103. Real-Time Analytics in Senior Living with SeniorLytics

In this episode, Amber Bardon welcomes Casey Allen from SeniorLytics to explore how real-time analytics is transforming decision-making in senior living. Casey shares the origin story of SeniorLytics, explains their “analytics as a service” model, dives into core integrations, reveals top use-cases and success stories, and offers a measured perspective on where AI fits into the analytics landscape.

Top Takeaways

• Industry Focus Matters: SeniorLytics’ metrics and integrations are built specifically for senior living operators
• Services-Heavy Onboarding: Whitel-glove services accelerate ROI and ensure sustained adoption.
• Actionable Analytics: LOC and portfolio dashboards uncover hidden revenue and operational efficiencies.
• Measured AI Strategy: AI pilots focus on well-defined business problems
• Partnership Over Product: Treat analytics vendors as an extension of your team for best results.

Resources & Links

• SeniorLytics
• Casey Allen

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Transcript

Welcome to Raising Tech podcast. I'm your host, Amber Bardon. Today my guest is Casey Allen. Casey is the co-founder and chief product officer at SeniorLytics, which is an analytics as a service.

Welcome to the show, Casey.

Casey Allen: Thank you. Thanks for having me, Amber. Good to see you. 

Amber Bardon: So analytics is the name of the game, right? That's where we're at. Everybody wants analytics and I think it's so necessary because I've been in senior living for 20 years. This has just been a huge lack, like the industry has just not had good data, right?

Like a lot of communities are used to getting their financials a month behind. And I think we're starting to just see more and more of this like hunger and need for real time analytics and information to make data driven decisions. So I'm curious to hear a little bit more about your story. So you're a co-founder, so how did you get into senior living and how did you get into this [00:01:00] specific area?

Casey Allen: Yeah, good question. So, We started the business late 2022, and I think we signed our first client like early 23. And we, my other co-founders, I have two others we've all worked together. In the past were former colleagues. We worked together at a previous startup in Chicago. And then that went through an exit multiple times and we all went off to do other things after that and then came together.

But primarily we all came from healthcare. So we all have. Have spent the majority of our careers working in he healthcare technology, so building and primarily building what was the precursor to ai, large language models. Now we were working with, Predictive analytics in the hospital and healthcare space. And then, our CEO, UBA Glu. And my co-founder, he was looking to branch out exit his current company and had started, one of [00:02:00] his friends actually runs, is an operator, and they started talking and he was expressing to him, all of the pains that he had to go through to just get, what seemed like basic reporting out of his business. Just to understand like how every community's doing, how this community over here is comparing to this community over here. And just, get a bird's eye view of operationally like what's going on.

In his business. And so UB and I got together with our other co-founder, Nathan, it, and we all agreed this is something we know how to solve. And we've done it for many years in the hospital and healthcare space. We took the dive into senior living, spent a lot of time sitting down with that first customer to really understand the space educate ourselves.

And understand how do we build a platform here that scales and solves this problem for both operators and owners? 

Amber Bardon: Yeah. Makes sense. And that is exactly what the use case is. The pro the issues and problems you described with your first customer.

I've been to so many communities and they're all dealing with a lot of the same things. So tell me a little bit about. SeniorLytics [00:03:00] and how it works. Are you integrating with the major applications in the industry? Is it a dashboard, is it KPIs? Do you do every components?

Just tell me a little bit about the product itself. 

Casey Allen: Yeah. We consider ourselves analytics as a service and a model that we having built analytics platforms in the past. Own them from a product perspective own them from a technical perspective among us as co-founders. We've learned a lot of lessons around like what's gone what hasn't gone well, what's necessary to really make an analytics platform add value to an organization.

'cause at the end of the day, if you're just giving them reports. And nobody's looking at them and nobody's really engaging with them, or it's not really showing them what they need. It doesn't really it doesn't really add any value and it falls flat and it becomes a, a negative ROI investment from a, from an operator's perspective.

And so one thing we did early on, or a decision we made early on was that. We would be we would have a very services, heavy [00:04:00] focus to and component to the platform. So in addition to technology, we didn't want to be just another drop off SaaS software that, you know, you. They sign the contract, you sign the contract, and then you hand them this kind of, the keys to this kind of big, bulky thing and, expect them to stand it up on their own or, with little help get it running for the organization and get it customized for their organization and what they need, need it to do.

