From discovery to insights - the best practice of data-driven customer journeys

What is a data-driven customer journey?

Businesses who adopt a data-driven customer journey strategy leverage customer data to optimize each stage of the journey - from discovery to post-purchase - ensuring that interactions are continuously relevant, timely and effective.

A data-driven customer journey is defined as a customer journey that is infused by data to understand, anticipate, and enhance every step a customer takes with a brand. This approach involves collecting and analyzing data from various touchpoints and interactions (such as website visits, social media engagement, email responses, etc.) to create a personalized and seamless experience for each customer.

While the potential is clear, there are inherent challenges organizations face when gathering this data, whether related to internal access restrictions, quality issues, lack of feedback collection, privacy concerns or data modeling complexities. The road to get there can be long, but if followed based on the framework proposed in this article it isn’t an impossible task, rather something that requires some rigor. Once achieved for one journey it is relatively easy to replicate the concept and reuse the data elements to serve similar purposes.

So, what does a data-driven customer journey look like and how does it differ from other journeys? How can businesses harness its power to maximize Customer Experience? Let’s explore the best practices for implementing this powerful strategy.

Moving on from assumptions: What are data infused maps?

Mapping customer journeys is the most common technique used to better understand customer needs and expectations, detect experience frictions and ideate solutions to solve them. The journey mapping process allows teams to share their own experiences, empathize with their customers and focus on the priorities that they think will bring the most value to the business and to customers at the same time.

Having said that, the mapping exercise, carried out in this subjective way, provides limited gains, as the inputs on which the map is based are solely the organization’s perception and not fact-based. In order to validate the journey hypothesis and make it more accurate, the use of data in correlation with the customer journey is required to create a data infused map that is updated in real-time.

What are the key differences between data-infused journey maps and traditional maps?

5 steps on how to structure a data-driven journey framework

The process of building data driven journeys is composed of several steps that act as building blocks. While there are different possible scenarios for the start point, it is recommended to follow the process from start to finish in this way:

1. Journey Discovery – collecting information and research data that will help you gain a better understanding of the way the customer journey flows. This will include actions such as: persona research, interviewing frontline employees, reviewing the brand values of the organization, thinking of the journey stages and more.
2. Journey Mapping – mapping the storylines of the customer journey, the interaction channels, pain points and opportunities to improve experiences. The journey map could include different levels of details, depending on if you are mapping a macro or micro journey.
3. VoC Data Integration – integrating feedback data from various sources directly to the customer journey map. The feedback data should contain mainly sentiment scores, the metrics measured, verbatims, traffic volumes and more.
4. Journey Orchestration – analyzing the data to drive contextual, real-time decisions, and recommend on next best actions. It enables the use of the customer’s entire experience to inform and personalize interactions that will improve customer experience.
5. Journey Insights – sits on top of the process performing the analytical tasks and pushing back insights to the journey map and journey orchestration engine, based on the algorithms defined by the user and the learnings from past trends.

Your end goal is to translate the designed experience of personas to personalized experiences of real customers adapted to their changing preferences in real-time. 

This is the job of the journey orchestration engine, which is the last part of the process, defining the flow of interactions between the organization and the customer and the triggers that will drive action.

How to build data-driven customer journeys

There are two techniques to build data-driven journeys:

1. Start with mapping the customer journey based on the organizational knowledge and then plug-in feedback data to validate the journey hypothesis, enriching the understanding of customer behaviors.
2. Start by collecting data from the different channels, analyze it to build customer routes and source from these insights the skeleton journey that will be completed by the journey committee members.

Both techniques are valid and the choice between the two will depend on the amount and quality of the data available to determine which way is optimal to reach the best result.

The process of creating the connection between the data and the journey relies first of all on collecting the data from the channels, such as online behavior, purchase history, feedback, etc. and defining the persona segmentation based on shared characteristics or behaviors.

The process of analyzing the data and producing insights, (i.e. using AI, machine learning and BI to understand current customer behaviors and produce recommendations) generates the actions that are fed into an automation engine that delivers a personalized experience at the right time in the preferred communication channel of the customer.

This is a repeated cycle, which regularly updates strategies based on new data and insights to continuously enhance customer experience.

From persona to person: How a modern Journey Management platform supports a data-driven strategy

A modern journey management platform, such as Cemantica, is software that enables organizations to manage an agile, continuously improving journey ecosystem.

The ecosystem of journey management starts with a journey atlas that is the repository of customer journeys of the organization and the links between them. The atlas will include macro journeys and micro journeys representing a more or less detailed outlook of customer experience.

The journeys mapped from the journey atlas will be designed by cross-functional teams and then infused with feedback data from customers, employees and processes. A journey insights mechanism will analyze the data and produce findings, recommendations and business drivers that will be used by the organization to define the right personalization algorithm to be used by the journey orchestration platform.

This will allow organizations to execute real-time personalization, moving from the theoretical design stage for a persona to hyper personalization for the real person.

One of the most powerful aspects of a data-driven customer journey is the ability to act on real-time data. Real-time insights allow you to engage customers while they are actively interacting with your brand, which can be incredibly valuable for conversion.
For example, if a customer abandons their cart on your website, real-time data can trigger an automated email or pop-up offering a discount to encourage them to complete the purchase.

