Tuesday, October 15, 2013

how I would visualize this data

A couple weeks ago, I posted the following visual (prompted by a question that came up at a recent workshop), along with the challenge to come up with a better way to show this data.

Big thanks to everyone who spent time with this data and shared what they came up with (see full original post, plus comments linking to reader solutions here).

I found myself with some unexpected free time this afternoon (that hasn't been happening so much lately!) and spent a bit of it playing around with this data. There were a couple challenges I was looking to address in my solution:

  1. First, the inclusion of Unaided Awareness. I can understand why it's important, but it doesn't seem to quite fit with the other categories, as many of the reader comments pointed out. Rather than complicate my visual with it, I omitted it entirely. Perhaps it belongs on another page of the deck that walks through this data?
  2. The other challenge from my perspective is that the remaining categories are each a sort of subset of the prior category: those who prefer a given brand are a sub-portion of those who would consider the brand, which is a sub-portion of those who are aware of the brand. I think this means that it doesn't make sense to stack the bars, or to compare only a single category at a time across the different brands as some of the user makeovers suggested.
For me, the goal here is to get a quick visual understanding of how the three categories for OUR COMPANY compare to various competitors, as well as how the three categories compare to each other. The goal is for our bars to be big to the right. There are many competitors with bigger bars than ours. But the ratio of Consider to Aware is actually pretty good for us (better than many competitors). Here is how I chose to visualize this data:

It isn't particularly creative. But perhaps it doesn't need to be. In the original visual, the two main things I wanted to change were 1) the idea that you should compare only downward - the layout of the original charts left me wanting to compare competitor A to E to H (first column), whereas really I should be comparing OUR COMPANY to each of the various competitors (not only those within the same column); and 2) center aligning the bars as is done in the original makes the relative values hard to compare, so I wanted to align each element to a single baseline to allow for easier comparison both within a specific brand as well as across the brands.

To those who read this blog regularly, my choice of a horizontal bar chart here probably isn't a surprise. I use them a lot. I use them a lot because I think they are easy to read. Note that this example isn't totally complete - there's still a story to be woven around this data to make it relevant to our audience. Given that I've anonymized the example so much here, I decided to focus just on the data viz in this case.

I think this works here. What do you think?

In case you're interested, the Excel file for the above visual can be downloaded here.


  1. Is there an elegant way to integrate the conversion figures from one bar to the next?

  2. I received a comment directly from a reader that I want to post and respond to here:

    I saw [your latest post] and that is a decent bar chart, but where is the story? A big part of the the story that is missing is the ratio between those values, the conversion. In your current bar chart, I can only easily walk away with is that Our Company is about in the middle and Company A is dominating.

    Your post on logic in order (link) was beautiful. What if you take the approach used there and apply it to the conversion funnel?

    The above comment is from Joe Mako, who recently posted this remake of the data using Tableau Public.

    And my response:
    Totally fair point - to be honest, I didn't spend a lot of time on this one, and that probably shows in the post.

    It's hard to say here, though, whether the story is conversion. It certainly could be. But it also could be that awareness for our brand is much lower than a number of our competitors, and to improve consideration and preference, the first thing we have to do is build greater awareness. If that were the story, I think the bars could work.

    As in many cases, it comes down to context and what story you want to tell (then what visual will work best follows these two things). It's hard to know specifically here, since I've generalized the example and neither of us have much context. :-)

    Thanks for your comment (and makeover), Joe!

  3. There you go again. You took some perfectly valuable free time and squandered it on something useful. :) Actually, I'm glad you did because I haven't slept well since that last chart up there.

    Let's face it, we're not likely to see this one hanging in MoMA anytime in the near future :), but the fact that it raises questions about the information in the chart, rather than about the chart itself, leads me to conclude that it's the best of the bunch. I think it is, anyway.

    To Joe: I agree with you that there's a ton of additional information that can be very useful, particularly if presented differently. Just looking at the chart as it exists now, the disparity between A & B's Preference conversions is causing me to wonder whether B is on the verge of bankruptcy or whether their respective cost structures and/or business models are completely different. Also, you alluded to the ratios which, at first glance anyway, seem to show certain patterns. For example, % conversions to Consideration trend downward as % Awareness decreases.

    The chart raises a host of questions about the components of the sales funnel for this particular product, for example, how long is each stage, and how much does each stage cost the company?; what is the ultimate cost to acquire a customer, in total and by sales stage and how does that compare to the lifetime value of a a customer's revenues? Given that Company A maintains the highest conversion ratio (Awareness to Preference = 100% x 70% x 50% = 35%, so the non conversion rate is 65%), there are also opportunities to understand why a significant percentage of each company's customers do not convert and what happens to them after that.

    I could go on, but if I did, I'd likely come up with a case study on a company that may or may not even exist, based on one chart!

    My primary point is that, as Cole may have mentioned, this post was the result of a question in one of her classes. So, the intent here, in Cole's words, was to "get a quick visual understanding of how the three categories for OUR COMPANY compare to various competitors, as well as how the three categories compare to each other." It may not be the prettiest thing in the world, but it does what it's supposed to do far more quickly and effectively than previous renditions.

    So, thanks for the follow-up post Cole. I so do enjoy your posts, perhaps because they most often make me think. An intellectual cocktail of sorts. Please find yourself a few more free afternoons and double your blogging schedule or something :) I'm sure I speak for all of your readers when I suggest that you'll likely not get any arguments.

  4. Jeffrey,

    Thanks for your comments, I will attempt to address each one.

    I agree with your primary point, the time Cole spent on this, no matter the length of time, creating a chart and sharing her process, is valuable. It is not easy to present oneself in public, it is vulnerable and courageous, a strength that Cole shows when she posts, and valuable in many ways.

