AI and Big Data in Customer Journey Mapping

Understanding customer experience has become increasingly complex due to the multitude of digital and real-world interactions customers have with brands. The concept of the customer journey has emerged as a way to navigate this complexity and research on customer journeys has grown significantly. AI and big data play a crucial role in understanding and managing customer experiences. By analyzing big data, businesses gain insights into customer behavior and preferences, allowing for personalized interactions. This data-driven approach also enables companies to make smarter decisions in areas that impact the customer experience. The customer journey consists of three phases: pre-purchase, purchase, and post-purchase. AI and big data help in each phase by providing a deeper understanding of customer needs and expectations. By connecting the power of AI and big data to the customer journey, companies can improve the overall customer experience and reduce complexity. The research highlights You know, it feels like just yesterday we were trying to map out how to keep customers happy with like a few surveys and maybe a comment card. Now, understanding what makes for a great customer experience feels like trying to hit a moving target while blindfolded. It's just complex. That's a spot on way to put it. The sheer number of digital and, you know, real world interactions a customer has with a brand today, it's created this incredibly intricate web. Right. It's far beyond a simple linear path to purchase anymore. It's this really multifaceted experience. Exactly. And that's where this idea of the customer journey really comes into play, right? This attempt to chart all those twists and turns in that experience. And it seems like we're all grappling with this. I mean, the amount of research on customer journeys has exploded. Apparently it's grown like sevenfold since 2012. Wow. Sevenfold. Sevenfold. It really underscores how vital understanding this whole thing is for any business that's really focused on delivering, you know, exceptional customer experiences. It truly does. Yeah. It highlights the central role the customer experience now plays in a company's success. Yeah. And for this deep dive, we've gathered some fascinating academic research. Okay. It looks at how the power of AI and big data is fundamentally changing how we understand and maybe more importantly, manage these increasingly complex customer experiences across all the touch points. Ah, okay. So think of this as like your direct route to grasping what's truly driving the evolution of customer experience management. So our mission today is to explore how AI and big data are giving us a completely new lens, a new way to view and optimize the entire customer experience from that very first flicker of awareness right through to hopefully long-term loyalty. That's the goal. Okay. Let's get into it. First things first, big data and AI. We hear these terms constantly. Can we get a clear picture of what we're actually talking about, especially in the context of, well, enhancing that customer experience? Certainly. So big data, as initially described way back, is really characterized by three things. The sheer volume of data generated, the wide variety of formats that comes in text, images, clicks, you name it, and the rapid speed at which it's created and crucially needs to be analyzed. The three Vs, right? Volume, variety, velocity. Exactly. Then artificial intelligence, AI, is the capability of machines to essentially learn from all that data, adapt to new information, and perform tasks that typically require human-like intelligence. Okay. That's how folks like Dwan Edwards and Delia Vieira framed it. So AI is kind of the intelligence layer that can take this enormous ocean of big data and actually extract meaningful insights, insights to help us create better experiences for our customers. AI tools are specifically designed to process these vast data sets and identify patterns, correlations, useful information that can inform our understanding of that customer journey. And while AI has really widespread applications, robotics, speech recognition, all sorts, it's increasing role in the business environment, particularly in understanding and shaping customer experiences, is becoming undeniable. Dwan's work really highlights this. Yeah. It's impossible to ignore how much our own behavior as customers is influenced by all these digital apps and platforms. I mean, every interaction leaves a digital footprint, right? Generating more and more data about our preferences, our behavior. Absolutely. And what's truly transformative for the customer experience is how the convergence of big data, AI, and marketing allows companies to create significantly more customer value and deliver superior experiences. It's a powerful combination. How so? Well, by analyzing this data, businesses can gain a much deeper understanding of customer transactions, purchase habits, even individual customer characteristics, like Zachary and his colleagues pointed out years ago. This allows for much more personalized and ideally relevant interactions. Right. It's about moving beyond just looking at like overall sales figures and actually understanding the individual customer's journey, what influences their decisions and their overall satisfaction, getting granular. Exactly. This deeper understanding