These sessions were unmissable for those keen to learn more about how to profile customers, define customer segments and perform basket analysis to uncover buying patterns. Included in this track were sessions that show how organisations can combine these results with demographic data to determine the most effective cross-sell or up-sell offers, and how businesses prevent churn, increase retention and become more successful.
|
Clementine
Up- and cross-sell with the right offer for each customer
Ola Gustafsson
Customer data analyst
Länsförsäkringar AB
Sweden
Länsförsäkringar Alliance is the market leader in non-life insurance in Sweden, with a market share of 29 percent. The 24 regional companies service more than three million customers and co-operate through Länsförsäkringar AB. The main business challenge for the organisation is to cross- and up-sell bank and life insurance services to non-life customers. Enter predictive analytics.
With analytics, Länsförsäkringar now uses historical data to build predictive models for customer acquisition, cross-selling and churn. In this session, they explained how implementing analytics produced a dramatic increase in campaign selection productivity that led to a response rate of nine percent for a car insurance telemarketing campaign. Previously, national campaigns with several products were, at best, produced twice a year. Now, the output level is five campaign selections every day.
Länsförsäkringar now considers predictive models to be useful knowledge assets founded on real business experience; the models are also used for outbound telemarketing campaigns that yield good response rates; and the organisation is starting work on new campaigns such as churn prevention.
As a result of implementing analytics, Länsförsäkringar Alliance estimates that it receives over 800.000 inbound calls in a year, and using predictive models it is able to calculate daily best-offers for each individual customer – and present them for staff to act upon.
Back
|
SPSS Predictive Enterprise Services
The Predictive Enterprise: driving profitable growth
Kris Neitz
Vice President, Enterprise Solutions
Stephen Cole
Vice President, Solutions Marketing
SPSS Inc.
USA
Organisations face tough challenges in trying to grow their businesses. They need to acquire more customers less expensively, increase customer and employee value, retain profitable customers, minimise the risk associated with transactions, and identify fraud and maximise operational efficiency.
Those attending this session learnt why successful organisations use predictive analytics as a key capability to drive profitable growth and attain greater efficiencies across the enterprise.
For example, they found out how to:
- Better understand their customers, employees or constituents by engaging in an on-going dialogue, capturing key information and combining those insights with existing data
- Apply advanced analytics to gain deeper insights and predict future behaviour and preferences
- Efficiently deploy the results of analysis and dramatically improve business processes across their operations.
A true Predictive Enterprise maximises the value of all its data assets, and applies analytics effectively across all parts of its business – even tuning its organisational structure to reflect the importance of evidence-based decision making.
Devising a roadmap for the adoption of predictive analytics ensures an early return on the investment and leads to the ultimate goal: reshaping and optimising operational processes across all areas of business.
Back
|
|
Dimensions
Going beyond market research
René A. Scherer
Head of IT Survey Competence Centre
Credit Suisse
Switzerland
Credit Suisse Group is a leading global financial services company headquartered in Zurich. As an integrated bank, Credit Suisse provides its clients with investment banking, private banking and asset management services worldwide.
The company’s IT Survey Competence Center is a full-service provider for Enterprise Feedback Management (EFM) across Credit Suisse’s customers and employees. It started in the year 2000 with paper surveys, and is now using Dimensions to develop up to 100 different projects per year.
The surveys are primarily performed for internal customer groups (IT, HR, Banking, Finance etc), but the centre also works as a contractor for the bank’s Market Research team, targeting external customers.
The portfolio includes regular surveys on IT customer satisfaction, IT project satisfaction and support centre surveys; ad hoc surveys about products, services and processes; and HR topics (eg mood barometers).
This session was about the benefits of using Dimensions outside the usual context of market research to conduct real EFM within a large organisation.
Back
|
SPSS, Clementine
Using data mining for more effective marketing
Christos Matsoukas
Head of Business Intelligence
EFG Eurolife
Greece
Eurolife, one of the largest insurance groups in Greece, proudly states that its aim is to provide its customers with comprehensive insurance through modern, understandable products. Recognising that each customer is unique and has distinctive needs, it offers a range of specialised but integrated solutions to suit individual cases.
Consequently, its first and most important requirement is to have a better view of its customers, allowing it to understand their needs and to devise more specifically targeted marketing campaigns.
