How to Use Different Funnel Visualizations to Effectively Tell Your Data Analytics Story

Written by Ansley Miller

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Here at Indicative, we’re all about making data more accessible and easier to understand. One of our goals is to democratize access to analytics so that everyone at a company can make better situational judgment calls.

Throughout this explainer, we’ll share some real-world applications — data visualizations for the e-commerce sector. There are two sections to this guide. The first includes general information about three kinds of graphs, when to use them, and their strengths/weaknesses in visualizing data. The second section covers how to use each chart to visualize user journeys, what information is in each, and why you would use one over the other.

Let’s get to it.

Real-World Applications: Practical Data Visualizations

This section will equip you with a short overview of 3 types of practical, every day charts. Most likely, you’ve already had some experience with these. By reading this section, you’ll gain a deeper understanding of how the tools that you already use, every day, work behind the scenes.

Pie Chart

Let’s start with a chart that most of us know quite well — the pie chart, which is also known as a doughnut chart. 

Over time, statisticians started using pie charts to compare units (i.e., slices) within a complete category (e.g., cake).  But statisticians have also encountered limitations with this visualization. For instance, pie charts can be misleading in representing proportions accurately. Effective data labels — and a commitment to using pie charts for their designed intents for showing a part to a whole — can make this visualization effective.

Indicative uses pie charts to allow you to add an extra layer of analysis to your funnels.

Funnels act like a map of how your customers navigate through your platform. Each step is represented as a pie chart so that you can see if the funnel is more successful for certain groups of users.

Chart 1: Funnels By Device Type

Chart 1: Funnels By Device Type

The chart above (Chart 1) has the pie charts grouped by the customers’ device types. As audiences move through the funnel, Android users make up a smaller piece of the pie than the step before, so it’s easy to identify that the website doesn’t engage Android users as well as other device types.

Chart 1 shows a hypothetical e-commerce company called PetBox, that sells monthly subscription boxes with supplies.

Chart 2: Funnels By Marketing Channel

Chart 2: Funnels By Marketing Channel

Similarly, the multipath funnel analysis shown above in Chart 2 compares the customer purchase funnel between marketing channels. 

While ‘at-a-glance’ analysis is great for small decisions, sometimes it’s important to have the cold, hard stats. Notice across the top of the funnel, Indicative highlights what we can see in the pie chart – paid traffic is about equally likely to complete the funnel than other marketing channels.

To gain this easy visualization of users who do complete funnel steps, there is a trade-off. Doughnut funnels don’t tell you as much about the users who did not follow the map laid out in the funnel. Those users are better represented in a bar chart.

Bar Chart

The bar chart is most useful when you’re dealing with categories that represent different proportions.

The beauty of a bar chart is that the visualization is easily remixable into the story that you want to tell. For one, you can place the categories in any order that you would like. You can also group your bars in a few different ways.

Visualizing your funnel with a bar chart is a great way to identify points of friction in your user journey. Each step your customer takes on their way to conversion has a barrier to entry with varying degrees of friction. If that barrier is too high, your bar chart will have a dramatic drop off like in the funnel visualization (Chart 3) below. The grey portion, representing users who haven’t completed a step, is huge compared to the step before. 

From a quick glance it’s obvious that getting users to the blog after opening the PetCam, a feature that allows website visitors to see puppies and kittens playing, is the main point of friction in this Subscription funnel.

While cute, the website feature is not conducive to driving conversions on the website.

Chart 3: Funnel Friction Analysis

Chart 3: Funnel Friction Analysis

Bar charts hide differences between user groups better than pie charts. In the funnel below, it’s difficult to tell that monthly subscribers complete this funnel more than average. Luckily, a quick check of the conversion metrics above the funnel can confirm this is true.

Chart 4: Conversion Analysis

Chart 4: Conversion Analysis

The bar chart above (Chart 4) shows a few points of friction in a funnel that begins with a user downloading an app. We know users aren’t converting, but what are they doing instead? For this, we’ll need a Sankey diagram.

