Speaker 0: And welcome to Psychology of Salesforce. I’m Cole Fisher. Um, my title is strategic solutions lead at ListEngage, which, uh, basically, it just means that I get to geek out, uh, especially on the sales and presales side of things on concepts like marketing cloud products and data cloud, even agent force and AI capabilities as they come up. Um, I focus a lot on strategy, technical, and functional details, especially around implementation, product capabilities, and configuration, and things like that. And I’m especially passionate about, uh, customer behavior and the psychology behind all of these, uh, concepts. So that’s what we get to dive into today. We’ll focus on a few key concepts around behavioral economics and what they mean for Salesforce marketers.

Um, so just to lay the groundwork, um, first off, you’re all crazy people, uh, and that’s okay because so am I, and your customers definitely are. Uh, I’ll I’ll preface this with a few. If you let me just kind of, uh, geek out a little bit in the academic world. In in the theoretical concepts, there’s sort of a spectrum of view of what rationality is. So on one hand, you have this neoclassical economics standpoint, uh, kind of your textbook, supply and demand, diminished returns, what we all took in high school and college. And it basically has this prototype of a human, and we’ll call it Homo economicus. Right? And and everything to Homo economicus is perfectly rational, and he’s fully informed, and doesn’t make, um, mistakes based on these, you know, pure textbook concepts. On the other end of the spectrum, we have sort of psychology world, which basically says we’re all crazy. We have emotions and habits and influences and distractions and all sorts of things that influence our decisions and impact our choices. And the prototype over here, instead of homo economicus, is basically this more akin to Homer Simpson, if you will. Somewhere in the middle of this spectrum is kind of this, uh, you know, where theory and, uh, the practical world meet, and it’s this world of behavioral economics. And it states that we’re all bounded under so we all operate under what’s called bounded rationality. And it’s our human nature to have limited bandwidth, limited resources, and limited information or access to information that we can either, um, acquire or retain. So it’s kind of this exciting blend of theories and principles and concepts that are widely applicable to nearly any field. That said, take with a grain of salt, uh, what you’ll hear. Not everything will be directly applicable, uh, or universally true for all customers and subscribers, um, but it’s meant to challenge what and how and why we think about and why we do things in a certain way. So, uh, like I said, this is all empirically tested and valuable framework for how we think about people and their behavior and customers and especially marketing. And certain components will be very relevant to marketing or customers, but it’s all worth considering and testing, even if some of this may not be directly, uh, you know, true or or quite one to one applicable to what what you’re doing today. So I’ll highlight three broad concepts that I found are especially relevant to marketers, and we’ll dig into law of least effort, paradox of choice, and survivorship bias in more detail. So the framework with which we’ll approach this is we’ll take a general idea and discuss this overarching theory or principle or concept found in psychology and behavioral economics. We’ll look then at instances of how this is pervasive in everyday marketing and consumer behavior. And then we’ll talk about practical applications, how we either manage or leverage these concepts in marketing, uh, or especially in Salesforce tools as we market.

So first, we’ll start with law of least effort. Uh, and basically what it states is we’re all lazy. Daniel Kahneman, if you’ve ever read the book Thinking Fast and Slow, it’s one of the bestsellers. I think it’s one of the top audiobooks of all time. He’s basically one of the godfathers of the field of behavioral economics and a Nobel Prize winner. What what he states in Thinking Fast and Slow, his book, is is there’s basically a like a a two part system of how human beings process. There’s high level processing and low level processing. High level is this abstract, intentional, more strategic thinking that we do when we process the world around us. And our lower level processing is kind of this autonomic cruise control, walking, chewing bubblegum type of process that we don’t have to put much effort into. And so in this lens, you can kind of liken cognitive bandwidth to currency. And so imagine you’re in a store, and you see two of the exact same products, exact same features, attributes, everything about it are the exact same, but two very different price points. Knowing that there’s no quality or any other difference, you would never spend more to get the same thing. You would always take the lower price, obviously. Right? You’d be silly not to. And Kahneman argues that this is true for nonmonetary resources as well. So if we think of, you know, that bandwidth as currency, this our our time, our energy, our attention, all of our resources become currency. And