Intro: The following SHSMD Podcast is a production of DoctorPodcasting.com.

Bill Klaproth (Host): On this edition of the SHSMD podcast, we're talking about how healthcare marketing teams can better measure campaign effectiveness and ROI. Isn't it something we all want? Yes, it is. So, you know what we're going to be talking about? That's right, we're going to be talking dashboards, data transformation, data modeling, and how to set up your campaigns for success with Jason Miller, Executive Director of marketing insights and analytics; and Apoorva Gupta, Senior Digital Marketing Analyst, both from Johns Hopkins Medicine. It's all coming up on this SHSMD Connections Preview—that's right—rght now.

This is the SHSMD Podcast, Rapid Insights for Healthcare Strategy Professionals and Planning, Business Development, Marketing Communications, and Public Relations. I'm your host, Bill Klaproth. Jason and Apoorva, welcome.

Jason Miller: Thank you, Bill. Thank you so much for having us.

Apoorva Gupta: Thank you, Bill.

Host: Yeah, great to talk with both of you. I know this is going to be a great session at SHSMD Connections coming up in Baltimore. Jason, let me start with you. So, your workshop is called Essentials of Measuring Marketing Campaign Effectiveness and ROI in Healthcare. So, question for you, why is it so important right now for healthcare marketing teams to move beyond basic activity metrics and start measuring outcomes like patient acquisitions, appointments, and revenue?

Jason Miller: That's a great question, Bill. And I think at this time, the evolution of marketing and measurement has continued to evolve. And many times marketers and the marketing team members and staff involved have measured those standard metrics for many years and they've had access to them.

But as it continues to evolve in healthcare, both leaders of the healthcare organizations want to move beyond those standard metrics and really understand the ROI of their investments in any marketing initiatives, and the bottom line impact that they have to the healthcare organization.

And as data becomes more readily available and the tools continue to advance, especially in this era of AI, I think most leaders will absolutely want to measure and have impact noted on any marketing initiatives that they have set off within their organization.

Host: Absolutely. As you said at the beginning, people want to know where the money is going and how is it working. Is it working or isn't it working? So, I think that's really important. And good point about AI. With AI, it should give us more tools that we should be able to measure these things. Would that be right, Jason?

Jason Miller: Yeah. Absolutely. AI continues to evolve. And it can aid us in developing and connecting data sets that maybe we previously had not had the time or programming time to connect. So, it gives us ways to use those algorithms, just as many AI prompts have today to give you answers quickly. That is the hope that that continues to evolve and applies in the marketing space as well.

Host: Yeah. And Apoorva, a lot of campaign measurement starts long before the reporting dashboard, like making sure you get it set up properly. So, what are some of the most important campaign setup steps across platforms and tools that teams need to get right so they can measure performance accurately?

Apoorva Gupta: Sure, Bill. So if you want to pull this off, there are really, I feel, five things you have to lock down before you spend a single ad dollar. First thing to start with would be get your campaign naming on lock. So, standardize all your UTM parameters in search, social, and every single click traces back to exact clinical service line.

Second here would be to capture offline site, like use dynamic call tracking and even hidden form fields. So, phone calls and web forms keep their digital attribution in place. And then, third would be—this one is huge to protect patient privacy—so, making sure that you're not tracking any PHI, PII, hashing out and tokenizing all your first-party identifiers like phone numbers, emails before they touch databases. And then, fourth would be to map our ads that directly link to these clinical care lines. And then, we would want to track real patient care pathways, not just generic web traffic. And then, finally, testing and plumbing and making sure that all these pipelines, which is test call, form fills before we launch, making sure that the data actually flows into click to staging and ready for EHR matching, which we do using our Epic data platforms.

Host: Okay. So, that's a great list, Apoorva. So, standardized, you said, UTMs across search and social, so everything is standard. Make sure you're capturing all of that offline activity. Three, always protecting patient privacy. That is so important. Four, make sure you're mapping the ads that are directly linked to that clinical care line. And then, as you said, testing the pipeline to make sure that all the data flows very properly. That's a great list, Apoorva. So, thank you for that.

