Caitlin La Honta (00:05)

Hello everyone and welcome to another episode of the Science of Sales and Marketing. I'm Caitlin La Honta, co-founder of Sirona Marketing and I'm hosting today's session with Letitia Rodley, who's a growth marketing leader and a Sirona Marketing partner.

 

And today we're joined by James Niehaus, who's the Director of Experimentation at Kaiser Permanente. So James, thanks for joining us today. And you've had a fascinating career across the industry as a whole, from your time at software companies like Veeva now serving as Director of Experimentation at Kaiser, in the sort of insurance and provider world. Can you walk us through this journey and how your perspectives on digital marketing have changed as you've moved between these very different environments, but within the same industry?

 

James Niehaus (00:44)

Sure, happy to and happy to be here. Thank you for inviting me. Yeah, so I've had a long and diverse career. I say 26 years and I guess digital across, think I counted 14 different full time companies. So I was doing the trend of skipping jobs every couple of years, even before it was a, you know, acceptable trend. But I've always kind of lived in digital marketing since my first role kind of always at the intersection of analytics,

 

Caitlin La Honta (00:59)

Bye.

 

Yeah.

 

James Niehaus (01:12)

product, marketing, and technology. So from all those intersections, the one I've always enjoyed most is experimentation, because I kind of felt it brought all four together. whether it served marketing, product, it always had that intriguing on the edge of seeing all the new stuff and trying to validate effectively with data, does change affect positive outcomes for the business? So I would say in that time frame, to your question about digital marketing,

 

I would actually have to say like digital marketing, while it has matured and evolved, I think the game, the high level game is still the same. Like it's still about trying to figure out what works for your organization through trial and error. And then one thing what's really changed me in the last 10, 12, 15 years is I think is with a lot more fragmentation, know, from the explosion in channels and now mobile and social or separate channels that also involve.

 

Caitlin La Honta (01:55)

Okay.

 

James Niehaus (02:00)

way more tools. like market technology wasn't really a thing in 2010. And now there's like 6,000 tools across those different diagrams. So I think it's just fragmented when I went to digital marketer where in the past it was like five things. Now it's like 35 to 40 things. And as a it's a struggle to keep up. And therefore I think you see more specialization, but at the end of the day, it's still that same like objective. Okay. I need to use some data feedback to optimize my message to this market with the right offer.

 

Caitlin La Honta (02:08)

Mm-hmm.

 

Mm-hmm.

 

James Niehaus (02:29)

⁓

 

So some things never change, I guess.

 

Letitia Rodley (02:32)

Right. ⁓

 

Caitlin La Honta (02:32)

Yeah,

 

and it's interesting just the title, it being referred to as experimentation. I know that other companies have that function, but call it different things. Do you know just the reasoning behind that being the term? It's cool, it sounds cool, yeah.

 

James Niehaus (02:43)

It sounds better data scientists. mean, it's cool with AB. Yeah, it

 

Letitia Rodley (02:48)

Thank

 

James Niehaus (02:48)

just, I mean, you know, at the end of the we want to sound more professional. We're doing controlled experiments as opposed to just, you know, growth hacking or AB testing. It's all the same general approach. think the language often reflects, I guess, the rigor of, I think, the statistics that power it. Cause I mean, I'm not a truest. So I kind of customized the program to the maturity of organization. But if you're just doing any type of, you know, decently solid AB testing,

 

Caitlin La Honta (03:08)

Mm-hmm.

 

James Niehaus (03:12)

you're doing things the right way because you're learning more effectively about what is actually working for your organization.

 

Caitlin La Honta (03:18)

Yep. Yeah. And I can

 

imagine operating in an environment with like Kaiser, where there's, I mean, so, so many things to measure, so many data points, you know, hundreds of thousands to millions of members. I don't know the full number. You know, that's a very, very different version of experimentation than maybe at a startup where it is more growth hacky and you're moving really quickly and it has to be maybe less rigorous, but faster. Yep.

 

Letitia Rodley (03:42)

James, I'm curious, you were mentioning that you've always enjoyed experimentation, you've always kind been in this digital lane. Are there two or three kind of tools that you just take with you from gig to gig to gig that are like your go-to that you use?

 

James Niehaus (03:56)

I don't necessarily say it's a certain tool. It's a category of tools. Like, obviously, you come in and you're hoping they have an analytics tool that's instrumented decently well for your organization, so it's giving you real information that's actionable. Then you hopefully have some way to introduce change, whether it be an AB testing tool, like an Optimize ly or Adobe Target, whatever it may be. But I'm less concerned about the tools. mean, typically, organizations have some version of those things running, right? So if you have...

 

Letitia Rodley (04:00)

Okay.

 

Caitlin La Honta (04:03)

Mm-hmm.

 

James Niehaus (04:21)

something to measure, something to initiate, and then maybe something to manage your campaigns, then you're like most people, at least at the 80 % level. Now, as you mature, there are certain tools that make more sense for your size, scale, scope, or need, but just guide me in the game. And if you have something that puts you in the game, then at least you have a fighting chance to get better.

 

Letitia Rodley (04:39)

Do you think AI has totally adjusted that for you?

 

James Niehaus (04:42)

the tool is not yet. I'm really intrigued about where it's going to be. Cause I think the big trend you're hearing is like everyone's trying to figure out how to add agentic AI into their application. So I think that is like the clear promise. Like I think right now what I see is, is not it integrated well within the product, at least for the stuff that is enterprise level. I mean, obviously I think at the startup level, there's a lot of, automation and other tools that are very niche or focused on say content creation. I think those obviously make a lot of sense, especially if you're in a startup space.

 

Caitlin La Honta (04:50)

Mm-hmm.

