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EP55: How Does Pricing Analytics Impact a Company’s Pricing Strategy with Neil Biehn

Neil Biehn is the Vice President of Business Analytics at Siemens Healthineers. He has over a decade of experience in analytics, segmentation, sales effectiveness, and pricing optimization. Neil’s specialties include Analytics, Segmentation, Revenue Management, Price Optimization, Operations Research, Willingness-to-Pay, and Data Science. 

 

In this episode, Neil shares with us how Data Science helps in pricing.  He also deep dives into why pricing works better with data analytics allowing you to compare and assess different pricing strategies.

Why you have to check out today’s podcast: 

  • Understand how Data Science helps in pricing 
  • Find out the disconnect between product managers and data scientists in terms of pricing a product  
  • Discover why pricing work better with data analytics  allowing you to compare and assess different strategies 

 

 

“Profit as a guide for price is limited and should be used only if it is in line with the strategy of a company. You are willing to lose customers to maintain a profit margin, ultimately, your customers don’t care about your profits, they care about the price of your services or materials or any products. Let Finance worry about running your profits.” 

– Neil Biehn

 

Get COV’s Pricing Metrics:

The Most Important Pricing Decision in a Subscription Business Course at https://www.championsofvalue.com   

 

Topics Covered:  

 

01:22 – How his path in Pricing started 

04:38 – A description of his current job   

05:45 – How does he describe the relationship of  “business people’ in an organization with ‘data science people’ 

10:17 – Logical comparison of the engineering team with the data science team 

13:40 – A look at one case study of coming up with good pricing at Siemens 

19:25 – How does he help solve a company’s price erosion problem 

20:24 – His one valuable pricing advice  

22:10 – How it looks like finding a solution to problems the data science way

 

 

Key Takeaway: 

“If you have data and have the facts, why wouldn’t you use it? Every single data point you have about price, every purchase someone makes at a price point will give you an unbelievable wealth of information and you should use it.” — Neil Biehn 

 

People / Resources Mentioned: 

 

Connect with Neil Biehn: 

Connect with Mark Stiving: 

 

Full Interview Transcript 

(Note: This transcript was created using Temi, an AI transcription service.  Please forgive any transcription or grammatical errors.  We probably sounded better in real life.) 

Neil Biehn: Profit as a guide for the price is limited and should be used only if it is in line with the strategy of a company. You are willing to lose customers to maintain a profit margin, ultimately, your customers don’t care about your profits, they care about the price of your services or materials or any products. Let Finance worry about running your profits. 

 

[Intro] 

 

Mark Stiving: Welcome to impact pricing, the podcast where we discuss pricing, value, and today the statistical relationship between them. I’m Mark Stiving. Today, our guest is Neil Biehn. Here are three things you want to know about Neil before we start. He spent 14 years at PROS, one of the very first pricing systems companies. He became VP of Science and Research and led 30 data scientists there, which is a huge deal and he’s now VP of Business Analytics at Siemens Healthineers and does way more than just pricing. Welcome, Neil! 

 

Neil Biehn: Hey, Mark! 

 

Mark Stiving: Hey, how’d you get into pricing? 

 

Neil Biehn: In 2001, I got a Ph.D. in Operations Research and Mathematics and you know, at the time, if you wanted to go into academia you could have some options, some interviews lined up, but I wasn’t that interested in academia. I wanted to apply it and go into the job force. And there wasn’t a data scientist around that. I mean it really was very little. I did an internship at Boeing and that looks promising, but it really wasn’t that many jobs around in pricing and specifically, airline pricing was a place where there was a lot of activity for people in operations research. Yeah. PROS interviewed me and they hired me in 2001. 

 

Mark Stiving: So the truth is you didn’t really care about pricing. You cared about the data. 

 

Neil Biehn: Yeah, I do Applied Mathematics to solve problems. And I was excited to do it and I had no idea what I was getting into. I remember my first day of the job I walked in, this is how clueless I was about companies, anything to do with working. I have two people sitting next to me and they were also on their first day and I said, ‘Oh cool, what are your guys’ PhDs in?’ I’m in marketing and I just got a bachelor’s, and the other persons are an accountant. Pricing was brand new. 

Mark Stiving: Well, you got to give PROS huge credit for hiring data scientists that far back. 

