The Business Issue
The modern world is stored digitally (e.g. from weblogs to the latest cameras) and popular culture creates the impression that information is easy to access and straightforward to analyse. Information Technology facilitates the capture and storage of a huge variety of information covering details of business transactions and customer interactions. However, most businesses use basic reporting, do not fully utilise the knowledge held within their data and do not identify valuable business relationships. There are many reasons given for not taking advantage of this resource: the volume, quality and complexity of the data or the lack of relevant skills. Capitalising on the insights held within data can give organisations significant competitive advantage in almost any aspect of their business, but especially in their relationships with their customers.
Our Expertise
Capgemini’s Customer Analytics capability lies within the Operational Research (OR), Knowledge Management and Business Intelligence practices. The OR Group has an unparalleled experience of solving problems and delivering solutions within a wide variety of industry sectors across Europe. We have knowledge of business intelligence and data mining tools and techniques, access to leading tools, and considerable experience of data analysis and statistical modelling.
Capgemini has world class technology and information partners. We use analytical tools and technologies from all of the world’s leading providers such as SAS and SPSS, as well as some of the many specialist tools available in the marketplace today. We have experience of using these tools in a wide range of sectors. Where appropriate, we use the recognised CRISP-DM methodology to demonstrate that our data mining projects are run in a highly rigorous manner.
The team is skilled in a wide range of forecasting and statistical techniques including ARIMA, Neural Networks, Decision Trees and Multiple Regression. They have experience of designing and building models using specialist analysis software packages such as SPSS, SAS and NAG.
Our Approach
We have methodologies suitable for quick proof of concept or for longer scoping studies. The proof of concept approach demonstrates capability, shows relevance to the business and estimates potential benefits, whereas the longer studies allow skills transfer so building capability in an organisation and develops the detail for building a business case for further investment.
From Data to Information: Customer Analytics covers a range of approaches, but typically begins with the integration of customer data from multiple sources in order to create a single view of each customer. To start with this is usually done for a sample of customers, but it can subsequently be extended.
From Information to Insight: Given a single view of each customer and their behaviour over time, it is possible to identify their likelihood of behaving in particular ways (responding to mailings, defaulting on loans, buying a particular product). Sometimes we use other data sources to enrich behavioural modelling, such as data on socio-economic groups. It is also possible to identify customer lifetime value.
From Insight to Action: Knowledge of customer characteristics enables more effective communication with and management of the customers. The right customers can be targeted for marketing campaigns - or customers can be offered products that will appeal to them while they’re speaking to the contact centre.
The process isn’t just restricted to customers – our data mining skills have very wide applicability. Techniques typically used include clustering, data visualisation, statistical analysis, neural networks, rule induction and genetic algorithms.
Business Benefits
There are many potential benefits to be realised from customer analytics by applying data mining techniques to specific business issues
- Better understanding of the segments/clusters in the customer base and their
patterns of behaviour:
- valuable/profitable customers
- customers likely to leave
- poor credit risks
- Better understanding of performance drivers within the enterprise:
- customer response
- sales team effectiveness
- supplier relationships
- store/factory productivity
- More efficient & effective use of marketing resources:
- better targeted campaigns
- better response rates
- Understanding trade promotions, optimisation of the supply chain and the links to product lifecycle
- Mass delivery of deep customer insight to the point of customer interaction
- Understanding of the value of internal data, such as “what lifetime value do we lose due to incorrect customer details?”
Our Experience
Capgemini has successfully delivered customer analytics and data mining work for a wide range of sectors and clients, as illustrated by our case studies (see below).
Related Success Stories
Customer segment-dependent costing
Customer segmentation analysis identifying public sector cost and efficiency
improvement opportunities, linked to influencing customer behaviour.
Download
Focus on Retail Performance Improvement
Retail store clustering based on Factor Analysis which identified £15M p.a. of
feasible initiatives and forecast the potential improvements at individual stores.
Download
Using Statistical Process Control (SPC) in the Forecasting Process
Statistical Process Control enabled the energy sector client to target their
improvements to the business processes.
Download
Economic segmentation and revenue enhancement
Leisure sector customer segmentation analysis, leading to increased profit and
improved client satisfaction.
Download
Customer profiling
Customer profiling as a predictor of response to marketing campaigns enabling
savings from targeting responsive customers, in the Travel sector.
Download
Analysis of patterns of life assurance policy lapses
Developed predictive models of ‘propensity to lapse’ for life assurance customer
segments. Our analysis identified the processes and products causing the highest
rates of policy lapsing and their financial impact.
Download