 

James Niehaus (05:09)

Like in the larger enterprises, don't think any of the tool, any of the major players have really fully, you know, in integrated in a way that's more like, I guess, operationalized, but you started to see, here's an AI genie or here's an AI widget. But I think we'll probably maybe a year or two from that being like materially impactful in terms of how you'd use certain applications.

 

Caitlin La Honta (05:30)

Yeah, I mean, one of the things that you sort of alluded to is that the tools are all the same, roughly speaking. Maybe there's a different product or a different software you're using, but it's the same idea. You're using it for A-B testing or whatever. But that there's actually vast differences in how different organizations are using those tools. So how would you speak to that in terms of it's not really the tool, but it's actually how the company is using it and understanding how to get the most out of those tools, or maybe you're not really leveraging them to their full extent?

 

James Niehaus (05:57)

Yeah, good question. I think it broadly speaks to think a culture in an organization which is about making decisions fast. I think you'll see the best way to think about it, not in just tools, but in general, I think it's the ability for an organization to make decisions and execute things quickly is I think the biggest factor.

 

whether it's a culture or tools, like to me, that is the big difference. So when you speak, specifically to say applications or technologies, where I go with that is that the better, more mature organizations pick a tool because they know they need one and they get off the ground and moving. The ones that are less mature, like let me spend 18 months on an RFP to figure something out, even though they had never used these tools before and they wouldn't really have great frame of reference.

 

And then when it comes to operationalizing the tools, it's like, let, you know, startups have the advantage because they have a necessity and also they have, you know, less, I would say bureaucracy or guess overhead. So they can execute rapidly, but they just don't have the scale or the resources to kind of do all the things on the flip side. know, larger enterprises typically treat even SaaS applications like managed services. So they will wrap overhead bureaucracy on top of it because it's more about control and standardization. And I respect the reasons why.

 

Caitlin La Honta (06:52)

Mm-hmm.

 

Mm-hmm.

 

Yep.

 

James Niehaus (07:08)

But

 

that's where you often have like people come from other companies say, well, it's the same solution. I know it could do this in an hour. Why does it take a week or a month? So it goes back to the ability to of just accelerate execution, as I always say that to me, that's the only way you get maturity. like, like there's no real like secret sauce. It's like you come in inexperienced, whether it be in tools or techniques or tactics. And the only way you get better is typically two things you

 

Caitlin La Honta (07:13)

Mm-hmm.

 

James Niehaus (07:34)

iterate through lot of trial and error and learning, I made mistakes, I grow, and that's how I become experienced, or you have a very good mentor that helps you avoid some of those pitfalls. But for most organizations, if you have low maturity on tools or tactics, the only way to get an organization out of that rut is to have them learn through rapid trial and error. the more you can, think the famous thing it's quite always say is like, the more expensive it is to change something, the less likely you are to change it. So if you can reduce the cost of change in your organization for whatever it may be, decision making,

 

Caitlin La Honta (07:58)

Mm-hmm.

 

James Niehaus (08:01)

picking a tool, using the tool, launching a campaign, executing whatever it may be. that feedback loop is the most critical thing and using a tool and maturity of an organization. Almost everything that we're going to talk about today is, I look back to how quickly can you go from idea to launch.

 

Caitlin La Honta (08:14)

Mm-hmm. Are there any pieces of advice you would give to people operating in larger companies where there are a lot of those, you know, those challenging roadblocks? Because some of them, like you alluded to this, some of them are required. There's obviously, like, compliance concerns and there's huge change of management. You don't want to throw something on a team and then everything is thrown into disarray and it creates more chaos than it helps solve. Do you have any pieces of advice there?

 

James Niehaus (08:36)

Yeah, I mean a couple. mean, you need to know the company or you need to know the culture of the organization you're in. So if it's a large company, you have to respect that's how they operate. So shame on you if you think they should act differently. That's what you signed up for in the job. But within that constraint, I think it's one you should understand, like anything else, understand why it's there, why it's there and what caused it to arrive at that kind of bureaucracy or overhead.

 

Caitlin La Honta (08:42)

Yeah.

 

James Niehaus (09:00)

And secondly, would say is, you know, learn who are the in a day, the people who are advocates who believe or think the way you do and have control of the levers. And from there, I think you'll be surprised that there's still ways to get things done rapidly in large companies. The only I think caveat is that it may not go anywhere, which is in larger companies, like it's the the the

 

Caitlin La Honta (09:18)

Mm-hmm.

 

James Niehaus (09:22)

Reorgs and other things will change in 12 to 18 months whether any of that had any meaning to it So I guess what I'm saying is that expect to build sandcastles that will collapse on you because With one reorg one new CMO or all whatever it is. It all starts over So I think you got kind of what I'll say is you got to love Playing the game which is like if you come in and then you burn oh this my project failed and now I'm bitter that's just the wrong approach like

 

Letitia Rodley (09:35)

you.

 

Caitlin La Honta (09:35)

Mm-hmm.

 

James Niehaus (09:46)

When I work with companies, especially larger companies, to me it's like a reality show. I'm in this reality show, it's kind of this weird place that has these rules, and it's my job to navigate the chaos and try to achieve my thing. And if I fail, which I expect to, like most of my, I'm very ambitious. I always try to like do large digital transformation of companies, even though it's unlikely to succeed, because that keeps me engaged in the job. Otherwise it'd be a job I would not want to come in on a Monday and...

 

Caitlin La Honta (10:08)

Mm-hmm.

 

James Niehaus (10:11)

with like, excitedly on a Friday, I actually enjoy the idea not of the task themselves, but like, how can I get this lower maturity organization or this to mature? How can I convince people that this is a better way, recognizing their values, their risk, their incentives. So I find that to be the more enjoyable piece, like in a day like doing AB testing is the same no matter where you go. Getting a company to go and say not run tests to start running tests to me, that's the large win. Like how could I

 

Caitlin La Honta (10:31)

Mm-hmm.