So Jason, one of the big challenges in healthcare is connecting leads or prospects to systems like Epic in a HIPAA-compliant way. We're just talking about patient privacy. What should marketing leaders understand about that process, and where do organizations often get stuck?

Jason Miller: I think this ties right into what Apoorva stated previously, is it all starts in the beginning. And the key aspect is getting the naming conventions of your campaigns and your UTMs set up correctly so that you can track it along that patient's journey as they may travel from different systems and tracking mechanisms from your website to web form, all the way to when they schedule an appointment at your healthcare organization and receive services.

So, I think that's the key starting point, and you definitely want to track many sort of demographic and attributes about a patient or consumer that is possible that falls within HIPAA and privacy guidelines. And again, that's why that naming convention and attribution is so important per campaign so that you can then use those lists and attributes to match to your EMR and determine if a match occurs.

You know, for example, you have a campaign that's cardiology related. You target specific patients and geographies. Those patients then fill out a web form. Once they complete the web form, you'd have those patient demographics information that you would then want to utilize to match to your EMR and see if some of those patients actually came in for services related to cardiology at your organization.

And I think often organizations do get hung up in just starting and, again, that attribution and naming. And if you don't do that correctly upfront, you often have challenges attributing those patients back to a specific campaign. And then, is it a new or return patient? Often patients within health systems come back for many services. So, how do you attribute it just to that specific service or campaign?

So, that's another area where a lot of organizations maybe get hung up or challenged when you talk to different service line leaders. That patient was already with the organization. But often, again, they're targeted for a different service.

Host: So, I know you're going to go in depth on all these things, right, in your session. So, we're just kind of touching the surface here. But these are really important things you're talking about, and I know you're going to discuss them more in length. So if you're like, "Man, I'm liking what Jason and Apoorva are saying," make sure you go to the session, because they're really going to get down deep into the weeds on all this stuff to help you out.

And I know, Apoorva, your session also focuses on data transformation and data modeling, other things you're going to talk about. So for non-analytics leaders listening, what does that mean in practical terms, and why does it matter for measuring campaign effectiveness?

Apoorva Gupta: So, both are very important ingredients in the recipe that we are trying to cook over here. In simple analogy, I would say data transformation is like prepping ingredients. So, raw data comes from all over the place, different ad platform, phone systems, clinical databases. And it's usually very messy and less formatted and full of different languages.

So, data transformation, I would say, is basically like washing, chopping, and prepping your raw ingredients before you start cooking. So, you're basically standardizing these phone numbers, cleaning up campaign names, hashing any sensitive information into a clean as well as a very uniform format so that you can actually be ready to cook with it.

And then, on the same time, I would say data modeling in simple analogy would be architecting this blueprint. How is it going to look like? So once your ingredients are prepped, you need a blueprint to build this meal. So, data modeling is like designing how all these separate pieces of clean data talk to each other. It's creating that structure, those rules. For example, like this ad click connects to this particular phone call or this form fill connects to this particular person who might have filled it, which links to this patient encounter and which connects to this net margin that we are trying to calculate here. So, it turns scattered prep data into a very clear map of customer journey and patient journey.

Host: Yeah, I love that analogy. Data transformation is like prepping ingredients.

Apoorva Gupta: Yep. Yeah.

Jason Miller: If anyone that's worked with healthcare data, they understand some of the challenges. And healthcare organizations are very complicated, and often it's the processes that create challenges with the underlying data from the systems. And that's why it requires a lot of transformation often to what we maybe cleanse and have it ready to actually build models off of to either measure or predict services. It's just often challenging and I think that some folks don't understand just how much effort and time has to go into some of those processes. And then, once you've started to build processes and cleanse, you can scale them more broadly. And I think that's a key aspect of something we want to highlight is creating scalable processes that are not only just for one service line. It could be for N number of service lines. However, you build your data models out for measurement of your campaigns, you want to allow them to scale so that you can scale across your entire organization.

Host: Well, it seems like that's such a necessary step, the data transformation, is getting all that data into the forms and the way you want it, like Apporva was talking about. Because if you don't get that step right, it's kind of garbage in, garbage out then. Is that right?