so we aim to get the most out of our spend, you know, the most out of something while expending as little as possible. So what that means is, you know, that’s why we always tend towards the path of least resistance. So, essentially, your parents were right. You should get off the couch, get a job because you’re a good for nothing lazy swindler. But we’ll always tend towards the path of least resistance. And this was originally studied in the field of linguistics. And so, uh, what we always found over time was that as languages evolved, longer, more complex words and phrases habitually got got phased out for, uh, in favor of shorter, simpler phrases and monosyllabic words. Uh, and and how the word monosyllabic is still in English language, I don’t know. Uh, but, anyways, that’s why we basically abbreviate words. It’s it’s just it’s more efficient that way. So, uh, unfortunately for us, though, as marketers, our customers and subscribers are faced with this same sort of option of laziness. And so, for instance, you know, an unengaged subscriber has two options. They can either unsubscribe, which all it would take would be a click, or visiting a profile center. Not that much to ask. But if they’re as lazy as I am, more often than not, they’ll just disengage. And so you have unsub or unengage, the latter of which is easier because it’s one click less. And so, you know, for us, there’s almost this as as marketers, there’s almost this irrationality to why we put so much effort into building preference centers, um, when we know almost no one ever goes to them, uh, and or or or even visits it once. Uh, it’s a very slim minority that actually goes to a preference center or does anything there, especially when there’s, you know, upgrading subscriptions and, you know, up, down, and set of out and all this. And then I’m not bashing preference centers, you know, as they’re a very, you know, important part, but we should probably do something that also caters to the other 95 to 99% of our subscribers as well. One of the things that we can be doing is progressive profiling. And so this is kind of marrying implicit as well as explicit data. Explicit being on a profile center, but implicit being things like marketing cloud personalization where we can gather, you know, browse behaviors or common attributes among products or content that they’re looking at. Or we can build profiles and affinities for what they’re interested in even though they’re not stating anything to us. Along the lines of that explicit data, interactive email is something that I wish were being leveraged more frequently. Interactive email is a great way to make it easier on your subscriber with you know, feedback surveys, post purchase, um, profile updates, things like that that I always kind of think about this as how are we removing friction from the end user. If I send a, you know, a follow-up or a post purchase feedback or something like that, and I ask them to open the email, then click on this, then open this website or this landing page, start to click there and then submit, that’s a lot of friction. And knowing that we’re all lazy, the odds of me as a consumer doing that, I know are very slim. So I’m trying to market to the laziest version of myself that’s possible. So if we could on interactive email forms where we can just keep all of the feedback short and succinct and just gather a few points, then we can move on, and they don’t need to visit. And we’re removing that friction and allowing the customer to be lazy while still catering them, you know, catering to them in the most effective way possible. Um, and I wanna make a point too that I’m not bashing on laziness. Uh, it’s okay for us to get lazy. In fact, I encourage you as marketers, all of us, just get lazy. A lot of us are already using things like Einstein features in Marketing Cloud, send time optimization, which goes back ninety days for behavior of of opens and and engagement. But if you want more than ninety days, which maybe you have longer sales cycles or things like that, it may make sense to you do your own custom modeling elsewhere and then bring those segments in. Uh, as well as there’s also engagement frequency or scoring. Uh, and not to plug Agent Force or or go outside of my lane here, but there’s a lot of Salesforce tools that are really new and exciting if some of us have access to those. If not, there are loads of free external AI tools out there for content generation, image manipulation, things like that that we could really be taking, you know, half of our sort of menial workload off of our off of our plates and really enjoying a little bit of being lazy. Um, and last point is just being you know, speaking of being lazy is, you know, make make things easy. Again, kind of that moving friction concept for the end consumer. So how are we doing things like recurring or consumption based concepts where, you know, if there’s replenishment or if they have seasonal purchases, things like that that we could be taking advantage of that we’re just serving up on them based on behavior we’ve witnessed in the past so they don’t have to go and tell us when and how they need these. So if we can come up with models that help with that, fantastic.

