Want the latest news and articles delivered right to your inbox?

February 23rd, 2022 (Updated 03/08/2022) | 24 min. read

Gabriel Smith
Chief Evangelist at Pricefx

The Benefits of Flexible, Simple and Transparent Pricing Software – Part 1

Flexible, Simple and Transparent Pricing Software is a key to driving profit growth in today’s modern business world. What’s more, pricing can help maintain profit growth even in a declining market, as we’ve learnt over the last two years since the global pandemic reared it’s ugly head.

Join Gabe Smith from Pricefx and Stephen Haggett Vice President, Pricing, Revenue Management, and Sales Operations at Iron Mountain, as they discuss The Benefits of Flexible, Simple and Transparent Pricing Software. Learn about the Role of AI and Pricing Optimization in driving fair prices, using pricing to keep your business growing in declining market conditions and how a flexible pricing strategy can help deliver simplicity to a complex market.

Listen to the podcast with the embedded player below or read along with the transcript. You can listen to the podcast on Apple PodcastsSpotifyStitcher, or wherever else podcasts are hosted. Want to see the conversation and not just listen? Watch on YouTube. 

Who Is Iron Mountain and What Do They Do?

Gabe Smith (00:41): 

Hi everyone. And welcome to pricing matters today. We are pleased to have on Stephen Haggett, the global VP of revenue operations at Iron Mountain. Welcome Steve, and thanks for joining us. 

Stephen Haggett (00:51): 

Thanks for inviting me, Gabe. Good to participate. 

Gabe Smith (00:53): 

It’s my pleasure. And it’s always great to be talking with you. So, for folks that don’t know about you or what Iron Mountain Iron Mountain does maybe you can start off by telling the audience a little bit about that? 

Stephen Haggett (01:05): 

Sure. We’re a large global B2B service provider. We manage a lot of corporate data. We manage cloud data. We store data on tapes. We manage physical data. If you’ve got a mortgage or if you’ve got tax records or things like that companies tend to store that with Iron Mountain. Iron Mountain is pretty global. We support the vast majority of the world’s large companies and a lot of small companies around the world.  

I’ve got a couple of roles. One is global pricing and revenue management. So I manage our new customer per pricing. How do we negotiate prices with new customers, the renewal pricing, which is a big deal. We’re a very sticky company. Customers tend to renew with us for decades. And so we oversee that deal review, new product price, anything having to do with managing price globally. Also I lead a global sales, operations and sales enablement. So our go to market approach sales training, sales, recruiting, and staffing, and so forth. 

Gabe Smith (02:13): 

Yeah, you got a lot under you and it’s a, it’s a complex business. I love the origin story behind Iron Mountain. Do you mind sharing that a little bit about how you got the name? 

Stephen Haggett (02:21): 

Yeah there, there really was an iron mountain in our history. The company was founded 70 something years ago by a mushroom farmer in postwar America. And he was growing mushrooms underground as mushrooms tend to grow and had purchased an old iron mine in upstate New York where he was growing mushrooms and in postwar discussions he’s talking to other business leaders and talking about the problems of their European partners who had lost all of their bank records.  

All of their employee records, all of their customer records in the war. And this is the beginning of the American cold war period. And so he said, ha. So, you know, I could, I could store bank records from New York city in my Iron Mountain, mine in upstate New York. And he started storing bank records next to his mushroom harvest. And eventually that became a records management business that originally was the Iron Mountain atomic storage company. And that was a little scary.  

Gabe Smith (03:35): 

That’s cool. And, and you guys have some pretty noteworthy are the historical items as well. Artifacts that I remember on your website that you had some pretty cool stuff. I don’t know if you have anything off the top of your head there that –  

Stephen Haggett (03:47): 

Oh, we store a lot of sort like the original photos from the whole Getty collection of all of America’s famous photos. When you see these black and white photos of people sitting on a skyscraper building New York city and the haunting eyes of the immigrants from the sooner expansion to California and so forth. A lot of real cultural legacy items that are stored insecure mind vaults. You know, we still have a lot of underground mind vaults, all of the original recordings from the thirties and forties films and so forth. We store a lot of art a lot of recorded history when, you know, folks like Steven Spielberg recorded the Showa testimony, we store anything like that, that has great cultural significance along with your healthcare records and your mortgages. 

