Skip to main content


Journey Science – The Next Frontier in Data-Driven Customer Experiences

Young Businessman Typing Computer Laptop With Graph Chart

General Webinar Transcript

Welcome, everyone, and thank you so much for joining us today.

I’m Sarah Dwiggins, I’m a Marketing Manager for Perficient and I’m excited to be moderating today’s webinar.

Journey Science, the Next Frontier in Data Driven Customer Experience.

Let me give you a quick overview of Perficient.

Perficient is a Global Digital Consultancy. We imagine, create, engineer, and run digital transformation solutions that help our clients exceed their customers’ expectations, outpace the competition, and grow their business with unparalleled strategy, creative and technology capabilities.

We bring big thinking and innovative ideas, along with practical approach to help the world’s largest enterprises and biggest brands succeed.

We have a broad network of locations across the US, as well as offshore and near shore facilities, and India, China, Mexico, Colombia, and Serbia.

And now a little background on our speakers. First we have Brian Flanagan, Digital Experience Strategist.

Brian combines CX strategy with technical solutions in order to maximize user engagement and drive business results.

Then we have Eve Sangenito a principle of digital marketing. Eve helps organizations deliver an immediate impact on revenue, growth, market presence, brand credibility, and productivity.

Jordan Kanter is a Director of Digital Marketing. Jordan is a proven leader of teams, design, implement, and scale, data analytics, and personalization programs.

And finally, Nico Frantzen Director of AI and ML, Nico combines his love for cutting edge solutions and problem solving to help clients establish visions and roadmaps for their AI journey.

We’ll go ahead and begin our presentation with Brian, please take it away.

Brian: All right. Thanks, Sarah. I’m going to start us off with little quote engaged you guy may have seen a sneak peak of that. But the David Louis Edelman said today’s customer journey is iterative complex pinball of touchpoints.

And when you think about it, I think that makes a lot of sense, Right? If you think of customer interactions, they’re bouncing around across different channels, and touchpoints and all the while as an organization, you’re trying to keep them in play, trying to keep them from falling through the funnel, and you want to reward them when they have a successful place.

I think that really talks about the experience of managing customer expectations. It really brings us to, really to create great experiences, it requires a journey focused mindset. You have to think across different channels. Your customers aren’t thinking about a single channel or business unit. They just think about their experience with your brand, and they expect you to understand their needs and help them on their journey, no matter what part of the organization they’re interacting with.

But a lot of organizations struggle with that and piecing it all together and you can probably all think of an experience that you’ve had, where it might have been disconnected, Right? So I recently had an experience with a retailer where I signed up for the Rewards program, on-site at the register, and put all my information in. And then I go to checkout and they asked me if I wanted a digital receipt. So I thought, “great a digital receipt, I like that,” and then the first thing it asks me is to enter my e-mail, and I just entered all that information. And the systems just weren’t connected, which really provided a disjointed experience.

And that’s where this, this, the key challenges that we see is really making sure that these channels are all communicating with each other, that you know who that customer is, and then you can create that cohesive experience.

So just thinking about some of the common challenges that we see is, you know, first, there’s a lack of data. So on the research side, when you’re developing an understanding or on your customer, some organizations don’t necessarily do all the research to understand the customer needs. They may develop proto personas or develop a representation of the customer based on their own experience, right? And it really, we really feel that collecting that data to have that foundation is really important.

So the personas and segments should really be based on data. Or your organization may have done a lot of great, great work around developing personas and developing customer journeys.

But they’re put on the shelf and they’re not used within the design process. They’re not used to optimize experiences, they become forgotten artifacts.

Also, you will see, a lot of times, vanity metrics are being used. So there, they may look good on the surface, but they’re not translating into meaningful business insights.

They’re not truly measuring the customer experience and ensuring that we’re supporting them on their journey. We’re just looking at things that may represent some of the parts of the experience, but they don’t really tell the full story.

Another challenge we see is there’s siloed views within an organization. So one team may look at their part of the world and say, OK, here’s where we want to improve that experience, And they’re doing a lot of great work there.

