How-to use data viz in products and a few constructive critiques

Data visualization or data viz has become common place and more and more products have incorporated analytics and often data viz into their apps. This article is going to explore the good, bad, and ugly of data viz in product. I have talked on this topic over a dozen times and as a person that has a foot in the product world and a foot in the data world it really appeals to me.

First off, let’s talk about what we mean by data viz in product. Product in product could mean an actual data viz in product but it could also be something that is used in marketing, support, or other component where the end user of the product uses the data viz. Further, data viz doesn’t mean that it needs to be a digital data viz with interaction but simply could mean data viz that is used in a cardboard display or a product sheet even.

Secondly, let’s take a quick step back on what data viz is and why data viz matters. We define data viz as the representation of information in a graphic or visual form as is often in the form of charts, graphs, tables, pictures, etc. Our goal is that any data viz should seek to have a defined user(s) and defined objective(s). The objective may be to inform the user or get the user to do a certain action or not do a certain action.

Before getting into the a mini-dissection of examples of what we think was done well and what opportunities exist, lets get into a framework if you are in product and looking to incorporate data viz. There are six things that we think every product person whether a product manager, product owner, user experience, or product marketer should think about:

  1. What is the customer need seeking to fulfill with the data viz? If there is not a customer need or pain point seeking to be filled then there is no reason to include a data viz. Remember if you are not providing your customer value with data viz then why are you using it. This may seem obvious but I think even from our examples below you will still see that this item can be missed.

  2. Does satisfying the identified customer need align with your organization’s strategy? Just because there is a need it doesn’t mean that every organization should fill it. Does it align with core strengths and strategy of an organization is the question and this is the same question all product people should be asking with whatever product-related effort.

  3. What customer behavior do you seek from your data viz? Once you have identified a need and that your organization’s strategy aligns with that need then the question is what is the behavior you seek as a result of the data viz. Generally this means either wanting a customer to do something or stop doing something but could also be having the customer feel something.

  4. Does the data viz fit into the overall product experience? The overall product experience is more important than ever as customers become more sophisticated. Understanding how analytics and data viz fit within that product experience is important for maximum benefit to be received.

  5. Is the data viz good? You can’t forget doing good data viz. No, 3D pie charts with 20 slices. Yeah the best practices of data viz still come into play. Don’t do the hard work upfront and drop these at the end.

  6. Are the assumptions and behaviors still as anticipated? It is critical that assumptions and behaviors be tested and retested on ongoing basis to ensure the data viz is functioning as planned.

I call this the Data Viz in Product framework and as a person that spent many years in product and data viz, this framework works helped me and hope it does so for you. Feel free to use it how you like and certainly love to hear your stories if you do on what worked and what didn’t.

Now let’s get into data viz and product examples. We will first go through a series of examples and do a mini-dissection of what we think was done well and what opportunities exist. Sometimes there might be questions we have. Our belief is that all data viz is part art and part science so there is no right answers.

Audible App: Starting out I want to say that I am a huge fan of Audible. It has allowed me to consume more information and in a way that is both convenient and also sometimes more emotionally resonate. In fact, I have used Audible for over 10 years consistently. But, sorry Audible but your data viz sucks in my opinion!

They have several uses of data viz in their app and includes a badge page which while aesthetically pleasing seems less useful and more ego wall but I have heard from others that this is indeed a feature they like. There is also a basic listening time analytics page that provides rudimentary understanding of listening and provides it on a daily, weekly, monthly, and total basis. There is also a “Listening Level” page which again seems more like a badge-like page. It appears from this page that simply the number of hours I listen in total equal to a “Listening Level” and uses terms like Newbie, Novice, Pro, Scholar, and Novice. But, the data viz I am going to dissect here is the “Audible Titles” page and graphic from my phone is below.

Source: Audible app screenshot

Source: Audible app screenshot

What is the purpose of this data viz? Honestly I don’t know and here are some questions I asked myself:

Is it meant for me to feel better about myself? Maybe it does a little but it also makes me feel bad at same time because I know a number of these books I started and didn’t finish or there are some I didn’t even start.

Is it meant for me to buy more books? I don’t think so because the number of books I have seem pretty high.

Is it meant for me to enjoy the app more? I don’t think so because I feel that there is not much to even do in this visualization.

What does Audible do well?

  • Audible uses an acceptable data viz given it is books over time. I might have chosen to go with line graph or something else instead of an area chart but an area chart can work.

What could Audible do better?

  • Audible could better understand it users and if appropriate use data viz to help its users in understanding users problems. An individual data viz needs to start out with a desired audience(s) and a desired purpose(s). I don't clearly understand either in this data viz.

  • Audible could better integrate its data viz into overall product experience. This is not just making it look visually aligned (which it does somewhat) but also just aligning into overall experience from user. I feel like this data viz was incorporated as basically a check-the-box around showing books purchased over time and a thought lets incorporate into a data viz.