Sankey Diagram

Understandably, the name “Sankey diagram” is a term that is less familiar than “pie chart” or “bar chart,” but you see Sankey diagrams all the time.

So what is it?

You use this chart when visualizing a state of flow. For instance, you might see it being used to depict materials flows, energetic states, or in life cycle assessments of products.

Chart 5: Sankey Diagram for Optimal Journeys

Chart 5: Sankey Diagram for Optimal Journeys

The more your platform develops, the more options will be available to your user, and the less obvious the ‘optimal’ journey to conversion becomes. 

Using a Sankey diagram to map out the most common paths customers take can help you find the path that brings in the newest customers and keeps returning customers engaged. See the example in Chart 5, above.

How to Create a Funnel Using Indicative

Charts are powerful tools for communicating standalone, data-driven stories. But you can make these stories even more powerful by weaving them together through a series of visualizations. In this section, you’ll gain an understanding of how to create a funnel.

The goal of Indicative is to help you visualize the relationships in your data. A best practice that we embrace at Indicative is that the graph or chart should speak for itself. Any text should create the context to make the visualization easier to interpret.

We’ll help you work with these charts using sample e-commerce and retail data as a practical example.

Example E-commerce Funnel Analysis

What an online environment enables is the ability to analyze shopper decisions in greater depth, through data. These judgment calls are often too complex to describe through a text-based story. But when you stitch together a series of charts for your e-commerce funnel analysis, you can quickly and easily empathize with your customers.

With this empathy, you can guide shoppers through a more personalized shopping experience — which translates to increased sales for your business. An e-commerce customer funnel analysis is about connecting dots between intent and outcomes to visualize the journey in between.

E-commerce Analytics Checkout Funnel

Every customer journey is unique. That can make your sales funnel analysis process complex. How do you effectively capture shopper sentiment in a way that tells the story about your business within your e-commerce customer funnel analytics strategy?

Consider the case of the hypothetical pet supply company that we referenced above, called PetBox, as an example. This is a fictitious store that allows you to build custom boxes of awesome products for your pet

Let’s say that you’re a member of the product and marketing team, and you’re looking to experience your website and mobile app from the perspective of your customer. Here’s how Indicative puts that puzzle together.

Website Funnel Analysis

One of the biggest questions that you might have is what your shopper journey looks like. Most likely, people have their own unique paths from discovery through the transaction. A Sankey diagram can help you visualize these relationships, as you gain an understanding of how your website visitors flow into different stages of your funnel.

By choosing a starting/ending event, you can see the most common events a user performs next — connected by paths proportional to the number of users.

Example:

  • Starting event: site visit
  • Of the users who purchase one petbox, 56% do ‘purchase petbox’ again
  • Of the users who do a site visit, 34% do ‘exit’ next

In Indicative, we call this particular type of Sankey diagram a Journey, which is best used when you’re trying to discover the most common paths users take. The information you glean from Journeys can be used in the funnel bar/doughnut charts to compare your top Journeys between user segments, to identify points of friction, and target users to increase conversion overall

Chart 6: PetBox Journeys

Chart 6: PetBox Journeys

If you’re curious about what users are doing after they view a product, Journeys can tell you exactly where your users are going next

In this example, most users are most likely to login next.

To hone in on our purchase conversion, customers either add a product to their cart or to their favorites. So now we can make an informed decision on how best to set up our funnels

Chart 7: Product Viewed to Purchase

Chart 7: Product Viewed to Purchase

This pie chart funnel above represents the optimal journey from viewing a product to purchasing that we found in the Journey above. By visualizing the journey in a Multipath pie funnel, rather than in Journeys, we’re able to leverage the strengths of the two tools together.

Now we’re able to compare how this optimal path is affected by the marketing channel that brought the customer in. We can tell that this optimal path is more successful when a customer comes in through a marketing email as opposed to another channel.