 

James Niehaus (10:37)

And then it's always different. Every company you go to, different culture, different incentives, different maturity, different stage in their life cycle. So it's like, it starts over again. So that's why it's always intriguing because every company is unique when you blend in all of those aspects. And that's why it makes my job infinitely rewarding because I am not basing my success purely on did I achieve that, like that basic outcome of how many tests per year or how many outcomes. It's more like,

 

was able to move the rock uphill and be satisfied in that.

 

Letitia Rodley (11:10)

Yeah. I have a quick question for you. It might be a little provocative. So as marketers, all of us here being marketers, AI has phenomenally impacted how we do our work every day. And in fact, in the meeting before the meeting, when we spoke with you, James, you were like, I've been phenomenally more productive with the advent of AI over the last 18 months or so. Where do you guys, and this is maybe for both of you, where do you think...

 

What is the trade off for marketers with AI? What is it that we maybe are leaving on the table as a result of that faster increased productivity as a result of dumping it into Claude or dumping it into ChatGPT and getting, writing the right prompt and getting the response. What do you guys think we're leaving on the table as a result of that?

 

James Niehaus (11:50)

I can, I guess, go first. I think there's a tactical value to AI, which improves your output, but there's a strategic value that I think is sometimes missed, which is like, you're like, I look at AI, like, I'm, know, I'm getting up there in years, like, like I have five kids from two to 15. So like AI is the most exciting thing for me, but also the scariest thing for me. Cause it either, it's either my success or what takes me out of the employment game.

 

Caitlin La Honta (12:10)

Mm-hmm.

 

James Niehaus (12:14)

So I look at it very much like I have to embrace it because it has that power more than anything, even more than I say the internet or mobile or social. It's a game changer. So I want to be on the other side of that. I want to be the one that's designing the workflows as opposed to being replaced by an agentic AI bot that does one of my workflows. I found it, since I don't have access to say AI specific tools, I've actually found more

 

Letitia Rodley (12:31)

within.

 

James Niehaus (12:40)

surprising, I guess, productivity enhancements in terms of how I operate daily. So like, I've only invested maybe the last three or four months in using AI more consistently. And I found that I've now used it for every single decision typically I want to make or thing I want to discover. Because I see it giving me value on multiple dimensions. I'll give you a couple of examples. Like I'm more on the technology side of marketing enabling capabilities.

 

So oftentimes every meeting you have like, okay, well, we should investigate that question about, you know, how does that thing work? How does that integrate? And that would typically mean either pass this thing on to someone else and they would pass it on to someone else and I get it back in a week. Now in the meeting, I will just query it, put the right prompt, drill down one time and I'll have the answer in the same meeting. So I think that's just one example. I get more quality answers immediately. And on top of that, then I find it easier to start projects.

 

because I've often had projects that are important that because I have to sit down for an hour or two and build it all out. It sits on the shelf for six months. Like I now realize last couple of months, like a couple of products, I just spent 20 minutes outlining what I wanted and I was able to get it off the ground in a rapid fashion. So that was another thing. And third part is like the output is just phenomenal. At first, like, I don't know what I don't know.

 

And this is why like, you know, in the old days companies, even now they work with the Accenture's and the blah, blah to, layer on frameworks and strategies. Now I can say, here's my, my, my thing. It's pretty sound. Can you layer in what best practice frameworks could make sense? And then the thing you see like this explode with here's six different tables and here's six different decision matrix. Do you want to have a flow chart? So I feel like I can now output the equivalent of having an Accenture in my pocket, but I do that.

 

Caitlin La Honta (13:58)

Mm-hmm.

 

Letitia Rodley (14:09)

Bye.

 

right.

 

Right.

 

James Niehaus (14:22)

you know, while I'm going to get coffee between like, you know, TV shows, can put in a prompt and walk away. So like, that's where I feel like, like, in the last three months, I can produce, can run projects where it I would have been just a cog that it took in six months to a year to produce something. I can now do it maybe in a couple of weeks. So it's just, it's just phenomenal. And that's, that's not including like actual tools. So when the tools come out, I expect another similar like bump in capability of productivity.

 

Caitlin La Honta (14:25)

Mm-hmm.

 

Letitia Rodley (14:27)

Yeah.

 

right.

 

Right. So how about you, Caitlin? What do you think?

 

Caitlin La Honta (14:51)

Yeah, I

 

have a lot of thoughts on this topic. I think that it's, I've gone back and forth since using AI regularly for the last, I would say about the last year.

 

Partially I was like, my gosh, this is gonna replace me I'm gonna be obsolete and then now I've swung in the opposite direction or maybe the middle of the road where it's like This is super powerful, but it's very clear how quickly it can Degrade into like the garbage and garbage out where if you're not using it correctly or you're just using it to fully generate things without thinking Giving some thought behind what you're doing with it It's just gonna produce a crappier generic response Especially in the context of like product and content marketing if you're not giving it good inputs like a good messaging foundation or really solid value propositions or

 

Letitia Rodley (15:30)

Right.

 

Caitlin La Honta (15:30)

clearly

 

well researched messaging framework, then you're just going to create something generic because it's literally assessing that off of just what it knows based on the context of information. And so the better information you get it and the better way that you use it, I kind of have come to the...

 

Letitia Rodley (15:41)

right.

 

Caitlin La Honta (15:48)

answer that it's sort of like being an artist or a musician where like a basic musician can use a guitar and kind of strum along and get some chords out. But the experts use the same tool, but they're creating masterpieces. And so it's your ability to handle that tool. You'd still be able to maybe get by at the basic level or do some basic things, but more about how you leverage it. And I think to what James, you are mentioning is using it a lot more as, know, something to bounce ideas off of or to work on ideas.