leads to better insights into consumer behavior, preferences, and overall market trends, as Yin and Knaack discussed. Okay. And importantly, this data-driven approach empowers companies to make smarter decisions in crucial areas that directly impact the customer experience, things like ensuring product availability, optimizing website navigation, even personalizing customer service interactions. Scheider and Gupta, Bradlow, and others explored this. Okay. So we have the raw material, big data, and the intelligence to analyze the AI. Now, how does this actually help us map and understand the customer journey specifically, you know, to improve their experience? Well, the customer journey itself is essentially a framework, maybe a metaphor, for visualizing and understanding the entirety of a customer's interactions with a brand throughout their purchasing process and, importantly, beyond. Meyer and Schwager described it as the complete sequence of a customer's direct and indirect encounters with a specific product, service, or brand. Encounters. Okay. So those happen to touch points. Exactly. These encounters occur through various touch points, both online and offline, and every single one contributes positively or negatively to the customer's overall experience. Touch points. That makes perfect sense. Every ad they see, every website visit, every chat with a customer service agent, all of these moments shape their perception and their overall experience. Precisely. And Lemon & Verhoof, back in 2016, broke this journey down into three key phases, all critical for shaping the overall customer experience. Three phases. First, there's the pre-purchase phase. This covers everything from the customer recognizing a need to actively searching for information and considering different options. And this is based on what they find online, in ads, through user recommendations, word of mouth, all that initial research. So that's the stage where they're kind of forming their expectations, gathering info that will influence their ultimate decision and their initial experience with the brand, maybe even before they buy anything. Correct. That's where initial perceptions are formed. The second phase is the purchase phase itself. This is where the customer makes their information-based selection and actually completes the transaction. A smooth, seamless experience here is, well, obviously crucial for satisfaction. Makes sense. Frictionless, ideally. Ideally. And finally, there's the post-purchase phase. This covers their experience with using the product or service, their level of satisfaction, and any subsequent engagement, positive or negative. Think reviews, repeat purchases, complaints. Ah, okay. So that phase is key for building loyalty and maybe even turning customers into advocates. Absolutely. It's critical for the longterm. So the research we're exploring today really aims to connect the power of big data and AI to this entire customer journey map, with the goal of not just improving marketing results, but maybe more importantly, enhancing the entire customer experience and reducing the friction, the complexity customers might feel. Exactly. That's the crux of it. And the research highlights 10 key applications, 10 ways AI and big data are significantly impacting our ability to understand and manage this journey for a better customer experience. 10. Okay. Let's dive into them. The first is customer profiling. How are AI and big data giving us a richer understanding of our customers, you know, to tailor their experiences better? Well, the sheer volume of digital information available today, both what people willingly share, like profiles, and the data that's collected passively, like browsing history, allows for incredibly detailed customer profiling. Verhoef and his colleagues really highlighted this. They even propose a framework called PLP people, objects, and physical environments to help create these comprehensive profiles from really diverse data sources. Think data from smart devices, environmental sensors. Wow. POP. So not just online clicks. Not just clicks. No. And what's really insightful here is how AI can now connect seemingly disparate pieces of online behavior. Say someone browsing articles about sustainable living, and then looking at eco-friendly product reviews, AI can link that and paint a much richer picture of their values, their potential needs, leading to more relevant and hopefully positive experiences down the line. So it's moving beyond just basic demographics like age and location to a much more nuanced understanding of their behaviors, their interests, even their interactions with the world around them, all to create more personalized, meaningful experiences. Exactly. Trusov, Ma, and Jamal pointed out how analyzing massive amounts of online activity data from search engines, website visits, online advertising can reveal key behavioral patterns. Patterns that tell us. That directly inform how we can best engage with and serve customers. And companies like Google, Facebook, Amazon, what Pochi and Hufenbach called PureClick companies, they have a significant advantage because their platforms are just constantly generating this type of behavioral data. Yeah. They're sitting on a goldmine of information about customer interactions online, and then there's user-generated content, right? Like online reviews, social media posts, and that must be