You've got the customer journey map. It's colorful. It's detailed. It's useless.
There are roughly 40 million search results alone around customer journey mapping and 644 million results around the customer journey. No wonder various CRM & survey tools and agencies bet horrendous dollars on associated keywords since understanding the customer journey with the different touchpoints and needs is probably the most pressing need of every marketer. With the rise of CRM systems and data collection, the hope of creating a data-driven customer journey map seemed tangible. But this is a fallacy and leads to frustration among marketers.
Most journey maps look great in a presentation but fall apart in the real world. They're built on assumptions, ignore real customer behavior, and treat everyone like they follow the same path. If you want journey maps that actually drive results, you need to stop guessing and start mapping with buyer personas at the core.
Quick definition of a customer journey
The idea of the customer journey dates back to 1998 when OxfordSM was called Oxford Corporate Consultants. The concept has remained the same ever since - it will help organizations understand and demonstrate the story of the customer's experience. It's a visual representation of the steps a customer goes through when interacting with your brand, from first awareness to post-purchase. It's meant to highlight key touchpoints, emotions, goals, and pain points along the way.
Challenges for Customer Journey Mapping
The Illusion of a Perfectly Trackable Customer Journey
The idea of mapping a complete, end-to-end customer journey for each individual sounds powerful in theory. In an ideal world, you could follow a single user from their first ad click to browsing your website, to downloading a whitepaper, attending a webinar, speaking with sales, and eventually converting. But in practice, this level of tracking is rarely possible, and the assumption that it is leads to flawed strategies.
The problem is simple: you don't have a reliable, persistent identifier across every touchpoint. A user may first visit your site on mobile without logging in, later return on desktop through a different channel, and finally convert under a different email address after speaking to a sales rep. Technically, these are all the same person, but without a consistent ID, your analytics tools see three disconnected users. As a result, the data becomes fragmented, and so does your understanding of the journey.
It's a math problem, at its core. If too many variables in an equation are unknown, the equation becomes unsolvable. Similarly, when too many customer interactions are anonymous, unlinked, or misattributed, you can't reconstruct the complete journey accurately. You end up filling the gaps with assumptions, which introduces bias and undermines the reliability of the map.
For example, a user might abandon a signup form. Was it because the form was too long, the pricing wasn't transparent, or they were just interrupted and intended to return later? Without context and identity, you can't know, and your optimization efforts risk fixing the wrong issue.
This doesn't mean customer journey mapping is useless- it means that expecting it to represent individual truth is unrealistic. The goal shouldn't be to track every user precisely, but to create representative journeys based on patterns across buyer personas, supported by real research and behavioral data.
Over-Reliance on Assumptions
The previous challenge automatically leads to the next flaw in many journey maps - the reliance on assumptions instead of data. So do marketers if they do not have enough data? They make something up. When teams create journey maps without real customer input or limited data, they make guesses about user behavior. For example, a SaaS company might assume prospects abandon their signup form because it's too lengthy. In reality, through user interviews or usability testing, the root cause could be a lack of clarity around pricing. Assumptions lead to misaligned strategies and wasted resources.
The Subconscious Decision-Making Dilemma
At the heart of many flawed customer journey maps is an oversimplification of purchasing decisions. A fundamental challenge is the misconception that customers make decisions through a purely logical, conscious process. In reality, research shows that up to 95% of all purchasing decisions are made subconsciously. This means that much of the decision-making process happens outside the customer's direct awareness, influenced by emotions, biases, and personal experiences rather than a clear, step-by-step evaluation of facts or product features.
Attempting to map a customer journey as a logical, linear progression ignores this critical subconscious aspect. For example, a consumer might choose one brand over another not because it's objectively the best option, but because it triggers a positive emotional response or aligns with a deep-seated desire for status, belonging, or convenience factors that are difficult to quantify.
Moreover, personality plays a vital role in shaping decisions. Two people might evaluate the same product but make entirely different decisions based on personality traits such as risk tolerance, openness to new experiences, or a need for certainty. Mapping a customer journey without considering these nuances is undercomplex and somewhat naive. Personal biases, cultural influences, and past experiences all color the way individuals think and feel about products or services, making it nearly impossible to predict every decision based on behavior alone.