 

and go back and forth versus only one output. I would never use AI or I don't use it as a, hey, do this one thing for me. Okay, I take it and go. I use it as a, let's work on this. Let's iterate, let's refine. And then I get it to a point that it's much faster than me doing that on my own. ⁓

 

Letitia Rodley (16:31)

Right.

 

Caitlin La Honta (16:32)

So I think that there's this element of just the best people are going to continue to learn how to work with it versus using it to replace a lot of tasks, at least from the context of maybe some of the deeper, more strategic disciplines in marketing. Though there are things that are fully automatable. Like I use AI to just fully automate certain data analysis work because I don't need, there's no strategy behind it. I just want it to match up company with this website and assess if it's in our ICP or things like that. That type of stuff is great.

 

more nuance to it. What about you Letitia? Yeah I'm curious.

 

Letitia Rodley (17:00)

Yeah.

 

I'm kind of in between. I'm in between you two.

 

So to your point, James, like the ability to, I'll say for lack of a better term, operationalize and take strategic plans and calendaring and omni-channel outputs and put it into a framework within minutes is amazing. Cause that was like one of the biggest, heaviest lifts of my job was like planning. I have a strategy now I need to document it and I need to organize.

 

it, I need to socialize it, and that just took a lot of framing and articulation and organizing and what's the best visual way to demonstrate this and that has been a godsend. However, what I feel like I could possibly be leaving on the table, and I hope this isn't exposing myself too much, but in especially product marketing,

 

So much of what you take in is through the research and going in and really digging in into the hood and really understanding it, especially in life sciences for somebody who doesn't come by it organically. You know, I was very reliant on that research and it was like, okay. And then you, then you build the thing, then you build the blah, blah, blah. It's like, but it was, it was the building, sausage that got me there. And so now I will take to Caitlin's point, especially working with Caitlin and Abdul, they've kind of got that inherent background in product marketing.

 

and I will take those prompts and I'll work and noodle it around.

 

to get where I want to go, which would have previously maybe taken me a whole week, but I was in the sausage making. So I could easily, I could walk away and go, okay, now I really get kind of, you know, the framing of this digital trial or the framing of this data warehouse or et cetera. And so I feel like I have to kind of monitor that myself and think don't lose that, because that's really critical to the value I can bring. But anyway, yeah, I was just curious, I appreciate you indulging me in your response to that, because I just think it's marketing.

 

Caitlin La Honta (18:35)

Mm-hmm.

 

Letitia Rodley (18:45)

We're so jacked and excited about what AI can do for us, but I'm just curious, like, do we think we're sacrificing anything as a result of this?

 

Caitlin La Honta (18:52)

Letitia will, I was just gonna as of one final comment on that from a product marketing lens for sure plus one that like it's very easy to get out of the habit of doing the.

 

James Niehaus (18:52)

It's a color.

 

Caitlin La Honta (19:01)

doing the learning part of product marketing or like the curiosity and digging in yourself. Because you could, okay, now I can have AI take all the notes, summarize those notes, give me the key takeaways and just now have the bullet point version when I'm like you. And I think a lot of product marketers are this way, especially in these deep industries is you need to get in there and read every single line, get the deep product demos, go through the product demos five different times from five different customer instances, et cetera. And so going the easy route will not be

 

beneficial.

 

reason that I find AI so useful is because you do the hard lifting and then when you get to the point of actually using it you've synthesized that into a really well researched messaging document or whatever your input is and then you can work really quickly with it. So yeah it doesn't replace all the hard work but it it replaces the tail end of once you've done all the hard work you can use it to now 10x your output of creating a sales slide here or a website here. So yeah but I think that that's definitely something but then you know you end up with

 

Letitia Rodley (19:36)

Right.

 

Right.

 

Right, right, right. That's a good point.

 

Caitlin La Honta (19:57)

in a case of product marketing, generic bland messaging, that's not going to be successful anyway. So you'll quickly find that that was a wasted effort. So very interesting. Yeah, and I guess, James, from the perspective of just growth demand digital marketing, mean, you're seeing now this

 

Letitia Rodley (20:00)

Right. Right.

 

Caitlin La Honta (20:16)

rapid change in AI and you're seeing it from the context of the Kaiser world which is like B 2 C or B 2 member. What are your thoughts around just the differences in digital marketing from like a B 2 B perspective which a lot of these companies are selling into like other pharmas or things like that versus what you've learned in the business to member, business to consumer world. And if there's any learnings terms of how you see potentially AI influencing any of that, that's always interesting insight too.

 

James Niehaus (20:42)

Sure, sure. I mean, I think with B2C, whenever I've been on both sides of the house, like B2C is more autonomous. Like you are responsible to, you drive the number in most cases. So it's, you have both the risk and reward factor. So you tend to focus more on scale, execution. You tend to be more data-driven because it's, you have to, like it's all within your control. ⁓ On the B2B side, obviously it's,

 

Caitlin La Honta (20:53)

Mm-hmm.

 

Mm-hmm.

 

James Niehaus (21:07)

It's more complex because of the buying process as well as with the account level stuff. But then secondly, you're all, especially in marketing, you are empowering typically the sales organization. So it's much more driven in relationships and also because of the buying process, it's much more content driven, much more, like said, research driven. Competitors matter way more on a B2B side than say the B2C side because there's more consideration for the purchase. So I would say, mean, when I...

 

Look at that from a digital lens. think B2C has, is almost purely digital play. B2B will always have to factor in the sales and human elements way more. So I think, like I to this day, like for one, when I was inside and I was like an evangelist, I went on like hundreds of sales meetings. Like I was like to help to close the deals and there's nothing like being in like

 

Caitlin La Honta (21:43)

Mm-hmm.

