Ep. 36 | Beyond the Obvious – Data Visualization

What does data storytelling have to do with customer equity? Everything. In this episode I summarize a month of data visualization and data storytelling best practices. I include clips from previous guests, Lea Pica, Alberto Cairo, Felix Shildorfer and Gulrez Khan. Finally, I wrap up with four insightful nuggets.     

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Logician Personality (INTP-A / INTP-T) What’s the Difference?

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Allison Hartsoe – 00:01 – This is the Customer Equity Accelerator. If you are a marketing executive who wants to deliver bottom-line impact by identifying and connecting with revenue generating customers, then this is the show for you. I’m your host, Allison Hartsoe, CEO of Ambition Data. Each week I bring you the leaders behind the customer-centric revolution who share their expert advice. Are you ready to accelerate? Then let’s go. Welcome, everyone. Today’s show is a summary of my past month of podcasts, focusing on data visualization disasters, data storytelling, and the corresponding best practices. Now, why did I choose to spend a whole month on this topic when the podcast is really about accelerating customer equity? How do those topics fit together?

Allison Hartsoe – 00:57 – Well, one of the things I notice all the time in my everyday work at ambition data is how analytical data is received. Then you might not know this, but only a small percentage of people are born to be analytic thinkers. I used to think it was about six or seven percent, but when I looked up the number, I saw that about three percent of the population are I, N, T, P, which is the analytic or logical type personality type on the Myers Briggs test. So that means the rest of us have learned or perhaps are trying to learn this way of logical emotion-free. I’m biased thinking. So if you, if you know the majority of your audience is not naturally an analytic thinker. That just lends quite a challenge to the act of communicating effective data.

Allison Hartsoe – 01:57 – So if I present a bunch of customer analytic findings to people who are not naturally analytical, what will actually prohibit the adoption of really good customer-centric strategies are you might think it’s, you know, great color or beautiful visualizations or data that tells a story. Well, all that is partly true, but the number one thing that actually blocks progress is the emotional connection. If I don’t care about the data that I’m looking at, that I cannot care about the people behind that data. So we must always connect to people. And, and by that I mean not only just connecting to the audience or connecting to the stakeholders, but helping them connect to our customers and the data that we know about our customers. And that is why I chose to spend a month on data storytelling and data visualization.

Allison Hartsoe – 02:57 – I began the month with a Lea Pica, who is a data storytelling expert and the founder of leapica.com. She is also the host of the present beyond measure podcast. And we talked a lot about getting your message through. Leah shared what she calls the peak of protocol, which covers five fundamental principles for good data communication. Here it is,

Lea Pica – 03:21 – a lot of us are just going at the data as a sort of shopping cart, and we’re dumping every possible thing that we can think of in there running it through the cashier and hoping that as they scan each item, one of these things is going to catch their attention. And there’s a lot to that communication piece as well in terms of assessing what your audience actually needs. And there’s a whole process behind that. Um, that I think would really serve anyone to look at their audience from a needs perspective. So it’s not coming in with a story, it’s coming in with a big shopping bag full of random items and just kind of spreading them all over the table.

Allison Hartsoe – 04:04 – I always like how others call that data puke. It awfully gross, but it is actually that in practice too.

Lea Pica – 04:15 – And sometimes you need something that’s striking to be, to really send a message home, you know?

Allison Hartsoe – 04:20 – Okay. Let’s move on to your five steps. You said, of our five methodologies.

Lea Pica – 04:26 – Yes. So it’s, it’s five questions to take your audience through that provide a lot of what great data storytelling or storytelling in general does. So the first thing, the first question is what happened? And you want to relay this in terms of observation, something that feels impartial and objective. You’re not too quick to overlay your judgment and your own assessment on top of it because you want to build trust and credibility. So that’s going to be the first thing is stating what you saw that happened. But then our job as analysts for me, is really to go deeper and say why do we think it happened and this is where the subjectivity and the unique lens of each of our experiences is really important, and you don’t have to be right because being right or wrong is also pretty subjective in our field, but at least using our experience to theorize and it may be even engaged the audience in a dialogue about,

Lea Pica – 05:29 – well, this is why we think it happened. What do you guys think? You know, that’s what’s going to start creating some real wheels turning and some really great conversation during that meeting. Then the next thing you can do as a presenter is a start to show even additional value and say, well, what should we do about it? If we had our way, these are the steps that we would take in order to take action on this particular item. And my great friend Evan LaPointe actually gave me this advice on my podcast where he said, always leave at least two recommendations for people because if you give one and they don’t agree with it, that’s going to create friction. And might alienate them a bit, but giving them two options that you can discuss empowers them with the idea that they’re in the driver’s seat with you in making a choice. So this for me is one of so crucial.