Acknowledging that customers are not purely rational means recognizing that the customer journey is far more complex, dynamic, and emotionally driven than most maps account for. Any attempt to understand customer behavior without considering subconscious influences and personality factors is missing a critical piece of the puzzle. If you want to learn more about customer psychographics, check out this article.
Lack of Emotional Context
Customer journeys aren't purely about actions - they're about emotions. Decisions are often driven by the desire to solve frustrations, reduce risks, or gain confidence. For example, an HR manager evaluating two software tools may not only be concerned about functionality but also about avoiding the embarrassment of choosing the wrong tool. If your journey map doesn't account for these emotional drivers, your strategy will miss the emotional triggers that influence decisions.
Linear Thinking
Traditional journey maps often follow a linear path: awareness → consideration → purchase → retention. But customer behavior is far messier. B2B buyers, for example, may discover your product, pause their decision-making for months due to budget constraints, and re-engage later. Treating this journey as linear ignores the real-world customer behavior's unpredictable, dynamic nature.
Lack of Data
One of the primary challenges in customer journey mapping is gathering comprehensive, reliable data. Data can come from multiple sources: surveys, interviews, analytics, CRM systems, and feedback. However, not all data is relevant or consistent. Some data may be outdated, missing, or conflicting. This leaves gaps in the journey map, leading to incomplete or inaccurate depictions of customer behavior.
Without reliable and consistent data, journey maps can become inaccurate, leading to misguided decisions that don't align with customers' behavior.
Stakeholder Buy-In
Customer journey mapping requires cross-functional collaboration, yet securing stakeholder buy-in can be challenging. Teams from sales, product, IT, and customer service need to understand the value of journey mapping and its potential impact on decision-making. Without this alignment, journey maps often remain isolated, disconnected from the broader business strategy. Without proper buy-in and support from all relevant stakeholders, journey mapping can become an isolated effort with limited influence on the overall strategy.
Actionability and Impact
Even when journey maps are comprehensive, marketers often struggle to translate the insights into actionable steps. The challenge lies in defining clear, measurable goals and linking them to customer behaviors at various journey stages. Without clear action items, journey maps can feel like theoretical exercises, not practical solutions.
Journey maps should provide direction: guiding teams on where to invest resources and which strategies will yield the most impact. Without this clarity, journey maps are often left on the shelf as unexecuted plans.
Tackling Customer Journey Challenges
To overcome the many pitfalls that undermine traditional customer journey mapping, like data gaps, emotional blind spots, and overly linear thinking, businesses need a more grounded, human-centered approach. This is where buyer personas come in. By representing real customer archetypes based on behavioral data, motivations, and psychological drivers, buyer personas bridge the gap between theory and actionable strategy. They transform flat, assumption-filled journey maps into dynamic tools that enhance engagement, boost conversion, and foster long-term loyalty. With personas as the foundation, companies can personalize journeys with greater segmentation clarity, connect more deeply on an emotional level, align content more precisely with buyer intent, and encourage internal teams to view the journey through the customer's eyes, not just their own objectives.
How Buyer Personas Tackle the Problem of Fragmented Customer Data and the Unique Identifier
While the issue of lacking a persistent unique identifier across every customer touchpoint remains a challenge, advancements in AI and data analytics have made it possible to overcome this barrier effectively. Through AI-powered segmentation, businesses can now identify distinct customer segments within specific stages of the journey by analyzing the questions they ask and the comments they leave across various online platforms. It is made possible by carefully listening to what customers are saying and analyzing it with the latest developments in NLP (Natural Language Processing). If you train your models with text data that reflects utterances about the different customer journey steps, your system can identify customers located within them.
Here is an example: Let us say prospects mention something like:
- “I am considering buying the product XYC, but I feel like it could…..”
- “I am contemplating buying product XYC because…..”
- “I am looking at product XYC, but I am unsure if…”
A well-trained NLP system will recognize expressions like considering, looking at, and contemplating, and assign them to the Consideration stage. Thereby, you can investigate why prospects are interested in your product, which features are essential for them, and what might hold them back from moving to the purchase stage.