Um, our next concept is paradox of choice. And you’ve probably all heard of this. Marketers are commonly very aware of this conceptually, but it’s still very counterintuitive for us to act upon this. And paradox of choice rears its ugly head in marketing and in sales and contact volumes and things like that. Think about, um, you know, a lot of times we we look at the volume of leads, and we look at it. It’s like it’s to be treasured because the more leads we have, the more conversions we’ll ultimately have. The error in that thought process is that this assumes a constant rate of conversion and that the quality is synonymous across a variety of different leads and lead sources. So if you’ve ever heard of paradox of choice in either a marketing or a psychology class, then you’ve probably likely heard of this study from more than twenty years ago now. But it’s the sort of the, you know, quintessential paradox of choice study where there was an experiment done in the, uh, in a grocery store. And it was the in cap aisles where they would display flavors of jam, of, like, you know, jarred jelly or whatever. Um, and they would have originally the control group had six flavors of jam. And they noticed the shoppers that went by, 40% of which stopped and looked at the display, and 30% of those actually grabbed one or more and put it in their cart and checked out. So what they did was they added 18 flavors in the experiment group. What they saw was was pretty, again, counterintuitive to what we’d expect. We saw 50% more shoppers get attracted to the endial. More impressions, more eyes were caught by this. But we saw the conversion rate plummet to a tenth of what it originally was. And so there’s there’s a lesson to be learned here in terms of the simplicity, um, and the fact that more is not always better. So there’s an inverse relationship, uh, in talk talking, you know, cognitively, there’s an inverse relationship about between the amount of choices one has and the action that they’re willing to take. So too much choice can motivate us to have sort of these counterproductive results. And there’s a great book called Paradox of Choice by Barry Schwartz. And it dives in and empirically shows that there are four common outputs of paradox of choice. It’s less satisfaction, cognitive dissonance, analysis paralysis, and lower conversions. And so just like in the jams, we see lower conversions. But also we see those that check out have less satisfaction. Because, again, keeping that currency concept in mind, if I’m expending currency, decision making bandwidth on buying a product or consuming a piece of content or whatever it may be, I expect, you know, at least my one to one return or hopefully as little spend and more as much return as possible. However, when I have things and this is also kind of contradicting true for us is what we see in things like ninety day money back guarantees and things like that. We typically don’t see the money back guarantees used as much. We see it as as possibly a lubricant to check out, but we also find is we’ll see, uh, empirically lower CSAT scores because now we’ve left the gate open for more decision making to be made, more constant processing of, did I make the right decision? A lot of that cognitive dissonance comes into play here with, were there other options? There were so many other options seed, I kind of wish I bought something else. And so if they do convert, we see lower satisfaction rates from that. So the reason this happens is twofold. First off, it happens because, you know, keeping that that finite bandwidth processing capability in mind, we experience decision fatigue. And our cognitive load can only handle so much. This is the reason that, you know, Steve Jobs would wear a black mock turtleneck and jeans every day, um, and or Mark Zuckerberg would wear, uh, you know, hoodies every day. What they believe they were doing, which there’s a lot of data to support this, uh, is that they were removing, um, the low level decision making, thus freeing up more bandwidth for higher level, more strategic thought processes. So it’s really interesting. If we remove things that we shouldn’t be paying attention to, we can focus on things or free up our bandwidth. Uh, the other reason that this this occurs, uh, paradox of choice, is because of loss aversion. Loss aversion is a a very big overarching concept throughout psychology. But what it states is basically that the fear of losing something is more salient to us than the potential of gaining something of equal value. And there’s prospect theory and a lot of other things that can go into this. But this fear of loss, this loss aversion, is really powerful. And so what happens in our case of the jams, when we had six options, we had a 17% chance of getting the right choice, which, you know, knowing my own personal preferences, what flavors I’ve experienced and and not experienced, you know, I’m likely able to navigate that just fine. So at one out of six chance, I like my odds of of being able to convert and be happy with my selection. When we have 18 new flavors added, now assuming the same constant of one of these choices is right and the other ones are wrong, I now have a ninety six percent chance of choosing incorrectly. And so as human beings, that loss aversion drives us to say, well, I would rather make no decision than the wrong decision. I don’t want regret. I don’t want remorse. I don’t want that loss looming over me. I’ll just make no action whatsoever. And so a lot of the times for us as marketers, we end up dealing with nothing. We like, that is the output, is that there there is no decision. And so a lot of the times we see this in, you know, all over just think about a website where you see or emails where you see recommendation logic that’s serving up new new content or new products. A lot of the times I’ve been in the shape where, as a consumer, I’m going to purchase something and all of a sudden a product that’s, you know, a competing product or just looks just as good but is at a lower price point or