How becoming data-driven helps build a continuous improvement cycle

Once the setup is in place and you are able to complete the full journey cycle from design to execution, the methodology can be followed to continue and run in agile cycles. Refining the customer journey, producing new insights and taking new measures to satisfy the current needs and expectations of the different customer segments.

Focusing on the things that matter most to the customer prevents waste and assures the resources of the organization are used to best serve the purpose of every organization, its stakeholders.

Sources and connection points throughout the customer journey

There are several types of data elements that can be connected to customer journeys, each providing us with different insights or a view of the truth. The combination of these sources is providing a better picture for decision makers and is painting a picture that is closer to reality. 

When you design a customer journey the risk is falling into misconceptions or assumptions that do not have a real grip in the everyday life of a customer. It is therefore recommended to combine the various sources of data to build a picture that will eventually allow you to design an optimal customer journey and solve the real issues bothering your customers.

The three types of data to collect are:

1. Behavioral data (Voice of the Process): transactional data that is collected as a result of the activity of the customer in the interaction channels. The transactional data is analyzed and transformed into behavioral patterns that uncover trends, patterns and relations between cause and action. This is also called the voice of the process.
2. Sentiment (experience metrics): the emotions and satisfaction levels expressed by customers collected through surveys, forums, social media posts, etc. The sentiment data is usually structured around experience metrics such as: NPS, CSAT, CES and others. They are a good indicator that can feed into touchpoints, stages, journeys or personas as illustrated below.
3. Operational data (business metrics): data related to the business KPI measured by the organization such as: churn rate, conversion rate, customer acquisition, customer lifetime value and more.

Having listed the different types of data elements that are potentially integrated into a modern journey management platform, let’s explore where these connections exactly happen:

So where does the data come from?

There are various places from where you can pull information, here are several commonly used sources:.

• Business applications such as CRM and ERP
• CDP (Customer Data Platforms)
• Journey analytics or insights platforms
• VoC platforms (such as Qualtrics, Medallia and SurveyMonkey) that collect customer feedback
• The interaction channels that hold information related to the communication between the customer and the brand (e.g. website, social media, in-store PoS, etc.)

The benefits of data-driven journeys

Integrating data into maps serves several purposes:

1. Validating the journey hypothesis
2. Focusing on customer needs
3. Establishing a connection between CX and the business performance
4. Taking decisions based on facts

More specifically the infused maps contribute to the business objectives by:

Personalization: Data allows the creation of highly personalized experiences by understanding individual customer preferences and behaviors. This leads to improved segmentation, more relevant recommendations, tailored content and targeted promotions, making customers feel valued and understood.

Improved Customer Retention: By analyzing data, businesses can identify potential churn indicators and proactively address customer needs, thereby improving retention rates. Happy, satisfied customers are more likely to stay loyal to the brand.

Enhanced Customer Insights: Data provides valuable insights into customer behavior, preferences, and pain points. This knowledge enables businesses to make informed decisions, refine their strategies and better meet customer expectations.

Increased Efficiency: Automation and data-driven processes streamline operations, reducing manual effort and ensuring timely and relevant customer interactions. This efficiency can lead to cost savings and improved resource allocation.

Better Decision Making: Data-driven approaches empower businesses to base their decisions on factual, actionable insights rather than intuition. This leads to more effective marketing strategies, product development and overall business planning.

Higher Conversion Rates: Targeted, data-driven marketing campaigns are more likely to resonate with the audience, resulting in higher conversion rates. Understanding what motivates customers to take action can significantly improve marketing ROI.

Enhanced Customer Lifetime Value (CLV): By understanding and predicting customer needs and behaviors, businesses can create long-term relationships and maximize the lifetime value of each customer.

Increased Customer Satisfaction: Personalized experiences, timely responses, and relevant offers contribute to higher levels of customer satisfaction. Satisfied customers are more likely to become advocates for the brand, driving positive word-of-mouth and referrals.

By integrating data into every step of the customer journey, businesses can create more meaningful and engaging experiences that drive loyalty, satisfaction and ultimately, sustainable growth.

Conclusions

Designing customer journeys that are not based on data means taking decisions that are based on assumptions, hunches and moods. Data-infused journeys are a better way to design experiences that will concentrate on the real needs of customers combined with organizational knowledge.

Data-driven insights can go beyond understanding past behavior - they can also help predict future actions. Predictive analytics uses historical data, customer behavior patterns and machine learning algorithms to forecast customer needs.

For example, you can predict when a customer is likely to purchase again, or when they might churn, based on their past behavior. With this information, you can proactively engage with the customer through personalized offers or timely reminders, improving retention and increasing customer lifetime value.

One of the positive consequences of infusing data into journeys is that it allows you to run a continuous improvement cycle, moving away from the one-off journey mapping approach. It fits well with the constant market fluctuations, benefiting from the knowledge cumulated in the organization about the customer journey, what customers are telling you as reflected from feedback collection and the insights you can derive from the behavioral data as reflected from the Voice of the Process (VoP).

The combination of the voices and their connection to the customer journey generates data-driven journeys and allows decision makers to take decisions based on facts.

With the right strategy and tools in place, your data-driven customer journey management will become a powerful engine for success. Please contact Cemantica if you would like further support in going to the next level in your journey management approach.

A special thank you to Ray Gerber of JourneyCentric CX for his contribution to this blog.