 

James Niehaus (21:56)

understanding the actual sales process. So I think that as you end up with an appreciation of the product, the solutions, the research. So I think B2B is always going be much more of a dynamic of digital plus human interaction and getting that. And I think the human side is actually more important. With B2C, it's really just numbers, game, and scale. So that's my reaction.

 

Caitlin La Honta (22:12)

Mm-hmm.

 

Yeah. Yeah, it's really interesting.

 

It's just interesting because one of the things we observe when we talk with customers, clients of ours or companies in the space that are B2B, is they're struggling now with a lot of those digital channels because perhaps they've tried to implement a lot of the B2C or more of those scale tactics on this type of sales process, which is still very human.

 

Oriented, but you're probably not seeing that I mean when we had chatted originally It's like that's not the case for you You're still finding those channels to be useful and successful when you're engaging with numbers Which is really interesting given like email is so hard now for a lot of the companies we talk with They're not really finding any you know successful social it's gotten to be so difficult on LinkedIn and other channels It's like a pay-for-play all of those types of things

 

James Niehaus (23:02)

I mean, I wouldn't say I'm so, I'm going to mostly enable other groups to execute. I don't think anyone's really like making it work killing it. Right? Like I don't think with B2C, think they had this more of a scale scenario. They can, there's more, there's more reps. There's more output they can do this because they have more customer base. They have more opportunity typically with B2B, especially on the higher price point B2B, like your market is very fixed. You have certain amount of

 

Caitlin La Honta (23:09)

Killing it. Yeah.

 

Yeah.

 

James Niehaus (23:29)

deals and play every quarter. It's just like, know, B2B, everything is more important because there's less reps. And so yeah, it's this, think, yeah, so it's, with B2C you can fail more often and be okay. With B2B, the room for failure is just, it's just limited.

 

Caitlin La Honta (23:36)

Mm-hmm. Yeah.

 

Letitia Rodley (23:43)

Yeah.

 

Caitlin La Honta (23:46)

Yeah. Yeah. It's interesting though, as you were describing the experience of, of driving adoption internally of, you know, digital maturity and trying to drive consensus or even just interest in some of these new tools. It reminded me actually of an enterprise sales cycle of like, find your champion and help grow within that. So it's funny how that type of sequence actually still does happen, but just in different contexts. Yeah. It's the same thing. Yeah.

 

James Niehaus (24:08)

It's the same thing. Yeah, it's the exact same thing. You're just selling an internal product versus an external product, but

 

it's the same. Like what I'm selling for like data-driven whatever is same thing Salesforce and Adobe are selling them from their perspective, right? So it's the same stakeholders. You just happen to have an inside track on the politics and the game, but then you don't have the credibility typically of an external vendor who's done it at 300 other places. But yeah, it's the same kind of input outputs in those worlds.

 

Caitlin La Honta (24:24)

Yeah.

 

Letitia Rodley (24:25)

right.

 

Caitlin La Honta (24:31)

Yep.

 

Knowing now that you've spent a lot of time on the business to consumer business to members side, are there any like learnings from from that world that you would bring back if you were to go back to the B2B world in terms of tactics that work? I know you mentioned scale and that's completely different in the B2B for this space.

 

James Niehaus (24:50)

think this mostly is this different, but the same, guess what I would focus on that when I, when I was doing like B2B optimization for as an agency side, I was focused more simplistically, like you don't need all the bells and whistles, but I think you need to think about what is the offer, right? Like, like you need to be very on top of what assets drive certain people to either raise their hand or engage or convert, at least on the digital side. I mean, I'm not speaking to like the human element of the events in the sales side, but like.

 

Caitlin La Honta (25:11)

Mm-hmm.

 

James Niehaus (25:14)

whether you're pushing this asset, this webinar thing, you need to know those offers so can pretty comfortably say, okay, next best offer, what should it be? So that allows you to not only have the most effective foot forward, but if you try to do personalization, then you can start thinking about, which offer warms them up, moves them through to this point in time and converts better. Just because again, you're not going to have the volume to do it. So you got to have...

 

Caitlin La Honta (25:37)

Yeah.

 

James Niehaus (25:38)

You got to be on top of your data to know which offers convert best in not only the immediate form collection, but also in the opportunity, you know, close won lost scenario. And again, it's never going be, it's still going to be, you're not going to kill it. Again, this like all of marketing is about, it's never about killing it. It's about understanding your mistakes faster and course correcting to the things that are, that are less bad. Like people would think like it's all about the, you know, your guru. No, no, like marketing 95%.

 

Caitlin La Honta (25:48)

Yeah.

 

Mm-hmm.

 

Yeah.

 

James Niehaus (26:06)

when consumers, especially 98 % don't convert at all. So you're failing the majority of the time. Your job in marketing is to quickly, with feedback, no, I should stop failing here. I should stop spending money here. This seems to have a slightly better promise of instead of 0.4 its 0.7. Like it's really around the margins of like, OK, how can I? That's why I go back to like maturities. Like an immature company will do a whole campaign for the entire quarter and then figure out next year, that didn't work.

 

Caitlin La Honta (26:11)

Yeah.

 

Yeah.

 

Mm-hmm.

 

James Niehaus (26:33)

A smarter company goes, we knew within a couple of days we weren't resonating, whether it be with data or feedback from the market. And that's all it is. quicker you can course correct your mistakes and capitalize on the small winners, obviously if something's killing it, you just get out of the way. And usually you're lucky you did it and you can't do it again. But the foundation really is, how do you fix your problems sooner? How do you capitalize on the small wins you have?

 

Caitlin La Honta (26:59)

Mm-hmm.