Lea Pica – 06:26 – They’re all crucial, but this one in particular and it’s saying once you’ve delivered those recommendations and everyone’s kind of moving towards agreeing to stuff, I don’t want you to walk out of that room without identifying who should do it and by when. So creating accountability around recommendations, I find after working with hundreds of practitioners is that we often, even if we get to the point of giving recommendations will go and that’s it. All right, bye. And for me, when I asked my student’s guys if we don’t assign the recommendation to someone who is going to take responsibility and everyone shakes their head, no one, and it’s the same thing. If we give recommendations like continuing to monitor or making this change and there’s no reasonable deadline, when’s it going to get done?

Allison Hartsoe – 07:24 – People don’t like it. Maybe at least what the manager was. Someone who has a feeling for this person would be good to solve this problem so that people just don’t sit there and go, oh, not me. It’s going to be the person who didn’t see any of the presentations.

Lea Pica – 07:41 – Uh, yeah. I think that’s a great idea. Actually. I always recommend collaborating with the manager. That or some senior level who’s going to be present at that meeting or is sponsoring you for this meeting. This is something we talked about on your appearance, on my shows. Having that sponsor advocate for you present at the meeting. And I think working with them in advance to say like, well, what are the best chances? Who’s going to be the best person to take this on? Because yeah, we don’t want to just look around the room and be like, who’s got this one? Guys? It’s my experience that depending on the kind of recommendation; there’s a natural group or person who would naturally be accountable for it. Um, and it would make sense. And of course, if there’s an overwriting person in the hierarchy there, they can make that final call. But at least having the conversation rather than just delivering the recommendations and then leaving it there I think is a crucial step in making these meetings really worth everyone’s while

Allison Hartsoe – 08:49 – I think that’s a really good point to underscore and it’s one that’s often missed.

Lea Pica – 08:54 – Yes. Not Anymore. So there is one more, and I actually, I’m trying to remember where I learned this one, but I thought this was really interesting which is communicating or trying to articulate in tangible terms what is the possible cost of not taking action if we did nothing, what’s gonna Happen? Is our campaign performance just going to plateau? Are we going to lose ground and performance with this way? Are our customers going to keep abandoning our lead generation process at an increased rate and this one’s tougher just because you know, it could require some projections and things like that, but if you’re really good with your numbers, this is a really powerful tool because then they are? That’s kind of lighting a fire under them to say, Oh guys, we can’t let that happen. That’s the worst trade off than having enough time to do nothing.

Allison Hartsoe – 09:57 – This is where are the customers angle can come in and be very powerful because if you think about it in terms of frustrating your high value customers and you know, watching them walk away, even if you could put it into that context, you would end up with either a customer voice or be that you could see the actual use cases and the activity of the customer’s changing over time. That would be a very solid way to say not only is there a cost in terms of the channel, you know, the actions that we normally see on a channel like click-throughs or engagements, but there is a long-term cost in terms of the amount of revenue we build for the business. When we, um, when we isolate, when we frustrate people who have high statistical propensity to buy from us again, which are high-value customers.

Lea Pica – 10:51 – Yes, exactly. That’s exactly what I was thinking was that the data that you help companies work with is really catered well to identifying that kind of risk.

Allison Hartsoe – 11:02 – And there’s few of them. You know, we almost always see that break out into a, a rough 80/20 split. And it’s usually less than two percent, so if you’re identifying your high-value customers who are oftentimes you’re very frequent engagers. Those folks who are frustrated, you know, even in a channel perspective can also be the ones that are not just the first to walk away, but there that 17 percent or-or less. There’s a small number of [inaudible], and they’re hard to get back.

Lea Pica – 11:36 – Oh yes. That’s also a really good point is I think the power and the kinds of data that you’re working with, you’re also understanding the loyalty factors and the expense of resources and trying to maintain even really high customers. So I think that’s an amazing Lens.

Allison Hartsoe – 11:57 – Then I spoke with Alberto Cairo professor at the University of Miami, specifically the Knight Chair of Visual Journalism and the leader of the visual trumpery tour. And if you haven’t heard about that tour, you haven’t checked it out. Please do. It’s really interesting. He’s a fantastic speaker. Alberto covered several dates of Alberto covered several data visualization disasters, but it wasn’t all about pretty graphics. Oh no. He cited our responsibility as chief analytics officers, as data scientists, as analysts, to prevent the misinterpretation of our data by using a proper scale and taking time to think critically about not only the data that we’re looking at but thinking critically about the broader context of how we’re communicating to our customers.

