Principal Consultant Carlton Hoyt recently sat down with Chris Conner for the Life Science Marketing Radio podcast to talk about decision engines, how they are transforming purchasing decisions, and what the implications are for life science marketers. The recording and transcript are below.
CHRIS: Hello and welcome back. Thank you so much for joining us again today. Today we’re going to talk about decision engines. These are a way to help ease your customer’s buying process when there are multiple options to consider. So we’re going to talk about why that’s important and the considerations around deploying them. So if you offer lots and lots of products and customers have choices to make about the right ones, you don’t want to miss this episode.
Before we get started I want to mention that grateful to have sponsorship from the Association of Commercial Professionals in the Lifesciences. And remind you that the deadline to register for the annual meeting at a 40% discount is coming up soon. It’s been extended for two weeks. So the deadline to get the substantial discount is now June 30th. A complete agenda for the meeting is available. Check out ACP-LS.org/agenda.
Now, let’s jump right into this, shall we? Carlton Hoyt is the principal consultant at BioBM Consulting – a full service marketing agency and consultancy working with innovative growth oriented life science companies to forge commercial success. Carlton, welcome to the podcast.
CARLTON: Thanks, Chris. Thanks for having me back.
CHRIS: Today we’re going to talk about decision engines and I just want to start off by – we’ll explain what they are in a minute but what’s the problem we’re trying to address with a decision engine? Why would we think about it?
CARLTON: So really there are two factors that are – or you can call them three – that decision engines are really meant to address that’s causing this problem that they need to address. One is that the internet and technology in general has made it so much easier for customers to access information. Because of that they’re presented with larger and larger sets of choices. The other one is that they are increasingly taking control over their purchasing decisions. They are waiting longer – studies have shown this – to contact sales representatives. They are owning more of their own decision making process. They’re not handing it over to suppliers or third parties.
So they want to be able to make their own decisions but at the same time the complexity of the decision is increasing because of this access of increasing information and also just more choice that’s being created in society in general as industries grow, etc. So there needs to be something that brings those things back down for them a little bit. Because what happens and what you see – and, again, this is also something that’s been studied and scientifically shown – is that when you have a purchasing decision that takes more time what you’re effectively doing is increasing the cost of that decision. Or on the other hand it would require making a decision that involves less certainty and more ambiguity as to what the correct option is and thereby making the decision more risky. And a risky decision has a decreased perceived value because you’re less certain that the ultimate selection is going to be really what you need.
So what these two things do is increase the likelihood of a no-decision outcome. So that would be where a customer doesn’t actively decide not to purchase a product but rather just simply does not make a decision. They’re mentally blue-screened. They walk away. They say, “I don’t have the time to deal with this right now. I’m just going to go do something else and deal with it later.” And that’s literally what it is. The problem still exists and they know the problem is still there. They’re not choosing to ignore the problem. They’re just not making a decision because the process of making a decision has become so difficult that they don’t want to deal with it.
CHRIS: Absolutely. I love how you describe that – they blue-screened mentally. That’s a fantastic picture.
CARLTON: We all do it.
CHRIS: Yeah. I understand. Lots of studies show that too many choices ends up in no choice at all and that’s not what you’re trying to do. Tell us exactly what a decision engine is and give us an example.
CARLTON: Sure. So a decision engine is really a tool that brings together choice information and also has funcationality which assists in the processing of that information. And that last part’s really important because you can have a lot of choices and a lot of information. That’s out there. But you need to not only have those things collocated but also provide functionality that helps them make an actual decision. So really what the decision engine is aiming to do is it’s aiming to own the buying journey in a sense. So the customers have a set of informational needs that they require in order to make an informed decision and the decision engine seeks to overlay on top of their purchasing decision in a manner which enables them to make the decision without having to go all over the place to find this different information.
A good example a lot of people would be familiar with are the websites that are out there for are out there for helping select airline flights or hotels – things like Kayak, Expedia, my favorite’s Hipmunk. It’s just a personal favorite. They’re aggregating all of this different information for you on different flights, different times. They have really good layouts that help you very clearly see what the options are in terms of price, in terms of schedule, in terms of layovers and a lot of other things that are really important in that decision making process so that you can just go to that website and make a decision then and there. It reduces the complexity. It takes less time. It increases the certainty because it’s presenting you with a lot of information and thereby enabling you to make a confident decision in a shorter amount of time in one location.