But they’re forgetting about the other interactions that customers have with other parts of the business, right? So, they’re not considering that full journey, and how a customer may move within different parts of the business in order to accomplish their complete journey.

And then the last piece is really limited analysis where organizations are collecting a lot of data, but they’re not sufficiently analyzing that they’re going to provide insights that are going to predict customer behavior. So, this is where data science comes in and really analyzing that data to inform future state recommendations.

OK, so that brings us to Journey Science and Journey Science is a cross functional discipline, that combines research based insights with data driven evidence, in order to understand predict, and optimize the customer journey at every touch point.

And a lot of organizations are doing different aspects of this, they may have a really great research team, they may even have a great data science team, but a lot of times, we see that those things aren’t connected, and the teams that are working together as a cohesive discipline, right. So, this is where journey science comes in, and there’s really six key core competencies that we see.

The first is research that’s gaining insights. That’s going to inform the strategy.

The second is segmentation identifying and empathizing with your customers.

And then, Behavioral Analytics is capturing data and activity across multiple channel, so you can see what people are actually doing across experiences,

And then journey design, so, examining the experience across interactions. So, designing out that journey and validating that journey based on the data that you see.

Then, predictive modeling is you analyzing patterns to predict future outcomes. So that’s really around that data analysis, looking at that data, what is it telling us? What do we predict based on what we’ve seen historically?

And then, finally, it’s experimentation, validating predictions, and optimizing those experiences. So, once we have a prediction, we think this is going to actually impact customer behavior. Let’s validate that the real experiments, and see if we can prove out that we are optimizing the experience.

So these are, you know, presented linear linearly here, but you shouldn’t think of them that way. They are a cohesive solution or components of a cohesive solution, and they all inform each other.

So in the experimentation, for example, you might find out that this pattern is emerging that people actually aren’t or clicking on the offer that we presented.

And so, you know, that doesn’t work, but you might not know why, right? So that’s where you go back and do some additional research, and say, OK, we wanna understand why this is occurring, and then get an example of predictive modeling.

You know, that’s going to provide recommendations, what’s going to happen, and then we can look that form, inform some of the behavioral analytics, that what additional data do we need to collect to make better predictions, right? So, we want to get back to that.

And then from a journey design perspective, it’s really looking at the experience across those interactions.

So, you want to say, Is this the journey, the journey that we created? Is this actually what people are doing?

All right, so, we want to say, OK, is this the behavior that we’re seeing? And then, tie that to initial segments. So, the journey may differ for different segments of your, your customer base. So, when you look at that journey, we may define that initial journey, and then modify that based on different segments in their behavior.

So it really should be a continuous process. And as I mentioned, we think of this is really a discipline that needs to be established at an enterprise level. All right. So with that, I’m going to turn it over to Eve to dive into our research-based insights and give us a little more detail here.

Eve: Alright Brian, so one of the areas that’s really important from our perspective to dive into is multidimensional, cross functional look at research and how it informs some of the processes Brian was talking about.

So, qualitative research is really about trying to understand the context of where some of your research candidates and participants are coming from, their, their opinions, their attitudes, their motivations, what is informing some of their perspective on things. That’s often done through exercises, like interviews and discussions. Quantitative research is really where you’re starting to look at trends, statistically identifying patterns that you see across segments of, of audiences, whether that be customers, employees, partners, et cetera, so that you can understand some of the defining characteristics that make up those segments. And by looking across both of those, you have an understanding of, kind of, the defining characteristics, but also the context behind it, because all of that allows you to really appeal to the needs that they have if you understand more of their motivations.

From a competitive standpoint, it’s really just important that you’re looking at the landscape that your organization is within. It may be that if you’re looking at research only from within your organization, and you’re not looking across the experiences they’re having with your competition, or in their consumer experiences in general, you might not have a full picture of what their expectations are or how those match to what they expect from your organization. one of the other important pieces that, that we’ve found incredibly useful is immersive research. So that’s where the researchers immerse themselves in the experience that a customer or partner or an employee may be having, so that they can experience it firsthand, to inform some of the research they’re going to conduct. But also some of the analysis of that research to really look at the opportunities where the experience they’re having can be improved.