huge too. Absolutely. Lu and colleagues emphasize how user-generated content, UGC, especially in sectors like travel and hospitality, provides another incredibly rich source of information about consumers' actual experiences and opinions, real voices, and AI techniques, including something called lexicon filtering and more advanced machine learning algorithms can be used for sentiment analysis of this UGC. Sentiment analysis. So figuring out if people are happy or angry. Essentially, yes. But often with more nuance, it allows companies to automatically gauge how customers feel about specific aspects of their experience, the service, the product features, the price. It provides immediate direct feedback on what's working and what's not. That's incredibly valuable for understanding the emotional side of the customer experience. Imagine automatically analyzing thousands of comments to pinpoint exactly what's delighting customers and what's causing frustration. And it goes beyond just understanding past experiences. Decision support systems, DSS, powered by AI, are even being used to proactively improve customer acquisition and tailor those initial interactions. Really? Like how? Well, the big chase system used by Blanco Santander, described by Carlos Sanchez and Liberator, uses social connections and operational data to identify the most effective sequence of clients for a manager to contact to achieve a specific goal, like signing them up. Wow. That's like using AI to personalize the very first touchpoint. Okay. Interesting. Application number two, promotion strategies. How do these richer customer profiles help us create more effective and, well, customer-centric promotions? Well, the detailed insights from profiling are invaluable for crafting more effective and relevant sales, promotions, and other marketing campaigns. Ultimately, this leads to a better customer experience with our outreach, less irrelevant noise. Bohalis and Forrest noted this. Right. Miros Petruan and colleagues even suggested using machine learning based click-through rate models to optimize online display advertising. Click-through rate models. So predicting who's likely to click an ad. Exactly. By analyzing data like age, time of day, browser, device type, advertisers can micro-target very specific audiences with offers that are far more likely to be of interest. This reduces irrelevant ad exposure and, ideally, improves the overall online experience. So instead of bombarding everyone with the same generic ads, it's about delivering tailored offers that genuinely resonate with individual customers, making the promotional experience itself more valuable, maybe even helpful. Precisely. Which should lead to better conversion rates and a more positive perception of the brand's communications. Okay. Third application, demand forecasting. This is about anticipating what customers will want and when, which is crucial for ensuring a smooth customer experience, mainly through product availability. No one likes out of stock. Exactly. And big data and AI are proving to be incredibly powerful here. Researchers like Chong and his colleagues have highlighted the importance of analyzing online reviews, customer sentiments, Q&As, even online promotional activities to predict product sales. So digging into what people are saying online before they buy. Yes. This allows companies to ensure they have the right products in stock when customers want them, avoiding those frustrating stockouts and improving satisfaction. What's particularly insightful is the level of detail they can analyze. They can even assess the impact of specific factors like free delivery or price discounts on future demand. Wow. Beyond that, Yang and coauthors found that web traffic data can be a strong predictor of demand, especially in tourism for things like hotel room bookings. Makes sense. More searches probably means more bookings coming. Right. And Tribuchi and colleagues pointed out the immense value of data collected through mobile apps for understanding customer needs and predicting product demand. This allows for proactive adjustments to inventory, staffing, you name it, to meet those needs. So it's about looking at all these digital signals, what people are saying, searching for, doing in-app to get a clearer picture of future demand and ensure we can meet those needs effectively, leading to a better experience by just having the thing they want available. Exactly. It avoids disappointment. That makes a lot of sense for managing inventory and production. Okay. Number four, new product and service development. How do AI and big data help us create offerings that truly meet customer needs and desires, thus enhancing their experience from the get-go? Well, Caldwell and Albana show that big data can significantly support the entire product development process. It provides valuable insights into market preferences, unmet needs, even competitor designs. So less guesswork and R and D. Much less. This allows companies to develop products and services that are much more aligned with what customers actually want, ultimately leading to greater satisfaction. Dew, Frank Wick, and Ramirez even highlighted how companies like Netflix use vast quantities of real-time user data. Oh yeah. Netflix is famous for this. Right. They use it to predict whether a new show will be a hit. This ensures they're investing in content that their audience will