  • Audible could make a better data viz by both enhancing the color and size of the labels on the axis and also reducing visual fatigue of the orange in the area chart.

I think Audible has failed on both (1), potentially (2), (3), potentially (5), and likely (6) as respects the Data Viz in Product Framework outlined above. Maybe instead Audible needs to think about its different user personas and break down how data viz could benefit them and align with Audible’s brand. Audible has loyal customers that are curious people like myself. One thing I might have interest in understanding is when I listen, how much I listen compared to others, etc.

Ring App: Ring is a doorbell where there is a video camera and also a speaker and microphone. It is a product that falls into the Internet of Things (or IoT) space which I find fascinating and have been involved with in the Twin Cities over the past 5 plus years. Ring is a classic IoT product in that it is taking in a ton of data while seeking to provide a service to end users. Ring allows me as a home owner to understand when someone is at my door and can even communicate with that person no matter where I am. It gives me peace of mind when I travel but it also allows my technical side be fascinated with the opportunities.

Image Source: Ring app screenshot

Image Source: Ring app screenshot

Now let’s get to the data viz in the app. Ok, yes this is my neighborhood and really it is a great neighborhood in the Twin Cities that has a diversity of cultures, diversity of residential and commercial, and great location. I and many others look at Richfield as a safe close suburb of Minneapolis.

From the Ring data viz you might be highly concerned about living in this area. It reports all the crime that is reported and does not delineate between violent and non-violent crime easily, it does not delineate between residential and commercial crime easily, it does not delineate anything as respects time of day or relative population count and other similar metro areas. Basically it gives me a map with points laid on it to make a judgement for myself.

I wonder how many people use this data viz for Ring. I also wonder what type of people use this data viz for Ring. I further wonder how many people would value Ring higher if they had a more meaningful level of data viz.

What do you think Ring’s objectives are in showing me this data viz in this format? Maybe it to scare me into buying a more premium ongoing monitoring service. Maybe it is a check-the-box effort and only limited amount of effort put on this.

What does Ring do well?

  • Ring provides fairly comprehensive data related to the area I live and not only in the app but also sends out a notification that the report is ready.

  • Ring arguably leaned in most with its audience that likely uses its product out of fear and protection and guessing a desire by its customers to know all crime around them.

What could Ring do better?

  • Ring could help users be able delineate data for users to understand their area both as a novice and a nerd. Things like: a) violent vs. nonviolent crime; b) commercial vs. residential; c) give me context on time of day; d) allow me to incorporate traffic pattern or allow Ring to understand my traffic patterns and give recommendations based on these; and e) context to whether the crime is a lot per person for type of area, increasing / decreasing, and other information that may help me make better decisions.

  • Ring could also help not just lean into fear though and maybe provide other delightful information around the neighborhood. For example, my Pocket Casts podcast catcher app provides some humorous items like 11.47 Trillion emails were sent during the time you listened to podcasts. Things like this can provide humor and delight in an app and in help users that might help bring users back from undesired negative tendencies like unwarranted fear and potential biases.

Fitbit App: I love my Fitbit. I actually used to love my Fitbit more and what I am showing was the prior UI prior to a recent update that I actually think is a worse use of data viz in their product. No matter what though Fitbit is a something that I have used for years and it tracks things like your steps, your sleep, and other habits related to your health and wellness. I can easily say it is the data viz in an app that I use more than any other where data viz is not the primary purpose of the app.

Source: Fitbit app screenshot

Source: Fitbit app screenshot


What does Fitbit do well?

  • Fitbit understands what users like me seek to use it for by quickly making metrics easily understandable and how I am tracking to my goals and does this on a daily basis. It even provides some easy to use insights where I can provide feedback on if I liked it or not.

  • Fitbit understands that some of its users are novices that just will use it to understand if they hit their step goal or other goals but it also understands some of its users are nerd users where they want to be able to dive into detail about the information. In addition to the the detailed screens Fitbit provides, it provides ability to download the data into .csv even. Great for a nerd like me and yes I have done this.

  • Fitbit understands that sometimes you need to use multiple encoding to help ensure the user easily understands what you are conveying. When saying multiple encoding it means relaying the same information in multiple ways. For example, you may encode information with color, shape, size, etc. Fitbit goes in and uses color and shape in how it fills items to let me know when I have hit goals for example.

  • One newer feature not shown above is Fitbit has created a sleep number itself on how well you slept. I am still in process of trying to understand it but seems like 0 is worst and 100 is best so it provides you a sleep number in addition to hours. This is sort of allowing a novice to go a little deeper into sleep understanding without breaking down the different phases of sleep and number of total hours.

What could Fitbit do better?

  • Fitbit UI prior to recent update had the top cards be able to flip days without flipping corresponding data below which meant a misalignment of data showing different items for different dates. This has been resolved though in recent up date but is a good reminder for us to have interactivity be well thought out.

  • Fitbit could do a better job leveraging notifications in tandem with the data viz. This is less a critique on the data viz and more on how notification of information are often more meaningful than the data viz itself because the notifications in theory should be more meaningful nudges.