Allison Hartsoe – 12:55 – And that is also something I see an awful lot of, that we as analysts sometimes look very closely at one pocket of data and we don’t communicate with the rest of the organization to get more of the context to unearth that. So Alberto was a big advocate of conversation. Here is more of our conversation in summary. Well, let’s summarize a little bit about what we’ve heard. We talked about why should I care about visual trumpery, you know, this, this visual mischief and, and deception that’s going on. And, and we came to the conclusion that there were five great elements that happen in most powerful charts are the most powerful graphics from them being truthful, beautiful, functional, insightful and enlightening. Those are the five key components that we’re really after.

Allison Hartsoe – 13:52 – But you know, we can marry that with what you said at the end, Alberto, which is the chart isn’t just meant to be a stopping point and I think that’s where the last piece in lightening becomes so valuable is if it is indeed in lightning, shouldn’t it provoke a conversation? Shouldn’t it caused people to say, oh, I didn’t know that. What about this? And, and indeed that’s what we oftentimes like to hear when people are engaging with our work is they find six different ways that they want to twist it and turn it to explore it and understand it can be a powerful way to, to get hold of the data and the story behind the data.

Alberto Cairo – 14:29 – That’s an interesting summary. You should deliver.

Allison Hartsoe – 14:35 – I don’t think I could do it justice, not like you. You talked about the different examples. We talked about the left and the right versions in the end at the editor. Uh, sorry, the electorial map, uh, the Obama care on the left and the electorial map on the right. And what you said here, that was really good, was it? And, and I’ve heard this before from other people that we’ve interviewed, which is to think critically, to think beyond what the data’s telling you to think beyond the chart. And, and especially here, we talked a lot about pausing, set your biases aside and really think that the chart is just the chart. It’s only showing you what it has. It’s not a; it’s not designed to tell you all the answers at once unless it’s extremely well designed.

Alberto Cairo – 15:25 – I’m going to interrupt you in there, but because there was another element to that which I forgot to mention, but I make this point in the swing, the in my, in my third book that I’m writing, which is that when we think alone, we know we’d done recently. We rationalize. That’s a very another, very relevant points. So when we only talk to ourselves or to people who are likeminded people who already think like us, we tend to basically use our reasoning skills to confirm what we already believe it is better. And again, this is connected to the idea of charts as part of, as part of our dialogue enabling process. Right? So don’t, don’t, don’t reason about the chart on your own, talk with other people who are not necessarily likeminded above the chart because every, every person will see something different in the chart and understanding I’m good reasoning may arise through the conversation about the data that is being shown to you until that other person,

Alberto Cairo – 16:25 – we don’t think well alone, we are social creatures and we only can them well when we don’t think on our own or in collaboration with people who already believe what we believe. We reason better when we partner up with people who are not necessarily like us, but who are a little bit different than we are,

Allison Hartsoe – 16:45 – I love that. That is going to be our closing note today. Don’t, don’t reason on your own. Definitely look for those opposing opinions to come to a proper unbiased conclusions. Next step I interviewed Felix Schildorfer. Felix is a principal data scientist at first retail who told me and a very interesting international story about a consulting data disaster. Now he wasn’t at first retail, and this happened, and that’s good because this disaster was so political and so messy that I honestly thought his team was just down for the count. There was no way they were going to recover. But it turned into a really fabulous lesson in perseverance. Here’s the summary of my conversation with Felix

Allison Hartsoe – 17:36 – when we talk about why should I care about the seeds of a visual data disaster? What I liked that you said in this section is that the idea of the data that came in as storage data, uh, was the legacy systems were originally designed to remember that you had certain sets of data but not to execute them for insights. And today everyone wants those insights, but the data structure has to be built with that goal in mind. And that has to do with flexibility with data governance was the speed with a whole lot of factors that drive analytics and hence visualization. Uh, so I thought that was a really interesting point, and it can be surprising to people how much work goes into displaying those really valuable insights. So what I thought was really cool in this example was the way that you created fake data to get that management team on board to really love it, to get them to give you air cover.

Allison Hartsoe – 18:41 – So you had to really express the vision, and you know, and also know that that vision was possible and then you’re ready to go, but you run smack into the wall, but you don’t give up. And so even though it is not willing to give you a menu of what’s available inside the system, you, you don’t stop, you express a lot of grit and persistence and you know, going upstream to the sources of the data and looking for different ways to cleanse it or bring it together. It’s almost like you really take the company into your heart and work so hard to bring forth what they can do with their own data. I thought that was really admirable.