All of that helps inform a number of things, but one of the first pieces that it informs in terms of segmentation. So, as I had mentioned, you really want to sort of understand the motivations and kind of the thinking and, and, and also the opinions, but the opportunities with all of the research that you’re conducting.

And you want to start to sort of look at those in categories of individuals that really might have common patterns. They might be common patterns between them through, you know, demographic attributes, or it might be through attributes in terms of desires and interests that they have, or ways that they engage with technology or perspectives they have from worldview point of view. So persona development enables us to start to create those profiles. So that we get better, get a better understanding of how to interact and communicate with one segment versus the other and better meet their needs that even cannot arise to things like technology. And some,  some groups of individuals that your organization might interact with might be more technology well versed than others, and might have preferences in one direction or another. And all of those insights allows you to create a better experience for them. And that isn’t one unique mass experience.

One of the, the sort of common questions or issues that comes up around persona development, as many organizations do them, but don’t know then how to activate them, and put them into the experience that they’re creating, are the audiences. So dynamic segmentation is where you’re able to do more precise targeting within environments from a digital experience standpoint, in order to actually activate on the insights that you have, and target some unique experience based on those segments.

And you can see, as we flip through, and you can get really in depth in terms of an understanding of your persona segments, because, you know, you can kinda look at the top level from our profile perspective. But, if there are key interaction points, or areas that they would interact with your business where it would be important to know. You know, what would they like that experience to be, what kind of content would actually be beneficial to them in that experience? What kind of features and capabilities do they have an interest in, in terms of how they communicate with you. All of that depth can be outlined and developed in a persona if you’ve done a multi dimensional in-depth approach to research. And then it can keep evolving as well, that’s another point I wanna make about researches. It isn’t a one-time research effort, and then done. you want to be evolving it over time? Because the environment within, which we, all live, is always evolving, and the experiences that we’re having, and your business is changing all the time.

So you want to sort of set a foundation, but then continue to target and refine based on the insights that you can continue to gather by watching their interactions or immersing in the experiences that they’re having. And you can see an illustration of that on the right of how that can be applied within a digital experience environment to target, based on your understanding of one segment versus another.

OK, thanks, Eve, so, we’re going to throw it over to Jordan to dive into the data driven evidence piece here.

Jordan: Thanks, Brian.

So, the next piece of Journey Science is building data-driven evidence related to behavioral analytics, and one of the key components of journey science, as it relates to behavioral analytics, is, how do we collect data specifically around the key touch points and journeys that allow us to deepen our relationship with customers, and prospects?

And what we found is that, in, in our research and, and in developing, journey science is that we, we both can develop key insights around these journeys, as well as deliver richer insights related specifically to the journeys and how they, how the visitor interacts with the brand.

So, for example, when a customer may be attending a retail store, one of the ideas is to understand first, what persona that customer represents, and then how does that person’s behavior within the retail store really translate to the way that those high level personas define interactions. And how the high level personas relate to set interactions?

In particular, does the behavioral data agree with how we’ve design those high level personas?

Is someone who is shopping for a pair of shoes showing us that they’re looking at, you know, lower price options as we perhaps have predicted they would? Or, in fact, does this person either not fit within this persona category or do our personas need revising according to the behavioral analytical data?

So, one of the sort of key features of Journey Science is that we have enabled sort of this high level strategic design based on behavioral facts or behavioral evidence. And that includes not only just path analysis, so understanding what the individual is doing from the beginning, middle, and end, but also overlaying heat mapping, behavioral heat mapping associated with the individual’s current behavior. So, whether that’s behavioral heat mapping as it relates to a digital experience or behavioral heat mapping as it relates to a physical experience.

And also, sort of more advanced analysis associated with a visitor’s mood, or how the visitor feels about the brand, if an individual purchases a particular product or service, and then moves over to a social network and makes positive or negative comments about the brand. Are we able to enable, are we able to build journeys, to allow, or design journeys, to allow a company to both interact with those types of feedback, but also, build journeys in response to those types of feedback and design journeys as they relate to those types of feedback?