Increasing Customer Conversion

Once you’ve mapped out your user journeys, you’ll develop a clear picture into your key points of conversion. For instance, in your user journey analysis, you may notice that a first-time visitor takes the following steps:

  1. Arriving at the homepage
  2. Clicking on recommended products
  3. Browsing product pages and descriptions
  4. Signing up for a mailing list
  5. Reading blog content
  6. Adding an item to a cart
  7. Forgetting about it
  8. Returning to the website to complete the checkout process, following a cart abandonment email reminder

Let’s say that you bring 10,000+ first-time users to your website each month. How do you determine where you should focus your attention with regards to converting people into buyers or newsletter subscribers? You likely don’t have the resources to pay attention to each step, equally.

Chart 8: Visualize Customer Journeys

Chart 8: Visualize Customer Journey

One way to establish this focus is to deep dive into your analytics, and create a visualization for clear understanding. A simple doughnut chart, like the one above, can help you get to your answer, sooner. In this chart, you can compare multiple paths within the same visualization and easily compare conversion rates between them. 

While Journeys are helpful for finding the most common ways customers engage, sometimes you want to know how the conversion will change if a user deviates from the optimal path.

Optimal paths are not always best for everyone. For example, in Chart 8 above we find that organic customers convert better than average on our optimal path, but what if they add something to their favorites first?

Chart 9: Multipath Funnel Analysis with Side by Side Comparisons

Chart 9: Multipath Funnel Analysis with Side by Side Comparisons

Multipath funnels enable you to compare different conversion paths side by side to answer these questions quickly. In the second funnel (Chart 9) above we can see that organic customers convert more when they add a product to their favorites.

Clicking on a user group — for instance, subscribers to your email newsletter — can help you see who’s converting at the highest and lowest rates. From this perspective, you can more easily determine where and how to focus your conversion optimization efforts.

Reducing Churn

Churn is a major challenge for the majority of e-commerce companies. It’s also a trend that you can work to optimize. For instance, it’s easy for shoppers to forget when they’ve added an item to a shopping cart. It’s also common for people to be shopping on their phones in situations where they may not have their credit cards nearby, or among environments of high distraction.

There are plenty of tools available to try and win back their attention, ranging from retargeting campaigns to personalized emails and even specialized offers.

But where should you focus your time and efforts to make the highest impact?

Here’s one (Chart 10) that highlights a 3-step conversion path from site visit to product purchase. A bar chart is one tool within Indicative, to visualize segmentation analyses.

Chart 10: Segmentation Analysis from Visit to Purchase

Chart 10: Segmentation Analysis from Visit to Purchase

This bar chart shows that churn in this purchase journey can be pinpointed to customers not viewing their cart. Conversion is very high once users do view their cart, so identifying this point of friction and solving this issue will drive purchase conversion through the roof.

This could mean that your checkout button is hidden or maybe users are finding a deal on the same product with a competitor. Conversion between other steps in the funnel is very high, so if we can get users to view their carts, we may see purchases skyrocket.

With the bars being the same height, you can easily visualize how many shoppers churn at each step. This perspective can help you figure out how to keep more people in your funnel — and develop conversion optimization campaigns with more precision.

Final Thoughts

Working with data can be tough. Data should be easy for anyone to understand and interpret.

At Indicative, we believe in making data relatable, intuitive, and digestible for everyone. By sharing some insights into our product, above, we hope to help you see how visuals can help you tell better stories about your data. 

When you’re browsing through your analytics platform, you need instant answers to in-the-moment questions. Applying the right type of chart to the question at hand will make a world of difference in how quickly you can make decisions with your data — without the need to write complicated code.

What can we help you visualize? We’d love to help, so reach out any time.

Contributors Statement

This blog post was a collaboration among the Indicative Team. While Ansley Miller was lead author, Marc LiebmannEsmeralda Martinez, and Tara McQuaide also contributed to the narrative.