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Funnels… Marketers use them, salespeople use them, but how about UX designers? You bet they use them! Funnel analysis can (and should be) part and parcel of their daily workflow. Gone are the days when we used to conduct user experience (UX) analysis only when prompted by bad user reviews. We live in a world where a single bad experience can be damaging – imagine if a major bug happens during the session of an influencer and they tell their 1.2M followers on Instagram. Looks bad, right? Well, it is. To remove as many usability problems as possible, product managers need to proactively align with team experts in the UX design process. We know that many are already using heatmaps or surveys, but these aren’t the only tools that are relevant to the task. Today, we’ll take a deeper look at one of the tools that can be used in advanced UX analysis – funnels.
In this article...
UX analysis comprises a vast set of activities that aim to achieve an improvement in customer experiences. It commences as a set of assumptions that need to be tested, involves the tests themselves, and ends (kind of) with the resulting list of improvements to implement. If done correctly, it can enhance user adoption, engagement, and conversion rates across the product.
UX analysis can be an iterative process that often includes heuristics analysis which can then be expanded to specific areas of interest that need to be validated as such with quantitative and qualitative data. It is at this point where funnels become truly important, along with a few other tools that we will tackle in another article.
Speaking of quantitative and qualitative data, it is important that we make this distinction now.
Quantitative data refers to numerical data that can be measured and analyzed using mathematical and statistical techniques. In the context of UX design, quantitative data can be used to measure various aspects of user behavior, such as the number of clicks on a button, the time spent on specific web pages, or the completion rate of a task.
Qualitative data refers to non-numerical data that is collected through methods such as interviews, surveys, and usability testing. In the context of UX design, qualitative data can be used to view the optimal user experiences from the perspective of the user, and to identify areas for improvement.
Funnel analysis provides a visual representation of the desired user journey, with discrete steps based on important triggers. These triggers might include events such as scrolling specific web pages to a specific percentage, watching a video, adding a product to the cart, or hitting the Finish Registration button. Apart from these steps, the funnel visualization also includes the percentage of users who moved from one part of the funnel to another – this is called the conversion rate.
To create a funnel, the first thing you must decide on is what are the key steps that users take to complete a specific action. For example, a simplified user flow for an e-commerce website may be the following: Visit product page > Add item to cart > Checkout. So, you need to set up the respective events in your data analytics software to capture when someone
1/ visits the product page
2/ clicks on the Add to cart button
3/ finishes the checkout process
Some digital analytics tools use manual instrumentation to do that. This is a somewhat cumbersome process but can be useful for tagging important events (the type that doesn’t change from iteration to iteration). Other tools use tagless autocapture to continuously record user data and provide the event needed whenever you need it, even retroactively. No matter the type of capture, make sure that you have all the user journey events you need to adequately track all steps in the funnel.
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There are lots of pragmatic benefits of using funnels for UX research. Depending on the goals you set, these are some common ways to extract value from your funnels:
Funnels provide a clear picture of where users are dropping off in the user journey, making it easier to pinpoint areas of the product that need optimization.
Funnels allow product managers to track conversion rates between funnel steps and identify areas where users are getting stuck or dropping off. This information can be used to optimize the product’s UX and improve conversion rates.
Funnels can be used to test the effectiveness of new features or changes to the product. By tracking user behavior in the funnel, product managers can see how users interact with the new feature and make adjustments as needed.
Let’s get our hands dirty and devise a funnel experiment – nothing speaks more clearly than examples! Let’s imagine that we are in the shoes of a product manager looking for ways to improve the conversion rate of their e-commerce website, and more specifically – the conversion rate of purchases that begin from the wishlist.
Step 1: Define the goals of the experiment
Increase the conversion rate of the Wishlist section from X% to Y% (use industry benchmarking or a business-driven value).
Step 2: Define the funnel
At this step, we need to define the steps that users are expected to take when purchasing a product from the wishlist. This could include steps such as adding a product to the wishlist, visiting the wishlist, clicking on a product in the wishlist, and completing a purchase. Note that sometimes people don’t use the most straightforward or obvious way to accomplish an action – keep your eyes peeled for deviations from the main path and make sure to include them in your analysis by creating a separate funnel for each scenario.
Sample events might include:
Visit product page > Add product to wishlist > Visit wishlist > Add product to cart > Initiate checkout > Complete purchase
Step 3: Set up funnel tracking
At this step, we need to set up funnel tracking using an analytics tool. Remember that if we are using a tool with manual instrumentation, we need to make sure all relevant events are tagged. Not the case with tagless autocapture where we can simply build the funnel and analyze ad hoc! This will allow us to track the user’s progress through each step of the funnel.
Step 4: Analyze funnel data
The fun starts here – let’s analyze the funnel data to identify where users drop off and the reasons for drop-offs! For example, if a large number of users drop off after clicking/hovering over the informational section of the product in the wishlist, this could indicate that the product information is not sufficient or engaging enough. Or there could be a technical problem with adding the item to the cart directly from the wishlist. Or… Wait. Here we need to add another analysis method to validate our assumptions as there may be plenty!
Step 5: Use Session Replay to visualize some problematic sessions
Using session replay can save us tons of time by providing us with the best type of user data – visualization of a whole session and everything the user did throughout their user journey. Here, you might notice that the Add to Cart button on the Wishlist page generates dead clicks preventing the user from actually adding to their cart (spoiler alert: sometimes this is all it takes to lose a customer!). Or they hover a lot over the informational section but never even touch the Add to Cart button. With session replay, your hunches can be validated through visual qualitative data.
Step 6: Address usability issues
This is the time to prioritize the improvement efforts – depending on our findings, these may be connected to fixing the dead click or adding more product information. Next up, we need to communicate the changes to the product development team, and finally – implement them.
Step 7: Test, monitor, iterate
Based on the insights gained from the funnel data analysis, we can now monitor how changes to the wishlist page have affected the funnel. Using the data from the initial analysis as a benchmark, we can assess whether the changes were a win – or not so much. We can also use A/B testing to compare the performance of the original wishlist page to the new version in real time. If needed, we can iterate and introduce further improvements until we improve the conversion rate to the value we set in Step 1.
Some other examples of how funnels can help include:
Product managers and UX designers carry the burden of the difficult task to proactively anticipate UX issues. Funnel analysis can be an indispensable tool in this quest – along with other visual analytics tools. This is why it’s always a good idea to consider implementing funnel analysis as part of your UX optimization strategy – it’s never too late, and it can provide actionable insights to boost conversion, improve engagement and retention, and reduce churn.
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