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You're listening to the Nonprofit Power Podcast.
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In today's episode, we share how to turn data into a story that engages and compels a decision-maker.
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So stay tuned.
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If you want to have real and powerful influence over the money and policy decisions that impact your organization and the people you serve, then you're in the right place.
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I'm Kath Patrick and I've helped dozens of progressive nonprofit leaders take their organizations to new and higher levels of impact and success by building powerful influence with the decision makers that matter.
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It is possible to get a critical mass of the money and policy decision makers in your world to be as invested in your success as you are.
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To have them seeking you out as an equal partner.
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And to have them Bringing opportunities and resources to you.
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This podcast will help you do just that.
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Welcome to the Nonprofit Power Podcast.
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Hey there folks.
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Welcome to another episode of the Nonprofit Power podcast.
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I'm your host, Kath.
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Patrick.
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I'm so glad you're here for today's episode.
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Tell me if this sounds familiar.
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You have some key pieces of data that you know should speak to your decision maker.
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It's obvious to you that these data points basically scream, our stuff solves your problem, and it solves it in a big way with a big impact.
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But then you share it with the decision maker and they don't really see those connections.
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They don't see the significance.
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They don't connect it to their problems and concerns.
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And you're left wondering why they can't see the obvious.
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Maybe they just don't care.
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The problem you're up against is that statistics and data are typically the least compelling way to convey important information.
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All the experts who've been telling you that you have to have data aren't wrong.
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They just left out a really important detail, which is your data won't help you if you don't communicate it in a way that engages and compels.
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Data are just difficult to get your head around in the abstract.
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So to make your data compelling, you have to make it concrete.
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Bring it to human scale and give it context.
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One of the best ways to do all of that at once is to create a narrative framework or a story around that data, that helps the decision maker realize that this data is the best news they've ever heard.
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Now.
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I'm a numbers geek.
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I love data, and clients share their awesome data with me all the time.
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And I got to tell you, when they hand me an infographic with five or 10 or 15 data points on it.
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I have to really focus.
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To get into each point and think about what it actually means.
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How does this translate?
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Now these are my clients.
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They're sharing their best stuff with me.
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Of course, I'm highly motivated to burrow into it and understand it at a deep level.
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And to make it make sense, to build in my own mind the story that this data tells.
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I'm highly motivated to do that.
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Decision makers are not.
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So we gotta do a lot of this work for them.
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So that when we share our data with them, they see immediately why and how it connects to them, and why it matters so much, and why it's so powerful and impactful.
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I'm going to walk through some core principles and techniques for how we do this.
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But then I think the best way to show you how to put them into action is to give you a bunch of examples about how these concepts can be applied to any good data that shows positive impact.
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Now of course you want to highlight your most compelling data.
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One of the mistakes I see nonprofits make a lot is that they want to include all their data.
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And that winds up diluting the most compelling pieces.
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It really pays to be selective.
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With data less is more.
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And we'll talk more in a little bit about how you pick which data points to use with a given decision maker.
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So our number one goal here is we've got to get the decision maker to focus.
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And to do that, you need to connect the impact you make with the problem or problems that the decision-maker is struggling with.
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And we talk about this all the time with your other messaging, but it is equally important if not moreso when we're talking data.
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So before you share any of your data, you want to ground the decision maker in the vivid reality of the problem they're dealing with.
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You want them to visualize that problem, the struggle to solve it, the frustration of not having been able to solve it.
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The pain of how much not solving it is continuing to cost them.
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And the relief they could feel if they could finally solve it.
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When you get them grounded in all of that.
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And then you share the story behind your data that explains how much you're going to solve their problem.
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Then you're going to get their attention.
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There is a subset of decision makers, often elected officials, who are going to care as much about how many people you serve and how big a geographic area.
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Whether you cover their entire district or not, whether you're serving all different quarters of their district.
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They're going to care about that because that's their job, right?
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They will be less interested if you only serve a handful of people in their district, versus if you serve lots of people in their district.
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So you always want to be prepared to share that kind of information.
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And that's data that they will understand fairly well on the surface of it.
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But even then you can make it more compelling by bringing it to life.
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Mostly in this conversation, I'm going to focus on impact data, but it is worth calling out that there are going to be other pieces of data that you're going to want to highlight as well.
