E26: To Sell With Data, Stop Leading With Data

What if the best way to influence decisions with data is to not lead with data? Welcome to Invisible Solutions. I'm your host, Steven Shapiro. Each week we tackle your most complex problems using the lenses from my book, Invisible Solutions. If you need the lenses, go to getthelenses.com with that. Let's get started with today's episode. On today's episode, I am thrilled to have on a good friend and colleague, Brad Kollar. Brad is a super smart guy that I've worked with for 25 years. We started working together at Accenture and we've stayed in touch over the years. In addition to his work at Accenture, he was also the Chief Learning Officer at the University of Chicago Medical Center.

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And his primary focus is on dealing with the fact that today leaders and organizations are overwhelmed with complexity. And his goal is to bring simplicity and clarity so that they can make better decisions and that they can solve problems more effectively and they can improve their communication. So Brad is an incredible thought leader and I'm thrilled to have him with us today. Well, welcome, Brad.

Hey, Steve. It's great to be here with you. I'm looking forward to our conversation today.

Yeah, you know, I always enjoy our conversations, you know, and as I alluded to in the introduction, you and I have known each other for a long time. I mean, it's about, I think, 25 years now, so that I just can't believe it. And you've been so influential in the way that I think about innovation and business. And so I'm just, you know, so honored to have you on the show today for us to tackle a problem, because, you know, this is a problem solving show. It's not an interview show. So I ask all of my guests to bring a problem they want to solve or they've solved or their clients have solved, and we will just have some fun with it. So, Brad, what is the problem you would like to solve today?

So the problem I'd like to talk with you about today is how can we drive data driven decision making forward without having to rehash the analysis and background each time we start a conversation.

All right, Brad. Okay, that's a mouthful. That's a big one here. And I know there's a lot of really juicy goodness in this one. So give me a little more background on this. Like, why is this a problem? What typically happens inside of organizations? Why do we need to deal with this one?

Yeah, great question. So I do a lot of work with executives and leaders on making data driven decisions. And one problem that I Constantly see them running into is their team will come and present some findings to them. But instead of being able to start with what are the findings and let's move forward, they'll sort of back the team up and say, take me through every analysis you did, show me all of the data you looked at. And so instead of moving forward, it seems like the executive is sort of redoing the work that the analyst team had to do, and then they could start moving forward. And I just feel like that wastes a lot of time, especially as we're trying to be more agile and more nimble. If we have to redo everyone's analysis, we're really not getting a lot of time to actually make the decisions.

Well, that certainly makes a lot of sense. I mean, it sounds as though what we're doing is we're redoing the work multiple times. So what have you seen? Like, why is this happening? What have you seen? Have been some of the good ways to deal with this, or is this just a big lingering problem?

I think it's, you know, I think it's a big lingering problem. I think it's a cultural problem. And I mean business cultural, not necessarily, you know, regional culture. I think one, we're all just used to asking to see the data all the time. And I think as you're growing up through a business, since you're the one who's always worked with the data, you have an expectation that you're going to continue to work with the data. I think that's one of the issues that has it. I think sometimes it might be that the leader wants to be sure that they would have come up with the same answer as the analyst team. So they want to see the data again. I think it's just a number of these factors, but a lot of it is just inertia. I think we've always asked to see it and we continue to ask to see it.

So have you seen any good solutions to this problem?

You know, some of the good solutions to the problem I've seen are really when leaders just ask their team to provide simple conclusions and evidence, and then they're able to sort of challenge the evidence rather than challenging the analysis, if that makes sense. So, for example, if I tell you that we need to increase marketing because our sales are down, our competitors are ahead of us, and we're starting to lose share, if a leader who listens to those questions, if those three questions make sense to them, because maybe they already know that they could just sort of move on and say, okay, let's just talk about this decision about marketing. A leader who's unfamiliar maybe with one or two of those points might want to go a little bit deeper to understand them better. But it's the leaders who pause to first ask themselves, do I understand the data that's been presented? And if they do, they don't need to go and do that drill down. So I think that's where I've seen it work best. Are the leaders who take that pause, reflect on, okay, does this make sense to me? Do I already know this before they ask their team to go deep into the details?

So I can see a number of different things going on here. One is, and I know when you and I have talked in the past, maybe there's a little bit of a trust issue associated with the data, but also, I'm guessing at some level there might be something else going on, which is the sequencing or the process. I mean, so one of the lenses which I like to use, and people who listen to this podcast know a lot of the conversation tends to be around the lenses. And one of the lenses is the resequence lens, which is number 11. I wonder if part of it is just a matter of timing. And what I mean by that is, is the issue that when somebody's trying to, let's use the word, convince a leader to buy into the data that they provided, it's happening after the fact, as opposed to somehow seeding the conversation earlier so that the leader feels as though they've been part of the conversation rather than being brought in at the last minute to now try to understand everything.