actually enjoy, which directly impacts their entertainment experience. That's incredible. Instead of just, you know, guessing what might be popular, they're using actual viewing behavior to inform decisions and create content that resonates. Leading to a much better viewing experience for subscribers. Exactly. And when it comes to understanding specific feature preferences for new physical products, Lopez and colleagues introduced a novel approach using something called support vector machines in conjoint analysis. Okay. That sounds technical. It is a bit, but it basically offers superior predictive power in identifying which features customers value most. Got it. Furthermore, Kuhl and coauthors emphasize the importance of monitoring social media, using machine learning to identify unmet customer needs, things people are complaining about or wishing for. Ah, mining social media for ideas. Precisely. And in tourism, researchers like Maureen Roig and Klawe and Under have shown how big data can even be used to predict popular travel destinations, helping businesses develop relevant travel packages and experiences. So from fine tuning product features to identifying entirely new market opportunities, AI and big data are becoming indispensable in the innovation process, ensuring new offerings are more likely to be a hit and provide a positive experience. That's the idea. Number five, pricing strategy. This is always a sensitive area for customer experience. Nobody likes to feel ripped off. How do AI and big data help get the pricing right? Yeah, this is a tricky one. Indeed. Danaher and colleagues demonstrated how big data analytics can be used to optimize pricing strategies for products influenced by trends and seasonality, like digital music. Okay. That could even determine how price changes for one song impact the sales of others, allowing for dynamic pricing that ideally balances profitability with perceived value for the customer. Dynamic pricing. That can be controversial though, right? It can be, if not handled transparently, but the potential is there. And both Weber and Schutt and Wirth have highlighted the potential of AI to inform pricing decisions as part of the broader marketing mix, potentially leading to more transparent and maybe customer-friendly pricing models over time. So it's about moving beyond static pricing to more dynamic data-driven approaches that adapt to market conditions and consumer behavior in real time, hopefully leading to prices that feel fair and reasonable. That's the ideal. Yes. Okay. Number six, distribution choices. How can AI help companies decide where and how to best make their products and services available for a convenient experience? Right. Getting the product to the customer. Wu and colleagues propose an AI-powered franchising decision support system. It can analyze the competitiveness and profitability of different distribution channels, like should we open a store here or focus on online or partner with someone. Oh, okay. It helps businesses make more informed decisions about where and how to offer their products or services, which ultimately impacts accessibility and convenience for the customer. That makes sense. Using data to figure out the most efficient and convenient way to reach the customer. Number seven, customer service. This is such a crucial touch point for experience and AI is already pretty visible here with chatbots and things. Absolutely. Motomari and colleagues discussed how big data analytics can help companies understand and improve customer service interactions for frontline human employees too. Oh, interesting. Not just replacing them. Not necessarily. It's about leveraging information gathered from various online sources, maybe past interactions, known issues to allow for more informed and personalized service delivery by the agent. This leads to more positive resolutions and a better overall service experience. There is a caution here about privacy though, which is important. Okay. Right. So it's about empowering customer service teams, human or bot, with the right information at the right time to provide more effective personalized support. Leading to happier customers. Exactly. Number eight is the analysis of consumer behavior, which really feels like it underpins a lot of these other areas. Understanding the why behind customer actions and experiences. Yeah, this seems foundational. It is. Hofacker and colleagues highlighted how user generated content online provides a wealth of data about consumers' relationships with brands. Analyzing what people are saying, the good, the bad, the ugly, can help identify early warning signs of dissatisfaction and areas where the customer experience is falling short. So listening in on the public conversation. In a way, yes. And techniques like text mining and automated sentiment analysis, as Kirilenko and Park and their teams have shown, offer scalable ways to measure customer satisfaction and loyalty from this text data. Right. And McCall Kennedy and colleagues introduced a framework for understanding the customer experience based on both emotional and cognitive responses to different touch points. They recommended using linguistics-based text mining to really capture these nuances. So it's about really listening to the voice of the customer, even when they're not directly giving feedback to the company and using those insights to continuously improve