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So you take the most compelling data that shows the impact that your stuff makes on the decision-makers problem.
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And then this is super important.
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You connect the dots for them.
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In addition to that, you're probably going to need to create a human scale to make your data more comprehensible.
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You know, you'd think that huge numbers would be the most impressive.
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But often they're just too big to really comprehend.
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And so we bring them down to human scale so that it's easier to visualize and therefore understand and retain.
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Percentages can be particularly problematic because they involve a second layer of abstraction.
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Numbers are already abstract.
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And then you make it be a percentage and that's another level of abstraction.
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Also, it is very concerning as a reality that a lot of people, decision makers included, aren't very good at knowing what percentages mean.
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Numbers and percentages are super useful, but you have to give them context and scale if you want the decision maker to understand their significance.
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And you want to make your data concrete, which is where so many of the story elements come in.
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And lastly, you need to remove all assumptions from your narrative.
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Unless you know for certain that a given decision maker is already well versed in a particular aspect of an issue or problem, and that they share your cause and effect analysis of the factors involved.
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Don't assume anything.
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Instead build in the explanation and the connections they will need to make meaning out of your data.
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And to make the meaning you want them to make out of your data.
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So plan to connect all of the dots for them.
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And then if in the course of the conversation with them, they jump ahead of you and say, oh, well, I see this means this and that means that, great.
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Then you can pick up the conversation from that point.
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But another of the big mistakes that everyone makes and nonprofit leaders tend to make in particular.
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Because we're so immersed in both our data and the meaning of our data.
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Because we're living the reality every day.
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We're serving clients, we're interacting with all of the pieces of the problem and helping people deal with that.
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That for us the impact data is just obvious.
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That of course well that means this, and this is what this means about how people's lives are changed, and how that changes this other thing over here.
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And all of that.
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That's super obvious to us.
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It is not obvious to the average decision maker who is not spending their entire workday thinking about the people you serve.
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So just make zero assumptions and everything will go much better.
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So let's put all of this together in an example.
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For the sake of examples, I pulled some data from a bunch of different nonprofits, some of which I've worked with and some of which I haven't.
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That I thought were nice examples of data you could work with.
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And it's not perfect data.
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Right?
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So that's the other thing.
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Sometimes your data are just so great and so perfect.
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You're beside yourself with joy.
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And other times you kind of got to make it work for you.
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Let's start with one that's really obvious that it is highly impactful and really impressive.
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This is some data around a nutrition intervention for chronic diseases.
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There's a lot of great data around this, but I pulled a few pieces that I want to use for this example.
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So let's say the decision maker in this situation is a prospective healthcare partner.
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Let's say they're a managed care organization for simplicity's sake.
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What are they worried about?
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Well, the whole idea behind managed care is that, like in the case of Medicaid, the state is paying them a per person rate to basically try to keep people as healthy as possible.
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They get a fixed rate for the people enrolled in their plan.
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And then it's their job to pay for the care that that person needs.
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And the incentive is for them to use interventions that are preventive as much as possible.
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The goal for them is to have the person need the least amount of healthcare, because they're healthier.
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But the reality for Medicaid in particular is that a lot of folks who are in Medicaid system have multiple chronic conditions.
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They're dealing with a lot of healthcare issues and their healthcare tends to be pretty expensive.
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So problem that a managed care organization decision maker has.
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They want to keep people as healthy as possible so that they can make more money off of each person.
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Because the state's going to pay them x dollars for the care.
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And if they can make it so that that person, because of excellent preventive healthcare and regular engagement with a primary care doctor and keeping all of their conditions under good control.
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That's a lot cheaper than having to say, pay for hospitalizations and emergency department visits and lots of medications to keep this person stable and functioning.
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But they have a problem because people with multiple chronic conditions wind up using a lot of health care.
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And they tend to use it in pretty expensive ways.
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They wind up in the hospital.
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They wind up in the emergency department.
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Those are high cost situations.
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The other problem they have is that that is a difficult problem for healthcare providers to solve.
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It's not easy to fix this.
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And lots of solutions you know, only work so much.
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So all of this is in the managed care decision makers head.
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This is part of the problem they deal with.
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So now here comes a nutrition focused program that's designed to improve the health of people who are dealing with multiple chronic diseases.