These are just some of the ways Fitbit has gone in and designed data viz in its app in a thoughtful way that provides me both delight and value.

Mint App: Mint is an app where you can bring your financial information together and it helps you understand your financial information and better plan and make decisions. I am not a Mint user myself but have talked to dozens of people that are so I am speaking at this from a less biased perspective maybe.

When looking at the screenshots below you can see there is a lot of good information with the screen on the left giving you an idea of where you are tracking on total budget for month along with how you are tracking on individual categories and subcategories. They use color to help you understand how you are tracking. Then, you jump to the middle screen and it is a donut chart where you have your monthly spend broken down. Lots of colors, not completely labeled, and no delineation of categories that are variable versus fixed costs. The screen on the far left is a simplified view of how your credit score is and an indicator of where it falls on a credit score scale.



What does Mint do well?

  • Mint does a lot of great tracking of financial data and helping display it in generally good data viz (ignoring bad donut chart in middle).

  • Mint uses generally intuitive colors although could better use hues to ensure there are not issues for those with color blindness.

  • Mint does a good job of separating out data viz in different screens and not forcing those together because different cognitive load can be placed on each.

What could Mint do better?

  • Mint could help differentiate between fixed and variable expenses because I generally cannot make fixed costs changes easily but variable costs I can.

  • Mint could help me better assess how I do against others in my peer group and relay this information for me. Not to make me feel overly good or bad but to understand where I compare and maybe also against a type of person I want to compare against.

  • Mint could drop the donut chart in the middle and put in a more useful form similar to the screen on the right or put it in a waterfall chart even.

Overall I think Mint does a solid job with data viz in its product and incorporating it into its overall experience and helping around customer problem and helping modify behavior but at same time I think it can improve.

Sleep Number: Sleep Number is a high-end smart mattress company that does some really cool things with its product but it also does cool things in how it uses data viz. Sleep Number is probably best known for what its name indicates, i.e. its sleep number. It simplifies the sleeping experience to a sleep number and believes it is different for different people and the beds allow adjustment and also adjustment for each side of bed.



Sleep Number's use of data viz in its product is kind of unique in that what I am showing is not even its use in the product itself but instead it is a data viz that is part of the Sleep Number selling experience. You go into a Sleep Number store and as part of it there is the ability to look how you sleep and how adjusting the sleep number could relieve the pressure points you have while sleeping. It is a simple data viz where it shows the two people laying down and where pressure is and uses color to differentiate pressure. There is also a Sleep number that is displayed. There is also a before and after view of things.

What does Sleep Number do well?

  • Sleep Number understands that its customers are more sophisticated and want to feel they are buying a premium mattress that helps them sleep better. Harnessing data viz in this case is to help them do just that.

  • Sleep Number understands that in-store sales people have a limited time to close a sale and are often of mixed sales expertise and by leveraging this data viz it helps empower its sales people on both fronts.

  • Sleep Number adds a level of credibility and authenticity to its product and seeing is believing. Using data viz in this capacity helps Sleep Number better support its product value proposition.

What could Sleep Number do better?

  • There are no clear opportunities as respect this data viz in my opinion. Certainly allow the sales person to print or email this data viz to the person especially in the case where sale didn't happen so maybe potential follow up.

  • Not sure if this data viz can be used in the hope but if so having that ability also would be great so people could adjust this with their app.

Data Pine Google Analytics Dashboard

Marketing dashboards are common and this one is Data Pine’s Google Analytics Dashboard to help understand and monitor Google analytics data for one or more sites.

There are a ton of examples of good and bad dashboards out there and this is a good example because I wanted to end on a good note but also I think we can learn from good and bad from others.

What does Data Pine do well?

  • Right across the top are cards that are labeled but also have relevant icons and colors. Knowing that items at the top of a page get the most eye attention this makes a lot of sense assuming these cards are indeed the most important items for consumers.

  • Cards are aligned together on dashboard to tell the "Google Analytics story" which is how could are the metrics measured performing.

  • Good use of data viz practices in carrying out visuals.



What could Data Pine do better?

  • Size of text is fairly small in some areas so enhancing the size of text and if it doesn't fit for example then allowing a shortened view of text that is larger would be beneficial.

  • There seems to be some confusion with labels in the cards at the top of the dashboard and charts below. They are using same label but seem to show different data.

Hopefully these examples were helpful for you. Remember data visualization is part art and part science so talented people may disagree. The opinions above are just that opinions and not meant to be endorsement of products or capabilities.

If you are a product person then we hope in the future you will take a closer attention to how information is being relayed in or with your product. Not everything needs to be a chart or a dashboard to relay data. However, think about leveraging concepts of gamification, behavioral science, and user experience as part of relaying this information is essential.

Good luck in incorporating data viz in your product and hope you leverage the six-step Data Viz in Product framework above so you more consistently deliver valuable information in your products that align to your users and your desired experience.

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