 

James Niehaus (27:00)

In my mind, that's 80 % of digital marketing and our marketing. The last 20%, you don't have control over, which is typically like product fit to the market and like certain brand appeal of the product. And that you just ride the wave. You pick the right company, you're scaling. And no matter what marketing says, very often you don't do that much to drive that. You just ride the wave and you just piggyback on that. But if you're not in a high growth company, you're pretty much trying to let's...

 

Letitia Rodley (27:23)

Yeah.

 

James Niehaus (27:27)

pick to the losers and cut the knots quicker and then find the small winners and wrap them up a little bit higher.

 

Caitlin La Honta (27:31)

Mm-hmm.

 

James Niehaus (27:32)

That's why whenever they reorg, like CMOs come in and change things, like no matter what happens, the revenues kind of stay the same because 80 % of the people are just trying to work on the margins and the edges. And therefore if you pause all that, the majority of the company still performs as we would expect.

 

Letitia Rodley (27:38)

sorry.

 

yet.

 

Caitlin La Honta (27:46)

Yeah, it's interesting when just hearing how much.

 

the scale piece actually matters. When you're at the level of scale you're at at Kaiser, like those margins of 0.04 % to 0.07 % or something do make a difference. Obviously, when you're in the B2B context in this industry, if your ICP is like 500 companies, you're not gonna potentially get that same, you know, that same change. But at the same time, we see a lot of leadership teams wanting to force more focus on...

 

digital marketing and measurement to like the level of a Kaiser, that level of sophistication. It's just an interesting conundrum because there's so much that happens outside of what you can measure in the B2B world, like we've all alluded to. And so we've seen the pendulum swing into the opposite where now a lot of companies are like maybe focusing too much on that and taking away from some of the more human elements of marketing in the B2B world of like brand and those types of initiatives. But for you,

 

it does matter to spend that to when you have that level of scale.

 

James Niehaus (28:47)

I mean, yeah, I'll get an example. I was doing optimizing AB testing for Salesforce, which for B2B is the biggest scale B2B company. And I had to leave that because for what I do, there's this more scale in a B2C organization. So yeah, it just depends on what you want to do. Obviously, in product marketing, Salesforce is like the dream job because it's all about positioning the product. Yeah, yeah, I digital and B2B is important, but it's

 

Caitlin La Honta (28:55)

Mm-hmm.

 

James Niehaus (29:11)

always going to be less important than the compelling content. Again, I'm very biased because obviously I'm from a direct response world. So I'm always about like, show me the money. Does it actually work? But I mean, I also recognize that marketing doesn't do as much as they think they do ⁓ in some scenarios, especially on the B2B side. So it's really about just, can you get people to align to your positioning on the product, you know, on the product? And do you have a good kill sheet and can you help?

 

Letitia Rodley (29:14)

Right.

 

Caitlin La Honta (29:20)

Yeah.

 

Mm-hmm.

 

James Niehaus (29:37)

someone in the buying position like be warmer when they go in front of sales. I mean, that's for the large enterprise. I would say if you're talking about like a low end B2B product, it mirrors B2C a bit more, but just depends on your price point and I guess the consideration phase.

 

Letitia Rodley (29:50)

Yeah.

 

Caitlin La Honta (29:51)

Did you ever thought I thought you were gonna jump in? ⁓

 

Letitia Rodley (29:53)

I think that's

 

a different podcast.

 

Caitlin La Honta (29:55)

Yeah, that's true. I know, was like, maybe we have a conversation around this, marketing's value at these different types of organizations.

 

But I only bring it up because I think we're seeing that on our side in terms of, you know, what is marketing's purpose? And if we can't measure it, should that be something we focus on in B2B? that's, I mean, I think litchi sugar is one of this, like we think that misses the point of a lot of...

 

what's happening that's subtle is the wrong word, it's a lot of marketing and B2B I feel like it's very obvious when you take it away, but it's hard to actually measure it in its presence because there's so many different ways that it can influence the sales cycle for these larger longer sales cycles.

 

Letitia Rodley (30:33)

Right.

 

James Niehaus (30:33)

It's

 

critical. I mean, it's it sets you apart. Just yeah, it's hard to measure. So to your point, don't know. I don't know. Yeah, I don't want to. You wouldn't. I wouldn't recommend spending all your cycles trying to like do true attribution in some ways. I think I think you, especially in a larger enterprise, like your customers, your prospects should be communicating to you like what resonated like it's that's where like I would obviously be on the closed deals, obviously. But like like you. So if I was a marketer in the B2B side it's more about

 

Caitlin La Honta (30:39)

It's hard to measure. Yeah.

 

Yeah.

 

Letitia Rodley (30:50)

Right.

 

Caitlin La Honta (30:54)

Mm-hmm.

 

Letitia Rodley (30:54)

Right, right,

 

James Niehaus (31:03)

As you probably do, talk to prospects and customers, understand what worked, what resonated, what compelled you. think that's the measurement part of B2B. Don't get me wrong, there's a foundational one. You've got to get volume of forms and leads and so forth. But as far as knowing what really drove the needle, that's where think data won't be able to tell you that in a meaningful way.

 

Letitia Rodley (31:21)

Right, right. And it also depends on the size of the enterprise, your point earlier. It's like a very different experience. You know, at now, I would say Veeva but Veeva's so big now, but like let's say Veeva back in the day when we were there four, five, six years ago, it was a very different experience than it would be now. And it was a different experience when we first joined when there was like 300 people. And it was all about, you know, kind of the marketing motions were really.

 

Caitlin La Honta (31:22)

Yeah.

