James Casper: Why technology fails without sound business processes

[00:00:00] James Casper: One of the fallacies that almost every company will have is that they look to technology to solve problems. And when they implement technology without a sound understanding of business processes then they're disappointed with the technology, and they blame the technology for the failure. Solid business processes that are based on understanding your customer, so your organizational design complements the business process, and that creates success.

[00:00:24] Kailin Noivo: Welcome to another episode of The Ecommerce Toolbox Expert's Perspective. Joining us today, we have James Casper. Welcome, James.

[00:00:32] James Casper: Oh, thank you for having me. Nice to be up on the podcast.

[00:00:36] Kailin Noivo: We always like to start off by learning a bit more about your background and how you kind of have ascended through your career. So, maybe kick us off with a brief background on who you are or where you came from.

[00:00:47] James Casper: I've had a bit of a non-linear career that's been informed by a variety of different dissimilar job experiences. At least it looks on paper. But all of it really was digitally focused and allowed me to center a lot of my experience around operational execution. And those unique experiences put me in a situation where I was able to start moving from more of a in function tactical perspective to being a lot wider to look at organizational operations. And just being a curious person by nature was very fruitful in that ability to continually have a wider aperture and to think about business operations in totality. So, as my career progressed more into digital, I was able to bring that digital capability in the forefront, but to really look at across an entire business function. And a lot of times in my past experiences with digital transformation, that was sort of a newer idea. But today, digital touches every aspect of a company, so it's more how it's contextualized.

[00:01:49] Kailin Noivo: Maybe to kick us off, a lot of B2B companies wanna grow their ecom. Based on your experience, what are some of the kinds of non-obvious constraints they'll typically run into when they decide that they wanna start scaling ecom?

[00:02:00] James Casper: Well, you really have to start to understand what parts of your portfolio are gonna be best served by an ecommerce experience. And there's gonna be certain elements where maybe you're just introducing through digital properties part of those portfolios, but they need a higher consultative sales. So, the role of digital has a different pretense than maybe other parts of the portfolio that are a little bit less complicated or maybe in a high transactional, high consumption type of environment where they're just natively ready for ecommerce capability. One of those things to think about is when you're doing sales transactions non-digitally, there's points of friction that maybe become less obvious to the customer. But as soon as you enter into an ecommerce perspective, you have to think about those points of friction because you're gonna be doing that at scale. And so, it's gonna exploit your weakest points of your infrastructure and your organizational design.

[00:02:56] Kailin Noivo: Makes sense. And once you start wanting to scale, you typically start looking at data. Talk to us a bit about what's the biggest challenge, right? Do people not have the data? Do they have too much data? Like, yeah, maybe start talking to us a bit about how decisions get made. Everyone always says they're data-driven, at least in my experience. I'm curious, yeah, from yours, like, do people typically have enough data? Where do these things start to fall apart?

[00:03:20] James Casper: Rarely is there an absence of data from the context of an individual function. But when you put it in concert and you try to lay that against a customer journey, then you start finding the gaps in your data. So, a lot of times when people are looking through the data, and they're measuring from the lens of their contribution in an organization, it doesn't usually look before and after that contribution. And so what happens is those are the become the points of friction that the customer actually feels. Because when a customer makes a transaction with the company, they will look at the material that you present to them to understand the product that they're gonna buy. They go through that selection criteria, and as soon as they add it to the cart and they commit to making the purchase, that's your contract. And usually at that point, you've already articulated what the product is, what the level of specificity of that product is, the price, and an expected delivery of that product. That goes inside that point of making that purchase. But what happens is inside the ecosystem of the company, each one of those functions that are creating a contribution in that ultimate delivery are measuring in their lane. And so success is measured in their lane, not from the perspective of what was that agreement with the customer, and what's my role of my data in that overall ecosystem, and that's where things break down. So, it's not an absence of data. It's an absence of connected data going back to the customer journey.

[00:04:54] Kailin Noivo: That's interesting. Would you say multiple conflicting sources of truth is a challenge there?

[00:05:01] James Casper: Well, absolutely. I mean, we witness this in our daily lives when people talk about measurements; say you can take a slice of a measurement and argue a particular perspective on it, and somebody could take the same data and argue it from a different perspective, yet it's that cohesiveness across that overall ecosystem. And it's difficult because you have to have that ability to come back from your, maybe a little more myopic view of what you're contributing to the end-to-end. And so the data has to be contiguous across that entire customer journey from the point of order, well, actually, from curiosity, from when they're first looking at the product all the way through the transaction, through post transaction, and to be able to understand if anything creates a point of friction along that journey, what are the actions that you're gonna take and which function needs to take the action against it. Those are the data gaps that tend to be missing. And so, if you build it from the perspective of the customer journey at the beginning, from the way that you think about your data and visualization of that data. You can sort of avert some of those points of friction upfront.

[00:06:10] Kailin Noivo: How much of this do you think potentially gets easier or worse with AI? Because now everyone's gonna have the ability to plug in other data sources into their kind of system of record. Talk to me a bit about that.