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And to get them more stable so that they are healthier and ultimately don't need as much crisis intervention kind of health care.
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They're better able to manage their own conditions, that they move toward their best health., And so the services are aligned with what the managed care organization wants.
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But the problem is that most people in healthcare don't think of nutrition as having that much to do with health outcomes and with moving the needle on cost.
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So here comes a nutrition program and here are their incredibly impactful data points.
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Their intervention for people with multiple chronic conditions resulted in 70% reduction in emergency department visits, 50% reduction in hospitalizations.
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70% reduction in emergency transport.
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And in the case of a particular subset of a population, 70% fewer skilled nursing facility admissions compared to a group that did not get the intervention.
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Okay.
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Those are big numbers, right?
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That sounds really good.
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And it is really good.
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It's amazing.
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But what does it mean?
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This is the problem.
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What does it mean?
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And how does it connect to the decision makers problems.
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Right.
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We get so excited.
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Oh, my God, 70% reduction.
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Everybody understands how big a deal that is.
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Well, sorta.
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But not completely.
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So what we want to do to make sure the decision maker gets it, is first we talk a little bit to remind them of the problem.
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And we say something like, you know, we share your struggle with the challenge of people with multiple chronic conditions, just having so much difficulty going on in trying to manage all these different conditions and all the impact that each of them has on their ability to function.
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It's certainly no surprise that they have exceptionally high hospitalization rates and emergency room utilization rates.
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And a lot of times they need emergency transport because they go into crisis and then they have to be rushed to the emergency department.
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It's a persistent problem.
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And it's an incredibly costly one.
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A day in the hospital is, and obviously this would vary by market, but let's say these are numbers that are right for the market I'm in.
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One day in the hospital is a minimum$3,500.
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The emergency transport bill's at 500.
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The cost of the emergency department visit is a thousand dollars.
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And if they're admitted, then that's$3,500, if they're there for one day.
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So there's one crisis, of a situation where the multiple chronic conditions got out of control.
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And the person went into crisis and there is an instant bill to you for$5,000.
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For that one episode.
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And how many times does that repeat itself over and over again?
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And every time it does, it's another$5,000.
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And that's if they're back home the next day.
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If they have to be admitted and stay for a few days in order to get them stabilized, then we're racking up additional days in the hospital.
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And pretty soon, this is a$10,000 event.
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And so you take that and you multiply it by the number of people with multiple chronic conditions in our community who are Medicaid recipients.
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And that number is somewhere in the neighborhood of 2000.
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And you know, I'm not sure what your share of those is, but whatever it is, that's a lot of potential expense.
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And I know that you are painfully familiar with how this goes.
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And it's so difficult to break that cycle.
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But.
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We have figured out an intervention that changes all of that.
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And that makes a huge impact on those costs.
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Because what it does Is it helps to stabilize the person's situation.
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and improve enough that they can manage their own conditions more effectively.
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And not wind up in crisis on a regular basis.
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The need for crisis care goes way down because their conditions are well-managed.
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So this relatively inexpensive intervention.
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It's not simple because there are many components to it, but this relatively inexpensive intervention can make a huge change in all of that.
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So let me just tell you how huge that change is.
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Let's say you have 50 people in your pool who are regularly costing you about$5,000 per crisis.
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And let's say they're having three of those a year.
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All right.
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Conservatively that's$15,000 a person.
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Our intervention cuts that by 70%.
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And what that means is,, for every 10 people costing you$15,000 a year in excess costs just for these crisis events..
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Instead of$15,000 a person on average, with this intervention that drops to 4,500.
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That's a net savings to you of over$10,000 per person.
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Every year.
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Just from this intervention.
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Which costs a fraction of that.
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So we bring it down to human scale.
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A number, you can get your head around.
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If you'd ask them to imagine a hundred people it's too many, but if you ask them to imagine 10, they can do that.
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Here's 10 people.
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$15,000 a person.
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Knock 70% off of that, it takes it down to 4500.
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But you've got to do the math for them.
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You can't assume they're going to do the math in their head.
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You do it for them.
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You take them through it.
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And then you can say, so you know, I don't know how many people that represents in your system.
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But if you can save over$10,000 for every one of those.
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That's a big deal.