How Pricing Helped Iron Mountain to Maintain Growth In A Declining Market 

Gabe Smith (04:41): 

Nice. So your business is evolving though, right? So, the core business of storing records and physical documents is, is in decline overall, right? People are producing less paper and you’ve moved into data management and storage. And then obviously the, the shred business is, is still a big part of your business as well.  

So, I mean, I think that that’s part of why your job is so key to the success of the company, right? In terms of revenue management and pricing and revenue operations is because the large part of your core business is in decline and picking up new business, getting pricing right, is really important. And you’ve got some pretty aggressive goals.  

You, do you wanna talk a little bit about that? The role that your team plays in the company’s success, and I’ve seen a lot of different pricing teams and, and what their goals are and, and how they get those handed down from the, you know, the C level. And I gotta say that your goals and, and your ability to execute on them are quite impressive, I would say. 

Stephen Haggett (05:38): 

Yeah. And this is all public information. Anyone that would enjoy listening to the analyst recordings when you have a quarterly release, we’ll hear all of this price really does drive our profit growth. Price is the key lever for profit growth. As you said, this is a mature company. Customers are very sticky. They renew for a long time. And we, we are a company that has grown up through a lot of acquisitions and where pure B2B negotiated sales and any, any of the folks listening that live in that world recognize that a legacy of acquisitions with B2B sales, it’s an environment of price variation.  

You just have a lot of price variation customer to customer because, you know, every company we purchased, every salesperson that sold all over history, didn’t set pricing the same way. And so we’ve got, yes, very aggressive dollar targets for price growth that, you know, drive all of our profit growth. But our strategy, I think is very simple and we can recite it very easily. With four words, we will price fairly. And in a world of a lot of price variation, when you’ve got one customer paying you a dollar and another customer paying you 50 cents for the same thing, I don’t think that’s fair. And so we try, try to use price strategy to reduce that inconsistency, reduce that variation increase the fairness in customer pricing. And at the same time that drives a lot of profit growth. 

The Role of AI and Price Optimization in Driving Fair Prices 

Gabe Smith (07:13): 

And I think it’s a good example of where you’re using data and AI based optimization to drive better decision making and more consistency and fairness and drive out some of the, the noise, so to speak, right.  

And the bias that come, come in when you have a complex environment like yours, where you’re operating in many different markets with many different sizes of companies, and you’ve gone through acquisition. And so you do have a lot of potential bias in a, an environment like that. Right. And so can you talk a little bit about, you know, the, the bias and the noise that you’ve seen and what you do about it 

Stephen Haggett (07:50): 

Bias and noise is a fascinating topic. And we’re doing a lot of work on that when you talked about using AI to drive out bias, and that’s where I’ve had, you know, a discussion with price effects. I’ve had a discussion with consultants in the industry. I’ve had a con dialogue with folks from Google and folks from Oracle that help us manage our corporate data, because we are concerned about some of the black box elements of AI.  

You know, AI is a pattern seeking tool, right? AI is looking for patterns in your data and not all of those patterns are the two B state that we want our pricing to be in. And in a lot of instances, AI simply reflects the biases of the past as, as you note. And so, when we implemented our pricing strategy, when we implemented a pricing tool at Iron Mountain, we went very light on AI, such that we could build a rules-based much more simple, much more flexible tool, no black box micro segmentation that the sales team couldn’t understand that I couldn’t understand. Instead we designed something that was much more simple and sort of rejects some of that pattern, seeking AI for simplicity, flexibility, ease of adapting as we change our, our decisions, change our settings. 

Gabe Smith (09:21): 

I remember, you know, first interacting with you and the flexibility really being key, because you do have a lot of different factors that influence how you go to market in any you know, given region and the factors that influence what is fair with regards to their pricing, because the, your different parts of the business are very different in terms of what those drivers are. Right. So I remember tips and trips, right? In, in shredding. It’s all about you know, we might have a base price that’s based on kind of a price curve, but at the end of the day, there’s a lot of factors that influence the cost, serve an account and how much value that they’re getting out of it as well. And those drivers are very different than the core drivers and data management. Right? 