their experience. Social media and online reviews become like a continuous feedback loop. Exactly. A very rich one. Okay. Number nine, customer relationship management or CRM. How do AI and big data enhance our ability to build and maintain strong customer relationships for a positive ongoing experience? Beyond just storing contact info. Way beyond. The online environment presents both challenges and opportunities for CRM. Steinhoff and colleagues discussed how e-commerce, AI technologies like chat bots, and big data analytics can be used to personalize products and services and build stronger online relationships, leading to increased satisfaction and loyalty. Okay. George and Wakefield emphasize using big data to understand how customers respond to different contact strategies over time. Like, does sending this email help or hurt the relationship? Ah, testing and learning. Right. And for service firms especially, big data offers valuable insights for attracting, serving, and retaining customers more effectively by personalizing interactions and anticipating their needs. So, it's about using data to build and maintain better, more personalized relationships with customers across all interactions, fostering loyalty and positive long-term experiences. Got it. And the 10th application, brand analysis. How do AI and big data fit into understanding and managing a brand so it resonates positively with the customer experience? Brand managers can leverage AI-based intelligence systems. One example proposed by Chika and colleagues is called Identimod. It uses fuzzy logic. Fuzzy logic. Yeah. It's a type of AI good at handling imprecise concepts. It helps model and evaluate intangible brand variables like brand loyalty and awareness. By inputting linguistic data like brand perception is strong or numerical data, these systems can stimulate different scenarios like, what if we launched this campaign? And help inform key branding decisions. Interesting. The ultimate aim is to create a brand perception that aligns with a positive customer experience. That's fascinating. It's like using AI to get a more data-driven understanding of how customers perceive our brand and how that perception might impact their overall experience. Okay, wow. That was 10 applications. So, let's circle back to those three phases of the customer journey, pre- purchase, purchase, and post-purchase and see how these AI and big data tools specifically enhance our understanding and ability to optimize the customer experience at each stage. Okay, tying it together. As Lemon and Berhoof outlined, in the pre-purchase phase, AI and big data allow for that detailed analysis of search activities we mentioned, like TrueSauce and colleague shows. Great. We can understand the exact search terms potential customers are using, as Hofekker's team highlighted. This enables the creation of targeted advertising campaigns that are more relevant, less intrusive, like Elliman and Merals Petchouan discussed. Oh, that's annoying. Hopefully. Hopefully. This data also informs pricing strategies, ensuring they seem competitive and fair right from the start. So, it's about understanding customer intent, even before they interact directly with us, and making sure their initial interactions are helpful and relevant, setting the stage for a positive experience. Exactly. Then, during the purchase phase, analyzing transaction data, geographic location, price sensitivity, as Chahan and colleagues noted, provides valuable insights for profiling, forecasting, and optimizing the purchasing experience itself, making it smoother, faster, more convenient. That's about understanding what happens at that critical point of sale, and using the information to eliminate friction, maybe personalize the transaction somehow, leading to a more satisfying purchase experience. Precisely. Finally, in the post-purchase phase, monitoring that E-word of mouth, online reviews, social media, as Hofekker and others pointed out, helps gauge customer satisfaction and loyalty. It gives crucial feedback on their experience after the purchase. AI-powered sentiment analysis, demonstrated by folks like Maureen Roig and Klabe, Kaladi and Cutler, Kirolenko, and Buhalas and Sonata, helps quantify these feelings at scale. AI can also identify those unmet customer needs from social media chatter, as Kuhl showed, allowing for proactive service or product improvements. Right. Fixing problems before they become widespread. Yes. Yes. Intelligent conversational bots, like the one Pradhana described, can enhance customer service and simultaneously collect valuable data on post-purchase experiences. So the bot helps and learns. Exactly. And finally, as George and Wakefield discussed, predictive models using big data can inform CRM strategies for retaining potentially at-risk customers by proactively addressing issues and ensuring continued satisfaction. So it's about understanding the entire life cycle of the customer experience, identifying areas for improvement after the sale, and proactively nurturing relationships to build long-term loyalty and positive word of mouth. Makes sense. Now, we also looked at another source, ImpactAICJM.pdf, which seemed to really focus specifically on the transformative power of AI in shaping the customer journey for a better experience. What were some key takeaways there? Yeah, that paper really hammered home the massive transformation