Customer Analytics
Customer Analytics
The Path to Customer Centricity
The emergence of information and data technologies, together with the sophistication of tools in data analytics, have led to a radical shift in the way marketing operations are run. The focus of companies has been shifted away from product-centric approaches and mass marketing campaigns to customer-centric campaigns tailored to the needs and wants of each individual customer. Customer-centric campaigns target a well-chosen subset of customers, at a well-chosen time, and with a well-chosen incentive. At their core, they require strong data analytics tools in order to be able to predict each customer’s behavior and derive optimal marketing interventions.
The Customer Analytics lab focuses on the development of predictive and prescriptive analytics to address these key challenges. Our team is involved in the design of new methodologies that can guide organizations in their customer-centric decision making. Our research portfolio is organized according to the three fundamental stages of the customer lifecycle:
- Enhance customer acquisition (e.g. customer referral programs, seeding strategies)
- Boost customer spending (e.g. loyalty schemes, customer engagement)
- Prevent customer churn (e.g. proactive retention programs).
Our research relies on state-of-the art machine learning, field experiments, and large-scale cluster and grid computing.
The Customer Analytics expert practice works in collaborations with several customer-centric business partners. Together, we deploy tools to foster customer analytics in business practice.
Research Projects
The research portfolio of the Customer Analytics expert practice is organized according to the three fundamental stages of the customer lifecycle:
- Enhance customer acquisition (e.g. customer referral programs, seeding strategies)
- Boost customer spending (e.g. loyalty schemes, customer engagement)
- Prevent customer churn (e.g. proactive retention programs).
Recent research:
- Ascarza, E., Neslin, S., Netzer, O., Anderson, Z., Fader, P., Gupta, S., Hardie, B., Lemmens, A., Libai, B., Neal, D., Provost, F. and Schrift, R. (2018). In Pursuit of Enhanced Customer Retention Management: Review, Key Issues, and Future Directions Customer Needs and Solutions, 5(12), 65-81.
- Belo R. and Li T. (2020). Referral Programs for Freemium Platforms: Evidence from a Randomized Field Experiment. Working paper.
- Chen X., Van der Lans R. and Trusov M. (2020). Efficient Estimation of Network Games of Incomplete Information: Application to Large Online Social Networks. Management Science. Forthcoming.
- Chen X., Van der Lans R. and Phan T.Q. (2017). Uncovering the Importance of Relationship Characteristics in Social Networks: Implications for Seeding Strategies. Journal of Marketing Research, 54 (2), 187-201.
- Chen X., Van der Lans and Trusov M. (2017). Integrating social networks into marketing decision models. In Handbook of Marketing Decision Models. Cham: Springer
- Donkers, B., Dellaert, B, WaismanM. and Häubl G. (2020) Preference Dynamics in Sequential Consumer Choice with Defaults. Journal of Marketing Research.
- Esterzon E., Lemmens A. and Van den Bergh, B. (2020). Targeting Donors: Increasing Fundraising Effectiveness by Providing Donors Opportunities to Target Their Charitable Gifts. Working Paper.
- Evangelidis I. and Van den Bergh, B. (2013). The number of fatalities drives disaster aid: Increasing sensitivity to people in need.Psychological Science, 24 (11), 2226-2234.
- Ferreira P.M., Belo, R., Zhang X. and Godinho de Matos M. (2020). Welfare Properties of Recommender Systems: Theory and Results from a Randomized Experiment. MIS Quarterly.
- Godinho de Matos M., P.M. Ferreira and R. Belo (2018). Target the ego or target the group: Evidence from a randomized experiment in proactive churn management. Marketing Science, 37 (5), 793-811.
- Jacobs B.J.D., Fok D. and Donkers B. (2020). Understanding Large-Scale Dynamic Purchase Behavior. Marketing Science, Forthcoming.
- Lemmens A. and Gupta S. (2020). Managing Churn to Maximize Profits. Marketing Science, 29(5).
- Puha Z., Lemmens, A. and Kaptein, M. (2020). Batch Mode Active Learning for Individual Treatment Effect Estimation. 20th IEEE International Conference on Data Mining Workshop Proceedings, Forthcoming.
- Tsekouras D., Dellaert B.G.C., Donkers B. and Haubl G. (2020). Product set granularity and consumer response to recommendations. Journal of the Academy of Marketing Science, 48, 186–202.
- Tuk M., Prokopec S. and Van den Bergh B. (2020). Do Versus Don’t: The Impact of Framing on Goal Level Setting. Journal of Consumer Research, Forthcoming.
Experts
- Aurélie Lemmens // customer lifetime value, personalized marketing, retention strategies
- Rodrigo Belo // recommender system, field experiment, customer retention, referral program
- Bram Van den Bergh // targeting incentives and psychological mechanisms
- Xi Chen // seeding strategies and policy evaluation
- Bas Donkers // marketing analytics, personalization
- Martina Pocchiari // customer and brand communities, network analysis
Business Partnerships
- KPN (Amsterdam//Rotterdam, The Netherlands), Telecommunications
- OptioPay (Berlin, Germany), FinAdTech
- Veepee Europe (Brussels, Belgium), e-commerce
- Agilytic (Walloon Brabant, Belgium), data analytics consulting
Dutch 
English