The Strategists’ Corner: MMM is a powerful tool, but not a silver bullet

Announcer: Welcome to the Ignite Podcast, the only healthcare marketing podcast that digs into the digital strategies and tactics that help you accelerate growth. Each week, Cardinals experts explore innovative ways to build your digital presence and attract more patients. Buckle up for another episode of Ignite.

Ben Dutter: All right. Welcome. This is Ben and Rich. Once again, another episode of the Ignite: Healthcare Marketing Podcast. We’re talking all things strategy in this series. We call it the Strategist Corner mostly because we’ve got two strategists sitting in corners. I’m Ben, chief strategy officer of Power Digital. I’m joined by my co-host, Rich Briddock here, who’s CSO of Cardinal Healthcare and also leads a lot of our healthcare marketing at Power Digital. Rich, welcome to the show again as always.

Rich Briddock: Thanks for having me, Ben. It’s so refreshing to be a guest now.

Ben: I know. I get to flip the hat.

Rich: Sit on the other side. Yes. I love this.

Ben: It’s fun. It’s fun. Well, today I think we want to talk, and in terms of strategy, a really important part is measurement and media allocation. I think you measure your paid programs and your marketing programs to understand what to do more of and what to do less of. One of the very common measurement vehicles that we’ve talked about in previous episodes and is really popular in the industry right now are three of my favorite letters, MMM, or a Marketing Mix Model or a Media Mix Model. Rich, for the uninitiated, why don’t you tell us a little bit about that?

Rich: It’s interesting, MMM. It’s gone from initially like a buzzword to almost like a mythical famous in the world of marketing, especially for those industries like healthcare where data is not as plentiful. It’s a level of sophistication that everybody is striving to get to this gold standard. It’s a predictive model that is trying to– and you can also probably describe it a little bit more eloquently than I can, Ben, but essentially, understand the effect that each channel is having in the marketing mix, and then using the historical data and using that model to then predict what you will drive with shifts in your media mix.

Even if you keep your media mix the same, but just based on things like the seasonality indexes that it’s ingesting, it’s essentially not just a more accurate or a prescriptive model of measuring through a different lens, but also it’s got this predictive element, which is why I think it’s so fabled and people love it so much is because they see it as this magic forecasting tool that’s going to tell them everything that they should do and how much money they should take out of Google and stick into meta and how much money they should take from channel A to channel B.

It’s something that everybody wants. There are obviously some limitations. The old adage is all models are wrong. It’s just that some models are useful. I know in this podcast, we’re going to get into some of the challenges around using MMM, but I think we like to think about it’s a fantastic measurement solution as part of the measurement toolbox if you are eligible for it, if you have the right amount of data, if you have the right amount of spend, if you don’t have huge amounts of volatility in your business, it’s going to make the modeling difficult.

Especially in a world where you may have pixel-based tracking constraints with HIPAA, the inability to produce ad platforms to sign BAAs that require you to remove some tracking from the website. It’s a great solution that sits inside of a broad measurement approach.

What it is not is probably a, “Let’s abandon every other way that we’ve traditionally done measurement and solely rely on MMM,” because what you’ll quickly find is you don’t have the level of granularity and detail that an organization is often looking for in order to make the right thing across the board if you just put all your eggs in that single basket.

Ben: Yes, for sure. I think most of our audience on this podcast are coming with a healthcare lens, so I’d love to talk a bit more around what are some common challenges that folks in that industry face when trying to deploy MMM? You mentioned HIPAA. You mentioned pixel tracking. What are some common breakdowns or limitations or strengths that the healthcare marketing ecosystem can take better advantage of with MMM?

Rich: Yes, it’s getting your hands on the data to begin with. The good thing and the bad thing about MMM is that you don’t need any website event data. You don’t need any pixel data, which is a plus column for a lot of compliance-constrained clients. You can use encounter volume, new patient volume. You could use patient revenue. There is a whole lot of downstream activity that you can use to feed the model and build that model off.

Now, what you need, though, is you need ideally 22, 24 months of clean data, and you need a certain amount of data in order to get an accurate model, even at the channel level. There’s different points of view on this, and depending on what kind of MMM solution you leverage, different levels of sensitivity. If you’re spending less than probably $60,000 to $100,000 a month on paid media, MMM is probably not a solution for you. Also, if you are typically spending pretty lower than that, oftentimes your strategy may only be activating on one channel. If you’re only on search-

Ben: Much of a media mix to [crosstalk]

Rich: MMM isn’t going to tell you all. Exactly.