And so we take on a lot of that burden with our services team. For our clients and we know that, that's just a part of the game and that's how, we can get a tool like, any analytics tool into a good spot and into a place that actually meets the customer where, you know, where they need to be from.

A, Hey, here's what we do today. It's working, it's in Excel or it's a patchwork of here, this over here, that over there. And from each of their core systems. And so we go and gather up all that data for them. We take on that burden. We, we put it into, we do all the data modeling 'cause [00:05:00] that's what we're good at.

We're good at the data integration, the data modeling the technology component. And then we work with them to, really then give them, what they want. So it's almost bespoke every time. To the point where, we take on the burden of all of the report writing, all of the report generation, all of that.

Take that off of them so they can actually focus on, running their business. And so yeah, so we do all the integration with all the core systems. And our services team plays a heavy role and we consider ourselves a, an extension of their team. They just get a bunch of data nerds instead of drop off software is how we put it.

So 

Amber Bardon: you used the word bespoke, which I've only ever heard British people say, 

Casey Allen: We have a jar, we have a bespoke jar in the office. Because we overuse it too much. Okay. And our COO's always he's thick of the word now, so we, every time somebody said that, we put a dollar in the jar.

Amber Bardon: We have some clients in the UK and they use that word all the time. And I'd never heard it in real life. And then [00:06:00] I was like, this is like a British thing. So I just had a comment 'cause. Are you American? You're not British? 

Casey Allen: I'm not, no. Yeah. Born and raised. Yeah. 

Amber Bardon: Maybe it'll catch on over here.

Yeah, 

Casey Allen: it has here anyway. 

Amber Bardon: Yeah. It's in our 

Casey Allen: office. 

Amber Bardon: Yeah. Oh, okay. So it's a Portland thing. 

Casey Allen: Yeah, maybe. Yeah. There you go. 

Amber Bardon: So tell me a little bit about the types of analytics that you are typically providing. Are you able to integrate with financial systems, HR systems, payroll systems, clinical, or are you focusing on one area over the other?

Just tell me a little bit more about that. And then I'm curious like what are the, what are you seeing are like the top requested data sets? 

Casey Allen: Yeah. So to answer your, the first part of your question, all of those, so we. We consider those kind of our core standard data integrations where every client we go to, we'll work with their financial system, we'll work with their EHR we'll work with their payroll labor system or H-R-R-I-S.

And then we also bring in reputation management. So [00:07:00] we go and get out go out on the internet, grab all their reputation, reputational kind of reviews and comments. And yeah. And then we bring all that in their CRM data as well. And so we'll bring all that as a standard kind of core dataset.

We bring all of that together and then, with that we can create new metrics that they wouldn't get before, so they can see. Commonly we'll deploy. Reports that help identify kind of areas of focus for them. And again, it's, it's unique to every client, so we help create a project plan that makes sense for them and help solve, we identify and then solve their most immediate needs and where they wanna put their focus on, almost like project by project.

So that if they want to go and if they know they're leaking revenue in terms of billing for level of care and clinical hours, we'll go and focus and we'll start focused there. And we can marry the financials to the clinical side of data and then give them, a more comprehensive picture and kind of, and also uncover hidden gems of Oh, okay.

You're, [00:08:00] you are, you have a bunch of people, a bunch of residents that are. This level of care and they're being billed at, a level of care different than that. And so out of your financial system. So we, we uncovered things like that. And the same is true for, identifying trends and interesting interesting analyses around, labor over time.

That their core systems may not, give them visibility into 

Amber Bardon: I have a bad habit as a interviewer to ask too many questions at once, and then people forget questions I ask. The other question I asked was just what are, if you could share, thank you, your, top findings, insights, like what are the data sets people are most commonly asking for?

I'm just curious the trends that you're seeing. 

Casey Allen: Yeah. One of them is related to the just understanding and having a deeper understanding of we call it our level of care analysis. Our LOC analysis that really just dives into caretaker hours level of care by resident [00:09:00] level of care by community.

So they then can see either at a. Group of communities or at a single community, within that, what is my what is my breakdown, across all of all of my residents. Where am my nurse hour go, my caretaker hours going, things like that. That's a very common one. We also commonly see and get asked for.

And build out is a portfolio analysis. So at the kind of home office or executive level, we give them that like 30,000 foot view that, they can put on a monitor in their home office and say oh, okay. Here's how the portfolio is doing. And so that's a very common one that, we deploy across pretty much every client.

