Customer Journey is the New Product

Published on Aug 16, 2019

Updated on Oct 16, 2019

Even when the customer has successfully added items to cart, it all means zilch until the purchase is complete. The average cart abandonment rate for all sectors worldwide was 75.6% last year. Simply put three out of every four digital customers leave websites without completing the purchase.

At the customer acquisition phase, you’ve to compete with zero-profit offers, despite the fact that your product is the best in its niche.

Although 80% of businesses claim they offer a great customer experience, only about 8% of customers express satisfaction with their experience.

So, what went wrong? Why are customers leaving your site without purchasing? And more importantly, what can you do about it?

Mastering the art of understanding user persona, discovering bottlenecks in the delivery process, and designing a user-friendly interface will help you build the product that supersedes customers’ expectations.

Mapping the Customer Journey

It involves understanding your customers’ behavior and feelings throughout their interaction with your products/services and visually mapping them to tell a complete story of their overall experience.

More precisely, the journey begins from the moment they first come to know about your product and the phases they pass through while making a final purchase decision.

“For brands, customer journey mapping is like walking a mile in their customers’ shoes and understanding their circumstances with empathy.”

Apart from managing complex user experience problems, you can tweak customer journeys to understand past, present or a future state surrounding a day in the life of your customer.

According to Gartner, Eighty-two percent of organizations have created a customer journey map, but only 47% are using those maps effectively. The mapping of the customer journey begins by creating a cross-functional team led by the CIO, CMO or even the CEO.

A journey map is different from process maps. They detail multiple channels and touchpoints before and during the buying process. Interestingly, the more value-added layers you can include, the better is your view of your customer.

Top Customer Journey Map Layers

Source: Uxpressia

Adding convenience to the customer experience value chain can create powerful moments of truth. For instance, while shopping on your portal, a customer might want to know more information about your product, it’s features, reviews, etc.

A proactive service that goes to the customer first using real-time messaging and custom product recommendations, is how innovative solutions can address such pain points, impacting the overall experience.

Choosing the Right Attributes

Before we look at how to select the most relevant inputs for outlining behavior, it’s important to grasp the fact that customer journey mapping is an iterative process. For instance, the questions answered by customer journey maps a decade ago is totally different from today.

Source: Harvard Business Review | Linear customer journey map in 2010

Source: Medium | The present-day non-linear customer journey map (eg: Ikea)

The goal is to build a comprehensive map that will conclusively identify gaps from multiple touchpoints — areas of customer experience that are disjointed or painful.

To achieve this, it is vital to map out each phase of the pre-buying and actual buying journey and map them alongside data-driven personas. Data is critical for customer journey stages — it is almost impossible to create customer journey maps without it.

The Modern Customer Journey
Source: G3 Com

The modern customer journey map needs to cover the complete omnichannel experience. Customers are now communicating with companies through 10 channels on average. Their expectations are fast-moving and rapidly evolving. They expect communications about the latest products from their favorite brands to happen in real-time.

For instance, Magalu, one of Brazil’s largest retail companies, recognized that its app was growing in popularity. They decided to enable deep linking, so that loyal customers who tapped on a Magalu ad were taken directly to the mobile app they already have installed, resulting in more than 40 percent growth in overall mobile purchases.

Customer journey maps are drawn from the customer’s point of view and are based on people’s mental models (how things should behave, the flow of interactions and possible touchpoints). They combine user personas, user scenarios & user flows to understand and predict how the customer will behave next.

5 Key Attributes of an efficient Customer Journey Map

Most relevant brand goals: The goals should be reflective of the inner aspirations of the organization that outwardly manifest into creating the best experiences consistently for the customer.
Key customer touch-points: Identify touch-points across all channels, and define the action and available paths for each. This layer is critical to understanding how the service structure forces customers into unnecessary interactions and take measures to avoid them.
Empathy Map: This map depicts exactly what the customer thinks, says, does and feels about your product/ service or the complete attitude towards the brand itself. One can also find utility in creating one-user vs multiple-user empathy maps.
Affinity Diagram: This is a great planning tool, and it enables you to organize the data and insights gathered up to this point into bundles.
Sketch the Journey: Now is the time to visualize the structured data into powerful story-driven narratives pointing out gaps in the process. This step will inform what solution or fix will remedy your customer’s pain-point for the long term.