[00:06:20] James Casper: AI absolutely is as good as the data that's available to it. Otherwise, it'll start drawing conclusions. In the early days, we called them hallucinations, where they would actually try to create the gaps in the data. Today, this happens a little bit more rampantly, but it actually has more data sources, as LLMs get enriched, and it uses repetitive data and inputs to refine it. But yeah, in the absence of data or incorrect data will absolutely exploit the actual result. So, data is very important. Master data management, which is what a lot of people will talk about, but don't really perfect the art of that because it's not a one-and-done; it's an actual living environment of how you manage them and orchestrate your data. That becomes a challenge for many organizations.

[00:07:08] Kailin Noivo: Have you ever had, kind of, at any of the companies you've worked with or heard from coworkers, how can I put it decisions that should have taken maybe minutes or hours taking days or weeks because of either a lack of bad data governance, or people don't have kind of the whole data story in one? Yeah. Talk to us a bit about how it can impact operational speed.

[00:07:29] James Casper: In many cases, the data is there. It's still this decision velocity that becomes the problem. So, because different elements of bringing it all together are owned by multiple functions, there's sometimes delays in understanding. So, if you're making price concessions or orchestration on how you're gonna set up e-procurement capabilities, or you wanna have conversations about supply chain management and how those flows would work through or even just maybe some healthy tension between what's gonna be sold digitally and how that is represented in a traditional selling environment because then you have who gets credit, who's supposed to facilitate what part of the sale, then you got the marketing aspect of it, of how a campaign logic going. And then how do you keep contextual messaging through the entire engagement with the customer so you don't have bifurcated messaging on the same experience? Those are the things that create decision delay, and those are points of friction in the process.

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[00:08:44] Kailin Noivo: Talk to me about in complex B2B environments like the, call it, traditional flow of, like, add to cart and start checkout, and all those things aren't really that straightforward. Yeah. Talk to me a bit about how are you thinking about the customer journey, and how do you measure the effectiveness of the customer journey on a more complex B2B website?

[00:09:02] James Casper: Well, you alluded to this a few minutes ago, where people will say they're customer-centric, and that can become a tagline versus actually the way that you operate. So, when I say that I'm sort of passionate about using the customer journey as a reference point, then you have to also believe that customer experience is really a leading indicator of success for organization, which would mean that revenue is a lagging indicator or a result of positive customer experience. And not a lot of companies talk like that. You know, what happens is you speak more towards the revenue, and you say, yes, we're customer-focused. Well, how many listening points of the customer do you have through there? And have you really observed that flow of what those steps are? And we've all been conditioned by the companies in a variety of different industries that do customer experience exceptionally well. And, you know, I can name off a few of them, but I think that we know them. We tend to speak of them in a common vernacular because they're the ones that have made an impression on us from a consumer standpoint. So, when you think about that, well, what is it that they were doing that made them rise above other companies? It's because they were maniacally focused on what it was like to be the customer. And so we do that in our daily lives. We make these decisions as a consumer, but I think sometimes we miss that when we come back to the business environment and think about, like, wait a minute. Am I recreating that positive customer experience, or the way that I'm holding a particular control or governance of something that's actually creating an impediment to that overall experience? And it doesn't mean that you don't have some of those controls in place, but you have to really evaluate, are those controls creating a better customer experience, or are they impeding the customer experience? So, when you use an example of add to cart, and you wanna make sure you understand the terms, how many clicks does it take to get there? How many clicks did it take to actually find the product to match the solution that you are looking for? And those are the types of pieces of logic that you have to have that customer experience moments of measurement along that customer journey, so you know exactly how to measure whether or not they're meeting a prescriptive SLA or expected outcome, and then have the controls and triggers in place to say when they don't meet those, I understand exactly where it happened. I understand what types of actions have to be taken. So, sometimes we talk about it in the ability to measure, but measuring without action is no better than no measurement.

[00:11:34] Kailin Noivo: Everybody has their own dashboards, like you mentioned. They're a lot of the times, they're siloed from data sources to availability. What's the difference between a company that reports on data and performance and one that actually uses data consistently to drive decisions? Like, what does that kind of look like in your opinion?