And, so, this relates specifically, we can move on to the next slide to journey design.

So, journey design really comes in two specific, in two flavors, flavor one would be high-level experience mapping. And the high-level experience mapping is essentially, right as a set of stages that the individual can proceed through, as well as on some, some sort of high level, right, overall, customer experience data.

Journey mapping takes that a step further. It describes a particular goal or particular intent of that particular persona, and then, it moves through the particular touch points based on that specific goal, or intent. And that’s what really, the journey mapping is what really allows us to build those experiences specifically.

And one of the unique features of jury science, specifically around these two types of mapping exercises well, first of all, these mapping exercises are not purely academic research activities, right? They, they really sort of translate into building these better experiences, but also, what journey science allows us to do is it allows us to fold in data-driven evidence and data-driven data collection to both of these types of journey design. So, for example, within experienced mapping, if we understand the particular sort of high level stages of what the individual is doing, we could also designed the particular, the particular types of data that may be relevant at those points, along the customer experience.

So for example, if this is an individual that’s tried that we’re building a high level journey about renting an apartment for perhaps one of the one of the potential signals along that apartment, rental journey could be on location, And so we, we can add to that location of interest or potential set of location of interest.

And so we can add particular data collection points along that journey in order to establish some knowledge about what the individual is trying to accomplish, or maybe we can customize the experience in order to satisfy, you know, an individual who’s looking in a certain part of Manhattan, for example.

Likewise, with journey mapping, we can do the same thing. We can establish data context, what we call in context data for each of the individual touchpoints. And then we can, better understand how we can build out those touchpoints in response to that in context data.

Nico: Alright. Thanks, Jordan. I was gonna, I was going to add on on top of that.

I think it’s important also to understand whether the current state of your journey maps are as was ideating what you would like them to be. And I think this is where you start surfacing in trying to, as you try to improve, you know, those customer journeys, you trying to predict their intent, and you’re trying to stay ahead of their needs.

And this is where, you know, we can transition to the next slide, into a predictive modeling. So, we’ve gone through, you know, all of the research we’ve analyzed, the data that we have available, measured, and surfaced all the behavioral analytics.

We understand what we want the customer journey to be and we have identified specific touchpoints that should we be able to accurately predict what they’re going to do next based on, but they’ve shared with us so far based on their behavior, based on their persona, based on their segment. Now we can predict what they’re going to do next and we wanted to do that at scale.

And accurately, and this is where, you know, we leverage machine learning and predictive modeling.

And starting by validating, although, those are all those assumptions that we surfaced through research and behavioral analytics, ensuring that, you know, we can explore and identify, you know, the similar patterns, understand the correlation between your, some of those patterns and the outcomes, the, or the intent of the customer that we want to predict.

We, then, in a traditional data science, you know, fashion, We go through model development. In that may also need, mean that we need to engineer many different new features that are not just first party data. Our raw data that we have available, but that we need to engineer, in order to, to better, represent and surface, you know, some of those patterns, they’re going to be important for machine learning model to learn from, and to accurately predict that outcome.

And obviously, on this part of the model development, is, in is really a science experiment, right? You have to iterate and trying to optimize, you know, through hyper parameter optimization.

But ultimately, you end up with a model, a machine learning model, that you can deploy in production, and utilize to predict those behaviors. And that’s going to help you then, you know, leverage those predictions, and validate those predictions to inform, recommend, and ideally even automate, you know, some of the decision making process. So, those are all the steps shown that are very much iterative, your traditional to data science, but very much informed. And, in many cases, bootstrap a lot of the other activities that even Jordan mentioned before.

So, it’s critical to really think as it, there’s all these activities as a joint initiative, you know, throughout the organization and not do them in, in a bubble or in a segregated fashion.