 

Neil Biehn: Yeah. I mean that’s, that’s what they are. That’s what they’d be called today. And they were just called signs back then. Yeah. Now, we’re special , we’re the sexiest job you can have, right? Number one job, five years running. I mean, it’s pretty good stuff, but it was great back then too. And I think that it really was, it’s just the data, the technology, it was in the airline industry at the time and it hadn’t made its way really to a lot of other places. There was some logistics companies. I think the supply chain was still kind of a big topic back then. 

 

Mark Stiving: I think it’s pretty fair to say that absolutely nobody who’s listening to us right now wants to go become a data scientist. Yeah, exactly. I mean, most of us probably are already in our jobs, but here’s what I love about talking to you is, you somehow transition from being a real nerd to being a business person and how do we go solve business problems instead of crunching numbers all the time? Yeah. What I’d like to explore today is how do we help those people get better at working with statisticians or data scientists so that together we can, we can solve real problems. 

 

Neil Biehn: Yeah, that’s fair. So first of all, you can’t take the nerd title away from me. Yeah, we’re just adding to it. So if you want to add a business person to this, I know I’m okay with it, but you’re not taking her away from me. That’s important though. It’s just to say that you know what you have to have, you have to keep your nerdiness, really important no matter how high up you go. 

 

Mark Stiving: Do you still crunch numbers yourself? I’m just curious. 

 

Neil Biehn: Yes, of course. Of course. It’s limited. We’re going through a big transition right now, so people got to move code and all this stuff around. And my load is considerably lighter to the point where I’m going to just let it die and rewrite it because I do mostly either research projects, which I’ll save that code of course, or I do more one-off activities where I write the code for kind of, that’s a one-on-one kind of, wouldn’t it be interesting to, and then if it ever turns into anything that I do and it looks like it needs to go into more of the production environment, it’s not fair to have me on the critical path of things, but yeah, I do a lot of this sort of leading-edge stuff and trial and error stuff for sure. 

 

Mark Stiving: I see that there’s this huge disconnect between the people who I would consider true business people, so we’ll call them product managers, product marketers, people who are going to set prices, and then the people who understand how to crunch the data. There just seems to be this huge disconnect between these two. Do you see that and do you see it getting better? 

 

Neil Biehn: Back in my days at PROS, we would walk into many companies and we would see, you know, we would see different parts of the organization interact, right? I got to probably go in and sell and implement it probably 60 different companies, including airlines, and that’s how companies and a lot of B2B, a lot of B2B companies. Yeah, you could say it. I mean, you see it in different forms. You see it in different ways. You see it when it works really well. You see it when it’s sort of strained and you just see every model out there and I think it’s tough, I think. Yeah. How do you take someone who sees themselves as a hardcore programmer or mathematician who doesn’t want to learn the business? Uninterested, right? They’re interested in doing math and writing code. How do you get them to interact with someone who’s not interested in writing code and not interested in doing that? It’s a tough problem to solve. It really is. 

 

Mark Stiving: Okay. I’ll just ask the hard question. How do you solve it? I would’ve seen that works. 

 

Neil Biehn: There you go. Yeah, and that’s the magic, right? That’s the key question to ask. How do you, how do you get at it? And I don’t know if I have the answer. I have an answer that we employ here. And one of them, I mean you’ve talked about before is, one, you have to have people on the data scientist team that care about the business. That you have to have it. If you’re only a shop, you’re a factory floor, then you’re going to get treated like a factory floor. And I gotta be honest, that’s not much fun. I don’t know anybody who has that much fun as a data scientist, it is micromanaged. Well, the stakes are made a lot of lost in translation. 

 

Mark Stiving: Well, can I interject something for a second? One thing I often say to companies is everybody in the company wants to work on a successful product. And I would guess this is true with data scientists as well. 

 

Neil Biehn: Very much so. 

 

Mark Stiving: Okay. And so in order to be on a successful project, you need to solve real problems that matter in the marketplace. 

 

Neil Biehn: It seems like a no-brainer to have to talk about this to some degree, right? It should be a self-evidence and it is like you start to, when you interact with other people, you start to see they don’t see it that way, right? They either it’s selfishness or maybe they feel ownership or pride in what they do and they want to be known for that and not for something else. So you can see some of the different motivations that can cause challenges.