Stephen Haggett (10:04): 

So in a shred business, you want to have one trip with a lot of tips, not one tip for a lot of trips. And for those that don’t happen to come from the shred industry, that simply means you’ve got a truck going out on a route. And if they go to a site where there are a lot of bins of, of paper to be shreded that we’re servicing, that they hit over into their truck, that’s very efficient and we’re willing to price at a lower per tip rate. Then if you’ve got multiple trips for one tip. And so it’s, you know, numerators and denominators and cost efficiency, route density and things like that. But yeah, we wanted to be able to use factors like that in our pricing that just get really complex with some of the, you know, the tools and proprietary algorithms that are out there. And so where we had trade offs between granularity and AI, robust technology and simplicity and transparency, we always opted for simplicity and transparency that comes back to the, the issue of noise, which, you know, allows us to see that variation and start to manage it. 

Gabe Smith (11:19): 

I think that’s the idea of being, you know, data driven, but keeping it really as simple as possible, and basically taking out a lot of that complexity allows you to, to separate the signal from the noise more effectively, right. And, and be more transparent, being able to move more quickly and being able to explain of stuff to the sales people. I mean, that’s really one of the keys, right? If, and one of the challenges with, you know, complex AI based price optimization, a lot of times is trying to explain it to a person that’s not a statistician that in some cases might not even have a college degree. Right. And so, although I’m, I’m sure most of your, your salespeople do have some level of familiarity with some of these concepts, but at the end of the day, you know, saying, okay, this customer’s getting this price because they order this much here and they’re similar to these customers. 

And this is kind of where we start the conversation and then having a set of kind of common sense rules. Like we were just talking about to adjust things from there is inherently a better approach in a lot of, in a lot of markets than having, you know, a black box approach where you just plug in a bunch of data. It’s applying some, some magic in coming back with a number and trying to explain that to a salesperson is you’re gonna be lost. And then they’re not gonna have com confidence in it. And they’re not gonna be able to go get the, the outcome cuz a lot of negotiation comes down to confidence, right? Giving sales people that that confidence in you know, how your position versus your competition, what you’re using to factor into the pricing and, and why it’s fair. Right. And explain it to the customer. 

Stephen Haggett (12:51): 

Why it’s fair. Yeah. Yeah, absolutely. And I, I think that’s, that’s a key part of our strategy and what has worked so well to enable us to achieve these targets is that it’s so simple. It’s understandable. And it, it just, as you say, it drives confidence, we measure pricing compliance. What is the ratio between what the salesperson closes a deal at to the price that the revenue management team recommends. And that just skyrocketed after we shifted out of a much more complex micro segmentation methodology to this much more simple, pure pricing methodology, because just as you say, salespeople could understand why is this a fair price?  

The pure pricing approach that we use says, this is what your peers pay. And the benefit that we have with this big global company is that we’re very highly penetrated and we’ve got a very good view of the market. And so we know what price is fair for different types of situations. And so now the salesperson knows I can win at this price and this customer can look around and with transparency, see that similar customers are paying the same price. And so they’ve got a lot of confidence and that just dramatically reduced that level of variation that we were experiencing in the market. Good. 

How A Flexible Pricing Strategy Brings Simplicity to a Complex Market

Pricing-Strategy-Working-At-Laptop-on-desk

Gabe Smith (14:16): 

Yeah. That makes a lot of sense. So we, I think we touched on simplicity fairly well. You also mentioned flexibility and I, I know, you know, I remember when we had the initial interactions with Iron Mountain, that was a really key factor because you had dealt with some, you know, some technology previously where, you know, it was overly complex to not really explaining the results and it wasn’t doing a great job necessarily at making recommendations and you couldn’t kind of correct it because it was a black box. Right. And it was overly complex, so that simplicity helps fix that. But the other key thing that you really, I remember being a really a key decision criteria for you and is for a lot of our customers was around flexibility and being able, you know, make the changes that are needed for any given market. 