And the kind of structure of reporting typically. Follows a similar structure of, you have. A bunch of executive level reports for home office and then you build that out and then you build out the next level VPs, RTOs, and then the community specific ones.

So you have this kind of top [00:10:00] down flow of, reporting so that you get from highest grain to lowest grain. And you have your ED looking at the ED report, which has, just their community's data, but just the things that, you know they should be carrying out, they should be focusing on and they should be carrying about and on up the chain and down the chain.

Amber Bardon: Yeah, it makes sense. I think that's all the kind of data I'm hearing that our clients want as well. So given that analytics is, I think, a hot topic right now, and there's several companies coming out there doing something similar, we've had some of them on our podcast. I'm curious, how do you see SeniorLytics as differentiating yourself?

If someone listening to this wants to pick an analytics platform and they're looking what would you say are the reasons or differences of your platform? 

Casey Allen: Yeah. So a and another kind of, competitor that we see is the operations team. Hiring in-house and deciding to build it [00:11:00] in-house is, which is another.

Don't do that, avenue. No. I always 

Amber Bardon: tell people, don't do that because I've seen people and five, six down the years in a row, they have 10 people they've had to hire to maintain it and it still doesn't work. So I agree. Go with the professionals. 

Casey Allen: Yeah, and we get, so we, we try to also, dissuade people from that as well, point out the kind of hidden costs that are associated with building an house that are definitely not apparent.

If you don't, if you're not familiar with building an analytics platform, pro program, outta the box that, you know are absolutely there. The maintenance costs, the, yeah, the longer term costs are, can be enormous in some cases and it takes forever to stand up. And yeah, so in comparison to others out there, including building it, DIYing, it is, we do this day in and day out.

So like we are professionals at the data component of it. And so we know we have this kind of proven model and this proven platform that is senior living specific. We often come up against or get asked about, oh, we have this other analytics platform, [00:12:00] but it's oftentimes, a just a kind of big ubiquitous, industry agnostic platform.

And, that's not gonna have or be able to integrate with all of their unique systems specifically in the senior living space. Or have any of the metrics that they really care about. And so that's what, that's what we bring to the table. We bring the industry specific platform.

And then another part of, what we consider our secret sauce is the services component. We have a ton of experience building analytics and other industries. And what didn't go well, there was not having a services component that can take the burden off of the operations team. And the end user from having to build the report, define the requirements, build the metrics, make sure that the integrate the data is coming through correctly and make sure the data matches.

All of the stuff that, that's hiding under the covers there. We take that on our team and we own that process for the life of, our customers. And so we [00:13:00] really think that kind of sets us apart. 'Cause it's a hard model to pull off from a business perspective.

It can be very expensive. But. We really think it's important and really our services team adds a ton of value to the actual technology component. So we see it as a two-pronged approach. When we go, go to the market. 

Amber Bardon: Yeah, I agree. I think that services piece is huge because, as senior living organizations typically don't have the resources to manage this kind of thing.

Correct. So that partner who can provide that for you, I think is really critical. So the next thing I wanna ask you about is another buzzword. So I can't believe I've waited however many minutes into this podcast to say this, but ai so AI is the next big thing. Everybody wants, never heard of 

Casey Allen: it.

Amber Bardon: Nobody knows what it is, but every, everybody wants it. And so what, one of the things though we're starting to see is trends is just the ability to incorporate generative AI into analytics and take that to the next level. So tell me a little bit about what SeniorLytics is planning to do with that, is already doing that.

Where do you see that going? 

Casey Allen: Yeah we're taking, [00:14:00] yeah. And you're definitely the very first person to ask me that. So I've never gotten that question ever again or ever before. Yeah, we are taking a very measured approach to how we approach ai. And again, we have a, collectively among our team, we have a lot of experience with ai.

It's precursor to what's now the most popular version of ai, which is the large language models. And we've seen kind of the precursor. Some things go well and some applications become, like adding a lot of value, other applications not adding value. And when you try to plug it into a business, and that, that kind of context is really feeding into how we're approaching it now, and really it comes down to what business problems are you trying to solve with ai? Because when you have this ubiquitous tool that can, on paper or in theory or on landing pages, can, do everything and be everything.