 

This obvious thing that says, look, you want to successfully work on things that are needed and that’s what, that’s one of the solutions, right, is that we have people on the data science team that cares deeply about analytics. We have some that care deeply about the business problem and we have some they really liked the back office stuff and don’t really want to be bothered. They’re okay with you coming to them and saying, here’s exactly what I need. They’re okay with it and they can be on the team too. I just can’t have all of them. I have some people on the team that, you know, they’re not rock star coders, they’re just not. They’ve never been. They grew up through reporting. They never learned formally how to code. They know how to code.

 

That just sounds good that there are other people, but they’ve been here 15 years. They’ve been here 20 years, 25 years. On the other side, if it’s a marketing team or an operations team, they can’t, we’re going to ask with a business problem. I don’t want to hear, I need a dashboard or I need something that predicts, no, I don’t want to hear what you think you need. We’re going to solve the problem together and you mandate it. You don’t let them get away with here’s a ticket. I wrote you an email about what I needed. Can you just deliver? No, actually I’m not going to. We’re going to sit and talk about the problem and so I feel like I can wear your shoes so that when I go back to code all the thousands of micro-decisions

I’m going to make while coding,I have all the thoughts about this problem I’m solving in the back of my head because you can’t tell me exactly what to do. No one can do that, it takes time. You have to let me make micro-decisions on how to solve the problem and if I know the problem well, I’m going to make those micro-decisions. 

 

Mark Stiving: What you just said is exactly what we teach product managers and how to interact with engineers who are developing products. Product managers can’t say, go build this, engineering team really has to understand the problem in order to do a great job solving the problem and that’s exactly true with your teams as well. 

 

Neil Biehn: That’s great. I mean, I think it’s a standard. I mean, again, how self-evident do you need to get, I mean, look, at the end of the day, you’re an engineer and a scientist and scientists are scientists. We follow the scientific method. No one says to a scientist like a hardcore scientist in academia, we want quantum computing by end of business Tuesday. It doesn’t happen in that field. Everybody knows that’s silly. Right? Yet in business it’s perfectly fine to say that I need it. I need an algorithm that predicts the number of clicks that are going, whatever. Okay. Right? Guess what, you can’t have it that, it doesn’t work that way. Let’s start by experimentation. We start by learning about the problem and you know it’s walking around the office and saying those things over and over and over again for four and a half years. I finally feel like I’ve trained people around here that it’s coming. When you ask us for stuff, that’s what you’re getting back and it’s shown to be very rewarding. They learn a lot about the business too and it just shortcuts all the problems with miscommunication because once we both had a deep understanding of the problem, then it’s just about solving it. 

 

Mark Stiving: Yeah, nice! 

 

Neil Biehn: Junk is removed, right? You don’t even know what the problem, you didn’t even know that solution’s actually going to be at the end of the day and you don’t even care. All that is forefront is that we’ve solved the problem here. 

 

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Mark Stiving: I think when you walk in you want to know that it’s solvable and what the problem is. And then let’s go figure it out. 

 

Neil Biehn: Yeah. And if you don’t know what’s solvable, there are things you can do in the short term and see. I call it looking for is scraping the surface and seeing if there’s gold there. You can do just a little dig and you don’t have to dig out the entire gold. You’d want to see if it’s there. Let’s do a test, right? There’s a lot of fishing expeditions that you have to be careful not to waste your time on. 

 

Mark Stiving: Hey, do you have a case study that we can talk through just for kicks? 

 

Neil Biehn: Yeah, sure, sure. This is Impact Pricing. So probably a pricing example. If you want a good pricing example, PROS has about a gazillion white papers you can go download. I was part of several of those in the past. I haven’t been there for four and a half years, so, all of the later ones won’t have my name on it, but they have so many success stories. What a great company with a great piece of software and a great science team that, you know, you’re going to get a ton there. Here at Siemens we have our own, though. A couple of years ago I came here from PROS, I think, you know, I kind of wanted to come in and just push pricing on everything and I did a good job I think of just not doing anything pricing for the first year. Well, I also came in with solve the business problem and if no one’s walking around saying we have a pricing problem and you’re saying we have a pricing problem without learning the business, I think you’re doing it wrong. So I need to practice what I preach. There just wasn’t a lot of screaming about it.