And Iron Mountain operates in, I don’t know how many different countries, I think, 40 plus countries, 60 countries. Yeah. And I don’t, I don’t know how many we’re live in now there, but I know our friend, Tom spends a good bit of his time, you know, traveling around and trying to figure out how to get better adoption and how to adjust strategy and tactics try to align them as much as possible. But at the end of the day, a system like this in a global company, that’s as large and as complex as Iron Mountain really does need to be flexible. So I’m curious as to your perspective on that and, and you know, why you saw that as kind of one of the key criteria for, for pricing technology. 

Stephen Haggett (15:40): 

Yeah. One of the important deliverables for a pricing team is the ability to measure customer price sensitivity, or if you’re an economist price sensitivity of demand, and you can only do that by testing different levels of pricing. And we constantly adjust the settings on our pricing tools to say in this situation, what’s the right price. And we want to be able to constantly adjust that and not have to go back to a, a software architect to Recode something. And so when we put this strategy in place, when we put our tools in place yeah, flexibility was the key differentiation for us. I wanted the ability for the pricing team to go in and make adjustments so that we could repair test and be, you know, a little more disciplined, a little more disciplined in managing that level of variation and see how does that impact customer behavior. 

And that way you’re not going to the sales team and say, well, look, the right price is a dollar. This customer’s paying 50 cents double it. What we’re doing is, is gradually understanding. How can we drive greater, greater fairness and what are the, the guardrails for how we can adjust customer pricing? And we’re not going to the sales team with a hundred percent change all at once. But if we’re saying last year, we adjusted these underpriced customers by 12% and we didn’t lose any because there’s still 40% below market. We’ll try 15%. And if 12 work, we think 15 will only for those really underpriced customers that simply bring them closer to the market and for the, or if you’re seeing a 15% price increase, you know, that you’ve got the best deal in town still because we’ll only do those very significant price adjustments to the customers that are well below market for some, you know, anecdotal legacy reason. 

Gabe Smith (17:52): 

Yeah. And, and I think that that kind of accessibility and the ability to, to kind of test hypotheses to iterate is really key when we think about, you know, noise. And we think about trying to get down to kind of the fundamental drivers of willingness to pay and of, of sensitivity and, and things like this. I know you’re a huge Daniel conman fan. And you know, you’ve kind of, we’ve had some conversations about the drivers of irrational or suboptimal decision making. Right. And we, we talked about kind of the, the role that complexity plays in that and the role that, that bias plays in it. But do you wanna maybe give the audience a, a little bit of background on Iman’s kind of theories and research, and then we can get a little bit more into some of the implications of that to, to pricing and to, to your role in your 

Stephen Haggett (18:39): 

Team? Yeah. I think there’s really been an explosion of thinking in academic economics that is very applicable to what we do in the pricing world. And it all falls out of the research that Dan Conman was performing with his partner, Amos Erky. And for those folks that aren’t familiar with Dan Conman there’s a hundred podcasts that talk about Dan conman, none focused on pricing other than this but conman’s research basically took on big economics and said that a lot of the foundation of classical economics isn’t exactly right in that it’s based on people with complete information, making rational decisions. And we aren’t designed to make rational decisions. You know, as we said earlier, we’re designed with some bias and there’s a lot of societal bias that, you know, we see right now in politics in society that we’re working very hard to address, but there are also neurological biases outside of social issues. 

You know, we are generally risk averse. And so classical economics will say if your expected value is higher between a two decisions you should ex you know, if the expected value is different, you should take the decision with the higher expected value. But, you know, if your annual bonus is $50,000 and instead marching comes to you and says, we have a new bonus plan, yours isn’t $50,000 anymore. You’ve got a 10% chance at $600,000, you know, economics would say, well, that’s great. You know, your expected value now is $60,000. Everybody should take that.  

And nobody will because you have a 90% risk of nothing. And that pain far outweighs the benefit of a windfall and conman in his research noted a whole series of these illogical approaches to decision making, according to economics. And we’re able to, you know, better understand how humans make decisions and based on his work, there’s been a lot of, of research done on bias and bias is a horrible thing and is ingrained into a lot of our decision making. 