something like that makes me reconsider my actual checkout. And so I was on the path to conversion, but now there’s too many recs. The recommendations don’t consider what life cycle stage I’m in. Instead of, you know, a nice, clean, linear, uh, progressing customer life cycle. I’m actually regressing all these recommendations because there’s so many of them. We’re actually seeing that I’m being reminded as a as a potential customer that I don’t have all the information to make this decision. I might regret it. Whereas if there were less less recommendation or if they were just complimentary, um, products or things like that, I’d be less likely to be deterred. Um, I I actually saw this with a customer when I was at at Salesforce. Uh, when I was on the the delivery side, we were playing around with recommendations and volume. What we would notice is more volume got tons of engagement, lots of clicks, longer time on-site, but ultimately garnered fewer conversions. Whereas less recommendations got them a little more to the point. And we steered a lot of that logic into let’s make sure that we’re not competing against them, that the price points are different, or that we’re doing add ons rather than you know, we’re complementary products rather than deterring them from checking out. Also think about our simplicity. Think about conversion. Like, what does a conversion look like in your message, your call to action? Um, a lot of the times, especially in, you know, I think in retail or anything dealing with memberships or anything like that, we know that the welcome journey or the initial life cycle stage is going to be the most engaged time for that customer. So a lot of the times, we make the mistake of inundating them with all sorts of messaging. Here’s what to expect with the program. Here’s our our publication list. Here’s, you know, download our app. Follow us on social. Sign up for our SMS program. And we have all these messages that are competing. And remember, as the end consumer, in my eyes, I see an inverse relationship between the amount of choices, the amount of calls to action, and the actual action that I can take. You know, remember, we’re lazy and we have this loss aversion. You know, we’re really playing with fire here if we’re trying to get them to do something. So simpler, more succinct and concise and personalized messages are gonna be a lot more effective. Uh, and the same holds true for audiences. You know, we mentioned, uh, you know, lead generation concepts or the amount of volume of leads or things like that in the sales cycle. The same holds true for our audiences. The smaller, more targeted audiences we always see, with very few exceptions, will almost always see greater conversions. And so think about, you know, the exercise with with the jellies in the grocery store. The larger the audience is, we saw more impressions, which means more cost, a lot more eyes on the product, but we saw significantly lower conversion. Remember, ten ten times lower, 30% down to 3% conversions. And if you’re in Salesforce Marketing Cloud or in Salesforce products, like, those super messages can get pretty expensive. So you’re gonna you’re gonna wanna keep an eye on those.

Um, our last concept is survivorship bias. And survivorship bias is think of it as a logic based error where we focus all of our attention on a subset of the data or subjects that pass a certain selection process while overlooking information that didn’t necessarily, um, make it into our make it past the filter, if you will, make it into our consideration set. And so what this looks like is, uh, there’s a story allegedly true. I’ve heard people claim either way. But during World War II, um, the, uh, the Navy wanted to understand more about, like, the Allied pilots and how to protect them, how to not lose planes, how to, um, make sure that that the pilots were coming back safely. And so they asked themselves, well, how do we reinforce our planes, you know, to stand up better against enemy fire? So they gathered all the planes that had run missions, all of it that had returned from any any sort of missions, and, uh, they collected data where, um, they could find any, uh, enemy fire and where they had incurred incurred the most damage. So the parts that were most decimated by enemy fire were, uh, the parts like wingtips, the the central body, uh, the elevators in the rear of the plane. And so the initial notion was, well, let’s this is where we’re incurring the most of the fire. It must be easiest to hit or whatever. Let’s make sure we’re reinforcing these and keeping our pilots safe. And at that moment, a statistician named Abraham Wald stepped in and said, actually, we’re only viewing a subset of what we need to view here. Um, we’re missing a big part of the data. And what we need to be focusing on is our nose, engines, and the mid to rear body of the plane. That’s going to be the most important part. And what the Navy thought that they were assessing was the most common damage to planes when actually they were assessing, you know, where they could incur the most damage without the plane actually going down as their dataset was limited to all the surviving planes. The data they didn’t have, the most imperative data, most critical to what they were trying to do here, as Walt pointed out, was that the data they were missing were the planes that never returned. And likely so, those planes would probably show enemy fire in very different areas than what we see in this diagram here. So the the concept is basically that there’s a lot of misinterpretation in in data in general. Um, little known fact, there are 87% of statistics are completely made up. Uh, that’s true. You can you can Google that. Um, but we but more often than not, um, even on small, insignificant levels, we we come with this preconceived notion that I have a theory, and how can the data fit this theory? And and is it is it factual? When actually, we should be taking an unbiased look at at the data and seeing what are all the possible theories that could fit this