Ben: That’s right.

Rich: Clients, when they start to diversify, there’s also this thought process of, okay, now we’re on Meta, now we’re on programmatic display, let’s get MMM into the fold. The issue is if you don’t have 22 months of data on Meta, if you don’t have 22 months of data on TikTok or programmatic display, the model isn’t going to do a great job of understanding the contribution that those emerging channels that you’ve just added to your mix is making. It’s not a case of, “I just got on these channels. Great. Let’s go ahead and get MMM in place.”

You have to be running these channels for a while if you want that model to give you a pretty accurate readout of what the contribution, what the effect share is of those different channels. Those are some of the challenges. I think the other major challenge is very seldomly, this is where you can slap my hand and tell me that I’m wrong, unlike, I think, some of our econ clients or even some of our B2B– well, B2B is a challenge with MMM as well, but some of our lead-gen clients where the lead is the lead, or a sale is a sale.

What we see in the healthcare world is invariably there is some level of segmentation or fragmentation, some granularity layer that clients want to and must look at their data at. If that is a location or a service line, we have that level of fragmentation. Oftentimes, even separate service line owners or separate location owners, regional owners, and they’re like, “Well, I just want to see what’s going on in this market,” or “I just want to see what’s going on with primary care or with OBGYN.” Most of the time, there is not that level of data to derive that insight from a model via MMM. You’re just not-

Ben: [crosstalk]

Rich: -going to get it, because don’t support it enough, don’t spend enough, don’t have enough encounter volume or enough new patient volume when you get down to that split. That’s where MMM, again, I go back to the point of, it is fantastic, and it does something that only it can do in terms of a lot of the predictive stuff, but it has to be used in conjunction with other measurement strategies if you want to get down to that level of detail that goes beyond just, “Do I need to spend more or less in paid social next quarter?” which is an absolutely legitimate question to answer.

Then you’ve got to figure out the next question is where. If I’m going to increase my budget from $20,000 to $50,000 in paid social, MMM is not going to answer where you then distribute those dollars that you’re adding into that channel. I know that this is the topic of this discussion. MMM is not the silver bullet that replaces everything else that you’re doing or that comes in and immediately solves all of the organizational measurement challenges at every layer of granularity that your business is trying to measure, report out, and optimize against.

Ben: I love the saying, “There’s no such thing as silver bullets, just lead bullets.” Everything is just a tool. It’s not the tool. You’ve got to figure out where it fits into the toolbox. Totally agree. The retail side, which is more where my area of expertise lives outside of healthcare, you’re absolutely right. There’s faster consideration cycles for most of these purchases, not all. If I see a shirt and I want the shirt, I buy the shirt in a week.

That’s a relatively quick cycle compared to maybe I’m contemplating healthcare treatment for years potentially. In my case, Rich, on a previous episode, we talked about those of you who are just listening to the audio, I have classes. I’ve talked a lot about when, if ever, should I make the jump to get corrective eye surgery or not? I’ve been thinking about that for decades at this point.

The consideration cycle in healthcare is super long. What are some ways that either that’s a benefit of using MMM? How do you work around that or is that just a blocker that makes MMM that much more challenging because of these very, very long consideration cycles in the healthcare space?

Rich: MMM is some of these more sophisticated models. By the way, we skipped over in our history of MMM, and I’m sure, Ben, you could give a great brief history of MMM. It used to be that you only had very sophisticated and specific vendors who could provide MMM solutions. The reason why there is this huge proliferation now of MMM across all these different verticals is because you have open MMM solutions from major players like Meta and Google. I’m sure people have heard of Robin and Meridian if you’ve looked at any–

If you’ve asked Claude or ChatGPT anything about MMM over the last six months, it’ll probably talk to you about some of these models and solutions that are now available. A lot of AI startup companies have built solutions off of those models. It’s become a lot more commonplace to be able to deploy MMM. You don’t have to have this huge budget. The beauty of it is you don’t have to wait six months to get a read. You can build this model, as long as you’ve got the data, and then you get the readout very quickly. Then you can go back, and you can tinker with the model in almost real-time and generate multiple versions of the same model, et cetera.

