abstract set that includes Voice of the Customer feedback (think open-ended questionnaires). Qualitative data can be gathered in a lab testing environment where real users interact with the product under supervision, or with a data analytics tool that offers session replay. This type of data puts the flesh onto the skeleton by giving you the exact reasons users don’t enjoy your product as much – and thus helping you decide whether you’re on the right track or not.
What does a typical user experience analysis workflow, you ask? It can be broken down into 5 discrete steps, as follows…
We have talked about the importance of setting specific and measurable goals before – this step is truly important if you don’t want to be shooting in the dark and wondering whether you’ve hit the target or not (your users might not be very vocal about problems – there is always plenty of competitors to go to, why bother sharing feedback?).
Google has set a specific goal framework for UX analysis – the HEART framework for user-centered metrics, which might be very useful if you haven’t set up a framework of your own already. The most important thing on your list should be your core business goals – and the metric you are using to measure it. Identify this goal (or goals, but don’t get carried away), and try to define metrics to measure it through the lens of your customers’ pain points.
For example, if you own an e-store and your main goal is to achieve a certain revenue goal, define the number of purchases needed and then work from there to devise value and incentives for customers to purchase. This may take the shape of a promotion but for it to gain traction, you must first optimize the e-store customer experience – a great metric to measure that is through specific conversion rates.
When you have pinpointed specific areas of improvement and devised a way to measure them, it’s time to roll up your sleeves. Sometimes, you won’t have the resources necessary to conduct a large-scale user flow survey or lab test. That’s okay – for these situations, you can always rely on benchmarking (benchmarking is actually a good idea no matter the scenario) or basic UX heuristics analysis. Or, you can rely on what you already have.
We bet that you have at least a tool like Google Analytics – it may provide invaluable quantitative data to set off your UX analysis. With it, you can also create funnels that will aid in pinpointing problematic areas. Unfortunately, the process in GA is rather cumbersome, as each step in a funnel should be crafted from scratch and you will not have historical data before its creation. Tools like SessionStack allow you to capture major events without manual tagging and provide historical data, too.
At this point, you will already have a clearer understanding of the weakest points in your product. But what is preventing users from converting? And how can you help them? Let’s see…
And we mean literally see. With session replay tools, you can empathize with your customers in a way that is non-intrusive and paints the whole picture. When recording user sessions, you intercept all the mouse movements and events, dead and rage clicks, network requests, browser console calls, and others – and have a visual recording of how all of this translates to the exact experience the user had. Click the Play button and watch all the steps they go through to reach the point of frustration where they leave your product and never come back. And don’t forget to take notes as this is what will guide you to create the best possible assumptions.
Speaking of assumptions, be wary of the built-in biases of the human mind as these can play tricks on your perception of what’s important and what’s not!
Summarising your learnings will help you spot the most critical UX issues and devise a prioritization strategy. You certainly don’t want to begin your UX improvement journey with the problem that has the least impact on your core business goals! This step is also essential as it gives you an idea of how to communicate your assumptions and improvement plans to the team so that you get them on board which is important for the speedy implementation of the improvements.
This last step is when it gets truly interesting – it’s when you get to decide whether your improvements are hitting the nail on the head. If yes, you should see an improvement in the core metrics you defined. Which should bring you one step closer to crafting the best user flow! If not, you might not notice any difference; or worse, you might even notice a decline in the statistics. Don’t fret, though – failure is the greatest way to learn and your next step should be to take the learnings and iterate until you get it right.
So, how does AI help in all of these steps?
Now, AI likely won’t take away UX designers’ or product managers’ jobs in the near future – but it might just make it much easier. And that’s great! By automating some of the most daunting tasks in UX analysis, you can spend more time working on your product. Here are five clever ideas to get you started!
Did you know that AI can help you build detailed buyer personas? This daunting task usually takes a lot of user research and let’s face it – the result is always somewhat abstract. No one really cares what Mona’s ice cream preferences are, right? AI comes to the rescue.
Ask: Create a buyer persona for /role/ of /business/ who has trouble /challenge/.
Example: Create a buyer persona for the CEO of a mid-size food delivery company who has trouble finding a flexible and efficient solution to monitor their organizational expenses.
Stuck UX design process? Ask AI. You can describe what you are working on and it will generate a description + sample copy (way too generic, but still good for a mockup)
Ask: Create a /type of page or section/ layout for /company/ that /specific need the page answers/.
Example: Create a landing page layout for a food delivery service that caters to the lunch needs of local residents.
User feedback is vital – but analyzing it can get overwhelming. To aid us, AI can scan the reviews you feed it and provide you with key takeaways for UX improvement or marketing.
Ask: Scan the following user reviews and extract the most common pain points users are sharing.
As always, remember that ChatGPT was trained with data up to 2021 and might need a few nudges to produce something truly meaningful and valuable. Once you have a response, try to dig deeper with specifics. For example, once you’ve figured out the main pain points, ask the AI to categorize them in order of magnitude based on the frequency of occurrence which will help you prioritize better.
AI goes beyond language models. Now, you can add visual elements to your designs, including color palettes and typefaces that complement your branding. When you don’t have a user interfaces (UI) designer at hand, this can prove indispensable for creating cohesive experiences that not only improve the UX within the product but also extend to your marketing collateral. In the context of UX analysis, these tools can also provide a great way to iterate quickly without swamping your design team.
Having to manually comb through data is a lost art in the age of the 3-second attention span. Not to mention the fact that our brains are not meant to be able to analyze vast amounts of data. So, we delegate to the machine. With artificial intelligence and machine learning, UX designers and product managers can feed the output of their research efforts and get a detailed report within a day. Again a victory in the name of speedy iteration!
User experience analysis might be a difficult task, but it is one of the few ways you can improve your product consistently. Going through the steps described above will ensure you get the most adequate results possible while playing with AI-driven tools in the process might speed things up and even show you previously unsuspected pain points or areas of improvement. Your conversion rates will validate the learnings. Thank us later!
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