And finally, you have, you’re trying to predict, you’re predicting customer intents, you have to then experiment with, with customers. So, obviously, going through you’re A/B testing, you’re split testing, comparing, you know, different concepts, different ways you want to predict, you know, the customer behaviors and personas based on, you know, basically performance and start now gathering data to measure the impact of those, you know, predictions the impact of those changes to the customer journeys through split testing.

We want to also build that feedback loop, you know, to our predictive models and measuring the impact, you know, the accuracy, the response, and engagement from those customers based on those predictions.

So, it’s critical to also ensure that there is that connected and direct feedback loop into our predictive models.

Then, finally, you know, using this split testing approach, we can then explore your new path exploration, and explore the different types of your journey flows, and different types of predictions that we’re making in those, in those customer journeys, to understand your effectiveness of a particular sequence of event triggers, and touchpoints the customer, you know, may have.

And, finally, as you find that perfect set that works that you demonstrated with data with analytics the impact on the customer journey, you that you now can exploit and deploy to true large audience and truly capitalize on that experiment, on all of this research and experimentation, you can now exploit and get the benefits of it.

But because if you have this whole frameworks of data driven analytics of research, you can also then measure the incremental accumulated your benefits of your, your changes going forward and really build a strong your business case for, you will continue to do additional experiments.

Now, obviously, all of these steps, and all those different competencies that we talked about have different layers of maturity that, you know, I’ll let your Brian kind of walk us through.

Brian: Alright, thanks, Nico.

Yes. We’ve talked through six gears, and you can see there’s a lot packed into journey science, but our maturity model breaks it down to be something a little bit more digestible. So we have a crawl, walk, run fly model, Aand for each of the six areas or competencies, and we’ve put an indication of, you know, what is the maturity at each stage?

So, if you think of, like segmentation, for example, you might start out with general market segments and these might be Claritas segments or cycle segments that you’ve purchased, but it’s a good foundation for understanding. And then the walk stage you’d want to derive, develop those data driven personas, so you’re doing the research to inform the personas, really making them your own. And in the run stage, you’re gonna utilize those personas to drive personalization.

So, not just at the design stage, but at delivering the experience, you leverage those personas to align to those customers and then personalize based on that criteria. And ultimately, in the fly, that’s where you’ve talked about dynamic segmentation, right? Where you’re really taking a trait-based approach and looking at some of the behavioral activity in order to drive some of those dynamics segments, and really small as the segment of one, ultimately, when you get to personalization.

So the maturity model breaks it down a bit, and helps kind of understanding, where your organization might be within journey science.

And then, you know, taking that a step further, we’ve developed something that we call Journey Science IQ. So, it’s a framework that helps you assess your organization across the six dimensions, and then identify those opportunities to improve the discipline as a whole, but also how do you improve the customer experience?

So, this is something where we can help organizations look into their operations – understanding, what they’re doing, where there might be gaps. We’ll assess each of the six dimensions, look at the different levels within those dimensions and it dives deeper than that maturity model. So we’ll, we’ll break it down to a little bit more fine grained actions and activities so you can get a sense of where your organization is, and where there are opportunities to grow. And, as we mentioned here, because we really think journey Science is, something that needs to be embedded in an organization. So, it’s not just something that, you’re gonna do upfront. We just did some journey science work, and now we’re done. No, it’s ongoing, right, there’s continuous discovery. There’s continuous improvement and optimization. So, just really needs to be embedded skill.

Alright, with that, I’m going to turn it over and see if we have any questions.

What would you say are the latest trends and developments in the field of Journal Science?

Brian: Seems for me, I’ll answer to start off and maybe Jordan and Nico can add to that. I think, you know, one of the key things is around, you know, first-party data. We’re talking about collecting customer data and what are those opportunities to gather more data about their profile, and really make that an owned experience. And so, that’s one of the things we’re helping organizations, when we look at that journey, it’s not just about, you know, what are the opportunities to use data, but it’s also what are those opportunities where we can collect data across different interactions in order to provide better experiences?