data. There’s a reason why we call this statistics and not facts because we always have a sample, not the entire population. And so we don’t have direct representation. And a lot of times, you know, it’s beneficial just to get an outside perspective. So whether that’s, you know, an outside consultant or whether that’s somebody who’s, you know, on the team next to you or adjacent to a product or project or something like that that’s not inundated with your day to day, get them to look at this. Get a fresh pair of eyes and don’t lead them into anything. Give them a, you know, let them have a fresh perspective without any sort of leading the witness, telling them what you think the data means or what they’re looking for. Just have them look at it and take an unbiased approach. So a lot of the times that’ll actually shed light on something that we’re not considering or at least give us a different perspective that, you know, maybe explains, uh, you know, a detail that we’re not actually, you know, keeping in set right now. So a lot of the reason this happens is because of framing. Basically, this is being positioned or told to us on what we want the data to mean or what we think it means, or we’re being told how we should interpret the data. Also, again, to hearken back to our laziness, we’re lazy and we’re routined. And, you know, we leverage biases and heuristics and cognitive shortcuts. And we just prefer simple models. We prefer, you know, even if it’s a partial data set, we we never have complete data, and we prefer a simple explanation rather than really diving in and activating that higher level strategic thinking on every seemingly, you know, mundane day to day task. And and, you know, I’m not saying that we should wait for necessarily all the data because you’ll never have all the data. Just be aware that you have a preponderance of the data, and you have to use that sort of subset of data to the best of your ability. And so a lot of the times we see this in action with, you know, lookalike audiences, um, where product x, you know, has a certain demographic. And we find that all the time that, you know, this is our key demographic. That might actually hearken more to where we’re getting our leads, where we’re, you know, picking up business, or where we’re investing most on on the sales or presale side. It could be that there could be a lot more here, but this is the data that we have. This I noticed this a lot in again, when I was at Salesforce, I used to get, um, a lot of deliverability issues would kind of come across my desk, and they would say, Hey. We’ve been blacklisted. We need help. We, you know, we need to reach out to the ISPs, get some, you know, mediation here on deliverability. It’s tanking, whatever it might be. And more often than not, we would zoom out and we’d say, you know, are we treating the symptom or are we treating the root cause here? Like, we’re seeing the symptom of deliverability. But if we look upstream, is there something wrong with our lead generation, our tactics, where these are coming from? Maybe there’s bot traffic. We’ve definitely had that where we had bot traffic and unverified emails and things like that coming in. And that’s our problem, not the subset of the output of the data that we were looking at. Um, and so this is also true for attribution. Um, I have a friend that says, you know, uh, multi touch attribution is a is a pipe dream. Uh, there there’s there’s some truth to that. Attribution is a goal for all of us. But the reason we always have a hard time kind of keeping a good thumb on the pulse of what attribution is is because it assumes static preferences, uh, affinities, uh, equal weighting, and impacts for different types of customers across different parts of the life cycle. And so our KPIs, impressions, tracking, dwell time, things like that may actually benefit a lot if we take other views for consideration, you know, marketing cloud personalization. We take some of their analytics and layer that onto our current analytics. Maybe we can get more of an impression of what some of these KPIs mean and what are really valuable. My last bit of advice would just be kind of get comfortable being uncomfortable. Don’t be afraid to push, prune, um, test, optimize, experiment, and just, you know, add new coefficients to the model, get an outside view. I used to have a professor that would always say, torture the data, uh, they will confess. And so whatever you do, just kind of slice and dice the data uh, in whatever you can and and, you know, find out if there might be a bias that we’re taking in our approach here. So key takeaways really fast. I know we’re running out of time. Uh, what I hope you learned is law of least effort, progressive profiling, uh, native functionality, interactive emails, uh, especially around, you know, some of our preference center practices and how we can gather implicit versus explicit data. Paradox of choice, more is not always better. Make sure you’re experimenting with, you know, the volume and logic of recommendations, um, that we’re not, you know, competing with our own messaging and getting out of our way with, you know, convoluted CTAs and and competing messages. And lastly, survivorship bias that we just covered. Just take an unbiased approach to our data and audience considerations, what our segments should look like, and challenge what the data tells us, whether we’re diagnosing symptoms or a root problem or a root cause of the problem. Don’t be afraid to consult, uh, someone outside of your your work area or office or team or whatever. Um, think about who our customer types are, if higher engagement is really better odds of conversion, or if we’re finding out that window shoppers are just very different shoppers than impulse buyers. Um, there were a couple other concepts, uh, I would love to touch on. We we’re all running out of time. Um, so thank you all for being here. Uh, if you have questions, comments, feedback, I’d love to connect. If you have any, uh, if you’re even a fraction as excited as I am, uh, about these