That’s why it’s becoming so much more proliferated across different verticals, including healthcare. To go back to the point about the lag, a lot of these MMM solutions are pretty good at understanding the tail of these different channels and understanding that TV, for instance, if you start running TV ads, you may see essentially no lift or no impact initially, but TV actually has a very long tail, where it’s still impacting your KPI months after that TV flight has ran.

What may feel like a very expensive tactic to execute in the short term has this residual impact that actually means that the effect share is greater when you look at it over a longer period of time, and therefore the ROI on actually running TV was significantly greater than you thought it was initially. Similar with CTV and, obviously, digital tactics that are more top of the funnel but then materialize over time. They have that longer tail, which is crucial in healthcare, because, to your point, it’s oftentimes a very long buying cycle.

We work with clients, especially when you’re thinking about very specialized treatment, long-term treatment that probably requires a relatively decent out-of-pocket commitment. Things like ABA therapy, where you’re making it– For ABA therapy, for instance, and I do use this example a fair amount, but you’re making a decision for your child. You’ve got to do due diligence. You’ve got to do a lot of research independently. You just can’t pick the first ABA place that you find. You’re going to go and ask your provider. They’re going to give you a list of referrals. You’re then going to go work fairly systematically through them.

Once you’ve reached out and connected with those groups, you then have to go tour those facilities. Imagine how long that takes. It’s in-house. You’ve got to go and check them all out, figure out which one you’re going to fall in love with. Then sometimes they may have a wait list. That actual patient acquisition could take six months. You’re dealing with a measurement solution that is trying to measure something that happens in a much shorter window.

For instance, we use pixel-based tracking. We use offline conversions. We use offline conversions for measurement. We use offline conversions for optimization purposes. The lookback window on an offline conversion is 90 days. For a lot of our clients, we can’t take the ultimate KPI and use the offline conversion from an optimization point of view, because the window has passed.

Again, with pixel-based tracking, you may have a view-through window of one day. You may have a view-through window of seven days. You’re not going to be able to look and understand if something that takes six months was driven by that impression or that exposure.

MMM is great for these solutions that have that longer lag. I think it goes back to how fast and how volatile is the business because if you’re measuring something that takes six months to come to fruition, and once it takes six months to get that readout, then the preceding six months, you’ve made swaths of changes to your approach, how you do things, both on the marketing side and on the operational side. How valuable is that data now? You’ve just got to understand that some of that data may be “a little stale,” but when using conjunction with other measurement frameworks, there is value in terms of understanding the lag and what’s driving the lag.

Ben: Yes, we see that a lot in consumer lead gen, especially sectors like education. It’s very similar to the example you gave earlier, where the parents are making a healthcare decision for their children, much with similar to education or the B2B also, where you have a buying committee, a group of people who are making a very expensive, very important decision.

What we see a lot of times in those instances is rather than optimizing or building a model around the end-all-be-all KPI, which would be the nice-to-have, we have to use some kind of lead indicator, something earlier in the journey or earlier in the function to model of a measure. What do you usually see how healthcare brands apply that kind of logic? Maybe they can’t get inpatient revenue for 12 months downstream. They need to do something sooner than that to make those quicker marketing decisions.

Rich: Yes. I will say that is often the case, which is there is usually some pretty valuable and qualified signals that exist earlier in the journey. If you think about it with what we would call more low-acuity healthcare. Oftentimes now in the sort of retail mode of healthcare, you’ve got an appointment that’s scheduled online. That is usually a high-volume signal that is relatively close to being able to proxy revenue driven from.

If you wanted to go a step deeper, you can look at kept appointments or you could even look at kept appointments that were driven by certain types of insurance, if you have enough data. It’s like kept appointments that were driven by commercial insurance is, maybe, a KPI that I want to look at, because once I’ve segmented out that payer type, I know roughly the revenue that I’m going to drive from that patient.

There is a fair amount of leading indicators to revenue that can be pretty predictable in terms of what we can expect. I think where people will fall down is wanting to do an MMM or a form submission or a phone call. At that point, when the variability of a phone call could be booking an appointment, or it could be calling because I’m trying to pay a bill or anything like that.

If the signal quality is so poor, you really don’t want to be modeling off of that, because it’s really very difficult to proxy revenue against that, and you could definitely get the wrong read. You could definitely over-index them to the wrong channel. If it’s too shallow and too unqualified an event that you’re trying to optimize against. Anything in the sort of booked appointment through kept appointment window, or even an initial thing like a tour scheduled for some of these longer sale cycles, or an initial assessment performed. Those can be gray signals that really meet that middle ground in terms of volume and also the lag not being too great that the data is stale by the time you’re actioning against it.