No, Steve, I think that's a great point, actually, because one of the issues that I've seen people talk about is if I come in to make a presentation to you and suppose I use three pieces of evidence that aren't the three pieces of evidence that you would have used to normally make that decision. I think the reason people are jumping into the data is now I need to sort of try to make sense of what you said and put it into my framework or my thought process. So I think your point is a really good one. If we get people involved a little earlier, not in the analysis, but if I could talk to the leader three weeks earlier and say, you've asked me to look into this problem, here's the questions that I might be looking into, or here's the data I'm going to look at to try to help make a recommendation. Do you agree with? I think if people could get consensus earlier on the criteria, they will trust the analysis and data more on the back end because they know that the data is coming through their thought process and I don't have to do that translation. And I trust it more. I mean, the reality is anyone can fool me when they cherry pick data that they want. It's much harder to fool me if you're actually using the data that I told you I wanted to see in the first place. And I think that might help with some confidence and some of that trust that you mentioned.

So I, and I really love that last point. Let's face it, you know, I mean, you're, you're a guy who spent a lot of your career focused on data and using it to make an argument. You could use data to make any argument you want, especially if you cherry pick the information that's going to support your hypothesis or your belief. So when you get the criteria defined up front, that certainly has to have some kind of impact on the way people absorb the data. So I think that's such a fundamentally critical point is it doesn't mean you have to have all the analysis done upfront or involve the leaders every step of the way. But at least if there's an agreement on what it is you're going to evaluate and how you're going to evaluate it, it feels as though it's a little more objective rather than, to use your word, I think it was cherry picking the data that supports what you want.

Yeah, absolutely. In US where I've seen some successes, I actually had a client who did that. This is a client who sells commercial products, consumer products to various retailers. And they started to actually do that with the buyer at the retailer because they said the buyer was always suspicious when they would come in and say, hey, we've got this new product, you should put it on your shelf. Well, of course, the buyer said, well, I know you're going to bring me a bunch of good data because you want me to sell it. They started to work with the buyer a little bit to say, well, how do you decide when you're going to put something on the shelf? And now when they come in, they present it through the buyer's lens of how they make their decision. And, and there's just a lot more. Again, it's trust and it's not ethical trust, but it's trust in the data, trust in the process. So they don't feel the need to go back. I call it an audit. Audit the data and audit the process. They don't feel that they need to do that as much.

Well, and I love that example. I think that really. Because, let's face it, we can make an argument for anything that we want, which then leads to another lens, which I think would be an interesting one for us to tackle. Here is when we talk about data, we're talking about analysis. We're talking about this very analytical part of the brain. And a lot of times, though, when decisions are made, you know this, when decisions are made, it's often not based on the data, but it's rather on the emotion behind the data. So that's lens number 14. Let's talk a little bit about that. Like, where does emotion come into play? And what I mean by that is sometimes if we're trying to sell something, we don't sell the data, we don't sell the statistics, we don't sell the spreadsheets. But what we do is we sell the emotion, the feeling, the impact, the pains. And then once people are bought into that, I've heard the term used before that there's warm cognition, which is sort of those emotional pieces, and then the cold cognition, which is very analytical, and it gets very easy to chop things from that cold cognition, that analytical part, whereas the warm cognition buys people into the process more. So what have you seen works from an emotional perspective.

You know, I think we. One of the most important things that works from that emotional perspective is really selling the problem. And it's really interesting. You said we don't sell them on the data or the analysis. We actually usually don't sell them on the solution either. What we sell them on is there's a problem, and that problem is something that you should care about because your life or your business is worse off because this problem is happening. And if you look at a commercial, that's what they do. And I always point this out to a lot of my clients. I said, look at how much of time in a print ad, how much space in a print ad, or how much time in a televised commercial you spend on the problem versus how much you actually spend time on the solution and the facts for why people should buy your product to solve that problem. And so I think people, as humans, we're naturally akin to problems. We're used to seeing problems. Our brain is sort of optimized to keep us safe, to keep us in survival mode. And even though our problems today aren't ones of direct survival, our brain still sometimes reacts to a problem as something like, hey, I'm going to really pay attention to this. So I often say that the framing of the problem is probably more important than the framing of the solution. Assuming the solution's relevant when you're trying to actually convince somebody of something.

Brad, you know, you're speaking my language because to me, the problem is everything. And I like the way you frame. Framed it too, because one of the things which I've studied for years were infomercials, you know, and there's a structure to the way like those as seen on TV types of things, you know. You know, you got Billy Mays, hey, I'm Billy Mays, and Or whoever it is these days, shows my age and you know. But it was always interesting to say, are you tired and be like, in black and white with this heavy music? Are you tired of trying to drive while put on your makeup and drink your coffee at the same time and spilling your coffee and getting into an accident? Well, no.