    In case my email to Cole was misunderstood (I apologize for any confusion, it is easy for tone to get lost in an email or comment, creating a gap, and it is easy for us to fill that gap in tone with something not intended). I intended it as a personal note to her as encouragement, that I know she can find interesting stories no matter how limited the data. The horizontal bar chart is no doubt a solid starting point, but I believe she can dig deeper and find some interesting stories. :)

    Here are my replies to your points addressed to me in your comment:

    > "the disparity between A & B's Preference conversions"
    The disparity is not just between A & B, it is A & everyone else. B is about on par with all other companies in preference conversion.
    See: http://public.tableausoftware.com/shared/S7KTWRCXG
    When it comes to Preference conversion, it is A and then everyone else is distant.

    > "on the verge of bankruptcy or whether their respective cost structures and/or business models are completely different"
    This data set does not enable insight on these topics.

    > "For example, % conversions to Consideration trend downward as % Awareness decreases"
    Maybe slightly, but it is not a strong correlation.
    See: http://public.tableausoftware.com/shared/67J6KZ49X
    You can see C's consideration conversion equal to I's while having 3 times the awareness.

    > "The chart raises a host of questions ... how long is each stage ... how much does each stage cost ... what is the ultimate cost to acquire a customer ... how does that compare to the lifetime value of a customer's revenues"
    While these are all great questions, they are all outside the scope of the data here. I would think these are all valid questions on their own.

    > "non conversion rate ... there are also opportunities to understand why a significant percentage of each company's customers do not convert and what happens to them after that."
    I am not sure how looking at non conversion rate is more insightful than looking at conversion rate since it is just a mirror number. Also, "what happens to them after that" is outside the scope of this data.

    > "I could go on, but if I did, I'd likely come up with a case study on a company that may or may not even exist, based on one chart!"
    Yes this is fake data, but I believe it is meaningful practice to try to draw potential insight from a limited data set. Yes we can all imagine lots of other fake data too, but that is outside the scope of the situation. To relate this to a real-world situation, I see this as what if additional data was not available, can we find anything potentially interesting in just this limited data set?

    Overall, I think we may have had a different perspective on the situation, that our view on the scope of the situation is different. I gather that you see this as an exercise in finding the next question ask. The practice of finding the next question to ask, thinking outside of the data presented, is a worthwhile task. I did not go that far, I simply find it valuable practice to attempt to work within constraints, in this case a limited data set, as that encourages creativity. It is a challenge to find interesting perspectives on a limited data set. I believe every data set, no matter the size holds interesting stories. What stories can we tell when constrained to a small data set?

  5. Thanks for your comments and clarifications, Joe. They were well-conceived and well-articulated. I believe that we all took a slightly different approach to this thing, which I find interesting in and of itself.

    I think Cole may have been thinking about the best way to improve last week's graph and, when a few hours became available, she took the opportunity to make it happen and post the results above. If I remember correctly, this whole exercise arose with little or no context during one of Cole's workshops. Her intention, if I'm not mistaken, was merely to put together a useful chart to highlight a single data point. Seemingly simple, yet surprisingly elusive. Once she had completed that, she stopped there. As I believe she mentioned, this time was a bit different in that she normally designs her visual representations with the data in hand and at least a general idea of the direction she's heading.

    Then, guys like you and I come along because this may indeed be the chart that solves world hunger. Our approaches are different, in this instance anyway. You remained within the four corners of the chart, analyzing relationships, while my attention was focused on questions raised by the data. I really could write an entire story from one data point, but I'm not sure what value it would bring, given that -despite its basis in bits and pieces of real world experience, and of course, the limited data, it would be entirely fictional.

    And yes, it is indeed difficult to recognize the tone of these paragraphs. Most U.S. courts would require a bit more than one bar about marketing from a random chart on the internet before allowing a group of creditors to force bankruptcy.

    It was good talking to you, my friend. Take care,


  6. I'm late to this game, but wanted to participate. :-)

    I took Joe's Tableau workbook (thanks for the data prep work, Joe!) and created a different view that helps answer the questions I would be most interested in. You can see it here.

    For me, the key story in this data is how our company is doing, both in absolute performance by metric (e.g. conversions) and in relation to our competition. So I put together a view that does that. In particular:

    1) I grouped the three primary metrics (Awareness, Consideration and Preference) as "bars within bars" so that a) I could see how each stage gets smaller and by how much, and b) regardless of how I choose to sort, I can easily compare the lengths of the relevant bars. Granted, the Preference bars become harder to discern differences between, due to their smaller lengths relative to the other bars, but a visual ranking is still established.

    2) I highlighted Our Company with a different color, so that it's easy to see even if its rank changes (which it doesn't, really, given its mediocre performance across pretty much all of the data points).

    3) I called out the conversion metrics as separate columns (and included the actual percentages), as I felt that those were probably more important than the base metrics of Awareness, etc. since conversion measures how effectively you guide your customers from one stage to the next, and by extension how effectively you are spending your marketing dollars as compared to your competition. I also chose to encode these as circles to set them apart visually from the bars on the other metrics, and colored them based on their deviation from our company.

    A few things become immediately clear: 1) Company A kicks butt over pretty much everyone, while Company B is just as splashy (i.e. same awareness) but nowhere near as effective at generating sales; 2) our performance is middle-of-the-road across the board; 3) the spread in conversion for Consideration is not nearly as wide as the spread on Preference Conversion, although that would change if you removed Company A from the mix; 4) Company H achieves the same Preference as we do but only has a little over half of our Awareness, suggesting that they achieve a similar customer base with a lot less marketing effort.


    1. Nice job, Mike. The "bars in bars" graph pretty much says it all and it says it quickly. The fact that some bars become harder to discern renders them irrelevant, which isn't a bad thing. You could even footnote the fact they're less than 2% or something.