Felix Schildorfer – 19:26 – Yeah, it was, um, it was a lot of work, but definitely rewarding when things actually did work. And I think I just want to come back to the thing that really kept us going was the positive feedback from the end user and we teach defender Dads really important. I don’t want to say all that matters, but it is really important for when you pursue a project that you know that the product you’re putting out is actually worthwhile

Allison Hartsoe – 19:54 – And that they plan to use it. It’s not that you’re creating something and casting it into a whole, you’re responding to our real desired need, and you’re not letting the lack of data stop you. You’re pushing to get the pieces together so the organization can make great decisions. And finally I wrapped up with Gulrez Khan a data scientist at Microsoft and Gulrez is really good at pulling through the emotional connection of story and he didn’t use his fables sometimes to create a connection. And I have seen this work occasionally with some of my presentations as well. When I use a certain story or a certain example, people will say, oh, I don’t want to be like, whatever that example was. And they’ll give it a name. It helps people get their heads around what to do and what not to do. So the conversation will go Gulrez was really good in terms of understanding how to make that connection with the audience. Here’s a little bit of our summary

Allison Hartsoe – 21:02 – along with the course of our conversation. We started with how do you get their attention and using that as the first place and that, that key being the first two minutes, if you use jargon, you’re, you’ve lost, uh, and, and that’s what proofing in front of your wife or your child or your daughter can help you get to a better communication strategy. But then you have to think about what are you going to get the audience to do a, what is it that they, what action do you want them to take? And I think in many cases in data science where sometimes, you know, we’re excited to find a nugget of information, but we don’t always take it all the way through until and now what? Especially when it comes to the order of operations. For example, your second story about the poet King, which was all about aligning incentives.

Allison Hartsoe – 21:58 – So, okay, I have this nugget of information, and I think there are some actions people should be taking based on it, but what is the second order of operation behind that? What is the incentive that’s driven behind that metric and then what’s the one behind that and kind of unpacking that? That leads to really great conversations, which again, you can queue up with a fable or a simple story to get into those conversations, but if you started with just the jargon and the metrics, you wouldn’t have connected with people’s heart. They wouldn’t care, and it’s hard to get to that level of engagement.

Gulrez Khan – 22:32 – Totally got it. You have captured that [inaudible].

Allison Hartsoe – 22:38 – So what should you take away from all these experts? Well, first newbies, data dump, but data experts like all of us should be sought to really communicate a sense of accountability as well as the cost of inaction. Further charts alone are just limited models of reality. We really shouldn’t expect them to capture a hundred percent of the complexity of what we might be experiencing in real life. They need context. They need further conversation to become enlightening, so don’t reason on your own. Talk to others about what they see in the chart, and that’s how we all succeed when we when we communicate, and we pull that information together. Another lesson was to persevere just like a drop of water wears away stone to break down something that’s seemingly impossible.

Allison Hartsoe – 23:34 – The action of trying to stay the course to break down those barriers and tell a complete story to try to reward the stakeholders that you serve internally or communicate the customer story that is where you will find the most impact. When you can pull that together and achieve something that hasn’t been possible before and finally emotionally connecting to your audience in the same way that Mary Poppins connected to the bank’s children before revealing the data and asking them to take action. That is how you actually get people to one. Remember what you said, and to sign up for action at the end of your story.

Allison Hartsoe – 24:23 – So there you have it for data visualization experts with four very different perspectives who share how to communicate with your data so you can ultimately create those changes that better serve your customer. It’s so much more than pretty charts and graphs. It’s really the deep connection with the audience. It’s the ability to make that ask because you have connected with the audience and helping them see how to serve the broader picture. Now, if you want to talk more about the subject, or perhaps you need a little help to sort out your own data disaster, you can reach out to me at Allison at Ambition Data or @ahartsoe on Twitter or Allison Hartsoe, and that’s H-A-R-T-S-O-E On LinkedIn.

Allison Hartsoe – 25:19 – We’ve just released a quick digital dashboard service to help reduce data disasters and improve your customer centricity, so feel free to reach out if you’re interested in that. As always, I include links to everything that we discussed, including the four prior podcasts at ambitiondata.com/podcast, and I’ll also include a direct link in our show notes to the quick customer centric dashboards that I just mentioned. Thank you for joining me today. Remember, when you use your data effectively, you can build customer equity. It’s not magic. It’s just a very specific journey that you can follow to get results.

Allison Hartsoe – 26:04 – Thank you for joining today’s show. This is your host Allison Hartsoe, and I have two gifts for you. First, I’ve written a guide for the customer-centric CMO, which contained some of the best ideas from this podcast, and you can receive it right now. Simply text, ambitiondata, one word to 31996, and after you get that white paper, you’ll have the option for the second gift, which is to receive the signal. Once a month. I put together a list of three to five things I’ve seen that represent customer equity signal, not noise, and believe me; there’s a lot of noise out there. Things I include could be smart tools. I’ve run across articles, I’ve shared cool statistics, or people and companies I think are making amazing progress as they build customer equity. I hope you enjoy the CMO guide and the signal. See you next week on the Customer Equity Accelerator.