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More welcome the. And then all of a sudden, like, the music gets bright, the picture gets bright. Introducing the. I have no idea what would help you put on makeup and drive and be safe at the same time. Hopefully there is no such device. But you get my point. They spend so much time, like you said, on the problem and really get the oh, yeah, I have that problem. I need that problem. So I love that. Now here's gonna. This might be a tough question, but I've seen this in a lot of cases is trying to convince, especially if we're trying to sell to leaders, trying to convince leaders sometimes that there even is a problem because we can try to sell it even analytically. Like if you don't. Let's talk about innovation for a moment. If you don't make this change, we know your competition is going to do it and you're going to lose 50% of the business. But there's the brain of a lot of people says, well, look, what worked in the past is going to work in the future. Especially if we're coming off of a really good quarter, which some of my clients actually had really good quarters. In the middle of all the pandemic. How do we convince people then? And I'm not sure I like the word convince, but play with it. How do we. How do we get people to buy in that there actually is even a problem, when in many cases, their past success might be their ultimate demise?

That's a. That's a great question. And I think there's two pieces to that. The first one is we often state the problem. We don't actually prove the problem. I mean, I think even, even what you said, I was sort of smiling because I wish people would even say we're liable to lose 50% of our business. Most people state the problem almost as a given and then just blow right by it. We have an attrition problem now. Let me go tell you what we're going to do about it. And so I always say you want your audience to see, feel, taste, smell, hear and empathize with that problem as much as possible. Which leads to the second thing I was going to mention, which is broadening our view of what data is in the first place. A lot of people think data has to be numbers. Sometimes the best data point is a story or an example. And if I lead off with we're having a big customer service problem, I could give you all sorts of metrics from our queue times and everything, or even customer satisfaction scores. But if I say, for example, the other day we had a person who was on hold for two and a half hours just to get their password changed, suddenly everybody's paying attention. That's a data point, it's not a numeric data point. Now that's a data point. And then what I have to do though is I have to make a connection to show that that wasn't a one off anomaly. That's where now I could use some maybe quantifiable data to say. And in fact that's not that uncommon. That happens to about 1 in 4 of our customers or 1 in 10 of our customers. So I think really it's the. How do you touch people across the mental with the numbers. But really how do you get them to get that feeling? How do you get them to really have that empathy, to really. I always say the more I could feel like I've experienced a problem, the more likely I am one, as you said, to even realize it exists and two, to care enough to actually get the problem solved.

That's great. And it sort of reminds me of a quote, not somebody I like to quote, but Stalin. Joseph Stalin once said that one death is a tragedy. 10,000 deaths is a statistic. And I think what ends up happening is we tend to go for the statistics. But I know from personal experience when I've seen and witnessed the impact on one person, it's much more powerful than trying to extrapolate that to 10,000. So there's definitely something about, like you said, that story. So how do you capture those stories? How do you gather those stories? Because it's easy to send out surveys, it's easy to do the types of analytical Number gathering. What are the things that you found that's been very helpful to gather those emotional stories that you can say, look, this is just one story, but it's not the only story. We've seen this over and over and over and something that people could relate to. How do you gather those stories?

Well, you know, I. I don't have, like, a formal technique for gathering them. What I find is everybody knows the stories, you know, so a lot of times you don't have to gather the stories because that person who was on hold for an hour, everybody's been talking about that person. Right. I remember when I worked at the University of Chicago Medical center, we had an issue once that came up on a patient safety kind of issue. Everybody was talking about that story. So it's actually funny. People know the stories of when things aren't going well in the business and. And when those stories are. And if the stories are credible and if they are things that happen over and over, even if they don't know your specific story, they've seen it happen somewhere else. And so I liked. I really think it's important to use the story to connect, use the data to validate. And I think that's the key. And when you were mentioning the quote, there's actually a lot of research in the nonprofit world that says the same thing. A lot of nonprofits like to say, we helped 100,000 people a year. We helped a million people a year. What they find is when they've done experiments side by side, when you say, how much will you give to help a million people get more water? They will get less money than when we say, how much will you give to help little Steve in this developing country get a lot more water? And so people connect with people. People connect with the emotion and the story, but then they want to know that Steve's not the only one getting water. But that's almost secondary.