It then comes down to what can it do well and what really does it do? Or, how do I use it in my day-to-day work? And so we are doing a lot of discovery and work with our [00:15:00] clients as well as internally to. Figure out like where can we, where can it, like tangibly add value by either reducing FDE hours reducing time of analysis, offering up instant analysis, things like that.

you know, We have all this data at our hands from in the platform. And analytics are essentially a regressive analysis of that data. And so how do we potentially also. Just, shortcut the time from analytics to Insight, which is really what a user cares about and really what, an ed at the end of the day, they want to they don't want to have to go to a report and be like, oh, okay, now I have to, interpret this trend for overtime and figure out what to do with that data.

What decision am I gonna make? And so we're figuring out, or testing ways that we can help. Business leaders in senior living make decisions faster and help them. Shortcut that time from insight to decision. And [00:16:00] so that's a big component of it. And then we also have a, among us on the team, we also have a kind of a deep technical background around automation and how and AI being a component of that.

And so we are doing pilots right now with some of our customers to automate, back office tasks for them and again, for us, it would be very easy to stand up, a chat bot or some sort of generative AI solution and plug it into the platform because the, the time to bring that up is almost near zero now.

But at the end of the day we really approach it from, where can we, where can this tool provide value to our clients? And so we're being very thoughtful about it. 

Amber Bardon: You mentioned back office process automation, so more like the RPA side of things. I have clients that are really interested in that.

Do you, is there, is that something you're developing through SeniorLytics? 

Casey Allen: Yeah, we have several pilot clients right now. And we have a lot of expertise on the team. Around automation. 

Amber Bardon: No, that's great to know. I'm gonna keep that in mind because I do have clients [00:17:00] asking me for a solution. Oh, great. Yeah. Great. I'm glad that came up. Casey, can you tell me about a success story? So a client that you have taken on and how are you able to enact transformation?

Casey Allen: Yeah. We had a recent client we onboarded, and one of the things like, and I mentioned this kind of briefly before as a use case and kind of one of the things we now commonly see because it was such a success story when we stood it up is they immediately knew, they came to us with a problem and they were, their problem was essentially, Hey, we know there's, our nursing hours and our caretaker hours.

Are going are going into this kind of invisible, realm of how much time they're spending with residents and and we have a lot of residents that we believe are either under. Being under reported or under assessed on their level of care. And we don't think there's a connection between, there's a one for one [00:18:00] connection between the level of care they're actually receiving and the the level of care they're being billed for either based on caretaker hours or otherwise.

And we built a report, several reports and dashboards around that problem. To number first, just bring visibility into, give them visibility into their caretaker hours how much they're billing for, and their level of care and assessment data all in one kind of view so that they could see all of those data points in various forms and in various configurations, in one place.

And then we built dashboards around that to track it and what it ultimately ended up. Doing for that client was they, they immediately recognized this big bucket of residents that were not being billed correctly. And so for their level of care. And so that ended up being, once they rightsized that that ended up being, a six figure recoup for them and revenue bump, every month in kind of new revenue that they didn't, they were basically leaving on the table.

Before that. And so 

Amber Bardon: [00:19:00] everybody is listening's ears perked up when you caught up. Identifying revenue, lost revenue opportunities, saving money. Yeah. 

Casey Allen: Yeah that's a big one. For sure. And that's one, like I said, since then, we've then probed our other clients and said, Hey, is this something you're thinking about?

Is this a problem you potentially have? And, we do similar. We. Deploy a similar report and a similar package for them as well. 

Amber Bardon: Yeah. Senior Lytics is definitely on the right track of being a great tool and resource for the senior living industry, like I said at the beginning.

This is just such a needed area. So I'm happy that you got into senior living, that you started the company. Congratulations on that. Is there anything that we haven't talked about that you wanna make sure our listeners know? 

Casey Allen: I think and you pointed to this it can be an easy trap to try and, build your own analytics platform.

And many have tried and unfortunately many have failed internally. And then we get pulled into a [00:20:00] conversation. And so yeah, I think it's more just we are here and we're new to the industry, but we're quickly learning and coming at it humbly.

And from a learner's mindset with our clients and with our prospects to really just deeply understand their business and what problems they're trying to solve. And then through a combination of, services and technology going to help them solve those. 

Amber Bardon: Where can our listeners find you if they wanna learn more?

Casey Allen: Yeah visit our website, senior lytics.com. We've, have everything listed there. You can contact us to chat. We'd love to, if you're interested in standing up in analytics platform or getting insight into your business that is, the best and the best way to contact us.

Amber Bardon: All right. Thank you so much for coming on the show, Casey, and it was nice to meet you. 

Casey Allen: Likewise. Thank you, Amber.

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