we're seeing in the customer journey, specifically because of AI advancements, particularly in refining CRM and optimizing business systems to better serve customers, as Ben Jala and Lankam Raju pointed out. Okay. AI's impact is felt across the entire customer life cycle, mainly through machine learning's ability to analyze these huge data sets in real time. This provides deeper insights into behaviors, preferences, sentiment, all crucial for delivering exceptional experiences. So it's not just about looking at past data anymore, but using AI to understand what's happening right now, and even predict future needs or potential pain points for individual customers, allowing for proactive intervention and a smoother experience. Precisely. That predictive element is key. This enables highly personalized and targeted marketing that feels less like an intrusion, more like a helpful suggestion. Tailored product recommendations that truly align with individual needs, and more efficient customer service that resolves issues quickly, effectively, all leading to greater customer satisfaction and loyalty. Right. The paper also highlighted the role of predictive analytics in forecasting customer behavior and anticipating needs, which allows for better resource allocation, getting help where it's needed most efficiently. Okay. And AI-powered chatbots and virtual assistants were also emphasized for their ability to provide instant responses and resolve issues quickly, 24-7, which significantly enhances the accessibility and convenience of customer support. It sounds like AI is moving from being just a behind-the-scenes analysis tool to a more direct and impactful part of the customer interaction itself, aiming to make every touchpoint more positive and efficient. Absolutely. The paper defined AI simply as machines mimicking human intelligence and categorized it into narrow AI, focused on specific tasks, and general AI, more human-like learning, still largely theoretical. Okay. They also outlined key AI techniques relevant to the customer journey. Machine learning, which we've talked about, natural language processing, or NLP. NLP, that's understanding text and speech, right? Exactly. Crucial for analyzing feedback-powering chatbots. Then computer vision for interpreting visual info, maybe analyzing how people interact with store layouts or product images online, and robotics, though maybe less directly in the typical journey map yet. Got it. So each of those techniques offers a unique way to understand the customer experience, NLP for feedback, CV for visual interactions. Exactly. The paper also discussed how AI helps companies understand customer needs more comprehensively, identifying new opportunities to serve them better, set more accurate business goals focused on customer satisfaction, and achieve what they call smart marketing and precision marketing, delivering more relevant, valuable experiences. Okay. It then delves into data mining, which is basically the process of discovering those hidden patterns and insights within the large datasets, supporting data-driven decisions about how to improve the customer journey. So data mining is the crucial process of sifting through all that big data to find the valuable nuggets of information, which AI can then use to personalize interactions and optimize the customer experience at every stage. Correct. The paper outlined the data mining process collection, cleaning, analysis, and stressed its importance in uncovering valuable info to support customer-centric strategies. It specifically highlighted its role in the customer journey by identifying behavioral patterns, preference shifts, pain points across touch points, enhanced personalization, better product recommendations, content delivery tailored to individual interests, making everything feel more relevant. And the Impact AI CGM paper also touched on generative AI, didn't it? That seems to have huge potential for creating more engaging and personalized customer experiences. Yes, it did. Generative AI, things like JANs and VAEs, which focus on creating novel outputs like images, text, even code, is also transforming the customer experience. How so? By enabling more efficient creation of personalized content at scale, like unique marketing messages or product descriptions, by improving customer interaction through more sophisticated human-like AI assistance, by fostering innovative new product offerings tailored to specific needs discovered through data, and by supporting data-driven decisions that put the customer at the center. So AI isn't just analyzing data anymore. It's actively creating new possibilities for more engaging and personalized customer interactions. Precisely. The paper then summarized the key impacts of AI on the customer journey again, really driving it home. AI chatbots and virtual assistants for immediate help. Hyper-personalization of content and offers. Predictive analytics for proactive problem solving. Improved operational efficiency that translates to a smoother customer experience. Okay. That makes sense. Less waiting, fewer errors. Right. Also, better informed decision-making impacting the customer. Ongoing enhancement as AI learns from interactions. Omni-channel integration for a seamless experience across channels. That omni-channel piece seems really important, but hard to achieve. It is. And finally, enhanced customer engagement, driving