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And the investment that it takes for this intervention is substantially less than that.
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So it's a win-win.
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People with some of the most challenging sets of healthcare conditions become more stable.
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Their lives are better.
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They're healthier.
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They feel better.
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They cost the system far less and they put less stress on the system.
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And they're able to go back and have a better life.
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So that's how you take data, you connect it to this decision makers problem.
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You bring it to a human scale.
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You don't make any assumptions about them doing any math in their head or them understanding why this matters.
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You walk them through it.
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And you connect the dots again and again, back to how this translates to solving the decision-makers key problem.
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Okay?
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So that's a really drawn out example of how you would do that.
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Let's take one or two more.
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So here's a housing program that I'm familiar with.
00:18:51.218 --> 00:18:54.038
And one of the services that they do is rapid rehousing.
00:18:54.788 --> 00:18:58.508
They have some amazing data from their rapid rehousing folks..
00:18:58.964 --> 00:19:10.917
But probably the most impressive thing that they can say is that 99% of the families who exit their rapid rehousing program remain stably housed.
00:19:11.657 --> 00:19:13.008
That's unheard of.
00:19:13.038 --> 00:19:15.018
That's very nearly a hundred percent.
00:19:15.821 --> 00:19:17.771
Now rapid rehousing works.
00:19:17.922 --> 00:19:21.281
So nationally, the data's pretty good.
00:19:21.582 --> 00:19:29.231
Nationally it's about 80 to 90% will stay stably housed depending on how you measure it.
00:19:29.837 --> 00:19:31.067
But 99%.
00:19:31.738 --> 00:19:32.397
That's crazy.
00:19:32.842 --> 00:19:35.902
So they've got a really good piece of data there.
00:19:36.701 --> 00:19:38.238
But here again.
00:19:38.538 --> 00:19:40.367
So whoever their decision maker is.
00:19:40.367 --> 00:19:52.332
Let's say it is the local housing authority and the organization is arguing to the local housing authority, why they should have more resources for their rapid rehousing program, because it's so effective.
00:19:52.991 --> 00:19:56.251
So what's that decision makers problem.
00:19:56.551 --> 00:20:04.115
They've got to make decisions about, there's a bunch of different rapid rehousing providers, all of whom want resources.
00:20:04.145 --> 00:20:13.140
They have city council members to answer to who are really upset about there's homeless encampments all over the city.
00:20:13.650 --> 00:20:17.739
There's nowhere near the supply of affordable housing that there needs to be.
00:20:18.098 --> 00:20:27.951
There are just so many struggles around helping individuals and families who are unhoused become more stable.
00:20:27.951 --> 00:20:34.551
And they've got pressure from the city council because when people are unhoused, there's all sorts of other cascading problems.
00:20:34.551 --> 00:20:39.021
There's impact on their health, there's impact on their ability to hold a job.
00:20:39.382 --> 00:20:41.163
It's just problem after problem.
00:20:41.828 --> 00:20:45.615
And so they know that rapid rehousing is effective.
00:20:45.996 --> 00:20:49.660
But they have a lot of competing problems in their head.
00:20:50.170 --> 00:20:52.912
And they're worried by all of them.
00:20:53.362 --> 00:20:58.898
And it would be easier to just like, say there's five rapid rehousing programs in the city.
00:20:58.898 --> 00:21:00.929
Let's just give the same amount of money to all of them.
00:21:01.173 --> 00:21:02.972
And then I could go think about something else.
00:21:03.633 --> 00:21:05.613
Because I am overwhelmed by all this other stuff.
00:21:06.306 --> 00:21:09.185
Overwhelm in and of itself is a problem.
00:21:09.935 --> 00:21:12.395
Decision fatigue, problem fatigue.
00:21:12.425 --> 00:21:13.836
Those are all problems.
00:21:14.105 --> 00:21:17.885
So this organization could choose to go at it this way.
00:21:18.336 --> 00:21:21.276
They could say, you know, we understand.
00:21:21.276 --> 00:21:27.766
We've been working in the housing space a long time and we get how complex this problem is and how challenging your job is.
00:21:27.766 --> 00:21:29.685
That this is just really hard.
00:21:30.145 --> 00:21:41.425
And so we're excited to share with you a couple of exciting developments about how we have been able to address not just one, but several of the problems that you're dealing with.