The same concept applies to the purchase stage. For instance, someone who mentions:
- “Yesterday I purchased XYZ, and I was so happy because it…”
- “I bought XYZ, and it sucks since it was not….”
- “I spent my last money on XYZ, which was the best…..”
This real-time behavioral analysis offers a more nuanced understanding of where each customer stands in their decision-making process, even if they engage with the brand through multiple devices or under different identities.
By leveraging AI to track and analyze the language, tone, and context of customer interactions, whether it's a product inquiry, a comment on a blog post, or feedback during a demo, companies can pinpoint the most pressing needs, required information, and pain points for each segment at every step of the journey.
How Buyer Personas Tackle the Oversimplification of Customer Decision-Making
As mentioned earlier, most organisations do not understand how vital the subconscious is for our decision-making processes. As a result, businesses often miss the deeper motivations behind customer behavior, making it challenging to create effective strategies that truly resonate with their audience.
AI models that analyze customer personality using the OCEAN Big Five model (Openness, Conscientiousness, Extraversion, Agreeableness, and Neuroticism) offer a game-changing solution to this problem. By leveraging AI to assess and segment customers based on these key personality traits, businesses can go beyond just tracking behaviors and dive deeper into the emotional and psychological drivers of decision-making. For example, an AI model might identify that a particular segment is characterized by high openness to new experiences, making them more likely to explore innovative products or services. Conversely, a segment with higher conscientiousness might prioritize reliability and value over novelty.
This approach provides several advantages for organizations. First, by clustering customers according to personality traits, brands can create more nuanced, persona-driven customer journey maps that account for emotional triggers and subconscious influences. For example, a risk-averse customer (low in openness) may need more information and reassurance at the evaluation stage. In contrast, a more adventurous customer (high in openness) might be swayed by bold, exciting messaging and product benefits that highlight novelty.
Moreover, personality-based segmentation allows businesses to tailor their messaging and content to resonate more deeply. Understanding that personality traits influence a customer's purchasing decisions helps organizations deliver more personalized experiences, whether it's offering a calming, data-driven approach for neurotic customers or a creative, boundary-pushing pitch for those with high extraversion.
In essence, by integrating AI-driven personality insights into buyer personas, companies can create customer journey maps that reflect the true complexity of human behavior. This not only improves engagement and conversion but also fosters more genuine connections with customers by addressing their subconscious desires and personality-driven needs, which would otherwise remain hidden in traditional, oversimplified mapping.
Enhancing Customer Journey Mapping with Emotional and Value-Based AI Personas
Based on behavioral cues, AI-generated buyer personas can analyze emotions using Paul Ekman's framework, which identifies universal emotions like happiness, sadness, fear, anger, surprise, etc.. By integrating this emotional intelligence, AI can better predict how different customer segments respond to messaging, experiences, and touchpoints along the journey. Additionally, combining this with the Schwartz Values Model, which maps core human values like security, achievement, and self-direction, enables AI to uncover the deeper motivations driving consumer decisions. These models allow for highly nuanced customer journey mapping that aligns emotional triggers and personal values with each stage, resulting in more empathetic, personalized, and effective engagement strategies.
Stop Romanticizing the Customer Journey- Start Grounding It in Buyer Personas
Traditional customer journey maps are often over-designed, overly linear, and built on shaky assumptions that fail to reflect real customer behavior. Marketers must abandon the romanticized idea of a fully connected, trackable journey and embrace a more realistic, data-informed approach. The key issue is fragmentation: users interact across multiple touchpoints without consistent identifiers, leading to misattributed and incomplete data. Furthermore, most maps ignore the subconscious, emotional, and personality-driven factors that dominate decision-making.
To make journey mapping truly effective, marketers must shift their strategy to center around AI-enhanced buyer personas. These personas integrate real behavioral data, psychographics, personality models (like OCEAN), emotional cues (via Ekman's emotion theory), and value systems (such as Schwartz's model) to provide a deeper, more accurate understanding of customer needs and motivations.
The takeaway? Stop chasing a perfect, linear journey. Instead, use intelligent buyer personas to map behavior patterns, emotional states, and value drivers, giving your team actionable insights, better segmentation, and ultimately, more impactful marketing.