Jason Miller: Absolutely. That's definitely correct. And that's part of why the focus is to almost map out all of your data sources for whatever campaign you're measuring, whatever platforms you have within your organization, and then to understand how can you set up the proper tracking and attribution in each one of those systems along that patient journey. And then, you can understand how you're going to get access to some of those systems to pull that data, extract it out, and what mechanisms will you need to perform to cleanse it, transform it, so that you have the ability to connect those disparate sources together.

Host: Right. So once you cleanse it, you prep the ingredients, then you connect it into basically the data modeling. So, those two go hand in hand, would that be right?

Apoorva Gupta: Yep. Just to summarize, I would say that if data transformation is taking raw, messy data from these different sources and translating it into clean language, data modeling, on the other hand, is taking those clean pieces and building the blueprint so they all fit together into this reliable ROI story that we try to present.

Host: So now, you've got the clean data, then time for the data modeling, and that's where you ultimately get the results.

Apoorva Gupta: Yep, absolutely.

Host: So, could we say if we're prepping the ingredients, then we're cooking in data modeling? And then, when it's all finished, we have all the information and the data to help us make more informed decisions?

Apoorva Gupta: Absolutely. Yeah.

Jason Miller: Correct.

Host: You must be a good cook, Apoorva. I mean, I like how you did chopping, the cutting, the washing. You guys, it was great. You got it all in there.

Apoorva Gupta: Yeah. I mean, I love cooking.

Host: Well, you put it in a way that was easy to understand, so I love that. So Jason, let's talk dashboards a little bit more. They can either clarify performance or overwhelm people. And you know this, and it's sometimes it's like, "Oh my God, okay, I love this dashboard. What do I do with it?" So, how do you decide which metrics belong on a marketing ROI dashboard? And this is the big one to me, how do you make those reports actionable for leaders? How can we actually make the informed right decisions from the data?

Jason Miller: This again is a challenge. There's so much data that we're working with. And when you build dashboards, it's easy to start putting different metrics out there, different visualizations. And I think it all comes back to what are the questions you're trying to answer? So, framing it from that perspective and putting yourself sort of in the end user or whoever is going to be the recipient of those dashboards in the audience. Is that, you know, a senior leader? Is it a marketing manager, et cetera? You need to make sure the metrics that you put on there help them answer the questions that they're trying to answer, and that it's framed in a way that's easy for them to digest and understand.

And again, it varies depending on the audience from a marketing standpoint. And often we always stay rooted in the funnel of understanding of each stage as a patient or consumer moves through that funnel, from the awareness stage to consideration to actually converting and becoming a patient. And we want to have metrics that yield results at each one of those stages of the funnel. The most important is coming all the way down to the bottom of the funnel and understanding did that patient convert, because that helps you better understand the bottom line performance of your campaign, any investment you've made in an ad spend.

But you can again measure it at each stage and look at different metrics and depending who's involved in the workflow and the process, maybe the marketers want to just see, are we generating more leads? But maybe the service line leaders want to know, are we seeing new patients? Are they coming in for the service? Because that's helping keep our physicians busy and helping with the bottom line impact to some of the revenue targets we've set.

Host: Yeah. So, there can be multiple questions that you're trying to answer along that funnel, if you will. So, I think that's so important. Isn't it a great moment, Jason and Apoorva, when you discover something, when that dashboard comes back and it either confirms something that you're like, "Aha, I knew it," or it gives you insight like, "Well, maybe I was wrong on that"? I mean, that's a great feeling, right? When you do all the data transformation, the data modeling, you get it out there and you get the answers back. That's a great moment, isn't it, Jason?

Jason Miller: Oh, absolutely. And when you're able to tell a story from the data that's very compelling, it's very rewarding to know that all that work, and although it may look like very little on the surface, there's a lot behind the scenes that goes into gathering data, cleansing it, and getting the output to answer the various questions.

But it is definitely rewarding to see difference to that depending on the audience, whoever you're producing data and metrics for, they can yield value out of what you've produced and continue to use it. That's another key fact that they continue to use it to evolve and keep informed decision-making and take action with what's being presented to them.