00:21:42.189 --> 00:21:45.249
We all know that rapid rehousing is generally pretty effective, right?
00:21:45.853 --> 00:21:51.278
A lot of people who enter rapid rehousing do wind up being able then to move into stable housing and stay.
00:21:51.638 --> 00:21:54.429
And then the rest of their life is more able to stabilize.
00:21:54.669 --> 00:21:54.939
Right?
00:21:54.939 --> 00:21:55.808
So this is great.
00:21:56.452 --> 00:21:57.202
Nationally.
00:21:57.202 --> 00:22:04.368
It's typical that about eight out of 10 families will manage to stay in stable housing once they've left rapid rehousing.
00:22:05.038 --> 00:22:15.222
In our world, with the quality of wraparound services that we provide, we have been able to take that and make it be virtually 100%.
00:22:15.732 --> 00:22:20.373
It is rare for any family to leave our rapid rehousing and not remain in stable housing.
00:22:20.613 --> 00:22:25.232
And the reason for that is the quality of the housing, but it's also the quality of all the wraparound services.
00:22:25.262 --> 00:22:32.873
We have figured out the recipe that makes it possible to have virtually 100% success.
00:22:33.875 --> 00:22:37.685
Let me tell you about the ingredients that go into that and why it's different.
00:22:38.346 --> 00:22:41.046
And here's how the family in rapid rehousing experiences that.
00:22:41.645 --> 00:22:47.388
And one of the huge values that we are able to bring is that we also are a healthcare organization.
00:22:47.449 --> 00:22:51.648
We do both because we see the connection between those two things.
00:22:51.949 --> 00:22:56.653
And so one of the most impactful things we're able to do is connect folks to regular healthcare.
00:22:57.192 --> 00:23:02.353
And we have an incredibly strong, supportive case management staff.
00:23:03.093 --> 00:23:21.093
We invest the extra time, we invest the extra attention to make sure that everybody involved is getting what they need so that they can be as stable as possible that they can have all the ingredients for success to stay in stable housing once they leave our rapid rehousing program.
00:23:21.923 --> 00:23:26.692
We know that our approach is unique in the city, and it is really fairly unique nationwide.
00:23:27.113 --> 00:23:28.655
And our results show that.
00:23:29.409 --> 00:23:38.252
And if there's a choice between having two of every 10 families not make it, when we have the ability to have all of them make it.
00:23:38.709 --> 00:23:44.278
It seems like a pretty good choice to go with a set of services that's going to make sure that all of them are going to make it.
00:23:45.046 --> 00:23:46.066
Something like that.
00:23:46.702 --> 00:23:48.623
Again, bringing it to a human scale.
00:23:48.893 --> 00:23:50.663
99% is super cool.
00:23:51.083 --> 00:23:52.192
But it's a percentage.
00:23:52.192 --> 00:23:53.153
It's abstract.
00:23:53.932 --> 00:23:56.393
So words like virtually all.
00:23:57.393 --> 00:23:59.163
People do understand a hundred percent.
00:23:59.516 --> 00:24:01.556
They do know that 100% means all.
00:24:01.796 --> 00:24:04.405
So you can use that, but anything else?
00:24:04.865 --> 00:24:09.088
It's better to break it down to here's 10 of these things.
00:24:09.566 --> 00:24:11.256
Eight of them are like this.
00:24:11.586 --> 00:24:13.476
But in our case, it's 10 out of 10.
00:24:13.986 --> 00:24:14.675
It's all of them.
00:24:15.113 --> 00:24:17.452
And why would you choose eight when you could do 10?
00:24:18.482 --> 00:24:20.913
And it makes no assumptions.
00:24:20.962 --> 00:24:26.425
It doesn't assume that the decision maker will do any of that math or that they'll understand why it's different.
00:24:26.425 --> 00:24:27.506
So you have to tell them.
00:24:28.323 --> 00:24:34.532
Now you have to make a judgment call about how deep you go into describing how you get your exceptional outcomes.
00:24:35.202 --> 00:24:40.413
Generally, it's a good idea to start out by saying briefly what's different about your approach.
00:24:40.833 --> 00:24:51.959
Our comprehensive services are of such high quality and they so thoroughly take into account all of the factors that impact success or failure for this family.
