We develop new methods, strategies and algorithms for individualised marketing, customer retention, optimised communication with users, personalised pricing and personalised recommendations or to maximise the probability of purchase of a product or other actions of the users. We exploit users’ behavioural measurements in addition to their more standard characteristics and external data (including competitors’ activity, market indicators, financial information, geographic information). We design and analyse comparative trials with well-designed user cohorts as well as large observational data. We exploit network topologies, informative missigness and temporal relations. A key point is to identify the actionable causes of customer behaviour.
Stochastic customer growth dynamics
Understanding how networks of customers grow in time and topology is important. The Vipps transaction data may be viewed as a graph with users corresponding to the nodes and the financial transactions between the users defining the edges. With an advanced statistical model we have analysed the growth of this graph. Our experiments show that the intrinsic quality of the nodes plays an important part in the evolution of the network. This insight may be used to identify influential nodes for viral marketing. The approach will be published.
Predicting customer behavior from time series
Multiple time series, related to individual customers have been used to predict, as early as possible, whether the customer will pay back his loan or not. We have developed a new solution based on deep learning of time series, which gives excellent results on the DNB cases it has been tested on. The method will be transferred to DNB and published.
Bayesian methodology for recommender systems
In many important situations, we wish to recommend to each individual user or customer, the items she might be most interested in, or the ones she would benefit most. Starting from data where user either rate or compare items or click on them, we want to predict user’s preferences on other items. In some applications, we are also interested in estimating the shared consensus preference of a homogeneous group of people. We have invented a new Bayesian approach based on extensions of the Mallows model, which allow making individualized recommendations, which are equipped with a level of uncertainty. We are working on public data, NRK data and plan to analyse a case from Telenor. The methodology was nominated to the Inven2 Idéprisen 2016 for the three best innovation projects at the University of Oslo and the Oslo University Hospital.
Stochastic models for early prediction of viral customer behavior on networks
Can we predict if a new digital service or product will be widely adopted in a market, or if it will be a flop? We look to markets where adopters are organised as nodes in a network, where links represent contacts which allow a user to “convince” a neighbor to adopt. We work on a case from Telenor. The aim is to be able to predict, as early as possible, how the adoption process will spread virally (or not) on the network. Our Bayesian simulation based approach looks very promising.
- Bayesian methodology for recommender systems
- Stochastic models for early prediction of viral customer behavior on networks
- Network dynamics
- Analysis of clickstream data in order to identify customer segments
- Default prediction using network data on companies