Corporate Services

A multinational resorts company needed a strategy to manage both customer and travel agent relations with reference to changes on their existing booking system. Quocom provided a solution that encompassed all the region's languages and character-sets and provided a platform for developing sophisticated multi-channel CRM programmes.

A major UK retail brand needed a market segmentation that made the most use of their understanding of shopper behaviour and attitudes while also being of practical use for buying media across all channels from primetime TV through to targeted email. Quocom developed a hierarchical approach which contained the key differences in audiences at the aggregate level for buying mass media, while at the same time providing the facility for more accurate targeting.

A multinational insurance company wanted to adapt its online strategy in response to the online behaviour of its customers, agents, competitors etc. Quocom undertook a statistically rigorous survey of the insurance conversations on the internet. It identified the key issues being discussed and to whom they were important.

In addition, a Visit Model was produced. This showed which messages were of most interest to customers and prospects. For instance, much of the volume of insurance-related information on the internet is produced by agents (and SEO sites) but the Visit Model showed that customers and prospects are far more interested in the personal claims experiences of other consumers.

The survey results were presented in a dynamic dashboard. Download an example here - the file is an Excel 2010 workbook without macros.

A UK online financial services provider was using automated text analysis of customer comments to identify those most likely leave. Quocom reconfigured the text analysis output and produced a model which identified 80% of the customers that would attrite in the following 6 months.

A UK mobile phone network needed an evidence-based estimate of customer lifetime values. Quocom produced a model which fitted the data well and gave radically different customer values to the assumptions that had been used for business planning. The model was used to develop a simple-to-use tool that could be implemented across the key finance and marketing systems within the business.

An insurance company had a good understanding of each product's overall financial performance, but it wasn't clear what this meant in terms of each individual customer's product journey. Analysis of customers records produced some key metrics for value and lapse-rate. These were developed into a simple report of key customer segments. This meant that relevant communication strategies, such as offering discounts to lapsers, could then be targeted to different segments.

A UK bank was using a propensity model to find the customers most likely to buy insurance. The problem was that some data sources needed for the model had become unavailable, so the bank needed the model re-built with fewer predictive data fields but using the same modelling methodology for compliance purposes. Quocom, through an improved analysis of the remaining data fields, produced a new model which was in fact much more effective than the one it replaced.

Within a customer database, what might be an important change in behaviour for one customer can be quite within the bounds of normality for another; each customer has to be monitored against their own set of criteria. Quocom has developed specialised techniques for monitoring individual behaviour, and flagging up significant changes. These have been adopted by a number of UK financial institutions and telecoms companies.

A neural network model, based on 220 data fields, enabled a 33% increase in conversion, compared to the client's previous targeting strategy, and at a lower cost-per-name.

Storing large amounts of customer in a data warehouse is expensive. In its raw form, the data is often of limited value for marketing. Quocom undertook a major project for a UK bank to derive more effective variables from the raw data within the data warehouse, to enable better marketing planning and predictive modelling.

A US-based insurance multinational accquired a prospecting database to serve a number of their UK subsidiaries. It was an expensive white elephant that couldn't fulfill its basic functions. Quocom rebuilt and managed it for one quarter of the original cost while it grew to 220M individual names and addresses.