And, you know, as, as people were trying to overcome that, but there’s also noise and the way conman describes bias and noise, he said, if you are in, in the example, in his most recent book noise, he starts out by saying, imagine you’re looking at a target and people are shooting at a target. And, and the first example, you see five hits inside the bullseye. So that’s accurate. And the second target is scattered all over the place and that’s noise. The third target, they’re all down into the left, still centered together, that’s bias. And then if there’s scattered down on the left, that’s bias and noise, and his research points out that absent bias noise is a much larger problem than society and companies realize. And his research has identified problems. For example, with judicial sentencing, where you’ve got, you know, hanging judges and bleeding heart judges and presented with this same case, different judges with the same law will sentence people very, very differently, removing gender, race, age, and so forth. Just because one person is more lenient than another. And we face things like this in pricing where people just not consistent decisions in individual situations. And so, you know, when you, when you look at all of this research on noise it’s for a pricing person, a huge problem, and a huge opportunity to tackle. And so at Iron Mountain, we’re doing a series of noise audits where we’re trying, trying to quantify the impact of inconsistent pricing decisions and apply this economics this new economics research directly to our work. 

Rooting Out Bias When Optimizing Pricing  

Gabe Smith (23:02): 

So I understand noise and, and the whole concept of auditing noise. But how does that work in conjunction with bias, or have you already kind of rooted out the bias and you’re just focused on the noise, or are you looking at both of those as you’re doing that analysis? 

Stephen Haggett (23:18): 

Yeah, we’re looking at both, but I, I hope we’ve done a better job already of working on bias, but noise is just the unexpected inconsistency pricing outcomes. And again, going back to the academic research, what they have shown, what folks like conman have shown is that a simple model will outperform human judgment. If you can consistently apply it. And within the pricing world, that’s where target price becomes such a powerful tool where we are providing an anchor to the salesperson to say, you know, based on the analysis that we talked about earlier, you will win it. This price, you should have confidence that you can win at this price. And when we can deliver that target price to salespeople consistently at the point of the deal then that reduces this inconsistency, this irrational variation. 

Gabe Smith (24:20): 

It makes a lot of sense. I mean, to a large extent, what we’re trying to do is look at at how we can, you know, group together like customers, or like products to some extent you know, and come up with target guidance that is fair and simple. And then really then put some checks and balances in place that reduce the variation or the noise off of that target price. Right? And to that, to a large extent, that’s what, what, what we’re doing with pricing optimization. And I, I, I really like the model that we’ve employed here because, because it is simple, it does filter out some of the bias. It gives you flexibility to then adjust based on, you know, market based factors and other things that are, you know, influence price and should influence price you know, allow you to get the outcomes, but in a way that’s fair to the customers. 

Gabe Smith (25:09): 

And, and I think that, and in a way that’s transparent and, and explainable to the sales people, like we said. So the combination of those things I think is really powerful. You know, when, when you were talking about conman, one of, one of the things that that occurred to me was, or I remember from my, my days at Berkeley was P professor that I had named Walter Freeman and what he talked about, and you might wanna read this book, it’s called the brain, the minded behavior. And what he talked about was that our frontal cortex, our, our rational decision making center isn’t really in control. It, it, it tricks itself. It tricks us into thinking that it’s in control. And in fact, you know, our old brain, our lizard brain is really what’s making the decisions. And he was able to actually show a cognitive gap a time period between the registering certain events in our, in our old brain and our Neo cortex. And I think that’s the, the, exactly what Kaine’s getting at right is, you know, if in a, in a completely rational world that, you know, economic the area is based on, we have all the information and we make perfect decisions because we’re not risk averse. And we’re not thinking about, you know, other factors that we’re really programmed at a very base level instinctually to respect more than what rational kind of decision making the direction that that might lead us in. Right? 

Stephen Haggett (26:26): 

Yeah. Dan chip heat use, I a very simple analogy in, in some of their writing where they say decision making is like a writer on an elephant. And so the path is the constraint and the, the world in which you live, and the elephant is the emotional component of your decision making. And the driver. The rider is the rational part of your decision making. And when there’s conflict bet on the elephant, that’s right. That makes a lot of sense.