CHRIS: Right. I love that example. I’m a big Kayak user and sometimes still there seems to be- of course, they will show you everything that meets your criteria. So you have to continue to whittle those down. In a sense you’re playing 20 questions, right?
CARLTON: That’s how decisions are made. Decisions aren’t linear. It’s not like back in the ‘60s or ‘70s or whenever it was when they had the funnel model where it’s just options are slowly being narrowed down as the customer has a better understanding of their criteria. We know that’s not what’s actually happening. You’re changing your criteria as you go along and you learn different things about the different options and you’re adding and removing criteria and solutions from consideration. So the ability to have that kind of flexibility where you can be dynamic in your search is important for a decision engine.
CHRIS: I really like that idea. I hadn’t thought about that – how you’re changing your criteria as you go. So we’re going to come back to that in a second. But let’s lay out the types of products that are suitable for applying a decision engine in the purchasing process.
CARLTON: For one, I’d like to bring that out a little bit and say that it’s not just products necessarily. This can be for services as well. It can be for anything. There’s no specific type of product that is suitable for creating a decision engine. It’s just that the difficulty in creating an effective decision engine varies based on the nature of the purchasing decision and also the nature of the solutions themselves. So, for example, if you have a physical product that’s usually pretty easy to define – they have a static set of attributes. They are often directly comparable when you’re considering other things that you would put in the same category.
If you’re purchasing, let’s say, a service there are a million shades of gray when it comes to a service. There can be an infinitely variable set of attributes that a service might have. So that makes it a little bit more difficult to work with, a little bit more difficult to build but it most certainly can be done.
CHRIS: So I image a couple things. Maybe you’re selling antibodies and, of course, you want to help people find the right ones and maybe that’s reasonably straight forward. Then I think about mass spectrometry and maybe – would a configurator be considered as a decision engine to say, “I want this feature and that feature but I don’t need that feature,” for example?
CARLTON: Usually when you have a configurator it’s being put forward by a brand.
CHRIS: Yes, exactly.
CARLTON: So let’s say, just to throw a name out there, let’s say Agilent has a bunch of different mass specs and they have a tool to help customers select which one of their mass specs would be the most suitable for their needs. Now that would not be a decision engine because they’re only putting forward their own products. So it’s not able to supplant the traditional activities that would be performed in a buying journey. It’s only enabling one small piece of it. If the customer wants to compare Agilent to something else then, they still need to go somewhere else. So that’s a useful tool and it provides a good customer experience for customers who are looking for an Agilent mass spectrometer. But more broadly, for customers who are looking for mass specs, that wouldn’t really do the job.
Now if say a third party was to go and create a configurator that compiled information from many different brands – now you have a decision engine potentially because now you say, “We’re not going to shove you in this box of one manufacturer which will probably not be a sufficient amount of choice for you to be confident in your decision. We’re going to show you this information for everybody.” So then you might be able to supplant the traditional buying journey.
CHRIS: I love this. I’m going to go off on this for a little bit. I think a lot of companies wouldn’t include their competitors’ products, right?
CARLTON: That’s absolutely true.
CHRIS: So we’re relying on a third party or we’re relying – let’s say I wanted to sell someone a decision engine – not that I’m going to. And you mentioned increased perceived value when they’re more confident about their choice. So does it become a question of weighing – if we put all our competitors’ products here maybe we could get a higher achieved price on our sales even though we might lose a few of them to other people. Does it make sense that way? But when they decide that they want ours, they really know that they’ve made the right choice and therefore we can charge more for it.
CARLTON: What you explained is definitely true that manufacturers don’t want to put other companies’ products on their website on properties that they own. Service providers don’t want to refer people whose attention they have to other service providers. As a marketer I wouldn’t necessarily encourage them to do so. However, what I think is definitely powerful is the ability to do this as a standalone thing. Kayak and Expedia are huge companies that have made a lot of money just being decision engines. It’s very, very powerful for distributors who often compile a lot of competing products anyway. It certainly can be used for manufacturers as well. I don’t want to completely dissuade manufacturers from considering building decision engines. But I think the considerations are a little bit different with regard to the role that they might play.