00:24:52.346 --> 00:24:56.246
And we have covered all the bases and that's why we have the success rate.
00:24:56.296 --> 00:24:58.006
We don't leave anything out.
00:24:58.006 --> 00:24:59.415
We don't leave anything to chance.
00:25:00.276 --> 00:25:05.496
And with that extra comprehensive support this is what we're able to accomplish.
00:25:06.526 --> 00:25:07.935
So you're setting yourself apart.
00:25:07.935 --> 00:25:16.135
That doesn't mean that you go into a detailed accounting of exactly how you do your case management and exactly how you do your counseling and this, that, and the other.
00:25:16.375 --> 00:25:20.816
Those are all details that you I'm sure are ready to supply, if asked.
00:25:21.586 --> 00:25:29.828
But if you've got a powerful piece of data and you have just connected it to the decision makers problem and shown them how it's going to solve their problem.
00:25:30.719 --> 00:25:34.378
Give them a chance to get excited about that.
00:25:35.002 --> 00:25:40.462
Once they do, if they have questions about how are you able to make this happen?
00:25:40.462 --> 00:25:42.353
What is different about what you do?
00:25:42.353 --> 00:25:45.202
Why do you have these results and no one else does?
00:25:45.502 --> 00:25:49.163
Then you can go in one layer of detail at a time.
00:25:49.976 --> 00:25:55.105
But feed them the first layer of detail and see if they want more.
00:25:55.615 --> 00:26:00.455
It might be enough for them to hear we don't leave anything to chance.
00:26:00.913 --> 00:26:11.326
We have identified the seven factors or whatever it is that are the most significant in determining whether a family is successful in staying in stable housing.
00:26:11.796 --> 00:26:15.546
We have made sure to address every one of those seven factors.
00:26:16.036 --> 00:26:20.496
And we have highly coordinated services so that, nothing is siloed.
00:26:20.736 --> 00:26:23.316
There's no chance of somebody falling through the cracks.
00:26:23.762 --> 00:26:26.972
And we are laser focused on that family success.
00:26:27.776 --> 00:26:28.766
And then stop.
00:26:29.353 --> 00:26:33.073
They may not need to hear anything more about how you get this done.
00:26:33.903 --> 00:26:36.336
This is a really good technique to use, by the way.
00:26:36.846 --> 00:26:47.338
If you take the time to identify what the key factors are that are essential ingredients for success that cause you to have the exceptional outcomes that you do.
00:26:47.818 --> 00:26:49.593
And break those out.
00:26:50.282 --> 00:26:52.628
And then once you've done that, count them up.
00:26:52.962 --> 00:26:55.885
If it's more than seven or eight, group them.
00:26:56.256 --> 00:27:00.276
Cause you don't want to say, well, we have a 27 step process.
00:27:01.205 --> 00:27:02.915
Three to seven is the sweet spot.
00:27:03.588 --> 00:27:09.239
And then obviously be ready to explain what those steps, or those factors are, ingredients for success.
00:27:09.689 --> 00:27:16.919
But to simply say we have identified seven specific factors that determine success.
00:27:17.769 --> 00:27:21.398
And we make sure that we address every single one of them and we leave nothing to chance.
00:27:21.818 --> 00:27:23.318
And we are highly coordinated.
00:27:23.318 --> 00:27:24.219
We bring it all together.
00:27:24.219 --> 00:27:25.838
We don't let anyone fall through the cracks.
00:27:26.368 --> 00:27:30.215
That alone is very likely not something they've heard from anyone else.
00:27:31.002 --> 00:27:35.292
They may not remember that you have seven steps versus six.
00:27:35.883 --> 00:27:42.058
What they will remember, because you identified a specific number of steps or ingredients.
00:27:42.566 --> 00:27:46.138
They will remember that you have something unique that sets you apart.
00:27:46.919 --> 00:27:53.608
That you've gone to the extra trouble to analyze this problem and break it apart and make sure that you're addressing all the pieces.
00:27:53.878 --> 00:27:55.679
That's what they're going to take away from that.
00:27:56.808 --> 00:28:00.858
So those are a couple of really good examples that will help you understand how this works.
00:28:01.199 --> 00:28:03.449
We could do more, but I think this gives you the idea.
