What the heck is Customer Journey Analytics?

Let’s be real, the notion of customer journey has been a thing for quite some time. It slowly became a priority for marketers; that’s no news to anyone. In this context, strategists have been mapping their customers’ journey with the hope of optimising their user experience. This led them to start thinking through how their prospects and customers were interacting with their brand at every touchpoint during their customer lifetime (awareness > consideration > conversion > retention). In general, strategists create those using customer surveys or feedback, a few personas and limited first party data. 

Customer journey mapping became particularly handy and a massive priority with the Covid-19 pandemic. Joana Quintanilha, VP and Principal Analyst at Forrester, reminds us in this podcast that

“Customer journey mapping can help firms find clarity during this chaotic moment. Journey maps are, by design, dynamic documents that can (and should) be amended to reflect reality on the ground. With good journey maps in hand, customer experience (CX) teams can determine where to put resources to fulfil customers’ current needs”.

Those events explain the recent rise of the term Customer Journey Analytics. The customer journey use cases, technology and methodology have changed. Fortunately for us, the era of digitalisation brought to marketers the ability to gain visibility and control over their customer journey. Not only this, they also gain much more information – obviously I mean data here. That’s where Customer Journey Analytics comes at play.

The foundation: What is Customer Journey Analytics?

Customer Journey Analytics is a new analytics practice, supported by the recent marketing technology evolutions. Nowadays, most new software platforms, such as CDPs (read what a CDP is here), capture customer data over time and across touchpoints. They then consolidate that around a single customer profile, while being channel agnostic. This allows businesses to look at and analyse their customer experience across all touchpoints, to further understand their behaviour

There are five common phases to customer journey analytics.


This step forms the foundation of a successful Customer Journey Analytics. Customer data is collected across touchpoints, online and offline (if doable). This data collection follows a clear, clean, and uniform data model solely based on the business’ objectives. Data collected can be various (behavioural, transaction, etc).

For instance, it could be behavioural data about about actions users take on the website or in the app; or transactional data from the stores or website.


Data is consolidated under a single user profile across touchpoint. In the best scenario, there would be a common ID across all touchpoints but we know this isn’t realistic. This is done by stitching and synching together various IDs (eg: emails, anonymised ID, etc) to reconcile the user’s identity.


Once user profiles are reconciled, the data is visualised to help the organisation monitor customer journey, but also hint early trends or patterns in customer behaviour. This is the foundation of an optimisation.

For instance, we can visualise the most frequent paths customers take that lead to a conversion. Are users using your mobile app to browse product before buying on site? On the other end, we can also visualise where users are typically falling off in the funnel. Let’s take the example of users visiting your website and signing up for your newsletter, but then never completing more than one transaction. This allows organisations to quickly identify points that need further attention.


Once an early trend has been spotted in the data, the business needs to do a deep dive analysis. This helps investigate the customer behaviour. This will allow to better understand the reason-why’s behind the pattern. Knowing “why” can lead to insights to inform the next best action to optimise the customer journey.

Let’s again use our engaged users who sign up for the newsletter, completed one transaction but never came back. That is a problem for the business as acquiring a customer can be very costly, especially if their total basket amount is small. This is where a deeper dive analysis comes handy. By looking closer, the business may realise that their CRM program is not driving additional transactions. It is not bringing users back to the website. Hence, it is not trying to augment the customer lifetime value through additional conversions

#5. ACT

The insights are the foundation of a recommendation. They allow businesses to optimise the overall customer experience and maximise business value.

In our previous example, the business may want to act on the insight and optimise their CRM program to better contribute to the customer lifetime value. For instance, the CRM manager in collaboration with the other departments, may want to include more conversion-focused CTA in the next newsletters. They could also start sending new product announcement emails with relevant content based on the unified user profile (including more than CRM data).

Why is Customer Journey Analytics so important?

There’s been a lot of attention around customer journey in the recent years. Customers have become much more connected and sophisticated in their expectations from brands. COVID-19 also reinforced the focus for businesses due to an accelerated shift toward digital channels in all major B2C industries in just a few months (like mentioned by Joana Quintanilha in her podcast). Today, making the customer experience seamless across touchpoints is not a nice to have anymore. It became an essential and a strong point of differentiation for customers when selecting a brand to purchase from.

In this context, Customer Journey Analytics is a game changer for marketers!

Customer data is not siloed by department or channel anymore but presented into a single customer profile. Keep in mind that those siloes often are the cause of inconsistent experiences. Organisations cannot follow through the entire journey properly. Each stage relies on a different entity, data source or even team. It’s easy to lose sight of the customer in this setting. Ultimately, this translates into higher drop-off in your conversion funnel.  

On the other end, having all the data in one place provides much more transparency and consistency into the path customers take to convert (or not). With this, you can now tell a consistent and comprehensive story about your customers’ interactions with your brand. This provides a much deeper understanding of their behaviour, in the moment, as they interact with your brand.  

Encouraging a Customer Journey Analytics mindset is also a step forward in adopting a customer-centric approach.

Indeed, analysing a single touchpoint at a time could lead to incomplete or not in-context decisions. For instance, you may come to a decision that a given channel is performing very well. This leads you to recommend increasing investment to support it. In this case, you are not accounting for the driver toward that specific channel. Was that previous touchpoint the reason why your customers were so well-nurtured and ready-to-convert?  

Putting the customer at the centre of your analysis, regardless of the touchpoint, will prevent you from making this mistake. You’ll be able to identify blind spots, drop-offs or areas of optimisation much faster.  

Indeed, using Customer Journey Analytics for your analysis and insight generation will allow your business to generate timely and more importantly customer-focused insights. This will result in stronger recommendations to increase your customer lifetime value, customer’s loyalty, reduce churn and ultimately drive business growth thanks to optimised and truly personalised user experience! 

About the Author: Elaine Lorent
Elaine is our Head of Data Strategy at NOBI. She spent 8+ years in Media and Digital Analytics, with focus on CDP, DMP and CRM strategies. Elaine has a Master of Science In Integrated Marketing & Analytics from the New York University. Her passion is to help brands leverage their 1st, 2nd & 3rd party data in a meaningful and responsible way, to become fully data-driven organisations.

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