 

Even though I secretly after everybody went home away from work, I would look at data and say, yeah, we have, you know, I didn’t scream it from the mountaintops or eventually we had this unique moment in time where we had shuffled up the marketing team. We had a new leader in the marketing team, new roles, and responsibilities and in particular, the pricing approval process was getting changed completely and it had been kind of a rubber stamp situation to some degree. I don’t want to label it too much like that. People not just filling it in, but it was hard to see where we were being discerned about Pricing. So we got together and we said, ‘Okay, how are we going to give the approvers something that is useful?’

 

Because the point of the problems that they all have is that they don’t really know what a good price looks like. I mean, they look at a bunch of tough stuff, but they still don’t really know. And so I sort of raised my hand and said I can help. And what is it like the second chapter of them that McKinsey book by Zawada and Baker, right. Where it has the histogram of Pricing. I mean that’s simple, right? And so a little bit of a segmentation work to make sure that we’re grouping customers and products in peer groups so that when we do that histogram, it is an apple to apples and then also providing basic percentiles right across the histogram. So you can either view the history visually or if you want it numerically, I can pull the person often just depending on how people wanted to consume it.

 

This process was so simple, you can’t ever request for approval. You log on to the dashboard, our click view dashboard, and you type in the segments’ information. It takes about 17 seconds to do that, three clicks and you have a histogram in front of you and you have the price that you need to approve in front of you as well. And you look where it sits on that histogram. And if it’s the cheapest price Siemens has ever seen, you’re going to need a pretty good explanation of why we’re selling at that level. 

 

Mark Stiving: That’s pretty one reason why they say no? 

 

Neil Biehn: Yep. And not only that, but you send the picture back. Every other salesperson on the planet has been able to get a price much better than you. And sometimes they come back with really good answers. They also limited the number of scans they’re going to do on the CT machine to only 200 a year. We’ll never have to service that thing. My lights went out. That’s always fun. Hey, seems it’s about saving the planet so it’s going to turn off the lights if it doesn’t notice. So yeah, they’re really good reasons for some special pricing and everybody knows it.

 

The difference now is that you have to provide that as well. And the results were, so we have contracts that we renew and the contract is come up for renewal and we were seeing a pretty substantial amount of price erosion. In other words, the new contract signing, it was quite a bit where there was quite a bit of erosion on the new one from the old one. So let’s say if I had a hundred dollar contract, the next one comes back might be $90.

 

Right? So the next five years, so that would be a 10% erosion, for example. We were able to reduce that, right from let’s say 10% something like 3%. So obviously if you do that, those weren’t the exact numbers, but if you do that, you’re talking about serious money. Right? And you know, you don’t get to go back into your historical contracts and raise them. That’s not most people salespeople are interested in that for good reason. But when you have the events right where there’s a renewal coming, right, you have the opportunity that points to try to protect it. 

 

Mark Stiving: Let me step back and talk about the business for just a second. You have the advantage of having walked in and done way more complicated pricing problems than the ones you’ve said. 

 

Neil Biehn: Oh yeah. 

 

Mark Stiving: And somebody had to have come to you and say, ‘Hey, we got this price erosion problem, how would we solve that?’ 

 

Neil Biehn: One is, I did not break out the words machine learning, automatic price detection algorithm that the price optimization, I didn’t do all those things. What I wanted to make sure everybody understood was that this is super easy and you can make a big impact.

 

Did we, at the end of the day, the price erosion gains that we got were not on the typical transaction. We stopped really stupid and then put your favorite curse word there, right? We stop this stupid stuff and then that’s what we did. We didn’t really optimize this thin veil of prices or you know, squeeze the orange or something. We didn’t, we didn’t do that. We just thought we’ve made it easy.

 

Change is hard. People got new tools they have, they want to create good relationships with salespeople and they want to own it. They don’t want the machine to determine that this, they’re making the call. Whether or not it’s a good price or not. 

 

Mark Stiving: No, I think that’s fascinating. That’s fascinating. So I think we’re going to have to wrap it up here in just a couple minutes, but if you were going to give our listeners a piece of pricing advice, what would you give them? 