 

Letitia Rodley (31:44)

And then as they created more product adoption and customer acquisition, then it became more and more about that kind of, to your point, James, like it wouldn't have made sense to dump all of our money into attribution marketing, because that's really not going to move the needle. What was going to move the needle was that customer satisfaction or the customer adoption or the enterprise relationship we had with those top 50 customers, et cetera. ⁓

 

Caitlin La Honta (32:07)

Mm-hmm.

 

James Niehaus (32:08)

Yeah, Veeva was very interesting experience. I remember this, when

 

Caitlin La Honta (32:08)

Yeah. Yeah.

 

James Niehaus (32:10)

I was interviewing the CEO, Peter interviewed me and it's like, we don't need to, we don't need to do Google ads, right? And I was like, well, maybe not. mean, like, like when I, after I joined, yeah, like Veeva is a great example where it's such a high price point, a very, very small, but known marketplace. Like the digital aspects of that is immaterial to the events, to relationships, the sales part of it. You just gotta figure out how to like not create friction really.

 

Caitlin La Honta (32:22)

Mm-hmm.

 

Letitia Rodley (32:30)

Right, right, right. Right, right, right.

 

James Niehaus (32:35)

keep

 

on top of, know, in their mailbox or whatever it is that's something you want to push them to.

 

Letitia Rodley (32:39)

Yeah, and I think to, and we could, I don't want to, we could go on and on about this, but to Caitlin's point, you know, especially some of the clients that we work with who are at the very, very small end of the scale from a growth perspective, early stage, you know, they kind of come with this toolkit that says we got to do digital ads. We know we got to do, and we got to do LinkedIn. And you're like, actually, those are probably the two areas you should not invest your money in right now.

 

Caitlin La Honta (32:40)

Yeah.

 

Letitia Rodley (33:04)

Content, absolutely. Strategy, absolutely. But it's really kissing hands and, or kissing babies, shaking hands and kissing babies right now. You know, when you're kind of building, you've to go get those early customer wins. You've got to prove your market fit. You've got to like build customer delight, you know. And then as you scale, you can start throwing money at Google.

 

Caitlin La Honta (33:22)

Yeah, I know it's like, why is my growth marketing not working? We're spending $10,000 a month on a LinkedIn ad campaign. It's painful. And then a bunch of bots, you get a bunch of bots. That's all you get for $10,000. I have one final question. Well, penultimate question before we get into our final question, which is always advice you have for other marketers. But just one quick question, switching back to the AI topic really quickly, because we talked about AI in the context of like our individual jobs and

 

Letitia Rodley (33:28)

Yeah, yeah.

 

Caitlin La Honta (33:49)

and how we sort of use it in our approach to it. But obviously, we're all N of one in the sense that when you actually think about AI expanding in an organization, and especially large organizations like a Kaiser, there is the reality of how quickly do we actually think AI adoption might happen. And I imagine lots of large organizations, potentially Kaiser, is also needing to be very careful with how they bring this in and in what ways they do so and not disrupting things, I guess.

 

What do you see in terms of a timeline for maybe larger, more conservative companies to bringing AI in? Do you have a sense yet of whether that's going to mean just tools or replacing staff? mean, we're seeing some things happening in other industries of layoffs and things because of AI, but curious if you have any thoughts on that or what you're seeing from the Kaiser context.

 

James Niehaus (34:32)

you

 

I guess I'll take it more broadly than Kaiser, but I think large companies, think they're laggers for various, you know, probably genuine reasons. So I think what's going to change that is either a change in leadership perspective or something changes in the marketplace, which forces them to adapt or die. So like that's what pushes laggers off of being, you know, last  to adopt. So I think that's the high level trend for the large companies. I think in terms of like

 

Caitlin La Honta (34:52)

No.

 

James Niehaus (35:00)

I guess what I'm seeing, I guess in the marketplace as far as, you know, impact on jobs. I I think the Spotify CEO memo kind of was like probably the best indication of like an early indicator, which he was saying like, there's no new hires unless you can justify why AI can't do part of that role completely. And I think that is the risk. And I think that's why being selfishly like, that's why it's important for me to get ahead of this trend because I feel near term, there's going to be like a musical chair scenario where

 

If you don't have a job now, if you have a job now, good. If you don't have a job now, there might be a point where people say, no, we're not hiring more. We have enough. We're going to get the people we have to get better. And that's more for the conservative, like the middle of the pack. mean, the innovators are going to take advantage of AI because it's a force multiplier. It's a leverage point. But if you're less mature and more or larger in scale, you're going to scale back. You're going to do and figure out how can I use this? Because when I think about a person, they're like,

 

Caitlin La Honta (35:41)

Yeah.

 

James Niehaus (35:55)

it becomes much harder to justify like we're talking about like bringing in an entry level person. Because we all know the ROI of an entry level person or agency or an offshore vendor is like three, six, nine, 12 months to get return on investment. Now it's going to say, well, if and plus now you know what you want. Like when I was as a manager, you always struggle with I know exactly what I want and I can do it right. do it myself, but I can't scale. So I need to delegate. And when I delegate, there's loss of control. I have to settle with the final product. So I think

 

Caitlin La Honta (36:00)

Mm-hmm.

 

Mm-hmm.

 

James Niehaus (36:23)

People who are on the other side who have experience, know what the final product should look like, I think you're going see them design systems that reduce the need to have entry-level cogs in the machine contribute to that. So I feel like, especially for my kids, entry-level, I don't know what entry-level will look like. feel like my advice to them is, OK, learn how to use those tools. Well, if you learn how use those tools, then you've priced yourself out of the entry-level market, and you should actually try to do your own thing.

 

Caitlin La Honta (36:35)

Mm-hmm.

 

James Niehaus (36:49)

Like I question what's going to happen. I feel like there's like a 20 year period of experienced people who are going to probably ride it out. I think entry level, I'm very curious about how that's going to go. And I don't know what happens next. And I think that's my concern for the next generation is I don't know what's left for them.