[00:11:53] James Casper: I've seen it probably in its worst and its best throughout all my experience. You know? And I actually had a former colleague just recently ask me permission to use one of my analogies in a LinkedIn post that they were putting up. And it was very rewarding to be asked that because it meant that I made a lasting impression. And sometimes I use analogies, and maybe they're not the best analogies, but it's to convey an impact. So, I had an experience where I had looked over 200 reports. And so the company felt very confident that they were measuring and looking at the data, but when I went through the data, I was very uncertain about how to measure whether or not the business was performing well. Because the comparatives were either incredibly elongated, so the time period was too long, or the currency was too old, it wasn't refreshed enough. Or I was being I was looking at something that was being measured at a point in time when I don't know if the point in time it was being measured against was a good day, a bad day, a good result or a bad result. And what I found through that was that there were many sources of reporting in addition to those 200 reports that were there, and everybody was going by their own accord to get those measurements, and then they would come into common settings with their cut of the data, and there were always mismatches. So, those mismatches just became points of conflict and debate, and you never got to the real business outcomes. So, the way around that, where I found success, is you gotta bring everybody into what we would refer to as a single pane of glass. Everybody needs to look from the same lens, from the same data source that's orchestrated in the same way, and that there's an opportunity to actually challenge the numbers if you see something that doesn't look correct, but everybody's challenging the same set of numbers, not disparate numbers. So, the analogy that this person who had reached out to me used was one where I talked about renting a car. And so if you go to a car rental facility, you know that you're renting a car. You intend to drive the car. But if you imagine that every time you got inside the car, you were aware that it was a car, but you had no clue on how to drive it because you couldn't find the steering wheel, you couldn't find the gas pedal, you couldn't find the brake, or any way to start the car, and you had to learn it again over and over. That's the fallacy of dashboards because everybody tends to put their own logic into their dashboards. So, unless there's a common framework on how you wanna visualize data, how you articulate that when you use certain controls, certain methodologies, and make those standard across the organization, it can be a real impediment to be able to understand what those business signals are. Because every time you look at the data, you're at a point of trying to figure out where am I and how does it work. I know why I'm looking at it, but I don't understand how to use it. And so that was a big critical success in moving from a very confusing environment to one that can orchestrate across the organization. But that's a very legacy view, to be honest with you. Today, because of AI, you have to have a mature way of data visualization with dashboards, but the dashboards are no longer enough. Because now AI needs to be able to interpret the dashboard, and you have to be able to have an interaction with it, either through typing or through audible mechanisms to consume the data. It's not just going to a dashboard. And you have to be able to do that whether or not you're in the dashboard or out of the dashboard. The dashboard and the data sources that sit below it are the interaction point where you and AI draw those conclusions. Because one of the things with data and analytics, and I know I'm spending a lot of time on this, but it's actually pretty important, is you need to be able to quickly adapt to the information that you see. And there's an insatiable appetite for data. Because once you start getting really good at it and you've solved all the top questions, the next time you convene on it, you're gonna get 10 more questions. And you get those answered, and you're gonna get 10 more questions because the curiosity and understanding of the business goes up and up and up. So, I would say dashboarding is not only important in philosophy and structure, but the desire to answer questions more quickly with AI has a dependency on that being robust and durable.

[00:16:25] Kailin Noivo: It's funny you say that. That's kind of where my brain was going, even in our business, we've been implementing AI, and I've been getting access to, like, for example, product data in terms of our product usage data, which is an area of the business I didn't really spend too much time on before because the dashboards were kind of confusing. But now that I'm diving in and pulling that in, I have so many questions, and it's interesting. Other people are getting access to other data sources, but we're struggling with the system of record for each data source, right? So, if you have, like, 20 different tools, one's, like, to manage your ads, one's to manage your revenue, one's to manage your customer sentiment, one's to manage your NPS. Like, NPS will live in three other systems, right? But it'll only have part of the data. So, like, which product do you use NPS as the source of truth, if that makes sense? It's probably not the same product as your product metrics, right, but we have some product metrics and another it is. So, it's anyway, it's very interesting. I actually think AI is gonna make this worse in the sense of, like, it's gonna like, someone's gonna have to solve this problem before it gets better.

[00:17:31] James Casper: Yeah. It's a tricky part of governance. So, a lot of times, people quickly equate the word governance with control. Governance enacted properly is really just structured orchestration. And you create this environment where you have a system, and inside that system, you rationalize those data sources. And where I've seen it very effective is when you have data stewards across the organization that are domain experts for the data sources that they're at, but then they come in the aggregate into data architecture team where they talk about where those relationships are and they orchestrate that together so that when the visualization start consuming, they're consuming from one standard source. Even though it's from one centralized place, those sources could be, you know, connected downstream in other data sources. It's that orchestration, and it creates that common view, and it helps create those relationships between different sources that are important, but what's the relationship between them that gets solved in the aggregate with governance in allowing the AI to be a little more effective?

[00:18:36] Kailin Noivo: James, as we look to wrap up, if you're advising a VP of ecom, or VP of digital, what's the first thing you would tell them to change, to avoid, what you like to call random acts of digital?

[00:18:45] James Casper: I could not harp or be more certain on professing that the customer journey and the customer experience are so incredibly important. They become an after-action to revenue in many cases, but if you can organize, I mean, one of the fallacies that almost every company will have is that they look to technology to solve problems. And when they implement technology without a sound understanding of business processes, then they're disappointed with the technology, and they blame the technology for the failure. But if we really play this out in orchestration, and this has been around for a long time, but it's one thing that remains true, solid business processes that are based on understanding your customer that be it translated into technology so the technology operates the way that you expect your business operations to be and to give an excellent customer experience, and then you organize your company around that, so your organizational design complements the business process, that creates success. But if you only target one part of the people, process, or technology, there's gonna be a breakdown, and there's gonna be a lack of connection to the customer. And if you truly believe that the customer is what drives overall company performance, then you have to be maniacally focused on the customer.

[00:20:06] Kailin Noivo: Very cool. Awesome. Thanks, James. This has been a great, great, great episode. Thank you again.

[00:20:11] James Casper: Oh, thank you for having me. Appreciate it.

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