So if you have a very good definition of your target market then certainly you can sort of slice your own niche and then say, “Well, we’ll build the rest of it around that niche. So we’ll do a really good job of leading customers who are within our really highly targeted niche to us and then make sure that those other customers who might consider one of our products but really would be best served by another product – we’ll guide those somewhere else. So we’re just going to be honest about our positioning.”
Also you could do something like, “We’re going to build a decision engine but we’re going to feature our own products more strongly in order to make them more visible or just make them stand out and make a customer more likely to choose one of our products.”
Another one of the benefits for a manufacturer doing this is that once you have a decision engine for something and it becomes fairly well adopted, now you’ve crowded out any other ones. The possibility that somebody else is going to do this and then it will outside your control is also something that needs to be considered. Because once that happens now you’ve lost your opportunity. You can’t do it. You can’t favor yourself. So there’s definitely benefits for manufacturers to do something like this but I think the benefits are greater if you’re doing it either as a standalone business model or if you’re a distributor or something akin to it.
CHRIS: Yes. So now I understand much better. The distributor or third party standalone thing makes complete sense and I really like the idea. Let’s go back to – when you said people are flexible with their criteria and those things may change. Of course I have a picture of Kayak in my head and I’m looking and of course I sort by price first. And then I try to tweak it a little bit and say, “How much more am I willing to pay to not have a three hour layover in Las Vegas” for example or something like that. So you’re tweaking the sliders to do that. There still has to be some base criteria that you have to define about how you’re going to build the engine. What I’m getting at is what is the thought process that goes into building this? How do you make sure that you’re giving the right options for ranges of choices or however you want to call it to the user?
CARLTON: Gotcha. Really what you’re trying to do with a decision engine is mimic the customer’s buying journey. The decision engine wants to say to the customer, “OK, we know the way you need to make this decision and we’re going to help you make that decision in the way that you would have made it otherwise.” So obviously that requires a complete understanding of the scientist’s buying journey. So you have to do normal marketing things like defining your customer personas, investigating the range of customer preferences, determine are customers are currently evaluating options, determine what they’re information requirements are. What information do they need to be able to be confident in making a decision? You need to compile all that information and fully understand it. Because, again, you’re not trying to change the basis by which customers are making their decisions. You’re trying to help them make decisions in a manner similar to how they would have done so otherwise. You don’t want to create resistance by changing the way they’re thinking about things. You just want to help them fully understand their options and obtain all the information that they need in order to make a very comfortable decision.
Once you’ve done that and you have all that information you are very confident in understanding the different buying journeys. Now you map that out and form a plan to recreate that process. So a lot of times that’ll just center on allowing a customer to select values for a range of attributes which you’ve determined to be of reasonably high importance to decision making. So if we’re going back to Expedia or Kayak or something – you have your price. You have your schedule slider or whatever it is and those are the really high importance things that they put at the top and that you’re playing with most of the time. You’re really trying to recreate that decision process. You don’t want to build too much linearity into it in favor of operational simplicity because that’s not really how decisions are made. You need customers to be able to play around and tweak things as they go forward.
Once you have all that done – you have your framework for your decision engine – and this is a pretty important point here – you have to build the assortment of options. That’s not necessarily trivial because it also involves some decisions of what’s the goal of the decision engine. Do we want to be able to have everything there or do we just want enough options to satisfy the customer’s desire for choice? And that’s going to be dependent on the goals of the business model – excuse me the goals of the decision engine, the business model of the organization who is creating the decision engine, etc.
And also, not just that, but the quality of the decision engine itself. If you have a really, really high quality decision engine that very efficiently helps customers distill their options down to a limited number of good choices, then you can have a very, very broad array of choice. However, if your decision engine isn’t quite as effective then too many options might confound your users and create an inability to distill all of those options into a digestible amount of potential solutions for them.
CHRIS: Yeah. Some of those things are testable, right? I can imagine you could create an engine and turn on or off some attributes to figure out whether they – I would think you could get the results and say it doesn’t matter whether we have this slider here or not the purchase rate’s the same or something. Is that -?
CARLTON: Absolutely. You can AB test a decision engine just like you would AB test any other kind of conversion funnel because that’s essentially what it is. You’re saying how many customers are going through and completing this process? If we provide this attribute as an option where they can input a value to, does that increase or decrease the number of people who complete this process? If we add 50% more choice in terms of the number of solutions, does that increase or decrease conversion, etc.?