 

Caitlin La Honta (36:51)

Mm-hmm.

 

Yeah.

 

Letitia Rodley (37:04)

Right.

 

Caitlin La Honta (37:06)

I'm curious just this trend, you you mentioned the Spotify memo. I know what you're referring to like.

 

If that will still hold in life sciences and healthcare industry where there is such a deep domain knowledge. I mean, you could definitely store and replace some of that like tribal knowledge with an AI system. If a company is adopting something like that, like an AI knowledge base. but yeah, it's, it'll be interesting to see. feel like with this industry, you know, you never know. And then there's always the expectation that things will take slower because of also just compliance and regulatory concerns and all of those factors as well.

 

you

 

James Niehaus (37:40)

But think to your point, life science, like I go back to life sciences, that's a many year acquired knowledge. So that group will persist in my mind. But when it comes, hey, I want to hire some people to offload the work. They'd be like, no, like you have the skillset. Now use these tools to output what you need. So I feel like we have a window where like the vet, the people who are certain skillset will I think be able to persist because they're the ones that design the systems. But it's.

 

Caitlin La Honta (37:50)

Mm-hmm. Yeah, we can do it ourselves. Yeah.

 

Yep.

 

James Niehaus (38:07)

If you're coming into the life science space as a marketer, I think it's tough. It's a, it's a tough terrain.

 

Caitlin La Honta (38:13)

Yep, yep. Well, on that interesting note, do you have any, yeah, on the positive note, do you have any advice? I I guess it could be in the context of AI or just advice more broadly for marketers kind of in this space who are trying to navigate changes with all these new technologies and sort of the unknown that you would impart, especially in the context of this industry.

 

James Niehaus (38:17)

On the positive note,

 

Hmm. Um, no, just be curious, right? I think like in the end of day, like if you're especially coming into it, like you don't know anything. So you have to, I guess, put it this way, like there's no secret formula. Um, if you want to get ahead, you have to put in more time. Um, that's just, think that's just open historical reality. I think there's a, maybe a parallel path to take on top of that, which would be, you yeah.

 

go where the trends are, figure out how to like be on the other side of innovation with like say AI tools and maybe you can capitalize not only on what you know, but also how you can scale what you know with these tools. So I think there's maybe two windows of opportunity, just not just learn how to do your craft, but then figure out how to scale your craft in a way that's gonna be valuable to the marketplace, which in capitalist world is how to do more with less, how to scale with little.

 

Caitlin La Honta (39:17)

Mm-hmm.

 

Mm-hmm.

 

James Niehaus (39:21)

Those are kind of things I people are to want to see from people.

 

Letitia Rodley (39:22)

Yeah.

 

Caitlin La Honta (39:25)

Yep, yep, it's definitely gonna change.

 

I think you've already alluded to this. We've already sort of talked about this, just what roles there are, what an employee looks like, what teams look like. We could probably have a whole conversation on that. We have a lot of thoughts. mean, what we're seeing working as like factionals and consultants, we won't use the word agency because to your point, those return on investments are way too long, which is part of what we're trying to also upset. But a lot more companies are trying to hire like a mixture of maybe they have a couple of FTEs, maybe they hire some outside work, maybe they're using AI in some contexts. It's becoming a lot more like weird.

 

hybrid models versus my traditional team structure with FTEs for all these functions, then middle managers, then senior managers, then whatever. That's being broken down very, very quickly, especially maybe more in the startup world, the clients we work with. Maybe a little slower on some of the larger company side.

 

James Niehaus (40:12)

Yeah,

 

Enterprise, I guess the Enterprise gig economy is coming, right? So the equivalent movers and everything for the Enterprise, the fractional CMO.

 

Caitlin La Honta (40:16)

Yeah, yeah.

 

Letitia Rodley (40:17)

Yeah.

 

Caitlin La Honta (40:20)

Yeah, exactly.

 

Awesome. Well, James, thank you so much. This has been a really great conversation. A lot of interesting topics. Thanks again for joining us.

 

James Niehaus (40:29)

Thanks for having me, both.

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Episode FAQ

 

Q. How is AI changing the way healthcare marketers work?
A. AI enables faster research, better content synthesis, and scalable campaign planning in healthcare marketing. 

 

Q. Why is rapid iteration important in healthcare and life sciences marketing?
A. In highly regulated industries, speed-to-learning is more important than speed-to-launch. Sirona focuses on shortening the feedback loop between campaign concept and market reaction, enabling clients to quickly validate or revise messages across clinicians, procurement teams, and scientific buyers.

​

Q. What are the most common MarTech challenges for healthcare marketers?
A. Most healthcare companies struggle with underutilized tools, long procurement cycles, and poor internal alignment. Sirona helps clients operationalize their MarTech stack for measurable outcomes—prioritizing analytics, enablement, and compliance-ready systems that support the full buyer journey.

 

Q. How should AI be used in life sciences marketing teams?
A. AI is most effective when used as a thinking partner—supporting product messaging, competitive analysis, campaign drafting, and persona development. Sirona trains teams to prompt and iterate with AI to enhance strategy, not automate away expertise.

​

Q. What are the risks of overusing AI in healthcare marketing?
A. Without human oversight, AI can create messaging that lacks nuance, scientific rigor, or regulatory awareness. Sirona ensures AI usage is guided by deep domain knowledge, rigorous review processes, and a clear sense of ethical boundaries.

​

Q. Why shouldn’t early-stage healthcare companies rely on LinkedIn ads or paid search?
A. For most early-stage healthcare startups, paid digital channels produce low-quality traffic and poor conversion. Sirona advises focusing on thought leadership, early customer validation, and founder-led sales efforts before scaling outbound or paid media.

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