00:28:04.075 --> 00:28:05.377
So to recap.
00:28:05.768 --> 00:28:12.478
You want to bring the decision makers problem vividly to life as part of your story, your narrative.
00:28:13.198 --> 00:28:17.892
And take your data that most tightly connects to that problem.
00:28:18.484 --> 00:28:21.815
What will make your data compelling is that it does tie to the problem.
00:28:22.238 --> 00:28:24.367
You could have all sorts of other cool data.
00:28:24.367 --> 00:28:25.597
You probably do.
00:28:26.117 --> 00:28:32.867
But that may or may not be relevant to share with that decision maker, if it does not tie directly to their problems.
00:28:33.468 --> 00:28:35.897
In fact, it may just clutter the environment.
00:28:36.498 --> 00:28:38.694
So begin with that.
00:28:39.248 --> 00:28:48.617
And as I said, if you have a decision maker who is an elected official, be ready to be able to tell them how many people you serve from their district or what have you.
00:28:49.142 --> 00:28:51.301
Because that does matter very much to them.
00:28:52.142 --> 00:28:56.971
But beyond that, you're looking to, what are the other problems that decision-maker is worried about?
00:28:57.001 --> 00:28:59.162
And then you are tying your data to that.
00:28:59.701 --> 00:29:01.801
You're bringing it down to a human scale.
00:29:01.801 --> 00:29:05.582
You're explaining it in a way that it is not abstract at all.
00:29:05.582 --> 00:29:06.751
It is concrete.
00:29:07.291 --> 00:29:09.541
You tie it to actual people.
00:29:09.751 --> 00:29:14.912
You tie it to small numbers of people, manageable numbers that people can get their head around.
00:29:14.942 --> 00:29:16.892
They can picture in their mind.
00:29:17.561 --> 00:29:29.134
You're creating multiple ways in which the numbers in your data become a vivid clear image in the decision maker's mind.
00:29:29.592 --> 00:29:30.758
That has meaning.
00:29:31.327 --> 00:29:37.694
And the way it has meaning is how it either helps the people that they're responsible to, or for.
00:29:38.414 --> 00:29:43.471
And how it solves one or more problems that they are struggling with.
00:29:43.892 --> 00:29:50.724
And if you tie your data to those things it will reach the decision maker.
00:29:51.494 --> 00:29:53.204
And then they will get engaged with it.
00:29:53.204 --> 00:29:58.157
And then you can go on and explain what it is that you need from them, and get to that part.
00:29:59.327 --> 00:30:04.637
I know that you have awesome data and I want it to hit the decision makers the way you want it to.
00:30:05.352 --> 00:30:12.672
And the way you have to do that is communicate it in ways that basically make your data be all about the decision maker.
00:30:12.672 --> 00:30:14.172
That's the bottom line.
00:30:14.652 --> 00:30:17.397
And give it to them in terms that make sense for them.
00:30:17.907 --> 00:30:19.798
Connect every dot for them.
00:30:19.827 --> 00:30:21.508
Don't make them do any math.
00:30:21.718 --> 00:30:23.847
Don't make them think about percentages.
00:30:24.028 --> 00:30:29.188
Don't make them connect from A to B to C to reach conclusion D.
00:30:30.028 --> 00:30:32.577
Walk them through it piece by piece.
00:30:33.417 --> 00:30:39.147
And do it with a story so that it's engaging and interesting and makes sense and connects back to the problem.
00:30:40.137 --> 00:30:53.251
When you do that you will have decision makers not only getting excited about the impact that your data represents, but they're going to want to talk to you about how can we make this impact be even bigger?
00:30:53.771 --> 00:30:58.718
How can we get more people enrolled in this thing that gets such amazing outcomes.
00:30:59.827 --> 00:31:02.887
Because this obviously is really great and works really well.
00:31:03.218 --> 00:31:04.268
That's what you want.
00:31:04.711 --> 00:31:05.612
That's your goal.
00:31:06.061 --> 00:31:12.991
So get out there and start building messaging that gets decision-makers as excited about your data as you are.
00:31:13.872 --> 00:31:14.801
Thanks for listening.
00:31:15.172 --> 00:31:19.132
And I'll see you in the next episode right here on the Nonprofit Power Podcast.
