Online Prediction and Recommendation of Volksbank Customer Journey under Concept Drifts


Within companies customer journey thinking is getting more and more established. Customers are getting more volatile and competition is fierce. With customer journey analytics is it possible to use a personal approach on large dataset of customer behaviour and customer experience. In the field of customer journey analytics multiple well-known fields are combined, like process mining, text analytics (NLP), stream mining and predictive analytics.

Business Goal
For companies, it is not enough anymore to look at their customer data in hindsight. The business is moving to real time analytics to allow intervention into the customer journey when necessary. Therefore, companies need to invest in techniques to predict next best actions. Hereby we mean the next step of the customer based on recent behaviour, customer characteristics and overall context. Additionally, what is the right intervention the company needs to take. Prevention is better than the cure and this will end up in happier customers and less churn and complaints.

Research goal
Together with De Volksbank, Underlined builds a generic framework in which all customer contacts are brought together as a unique dataset, which is further enriched by Underlined with relevant analyses that can be linked to customer events. Underlined co-created multiple of their customer journey mining algorithms together with TU/e (dr. ing. Marwan Hassani at the Process Analytics Cluster). This research showed that it is possible to distinguish the different journeys per customer and to predict future interesting interactions with the customer in the insurance journey (find it here). Moreover, also in collaboration with Dutch universities, a driver model has recently developed to distinguish relevant drivers and to predict the NPS-score.

Research question
In a collaborative research between TU/e, Underlined and De Volksbank, the research focus will be on developing an online process prediction model that works under concept drifts to predict the next interaction touch point between the company and the customer. Consequently, the research will advance the developed model to trigger interventions that maximize the company’s KPI. The scope of the current master project will be:

Designing a (standardized) online prediction model for
predicting next touch point in customer journeys under concept drifts and recommending follow up (tangible) actions.

More precisely, after a proper pre-processing of the different types of data, the master project will start by addressing the following list of questions:

  1. What type of model works best on customer journeys?
  2. What has to are the requirements on the data for using that model for different type of companies in different types of industry?
  3. How can an online process prediction model be best designed to work under concept drifts?
  4. How can recommendations be inferred from the previous model to prescribe interventions to the company?

In the above-mentioned analysis, all available data will be leveraged. This includes logged data of the journey (workflow process data, call center data, online click trails, social media data), online and offline feedback and non-transactional data (product and background information of the customers).

Underlined has a proven approach and toolset for providing insights into how to improve customer experience during the customer journey. Using data from customer contact channels, online environment, customer feedback and research response, we can reveal the actual customer journey and customer experience. This enables companies to measure the customer journey of their customers’ behaviour and emotions continuously and to manage and improve the journey across all channels. Underlined calls this Customer Journey Management. More information about Underlined can be found at:

De Volksbank
Being one of the large banks of the Netherlands, it delivers products and services to more the three million customers under four brands; SNS, BLG Wonen, RegioBank en AS Bank with a shared back-office. Each of the brand has its own focus within social responsibility to be more than just a bank.  The personal approach of customer journey analytics really fit their mission. More information about De Volksbank can be found at:

For more information, contact dr. ing. Marwan Hassani.

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