Host: And then, refine it even more, right? Apoorva, then you're going to go back in. What'd you see what we learned? Okay, let's refine this a little bit more now, right?

Apoorva Gupta: Yeah, definitely. I feel like honestly it feels game-changing. For a long time, even here, we were not able to do that. But for marketing and healthcare, it has been seen as cost center and you spend budget, hope it drives patient volume, and you have to prove to your senior leaders that what marketing is doing is actually working.

So, with numbers, it becomes like finishing a report like this changes entire conversation. And especially for our marketing team members walking into a room with finance and executive leadership and showing that the ROI we've earned after spending XYZ amount, that transforms marketing into a clear growth driver for health system. And there's no like, "Okay, why are we doing it? Can you prove it is working or not? All those questions get answered by looking at those numbers instantly.

Host: Yeah, I like how you said it can change entire conversations.

Apoorva Gupta: Yep.

Host: Right? Once that data comes back and be like, "Oh man, we were talking about this. We should be talking about this." So, what a great point that is. Well, I want to thank you both for your time today. This has been awesome. One more question I'm going to ask each of you the same question. I would like to get each of your individual insights on this. Jason, let me start with you. So when it comes to proving marketing's impact on business growth, very important, what is the most important thing healthcare marketers need to know?

Jason Miller: This is really multifaceted, Bill. It's not just one sort of metric, and I think—

Host: Cliff notes. Give me the cliff notes.

Jason Miller: Yeah, I think to summarize it, it comes back to what we've discussed is you've really got to do your due diligence upfront with understanding your market. Do a market analysis with whatever data sources you have to understand which market you're targeting.

Then, you want to set up your campaigns and have all the right naming conventions set up correctly so you can track and attribute to measure along the funnel. And then, have really strong ROI reporting so that you can justify the marketing spend that you may be putting forward for any specific campaign and understand that impact, the bottom line impact.

As marketers and healthcare continues to evolve, I think the latest metric is lifetime value, and I think that's going to continue and something we aspire to have a very strong figure on to understand for every patient we bring in for certain service, it generates dividends in various specialties and areas to the entire organization. And we really want to get and measure that. And I think that is sort of the holy grail, so that you understand for every new patient. And other industries do this really well as, for certain products, they know they're going to display and that's how they display ads and other products that recommend because they know other shoppers that are purchasing similar products.

And I think healthcare, we haven't got there yet. But I think as data and everything continues to evolve, we will be able to measure and have that similar approach.

Host: Well, knowing that metric, that lifetime customer value really, really important. So, good point, Jason, on that. Apoorva, how about you? What is most important for healthcare marketers? What do they need to know now?

Apoorva Gupta: I just feel first it moves you from cost center to a true revenue driver. They definitely need to know that. So, you are no longer like depending on ad budget. You are showing leadership exactly your financial returns with this. And then, secondly, I feel like it takes away the guesswork. So, there's like no more awkward meetings where people are asking, "Okay, but did those clicks turn into real patients?"

So, like Jason mentioned, the math is right there on the screen. And honestly, it's just pure relief connecting marketing, finance, IT into one pipeline. It's a puzzle that stops most teams in their tracks. So, crossing that finish line with just solid data behind you is a huge victory for us.

Host: Yeah, very well said. Well, this is going to be a great session at SHSMD Connections. Jason and Apoorva, thank you so much for your time today.

Apoorva Gupta: Thank you, Bill.

Jason Miller: Thank you so much, Bill. We really appreciate the opportunity, and we're looking forward to presenting at SHSMD.

Host: Absolutely. It's going to be a good one. So, thank you again. Once again, that's Jason Miller and Apoorva Gupta. And if you want to see their session at SHSMD Connections, make sure you register at shshmd.org/education/annualconference. And if you found this podcast helpful, please share it on your social channels, and please hit the subscribe or follow button to get every episode.

And to access our full podcast library for topics of interest to you, visit shsmd.org/podcasts. This has been a production of DoctorPodcasting. I'm Bill Klaproth. See ya!