With so many touchpoints in digital marketing campaigns, it can be hard to hone in on what stages are causing problems with conversion rates. A data-driven funnel analysis can give you actionable, granular insights that directly impact your customer experience. This powerful process helps you not only to understand your customer journey but also enables you to make that journey as easy as possible for your users.
In this article, we'll define funnel analysis and look at some of the benefits it adds to your marketing campaigns. Then, we'll take you step by step through conducting a funnel analysis of your own.
Data-driven funnel analysis helps marketers identify conversion bottlenecks and other issues within the user journey. To do so, it uses customer information collected from key digital marketing channels.
For instance, contract management software company PandaDoc runs social media campaigns on LinkedIn and Facebook. Their funnel analysis would draw heavily on social media data like clicks, demo requests and deals signed.
It's important to note that a funnel analysis is used exclusively for identifying conversion rate issues in a linear user journey. You'll need to outline a specific series of steps to analyse for places where users drop out of the funnel.
If you have a more complex marketing funnel, you can hone in on different branches of it and analyse those linear paths individually. An example of this in action could be one funnel for organic search and another for email marketing. Then, you can identify broad conversion trends across your marketing funnel.
As mentioned above, a funnel analysis leverages data to address conversion rate issues. You'll easily be able to identify the exact step of your user journey where conversion rates drop off unexpectedly. Then, you can break down each piece of that process to optimise the user experience.
For instance, if you notice that you're getting lots of click-throughs from your marketing emails to your landing page, but conversions drop dramatically at the form submission stage, it's time to take a look at your landing page.
As you optimize the landing page with the right topic clusters for conversion rate, you'll move more prospects to the next stage of the customer journey, sending a positive ripple effect throughout the funnel and your business' entire value chain.
It's important to remember that you'll see a certain amount of funnel abandonment at each stage, no matter how much conversion rate optimisation you do. The funnel analysis shows places where the conversion rate is unexpectedly low, signalling a problem. In short, a funnel analysis helps you do the following:
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Now it's time to get to work on your first funnel analysis. Follow these six steps to uncover new insights about your marketing funnel.
While you can manually aggregate your data to build funnel analysis, there are many common funnel analysis tools you can use to get started. You might already have two common funnel analysis tools set up: Google Analytics and HotJar. If you use one of these, you'll just need to make sure your funnel is set up according to step two below. If your existing analytics tool doesn't offer a funnel analysis view, set up one that does. You may need to give it a few days to make sure the data is processing correctly.
Next, you'll need to list the linear steps of your user journey. These should mimic your overall sales process, including every action a user will take to get from your marketing campaign to making a purchase.
You can use the common model of the marketing funnel shown below to help you outline your user journey. List the specific action a customer must take to move from one stage of the funnel to the next.
Let's say you're running an Instagram ad campaign. Awareness starts at the first ad a user sees. When they watch a certain amount of the ad, you can count that as a specific action that demonstrates they've moved to the interest stage. There will be a specific action or two associated with each stage, right until they complete their purchase. Make sure each step is correctly set up in your funnel analysis tool and gathering data.
Now it's time to generate the funnel visualisation in your analysis tool. You'll likely see multiple layout options, so choose the one that gives you the clearest view of your funnel and its bottlenecks.
With your visualisation in hand, it's time to look at each stage to identify conversion drop-offs. Remember that some stages of the user journey have high abandonment rates. For example, the average cart abandonment rate across online stores is 69.8%. You'll want to know the average benchmarks for each stage of the marketing funnel for your niche and business so you can accurately analyse the data.
By altering your data view to display the time between actions, you can also look for steps where users take an unusually long time to move on. This is called a time bottleneck. Eliminating or reducing these is key to optimising the user experience. Once you've identified the most significant areas for improvement to prevent cart abandonment, keep a list of them handy for further analysis in the next two steps.
By segmenting your customer data, you can hone in on specific ways to improve the user experience for certain groups. For example, try segmenting your funnel visualisation by the type of user device. If you notice that desktop users convert at a higher rate than mobile users, that's a sign to look at your mobile checkout experience and make sure it's as smooth as possible. Here are a few other ways to try segmenting your audience.
Don't be afraid to try unusual comparisons or segments. You never know what insights you might uncover. Keep track of the segments that require a closer look at certain stages. In the next section, we'll look at how to analyse and solve your bottleneck problems.
Pick one of your conversion bottlenecks to analyze first. Look at the specific action causing the bottleneck and list out each element of that action. We'll take a paid search campaign as an example. Let's say you're getting a decent click-through rate, but users abandon the landing page almost immediately, even across campaigns.
This is where you'll want to draw from other data sources that help manage the marketing campaign in question. In this case, we're looking at the advertising dashboard on SEMRush, a digital marketing analytics tool.
Now, you can systematically look for potential problems in the landing page URLs, layout, content, or other factors. Once you identify the more specific problem, design conversion rate optimization tests to determine the best path forward. Repeat this process for each problem area you've identified in your funnel analysis.
In some cases, you may need to look at an earlier stage in the funnel to impact a problem further down. For instance, if prospects are dropping off after the product trial or demo stage, you may want to revisit your awareness stage to see if you're establishing the wrong expectations for your customer base.
By using data to conduct a funnel analysis, you can optimize your user experience in more detail than ever before. That means a more personalised experience for your customers and a higher conversion rate for you.
It's okay to start small with a single funnel analysis. As you get more comfortable with the process â€” and see just how powerful it can be â€” use them regularly to optimize your user experience and watch your conversion rate rise.
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