Why You Need Customer Journey Mapping?

You’re doing great if you understand your customers and are able to exceed their expectations. Retaining a loyal customer base might make you think about the essence of understanding the customer journey.

But, how are you planning to face the competitive landscape? Because customers constantly lookout for change. What if your competitors satisfy your customer’s needs with a better emotional connection? Mapping the customer journey will allow you to transform the experience delivery process creating ‘wow’ moments that strengthen loyalty.

“The term ‘customer experience’ won’t exist in the organization of the future. It will be deeply entrenched in a company’s product, process, and culture that it will be synonymous with the brand and represent the only way to do business.”
Ann Lewnes, EVP and CMO, Adobe

We specialize in helping organizations build attractive and easy-to-understand user journey maps for faster omnichannel integration. Reach out to us on hello@mantralabsglobal.com, to learn more.

In 1997, the world watched in awe as IBM’s Deep Blue, a machine designed to play chess, defeated world champion Garry Kasparov. This moment wasn’t just a milestone for technology; it was a profound demonstration of data’s potential. Deep Blue analyzed millions of structured moves to anticipate outcomes. But imagine if it had access to unstructured data—Kasparov’s interviews, emotions, and instinctive reactions. Would the game have unfolded differently?

This historic clash mirrors today’s challenge in data architectures: leveraging structured, unstructured, and hybrid data systems to stay ahead. Let’s explore the nuances between Data Warehouses, Data Lakes, and Data Lakehouses—and uncover how they empower organizations to make game-changing decisions.

Deep Blue’s triumph was rooted in its ability to process structured data—moves on the chessboard, sequences of play, and pre-defined rules. Similarly, in the business world, structured data forms the backbone of decision-making. Customer transaction histories, financial ledgers, and inventory records are the “chess moves” of enterprises, neatly organized into rows and columns, ready for analysis. But as businesses grew, so did their need for a system that could not only store this structured data but also transform it into actionable insights efficiently. This need birthed the data warehouse.

Why was Data Warehouse the Best Move on the Board?

Data warehouses act as the strategic command centers for enterprises. By employing a schema-on-write approach, they ensure data is cleaned, validated, and formatted before storage. This guarantees high accuracy and consistency, making them indispensable for industries like finance and healthcare. For instance, global banks rely on data warehouses to calculate real-time risk assessments or detect fraud—a necessity when billions of transactions are processed daily, tools like Amazon Redshift, Snowflake Data Warehouse, and Azure Data Warehouse are vital. Similarly, hospitals use them to streamline patient care by integrating records, billing, and treatment plans into unified dashboards.

The impact is evident: according to a report by Global Market Insights, the global data warehouse market is projected to reach $30.4 billion by 2025, driven by the growing demand for business intelligence and real-time analytics. Yet, much like Deep Blue’s limitations in analyzing Kasparov’s emotional state, data warehouses face challenges when encountering data that doesn’t fit neatly into predefined schemas.

The question remains—what happens when businesses need to explore data outside these structured confines? The next evolution takes us to the flexible and expansive realm of data lakes, designed to embrace unstructured chaos.

The True Depth of Data Lakes

While structured data lays the foundation for traditional analytics, the modern business environment is far more complex, organizations today recognize the untapped potential in unstructured and semi-structured data. Social media conversations, customer reviews, IoT sensor feeds, audio recordings, and video content—these are the modern equivalents of Kasparov’s instinctive reactions and emotional expressions. They hold valuable insights but exist in forms that defy the rigid schemas of data warehouses.