In this session, Eurolife described how they use data mining to find common characteristics and behavioural patterns among its customers and generate customer-centric segmentations and loyalty propensity models for cross- and up-selling campaigns.
They further explained how they use multi-attribute segmentation to find products that are related to customers’ existing portfolios, and then find the right time to offer them. Eurolife has achieved considerable success with its methods, which have increased positive responses to health insurance marketing campaigns from two percent to 25 percent.
Back
|
|
Clementine, Text Mining for Clementine, Dimensions
The Predictive Enterprise at work: revealing the causes of dissatisfaction through analytics
Simon Dudley
Customer Analytics Manager
Royal & SunAlliance
UK
Royal & SunAlliance is one of the world’s leading insurance companies, writing business in over 130 countries and providing general insurance products to over 20 million customers worldwide.
This presentation discussed how Royal & SunAlliance and SPSS formed a partnership to understand better the interactions that take place across their sales, service and claims call centres, and how this insight drives operational change to enhance customers’ overall experiences.
This real-world example of the Predictive Enterprise at work described the benefits that have been realised in the areas of increased customer satisfaction; improved customer retention; and optimised operational processes through understanding the root causes of customer dissatisfaction – why people phone call centres and how many calls they make before the issue is resolved.
The session also included practical advice on how to approach this type of analysis: the challenges to expect and the lessons to be learned.
Back
|
SPSS Predictive Enterprise Services
The Analytical Revolution: reaping the benefits of a predictive analytics infrastructure
Kathy Konkel
Product Marketing Manager
Colin Shearer
Senior Vice President, Marketing Strategy
SPSS Inc.
UK
More and more organisations are turning to predictive analytics to solve their business problems. While point solutions can deliver significant returns, the most visionary companies are going to the next level by adopting a predictive analytics infrastructure: a key step to becoming a Predictive Enterprise.
Attendees at this session learnt how to take their organisations beyond ad hoc analysis by:
- Delivering better results more quickly with a predictive analytics infrastructure
- Adopting a standard platform to share best practices, experiences, common analytical components, analytical data views and processes across applications and business areas
- Developing repeatable, scalable processes
- Monitoring their deployment of analytics across the organisation
- Combining the SPSS Predictive Enterprise Services platform with existing analytical tools to form the core of a predictive analytics environment.
Back
|
|
Clementine
Making a profit from customers’ calls – sell in real time
Omar Rois Merino
Customer Analysis Manager
Angelica Dominguez
Customer Analysis
DIGITAL+
Spain
Digital + is the leading pay-per-view television provider in Spain. It reaches 2,050,000 homes, giving it a total of some six million customers.
The challenge for Digital + was to improve profits by increasing the customer retention rate and the customer loyalty rate. In addition, they wanted to acquire new customers and provide better customer satisfaction through enhanced call centre operations.
To achieve this, Digital + has used SPSS to implement customer pop-up cards for its call centre agents to use as an information, segmentation and sales tool.
This session was about how these cards help call centre agents by giving them a picture of each customer, and work as a segmentation tool that makes it possible to customise the interactions and have one-to-one conversations.
Powered by SPSS Predictive Analytics, these cards provide information such as the length of the contract and the product package, and a wide variety of other data to inform and enrich the relationship between Digital + and its customers. After a two-month pilot programme, predictive call centre operations produced a 20 percent increase in the retention rate.
Back
|
SPSS, Clementine, SPSS Predictive Eenterprise Services
Managing unstable processes in automotive manufacturing
Hans Doermann-Osuna
Quality Management Specialist
BMW Group
Germany
In the automotive world, ‘quality’ and ‘BMW’ are one and the same. However, some automotive manufacturing processes, such as the casting of alloy engine blocks, are non-stable.
The process is complex and subject to disturbance by a number of factors that cannot be easily controlled, making it difficult to predict the quality of a casting until six to eight hours after being cast.
Consequently, every single casting must be inspected before it is passed, sent for rework, or scrapped. In addition, the non-stable nature of the process means that classical quality management procedures cannot be used.
This session described how BMW used data mining to examine both process and final quality data to build an on-line scoring system for casting. As a result, the car maker was able to improve the overall process, stabilise and optimise scrap and rework levels, lower production time scales and significantly reduce production costs.
Back
|
|
Text Mining for Clementine
Text Mining for Clementine: overview and roadmap
Eric Martin
Product Marketing Manager
SPSS Inc.