conversions and repeat business through positive experiences. That's a pretty comprehensive overview of how AI is fundamentally changing how businesses can interact with and serve their customers, all for a much better overall experience. It is. The paper also stressed the importance of customer journey mapping tools for visualizing these interactions and spotting improvement areas and hyper-personalization strategies like proactive communication and adaptive journeys that respond to individual behavior in real time. Adaptive journeys, meaning the path changes based on what I do. Exactly. Omni-channel CRM for that unified customer view across all channels was also highlighted along with the huge impact of digitalization on consumer behavior and the need for businesses to adapt their models. Right. Data management and analytics in CRM were underscored as crucial for tailor-made solutions and better service, while acknowledging the challenge of unifying all that data from different silos for a complete customer profile. Yeah. Getting all the data in one place is a perennial problem. It is. Finally, the paper reiterated how AI integration with CRM empowers deeper engagement, predictive analytics that anticipate needs, and truly personalized experiences that build loyalty. It really paints a picture of a future where AI is deeply woven into every aspect of the customer journey, from initial awareness right through to creating a loyal advocate. Now, our third source, journeymap.pdf, took a slightly broader look, right, at the research around customer journeys themselves. What were some key themes emerging from that literature? Yeah, that paper did a systematic review of a large body of customer journey literature, and identified five major recurring themes in how researchers have approached understanding customer experiences over time. Okay. Five themes. What are they? They were service satisfaction, failure and recovery, basically, how companies handle mistakes, customer response, both emotional and cognitive reactions, co-creation involving customers and value creation, channels how customers interact across different touch points, and interestingly, technological disruption was identified as a relatively emerging theme in this body of work. Ah, interesting. So while the AI paper focused heavily on technology, this broader review shows that technology's impact on the journey is a more recent area of formal study compared to, say, basic service quality or complaint handling. Exactly. The service satisfaction, failure and recovery theme focuses on how customers evaluate service at touch points, and how effectively companies address issues, both critical for the overall experience. Customer response looks at how customers react, their thoughts and feelings. Co-creation explores actively involving customers. The channel theme examines channel usage and management for a seamless experience. And as we said, technological disruption investigates the impact of new tech, which is obviously growing fast, but is newer in the literature compared to the others. It's interesting to see those different lenses. It's not just about the tech. It's also about those core principles of good service, understanding the customer's perspective, handling problems well. Precisely. And the paper noted that while the literature has grown dramatically, a comprehensive, fully integrated understanding of the customer journey is still developing. There's still lots to figure out. Okay. So bringing it all together, it seems incredibly clear that AI and big data aren't just abstract concepts anymore. They are powerful tools, fundamentally reshaping how businesses understand, and more importantly, enhance the experiences of their customers. At every single stage of that journey, they're enabling levels of personalization, prediction, efficiency improvements, things that just weren't possible before, all with the ultimate goal of creating more positive, more valuable customer experiences. That's absolutely right. In today's increasingly digital world, having a deep data-driven understanding of the customer journey isn't just a nice-to-have or a competitive advantage. It's becoming essential. Table stakes, almost. Yeah. It's crucial for any business that wants to truly understand and delight its customers, fostering loyalty and long-term success through exceptional experiences. And this field is evolving so quickly. I mean, generative AI wasn't even much of a topic a couple of years ago in this context. It's exciting, maybe a little daunting to think about the future possibilities for even greater innovation and how we understand and continuously improve the customer experience. It really is. The pace is incredible. So here's something to really think about, maybe for our listeners to mull over. As AI becomes even more deeply integrated into our interactions with businesses as customers, how will our own expectations evolve? What new kinds of experiences will we come to expect, maybe even demand? Hmm. Good question. And how will businesses need to adapt to meet those ever-increasing demands for seamless, personalized, and truly exceptional customer journeys? And, you know, where's the line between helpful automation and genuine human connection in all of this? Yeah. The ethical considerations and the balance. That's a whole other deep dive problem. Definitely something to ponder until our next deep dive.