Eve: I think there’s a move towards just the development of assets and artifacts, and the activation towards more of an activation of the use of those, and orchestration across the organization, about how you can leverage those insights to create a better ongoing experience, both internally and externally. There are organizations who’ve been mature like that for some time, but, um, but I think it’s becoming more widely adopted.

Nico: It’s important to consider not just the digital experience, but also you’re connecting the data from physical experience, and how do we capture data from physical experiences from, you know, walking into your brick and mortar store, and really thinking about that holistic, like, because customer journey, now, not just from your digital assets, or touch points, but also from the physical ones?

What sort of challenges do organizations have in adopting journeys science? What have you seen?

Brian: I mentioned it a little bit, and say, you know, the big thing is having commitment at an enterprise level, like, looking across the organization, that it’s not just one team that is looking at journey science, and adopting it, it’s that everybody in the organization adopts the mindset, and that it becomes embedded, so that, you know, every product that’s developed, takes a journey-focused approach, that the organization, that different teams across the organization, are talking to each other and aligning on that customer journey. Think that’s one of the shifts.

And then, you know, sometimes some of these capabilities have been siloed in different teams across organization. So, connecting them as one cohesive unit is really important and can be a challenge.

Eve: I’ll jump in there as well. One of the things that we see often is, there are, depending on, you know, sort of, the type of organization, or how long they’ve been in business. Often, organizations may not have as much interaction or dialogue with their customers as you might expect. So, they have interactions through, you know, purchase channels and things like that, but, not a lot of dialogue getting their feedback or voice of the customer. And so, that’s an area that we’re really encourage because it can make a significant amount of difference. But that’s a hurdle sometimes, for certain types of organizations that just have historically not done that, and it’s a shift in the way that they think about going, about gathering inputs to the way they do business.

Jordan: Yeah, I think, I think some challenges or traditional challenges as well with that one would have with data ingestion programs, I think data quality and the quality of the insights being able to gain from said data is definitely a challenge. That’s not unique to journey science necessarily, but it’s definitely something that would impact the ongoing adoption of the program.

Nico: I was thinking the, this is, uh, this is an old challenge, it’s nothing new, but obviously you, Brian, you talked about the common challenges at the, at the beginning of the presentation of the siloed data.

It’s great if you can still get all the right people in the same room. But if you don’t have a way to connect and stitch the data from all those siloed and legacy systems, that will remain a challenge. And that’s probably not something that, most of you on the call, in your own line of business organizations, a challenge that you’ve been trying to tackle for your, for a long time.

And one that is still, there’s a lot of importance, right, to, to support, you know, these kind of approaches around evaluating and truly transforming customer journeys.

How would an organization go about getting started with Journey Science?

Nico: I would say talk to Brian. That’s the most important part. But now, Brian, just just joking aside, I think even thinking about it and realizing, you know, it’s important to, you know, to see, know, how you would even consider implementing your own flavor of journey science. It’s like, how do you function around evaluating customer journeys today?

Would you just do an assessment ask, Ask Who, Who in the organization is working on some of those competencies? Just identify the key players, and start having a conversation, and if you’re not already talking on a regular basis.

Brian: Agree, and I think it’s the first step, is looking to see where your gaps are. So we’ve outlined the framework organizations can look at that. We can also help and get dive into that deeper, but just seeing where there might be gaps in your organization. And then working to fill those gaps, by making that bigger commitment to adopting journey science.

Sarah: We’d like to thank everyone for joining today. I’d like to thank our speakers, our very knowledgeable speakers and we’d love to invite anyone who has any additional questions about Journey Science to get in contact with us and you can take a look at our Journey Science on our website.

And, you know, I encourage you to follow proficient online as we have new information added daily. Connect with us on linked in, and you can visit perficient dot com as I said, you know, to learn to take a look at some of our blogs, guides our upcoming webinars and more and we will send out a follow up way, you know, our link to our data as well as the webinar.

And thank you, again, for joining us today.

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Sarah Dwiggins

Sarah Dwiggins is a Perficient Marketing Manager over the Customer Experience and Digital Marketing Pillar. She has worked in technology for more than 8 years.

More from this Author

Follow Us