CHRIS: Yeah, that’s cool. There’s a big database that sits behind this thing and it’s built based on attributes and values for the most part. Is it more complicated than that?
CARLTON: Not really. That’s pretty much how it usually works. It’s usually on some kind of database that has all the information on the different options and serves customers solutions based on the values that they input and how they cross-reference that database. And there’s a lot of technical stuff that’s not worth getting into in terms of how it can work, etc. But generally, yes, there is some database that sits behind it. I suppose you could potentially do something else and not have it be based on a database but I don’t know how that would work.
CHRIS: I don’t either. You mentioned something in your paper that I’m going to link to in the show notes. But, for example, helping academics decide where to publish. And I like that. It’s content oriented. Can you talk more about that? I’m trying to think – would a scientist use that at all? That’s sort of aside from the whole thing. Opening people’s minds to the many different ways they can use these. But let’s take that example and what that would look like.
CARLTON: Way to put me on the spot. Hey, Carlton, build a decision engine for me right now on my podcast.
CHRIS: Well, you brought it up in the paper so it’s on you.
CARLTON: OK, great. I’ve now committed myself to this. I guess if you were going to have something where you’re helping scientists decide where to publish, having something that would narrow down journals by topic would be good. Maybe they would be able to put in a keyword and be presented some kind of weighted list based on how often that keyword appears in that journal. Certainly impact factor is something that you would want to present to scientists. The actual way that it’s presented – and I don’t want to downplay the importance of it but the big factors are, what are the criteria that are being used to make the decision. I think that’s definitely some of the major ones. There are some other things as well. Are they open access or not? A lot of people prioritize that. How much does it cost?
CHRIS: Turnaround time?
CARLTON: Yeah, turnaround time if you can get that information. Good luck getting that information from the publishers. “Hey guys, how long does it take you approve a journal article?”
CHRIS: You could imagine users could create that database. They would somehow feed into it if it’s a third party thing. How long did it take to publish your paper here? And eventually you could build it up.
CARLTON: Now we’re getting into another one of my favorite topics which is recourse marketing. Cause now you have something where – a decision engine, for people who pay attention to my writings and things that we put on the BioBM blog and things like that. A decision engine can definitely be a resource. It’s something that actively provides value to customers and builds brand affinity by the fact that they can go back and derive value from these types of tools over and over again. And by putting in some kind of functionality where the users then had feedback to the decision engine – like in this case you had mentioned you could solicit user feedback on how long it took to get an acception or rejection. So basically an average time to decision or something like that. And collect that information and then integrate that into the decision engine. Now you have an element of co-creation so you’re really increasing the brand affinity as well. And that would be good. That’s a good idea. That’s probably the best idea that you might have had for this particular thing.
CHRIS: Nice. Thank you. I’ll put a link to your report on decision engines in the show notes so people can have something they can read. Of course they’ll be a transcript of this podcast on my website. How can people get in touch with you if they want to learn more about this?
CARLTON: I would encourage people to – aside from downloading the report, they’re welcome to shoot me an email: email@example.com or give me a phone call 313-312-4626. I’m happy to talk to you. You can visit the blog on our website biobm.com. We do write a lot of stuff there. We have a resource center. You can poke around and see what different topics you’re interested in. We have a bunch of different papers, webinar recordings – all sorts of tasty num-nums for people who are interested in life science marketing.
CHRIS: Nice. All right, Carlton Hoyt, it’s been a pleasure talking to you again. It’s been a year since we did this.
CARLTON: A year already?
CHRIS: Yeah, we’re going to do another one on some other resource thing in the future. Oh, what was it you said? Something about resource marketing – I think that’s what you said, right?
CARLTON: It’s a topic that we are very passionate about.
CHRIS: Yeah, I know. So we’ll set that up for some time in the future. Thanks very much for all this today. I think it’s been really interesting and definitely opened my mind a little bit about how these things are used.
CARLTON: Awesome. Thanks a lot for having me. It’s a pleasure – always.
CHRIS: All right. Take care. Well, that was an eye opener for me. It was a little different than I had imagined and I really like the idea of a third party creating these or certainly very applicable for a distributor who is offering many choices from many vendors and can increase customer confidence in their decisions by guiding them with one of these decision engines. If you liked the podcast as always tell two friends. Very much appreciate it and I will talk to you in a couple of weeks. Bye.