Ben: Yes, just like any measurement system. Your signal-to-noise ratio has to be good. You have to make sure that all your proxies lead appropriately. Do you recommend some kind of reconciliation process of, “Hey, the model predicted this amount of appointments, which translates to this amount of revenue,” and go back and look at how far off were we–? How do you chew that up to make sure that you’re making good decisions moving forward?

Rich: Typically what we’ll do is we will withhold a certain amount of data from the model when we’re building it. Typically, if we go and get 24 months’ worth of data, we withhold the previous 3 months from the model. It’s essentially like a holdout, and then we’ll have the model predict what it thought was going to happen in those three months. Obviously, we already have the data, so we know exactly what did happen. Then we’ll see how far off the model was from what actually happened.

What we’re also trying to do in that situation is we’re trying to factor in the impact of any environmental factors. I know we haven’t talked much about environmental factors that we know happened within those three months that could’ve potentially been the reason for the delta between what the model predicted and what actually happened.

For instance, I just hired three new providers at my best location last month, and they brought a ton of their patients with them. In my actuals, I see– last month, I actually surpassed the model’s forecast by a huge amount, but it’s purely because I just acquired three new providers, and they brought their patient base with them of 500 new patients into the system. There’s no way that the model could’ve predicted that.

We do the holdout, but we also then look at those environmental factors that would be causing spikes the model would not be able to see. Then once we’ve normalized that data, we then compare the two, and we hope to see a pretty small variance between what the model predicted and what the actuals were. Ideally, you’re hoping to be within 10%, I would say, is fairly good with the amount of data that we have.

I know on the e-com side, it might be more like a 5% MAPE or something like that. I think with the amount of data that we have is you can get within 10%. Most people would accept that. If I was like, “I can predict your new patient volume within 10%,” they’re probably going to be like, “That sounds great.” [laughs]

Ben: Pretty good.

Rich: I’ll take that.

Ben: Give us the range on low or high, right?

Rich: Yes.

Ben: That makes sense financially, then let’s go. On the environmental piece thing, it is really critical. On the retail side, there’s a lot of large public data sets that are used in more sophisticated marketing mixed models. That’s somewhat of the nerdy jargon distinction between a marketing mixed model and a media mixed model is a marketing mixed model’s factoring in things outside of just media controls, like spend and impressions and channels, pulling in stuff like macroeconomics and weather– and even things like competitor data.

In the retail space, it’s a lot easier to get at that information. I can go and pull something like the S&P500 index and use that as a variable in my model to help predict consumer spending. There’s also these key seasonal windows, like the holiday shopping season in Q4, Black Friday, et cetera. What are some of those environmental factors outside of media that you have used or would recommend in an ideal state? Not every client can do it, but in an ideal state that you would look at when building an MMM for a healthcare brand.

Rich: It’s definitely a lot of first-party information initially. What new locations have we opened? What locations did we close? How is that provider mix shifted? Have we staffed up? Have we lost providers? Understanding if the business is a situation where patients tend to flow with providers or tend to stay with the provider group, irrespective of whether a provider leaves and how that potentially impacts patient volume.

I also think looking at things like claims data, when you talk about data sources and data information, claims data can help us understand things like new competitors opened within that market, and this is the type of volume that those competitors are now doing inside of that market can also help us understand referral paths. If we’re a specialist group and we are reliant on five key primary care physician groups inside of that market to refer patients to us, and we suddenly start to see in claims data that now PCP Group A, which is our biggest referrer, is now referring to this specialist competitor.

That’s also going to impact us. That’s a pretty significant environmental factor, especially if maybe 70% of our business comes through referral. There is some data that we can get our hands on similar to the lag challenge. Claims data isn’t updated the next day. You’ve got to wait for that to trickle through. It’s got to come through a clearinghouse and then come to us. We use that data where available to validate.

I think the other big one, which you see a lot, is payer contracts. I was in network with Anthem. Now I’m not in network with Anthem for whatever reason. Therefore, all those patients that have Anthem that were my potential patient base are no longer my potential patient base. That is a huge environmental factor. That’s a negative way of looking at it, more often than not, provider groups that we work with are getting in network with more payers. Usually, it’s an opportunity, and it opens up more. That, again, is critical information for us to understand.

