From Digital Analytics to Continuous Customer Journey Optimisation

At Philips, we had extensive digital analytics capabilities and millions of customer interactions available for analysis. But having that data did not automatically mean we understood how customers actually moved through their journeys.

Traditional digital analytics was very good at answering specific questions about traffic, pages, campaigns and conversion. What was harder to understand was the sequence behind those interactions.

How do customers actually move across digital touchpoints? Which paths lead towards conversion? Where do customers experience friction or drop out? And how do these journeys differ across markets and customer groups?

I led the design and global rollout of a customer journey intelligence solution to address these questions. We used process intelligence technology, with Celonis as the technology platform, to analyse digital customer journeys.

From individual interactions to customer journeys

We translated millions of digital interactions into meaningful business events and established advanced KPIs to measure journey performance.

Celonis could then read these event sequences and reconstruct how customers actually moved through their journeys. Instead of presenting the data primarily through two-dimensional charts, process intelligence visualised the different customer flows and their performance.

This created a fundamentally different way of interacting with customer data. Users could explore the process visualisation, select particular paths or behaviours, compare outcomes and adjust their analysis as new questions emerged.

Rather than analysing touchpoints largely in isolation, we could study the sequence of customer behaviour and see where journeys converged, diverged or encountered bottlenecks.

A more intuitive way to understand customer behaviour

One of the things that stayed with me was how stakeholders responded to these process visualisations.

For the first time, I saw people look at complex customer interaction data and feel that they could actually see what was happening.

They could start with the overall customer flow, identify something unusual and immediately drill down. As their understanding developed, they could change filters, isolate particular behaviours and explore new questions on the fly.

That is an important difference from many conventional analytics environments. Two-dimensional charts are excellent for monitoring defined metrics and answering known questions. Process intelligence is particularly powerful when you want to explore how a result came about.

It provides an interactive representation of the underlying flow rather than only a collection of aggregated measures.

Turning journey insights into business opportunities

The objective was not simply better analytics. The resulting insights had to help commercial teams and leadership understand where customer journeys could be improved.

Journey patterns could reveal conversion opportunities, unnecessary friction and meaningful differences between markets or customer groups. Analysts did not have to start every investigation with a fully predefined hypothesis. They could explore actual customer flows, identify patterns and then investigate where attention was most valuable.

This is where I see an important advantage of process intelligence for customer journey analysis.

It creates a bridge between customer behaviour and business performance. Management and commercial teams can move from an aggregated KPI to the actual customer flows behind that KPI, creating a stronger basis for deciding where to improve marketing, digital experiences and customer interactions.

From analytics project to organisational capability

The longer-term objective was not another dashboard or analytics application.

We established customer journey optimisation as an ongoing, data-driven capability.

I worked with senior stakeholders and analysts to drive adoption and embed the approach in the organisation. The analytical foundation was designed to scale across markets and support further development.

The result was better visibility and control of customer journeys, faster identification of improvement opportunities and a scalable foundation for continuous journey optimisation.

Looking back, that is also what makes the case relevant to marketing transformation today.

The technology has evolved considerably. Customer data platforms, journey analytics, decisioning and AI have become much more capable. But the underlying management challenge remains similar.

Organisations increasingly want to personalise interactions, predict customer needs and determine the next best action. To do that well, they need to understand customer behaviour across the journey rather than only within individual touchpoints.