 

Neil Biehn: Yeah, number one is if you have data and have the facts, why wouldn’t you use it? Every single data point you have about price, every purchase someone makes at a price point will give you an unbelievable wealth of information and you should use it. Number two, profit as a guide for price is limited and should be used only if it is in line with the strategy of a company you are willing to lose customers to maintain a profit margin, that’s fine if that’s your strategy then. But ultimately your customers don’t care about your profits they care about the price of your services or materials and your products. If you need to be involved in market research, you are willing to pay and those as a product person or marketer should be your almost laser light focus. Let finance worry about running profit numbers. 

 

Mark Stiving: And that was beautiful. I absolutely loved it. I actually have to ask him to follow up question on the first piece you gave and that is if you’ve got the price data, it’s really valuable, you should use it. Here’s an interesting question. What if I handed you a whole bunch of data? How can you tell me what interesting findings you could get out of that data? 

 

Neil Biehn: You’re going back to the start of the podcast, ask the questions, research, right? Find the problem. Where’s the pain point, right? Get in it, get into the business and get those hypotheses out of people. 

 

Mark Stiving: I see it as two different sides. And by the way, I’m with you, right? But I see it as two different sides. I see the, let’s say product manager says here’s some problems that I want to go solve. And yet I see that there’s all these data that maybe we can go solve problems the product manager hasn’t thought of. 

 

Neil Biehn: Yeah, I mean you can, so you just have to be careful with it. So you can, you could imagine like throwing up 500-piece-puzzle on the floor and trying to pick out the red pieces because you know they’re probably going to go together, right?

You can do this kind of the same kind of thing with data. You can get in there and start showing things. Hey, I looked at it by geography. I looked at interesting things. I look at price points by geography. I looked at price points by product type. I looked at margins by a salesperson. I looked at and I think I found something interesting. My CFO here at Siemens, she, just retired. She liked doing that a lot. And I doubtedly, I said, look, I’m not so sure about this. We need some hypotheses. She’s like, ‘Nope, I want the data. I want to look at it. I want you to make me a dashboard and let’s explore’. And we did. I wasn’t going to fight that for so long. And you know what? Interestingly enough, we found some really amazing things. We found some really cool things. It wasn’t clear how to take action on it though, right? Because there was no control variable.

You know, there was no you didn’t tie it from a pain point to a control mechanism to how are we going to implement to, is it actionable that you normally would do in an analytics project? Finding really interesting in the data, but yet you’re not sure and it’s a little dangerous if you sort of post them on the wall and say, look, I found something interesting. Right? Who knows how it will be taken. Right. What if you find that a salesperson only sold two big deals last year and basically took the rest of the year off? What did you find that, what are you going to do about it. I mean, are you gonna post that on the wall? Do you tell the boss? It’s fascinating, right, that he was able to do that or she was able to do that. 

 

Yeah, and then when you find those correlations, you often infer causation from it. It’s just horrible. 

 

Well, but you know, if you’re in marketing, you know, something simple as you’re not going to, no one’s gonna die. But yeah, dangerous. Right? 

 

Mark Stiving: No one’s going to die, but we’ll lose money. Maybe. 

 

Neil Biehn: Yeah, you could lose money or are you just wasting more often than not, you would just be wasting a lot of time. 

 

Mark Stiving: Neil, I love talking to you. This is just so much fun, but we do have to wrap it up. Thank you so much for your time today. If anyone wants to contact you, how can they do that? 

 

Neil Biehn: Yeah. As I said, I think the last time I talked to you, I’m the only Neil Biehn on the planet. You just have to Google me on LinkedIn and find me. I’m more than happy to talk to you over LinkedIn and I think it has my email there too. So just reach out and I’ll be happy to speak with you. 

 

Mark Stiving: Excellent! Thanks, Neil. Episode 55, is that a special number? 

 

Neil Biehn: Not that I know of. I can tell you that. 

 

Mark Stiving: 55 is all done. So let us know in the comments what you thought of the podcast. While you’re at it would you please give us a five-star review wherever you download it and listen to this from. Don’t forget, we have a community at championsofvalue.com and if you have any questions or comments about the podcast or about pricing in general, feel free to email me, mark@impactpricing.com.

Now, go make an impact!  

 

**Note: Mark Stiving has an active LinkedIn community, where he participates in conversations and answers questions. Each week, he creates a blog post for the top question. If you have a question, head over to LinkedIn to communicate directly with Mark.

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