Ben: I think that level of context and industry knowledge is super impactful. We see that on the retail side. A great example of that is in auto industry. We’ve had a number of auto parts clients in the past where they’re not selling cars, but they’re selling parts to repair, refurbish, remodel cars and vehicles. If I’m selling a bunch of pieces for a Mercedes and, in my state that I’m selling in, there’s a very low amount of Mercedes cars on the road, it’s going to have a weaker marketing impact than a state that has a lot of Mercedes on the road.

That comes back to understanding the context of the market that you’re in beyond just both geographically, but also demographically and all the industry specifics like that. I would say, if I’m reading between the lines of what you’re saying, Rich, is working with a measurement partner who understands the nuances of that industry so that they can bring in the right variables into the model will change a lot of the output and change the results of what the model says to do.

Rich: I know this is a bit of a sidebar, but obviously, the utility of having those datasets is highly valuable outside of just modeling. Going back to the claims data and to build on your Mercedes analogy, for example, we’ve had conversations with clients in the past where they’ll say, “I have a new patient acquisition goal in Minnesota, in Minneapolis, and I need to hit 5,000 new patients next month.” You’ll look at the claims data that has the diagnoses, the relevant ICD-10 codes in it, and diagnose volume in that market [chuckles] for the thing that they treat might be 5,000 new patients a month.

It’s like, “Okay, hang on, let me get this straight. You want to get 100% share of market? Power to you, my friend. Power to you. I wish I had that business, too. Very aspirational.” Sometimes, it’s even worse than that. Sometimes, it’s like, you need to treat every patient three times because there’s only 1,500 patients. Yes, absolutely, to inform the model, but I think having the ability to tap into those datasets is crucial for planning, performance, expectation setting, et cetera, as well.

Ben: Yes, we’re seeing that a lot in the CPG space. There’s a certain amount of just TAM, just Total Addressable Market, to go and get. A lot of our, I would say, more mature retail brands are building more towards a brand equity or a share of market model as opposed to just getting an ROI. If I spend a million bucks, I’m going to get three million bucks back, and it’s a three ROI. As opposed to that, they’re looking at what’s the actual total amount of market that’s even available to grab, and then they use the model to work backwards to say, “We can spend this amount in order to capture this amount of market share.”

That makes it a little bit more practical for those mature marketers out there. I think starting with the audience, it’s a super valuable dataset in any industry that you’re in because there’s only so many customers in any category. On that note, I would love to talk a bit about how to make this more practical. Let’s say we get all the right data. We run a perfect model. It’s very predictive. It’s within that 5%, 10% that we mentioned earlier, the holdout months. Now what? We present that to who? How do we discuss that? How does that actually influence a marketing program?

Rich: Yes. Typically, the way that we will operate that motion is we’ll meet with our point of contact. I think the nice thing about MMM is because you’re collecting a lot of information, everybody knows that you’re in the modeling cycle. It’s not falling out of the sky for anybody. We’ll present the initial model readout to our client, and we’ll have an initial set of recommendations.

What we might expect to see is usually with the amount of data that we have in healthcare, we’re really just looking at usually one model that we’ve developed. It’s giving us a channel mix readout essentially of our spend share is 30% in non-brand. We’re spending 30% in non-brand, so that’s our spend share. It’s driving 35% of our KPI. Therefore, that’s the effect share.

The effect share is outpacing the spend share. That’s good. We can invest more into that channel, albeit just marginally more, but it’s not going to tell us where and how. I think that’s the critical piece. That’s where you’ve essentially got to take the MMM and pair it with other testing strategies and measurement strategies that is going to help you get to that answer that aren’t predictive, but they are prescriptive in nature and that they help you measure both the acquisition of new patients, if that’s the KPI, but also ideally the acquisition of incremental new patients that you would not have got had you not deployed this type of effort.

That’s where I think MMTs really come into play for us is we may have a recommendation– we may see from the MMM readout that the effect share of MET is much greater than the spend share, so we want to spend more in meta. We may go to the client and say, “Okay, we want to spend more in meta.”

In order for us to measure the incrementality that meta is going to bring when we spend more, let’s set up an MMT alongside that incremental spend approach to really understand how much incrementality meta is driving at a more tactical level and at a more location-based level so that we can deliver more granular insights, both to help feedback into the model, but just also to feed into our strategy of saying, “Yes, we know from the model that meta is effective,” but from running MMTs, we can see which kind of markets it’s effective at, what type of service line support it’s effective at.