Data lake is the system designed to embrace this chaos. Unlike warehouses, which demand structure upfront, data lakes operate on a schema-on-read approach, storing raw data in its native format until it’s needed for analysis. This flexibility makes data lakes ideal for capturing unstructured and semi-structured information. For example, Netflix uses data lakes to ingest billions of daily streaming logs, combining semi-structured metadata with unstructured viewing behaviors to deliver hyper-personalized recommendations. Similarly, Tesla stores vast amounts of raw sensor data from its autonomous vehicles in data lakes to train machine learning models.

However, this openness comes with challenges. Without proper governance, data lakes risk devolving into “data swamps,” where valuable insights are buried under poorly cataloged, duplicated, or irrelevant information. Forrester analysts estimate that 60%-73% of enterprise data goes unused for analytics, highlighting the governance gap in traditional lake implementations.

Is the Data Lakehouse the Best of Both Worlds?

This gap gave rise to the data lakehouse, a hybrid approach that marries the flexibility of data lakes with the structure and governance of warehouses. The lakehouse supports both structured and unstructured data, enabling real-time querying for business intelligence (BI) while also accommodating AI/ML workloads. Tools like Databricks Lakehouse and Snowflake Lakehouse integrate features like ACID transactions and unified metadata layers, ensuring data remains clean, compliant, and accessible.

Retailers, for instance, use lakehouses to analyze customer behavior in real time while simultaneously training AI models for predictive recommendations. Streaming services like Disney+ integrate structured subscriber data with unstructured viewing habits, enhancing personalization and engagement. In manufacturing, lakehouses process vast IoT sensor data alongside operational records, predicting maintenance needs and reducing downtime. According to a report by Databricks, organizations implementing lakehouse architectures have achieved up to 40% cost reductions and accelerated insights, proving their value as a future-ready data solution.

As businesses navigate this evolving data ecosystem, the choice between these architectures depends on their unique needs. Below is a comparison table highlighting the key attributes of data warehouses, data lakes, and data lakehouses:

Feature	Data Warehouse	Data Lake	Data Lakehouse
Data Type	Structured	Structured, Semi-Structured, Unstructured	Both
Schema Approach	Schema-on-Write	Schema-on-Read	Both
Query Performance	Optimized for BI	Slower; requires specialized tools	High performance for both BI and AI
Accessibility	Easy for analysts with SQL tools	Requires technical expertise	Accessible to both analysts and data scientists
Cost Efficiency	High	Low	Moderate
Scalability	Limited	High	High
Governance	Strong	Weak	Strong
Use Cases	BI, Compliance	AI/ML, Data Exploration	Real-Time Analytics, Unified Workloads
Best Fit For	Finance, Healthcare	Media, IoT, Research	Retail, E-commerce, Multi-Industry

Conclusion

The interplay between data warehouses, data lakes, and data lakehouses is a tale of adaptation and convergence. Just as IBM’s Deep Blue showcased the power of structured data but left questions about unstructured insights, businesses today must decide how to harness the vast potential of their data. From tools like Azure Data Lake, Amazon Redshift, and Snowflake Data Warehouse to advanced platforms like Databricks Lakehouse, the possibilities are limitless.

Ultimately, the path forward depends on an organization’s specific goals—whether optimizing BI, exploring AI/ML, or achieving unified analytics. The synergy of data engineering, data analytics, and database activity monitoring ensures that insights are not just generated but are actionable. To accelerate AI transformation journeys for evolving organizations, leveraging cutting-edge platforms like Snowflake combined with deep expertise is crucial.

At Mantra Labs, we specialize in crafting tailored data science and engineering solutions that empower businesses to achieve their analytics goals. Our experience with platforms like Snowflake and our deep domain expertise makes us the ideal partner for driving data-driven innovation and unlocking the next wave of growth for your enterprise.

Customer Journey is the New Product