Case studies

Large scale cloud application at Ticketmaster International

The CRM department at Ticketmaster International faced with the problem that their existing predictive analytics stack without significant investment was not able to handle the increased load caused by the new BIG data sources integrated recently. They realized that the integration of new BIG data sources and the analytics capabilities on the top was relatively painless due to the availability of cloud infrastructure and BIG data tools, however the low latency large scale predictive utilization of this information in their marketing automation suite is blocked by their predictive analytics stack that was purchased from a big vendor a few years earlier for a significant investment.

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Rotational Churn Estimation for a Telecom Provider

Our client, the subsidiary of one of the biggest mobile telecom provider in the EU, was aware that its churn models have suboptimal performance which tended to overstate the churn rate and the resulting success rate for acquisition campaigns were equally fantastical.
These churn models, it turned out, were trained on events that were not real churn but on rotational churn (sometimes called spinning). These events are typically triggered when an existing customer gets a new subscription from the same provider usually to take advantage of promotional offers targeted new customers.

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Data Fusion Approach to Customer Choice Modelling for a Telecom Company

This was a follow-up project for a leading telecom operator, now pushing to increase product usage and penetration across its network footprint.The operator already offers triple play services (broadband, landline and television) to its customers but needed to sell additional high value services like on-demand video service, domestic security monitoring and device protection insurance. By selling these additional services, it is thought that customers will use more of the operator’s products — reducing churn in the longer term by creating higher switching costs for such customers.

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