Yeah, that makes perfect sense. And if I think about pretty much every commercial that's trying to get money for a nice, worthy cause, the ones that do it, well, exactly like you're saying, it's. It's either a little kid in a hospital or it's a dog in a pound, and you see their sad eyes or whatever it is, and it's like, okay, I have to open up my wallet. But when it's the numbers, they're just numbers. They don't. They don't have the same emotion. And so I hope people are getting it. This is like a really powerful conversation. If you want to make an impact in your organization. You could be the most brilliant person with the most brilliant solutions, the most brilliant ideas. But if you can't, say, sell them, if you can't get people to buy into them, it's not going to matter. And so the whole point of this conversation is impact comes when you can get other people bought into, involved with, engaged with your solutions and your ideas so that they can actually be brought to fruition. So this is a really powerful conversation. Let's just try one, one last lens. And I'm actually gonna. This is gonna be weird, Brad, but I think you know the lenses well enough because we've talked about it. I'm going to give you a choice of which lens you think is going to stimulate the most interesting conversation here at the end. And I'm going to give you three lenses. Let's see how this works. All right, so lens number 12, the reassigned lens, which basically says shift who does the work. So this could be with the data gathering, but more likely the sharing of the stories. The next one would be the variations lens, which is 24, which says, well, let's not treat all data the same or let's not treat all stories the same. And then number four, which could lead to something interesting is eliminate. Like how could we eliminate data completely from this conversation as a way of being better at selling? So reassign variations or eliminate. Which one seems to feel good to close out our conversation with something controversial.

Well, I'm going to cheat a little because I definitely actually have a point of view. So you might have to knock me out of my comfort zone, but I'm going to go with the eliminate lens.

I was hoping you'd say that. So go for it. What do you got?

Well, the eliminate lens is pretty simple and it's the message I give everyone is if you don't want to have a discussion about the data, stop showing the data, which freaks people out at first. But I always say if you put numbers on a screen, people are going to talk about numbers. If you put ideas on a screen, people will talk about ideas. And if you put decisions and evidence on a screen, people will talk about decisions and evidence. So this isn't, you know, this isn't a new idea, but it's the move as much data to the back appendix of the presentation or email or whatever it is and really decide what you want to focus on. And a data driven conversation doesn't mean a numbers driven conversation as long as everything I'm saying can be supported by something in the appendix. And I could pull that out as needed if somebody asks me a question. It's a data driven conversation. And I think it's this misunderstanding of a data driven conversation has to actually show data. No, it just has to be based on data. And the data has to exist and be accessible. So eliminate the data, stop showing the chart in the graph, because what we really care about isn't the chart. It was one point on the chart. If you tell me that point, we could have a great conversation. If you show me the chart, someone's going to bring up another point where the line dipped for a little while. Now we're going to have a conversation about where something dipped. It might not even be relevant to the overall story because it was a one off anomaly. But if people see a dip on a chart, they're going to ask you about it, whether it's relevant or not.

Well, I love that. I love that. And I want to sort of wrap this all up with a quote that to me just always makes me smile every time I say it. It's from Scott Cook, the founder of Intuit. And he said for every one of our failures, we had spreadsheets that looked awesome. And I just think that it's true. Look, and this comes back to the beginning of the conversation is we can use data to support whatever belief we have, whatever point we're trying to make. And so just to sort of summarize, what we've done is we covered three different lenses here, Brad. We talked about the resequence lens, which was sort of the timing of things. And I loved your example of really getting people bought into the process earlier by defining the criteria early on. We talked quite a bit about the emotion lens, which is about how do we tell and sell with stories versus the data. And then the eliminate lens sort of built on that, which is, well, put a lot of the data in the back rather than the front, and you're going to have a better time with it. Anything you want to do to sort of wrap this up and put a bow on the conversation?

No, I think, Steve, that you really summarized it well. And I think the key is we should be thinking about where the value comes in with anything that we do. And the value of data is never the numbers, it's never the chart. The value of data is a decision or an action. So the degree to which we could start the conversation as close to that increases the probability that we'll actually get that taken care of and we'll get the decision made. And again, it's never about when I say move the data to the back. The data is always accessible. If people want to see it, we could show it to them. But I think making the assumption that either we have to see data that we already know or making the assumption that our audience has to see all the data without letting them know what they actually are interested in seeing is probably something that gets in the way of good data driven decision making.

All right, Brad, that's fantastic. What a really fascinating conversation. And every time we talk, I learn something new. So I just so appreciate your being here with us today. And if people want to get in touch with you and learn more about what you do, how can they reach you?

So they could reach me at my website, availaviladvisors, a D V I S R or certainly on LinkedIn. So whatever's easier.

Excellent. And I'll put all that in the show notes. Thank you so much, Brad. And with that, we are at the end of this week's episode. To submit problems you want solved on this podcast, go to invisiblesolutionspodcast.com and remember to download the lenses, go to getthelenses.com I look forward to being with you next week. And until then, happy problem solving.

Sam.