We can start to get more granular and more– I want to say real-time because MMT takes weeks if not months to get readouts on, but quicker readouts than potentially running an MMM, and it gives us that more granular level of detail and insight that we can never get from an MMM. It’s the next level down in terms of Marci application that’s going to give us a more detailed readout on what’s working and what’s not working than MMM would do.

Ben: For those that haven’t listened diligently, which I’m sure is very few people prior episodes, an MMT is a Match Market Test. That’s a geo-based test. For us, we definitely, on the retail side, definitely look at MMM as, like you said, the predictive engine. It’s saying, “Hey, we think that this channel could do better or worse,” or “We think that this channel is very performant.” How do we validate that? Well, we got to do an experiment. Just like in medicine, you got to do an experiment to get stuff through the door and approved through regulation.

I think the same level of, maybe not same degree of intensity, but the same type of approach of a good AB test is important in your marketing mix. Those MMTs are crucial to get that snapshot of that real-time data and that incremental data. Let’s say that you’ve done that MMM work, you validate it with an MMT, and the result comes back, and maybe a channel is not profitable. It doesn’t fit within the unit economics. What do you advise marketers out there in the healthcare space? How do they approach that? Do they communicate that to the CFO? Do they try to optimize? What’s their order of priority?

Rich: I think the answer to that question is, what are your other opportunities? Also, ultimately, what are your goals as an organization? Potentially, to that point, I run MMM, so the channel’s not that performant. I run an MMT to try and get more detail, more insight. It’s still not performant. Then I have to ask myself a question. Can I redistribute these dollars to some other mechanism of marketing to drive new patient growth?

If so, great. Maybe that is the easiest solution. That’s the easiest win. It’s just pull back and redistribute or if I don’t have that luxury, do I feel really confident that the way I’m running the channel is the correct way? Do I have the right funnel strategy? Do I have the right hygiene inside of my account, not to get into the nitty-gritty? Do I have the right post-click US experience? Do I have the right test-and-learn approach to optimize that channel and really make sure that I’m putting the right elements in place like my messaging is as strong as it can be? I’m leaning into the algorithmic best practices?

I think it’s going to be situational and dependent upon those things. Ultimately, you’ve got to act upon that information. The worst thing that you can do is get that readout to your point, Ben, and then just sit back and do nothing and not tell anyone or just something I’ve actually heard.

Ben: You’re right.

Rich: Yes. [chuckles] Well, something I’ve actually heard is like, well, the CMO or the brand just feels like we need to be on Channel X. That’s why we invest in it.” You, as an internal marketing team, and we as an agency, have to be bold enough to challenge those assumptions and just say, “Hey, there’s likely a better way here,” especially in our world where there’s a lot of marketing inventory that just remains untapped that we haven’t pushed into and that we haven’t tested. It is about being brave and pushing back against the status quo and just operating off the data that is available to you.

Ben: Yes, I love that. I think that’s a great segue and bookend to this series of episodes. I think the next series, we’re going to talk about more on the media planning, the reallocation side. These last few episodes have all been focused around the change management, measurement, right things to do. At the end of the day, it’s about getting the best ROI in your marketing that you can and how to drive growth as profitably as you can, and where are those trade-offs.

I think that’s a great tee-up for the next episodes as we get into portfolio reallocation, how to test new channels, what that looks like between retail versus healthcare, and we can go from there. Rich, love the conversation. We could talk about this all day. Appreciate you co-hosting with me, as always, learned a lot, and hopefully our listeners learned a lot, too.

Rich: Yes, absolutely. My pleasure, Ben. Just a shameless plug, guys. We have Scaling Up, a healthcare conference, October the 15th. I’ve got the swag on. I’ve got the hat on. Obviously, if you want to come and learn all about MMM, I believe we’re going to have Ben there doing some presenting. We’re going to have some other amazing people from the Power Marketing Science side, the Cardinal Marketing Science team. Come to the conference and learn about how you can deploy and leverage all of these things that we’re talking about on the podcast.

Ben: Love it. Looking forward to it. Thanks again, folks, for listening to the Ignite: Healthcare Marketing Podcast. Appreciate it. This is Ben signing off, and have a great rest of your day and week.

Rich: Thanks, guys.

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