France
This session was intended for users of Text Mining for Clementine – or anyone who wished to learn more about how to leverage analytical or predictive applications to turn unstructured information into actionable knowledge.
Attendees heard how extracted concepts and categories can be combined with structured data and applied to create models to yield better and more focused decisions using Clementine’s full suite of data mining tools. In addition, they were told about automated translation and speech-to-text capabilities that can be added to Clementine to maximise the return on investment of any predictive application.
Among other topics, the sessions discussed these new features in Text Mining for Clementine 12.0/12.0.1:
- Enhanced support for verticalisation
– New Template Editor
– Updated libraries – Sentiment Analysis, CRM, Security Intelligence, Competitive Intelligence, Life Sciences (Genomics, Mesh®), IT
- Enhanced multi-lingual capabilities
– Support of Sentiment Analysis in five Languages (Dutch, German, English, French, Spanish)
– More than 14 languages available through Language Weaver.
Back
|
SPSS, Clementine
Why do customers defect? And how can you stop them?
Dr Thomas Fender
Senior Data Mining Specialist
Union Investment Privatfonds GmbH
Germany
Founded in 1956, Union Investment Privatfonds (UIP) ranks among the three leading German fund managers by market share, with more than €171 billion under management as of June 2007.
Like all financial institutions, UIP values loyal customers and strives continuously to prevent them defecting to a rival. But as well as using statistical modelling to predict the probability of churning, UIP takes the concept a step further by using data mining techniques to discover the reasons why it may be forfeiting customers’ goodwill.
In this session, UIP explained how predictive analytics allows deeper insight into customers’ behaviours, how the it meets the challenge of identifying customer segments with similar churning patterns, and the methods used to model rules for use with highly dynamic data.
The results, they revealed, help to address churning through campaigns and mailings aimed at the segments at risk, with an estimated return of between 70 and 100 percent.
Back
|
|
Clementine
Data mining – what it really is and what it can do for your organisation
Richard Hren, PhD
Director, Product Marketing
SPSS Inc.
USA
‘Data mining’ is a frequently used yet often misunderstood technical term, and in this session talked about the ‘true’ meaning of data mining: what it is – and isn’t; what it does – and doesn’t do; and how it can integrate and inform the day-to-day decision processes of many organisations.
As importantly, the session gave an appreciation of the incredible value that predictive analytics delivers to an organisation – how the use of analytics drives better decisions to provide rapid and significant returns.
In fact, the ability to deliver impressive return-on-investment figures is the critical proof of the rationale for deploying predictive analytics and becoming a true Predictive Enterprise.
Back
|
Clementine, SPSS Predictive Enterprise Services
Building complete understanding of customers through data integration
Dag P. Svendsen
Analyst – Data Mining/Statistical Modelling
Komplett
Norway
Komplett is a leading European e-commerce player operating in 10 countries selling computer components, PCs, home electronics and related equipment to end-users and resellers. Komplett companies have close to 1,6 million registered customers.
During this session, attendees found out how, through data mining, Komplett.com is able to collect and integrate behavioural, attitudinal, and other data from several sources to get a complete understanding of its customers.
In particular, they heard how Web usage data is integrated with customers’ purchase histories, and then merged with socio-demographic and attitudinal variables from surveys.
The richness of the data resulting from this integration permits advanced predictive modelling in the development of, for example, marketing campaigns and micro-targeting. Komplett is also using modelling to make product recommendations for visitors to its Web shops.
Back
|
|
PredictiveMarketing
How a creative approach to data leads to a better customer contact strategy
Frank van der Spek
Information Manager
AD NieuwsMedia BV
The Netherlands
Didier Nieuwenhuis
Client Value Consultant
Client Value Lab
The Netherlands
Netherlands’ second largest national newspaper, with a daily readership of more than 1,6 million people, uses SPSS to provide better services to its customers and to identify target groups more effectively.
This outlined how analytics enables AD NieuwsMedia to sell additional subscriptions to specific groups within its existing customer base by approaching them with a targeted proposal at the right time and through the correct channel.
A creative approach to using a customer database creates a better contact strategy, and developing an analytical and operational environment puts this strategy into operation. For AD NieuwsMedia, this led to a 23 percent saving on its first telemarketing campaign after implementing analytics.
Back
|
Clementine
Constructing a robust definition for churn modelling high-value customers
Sarah Gray
Customer Insight Manager
Panyiotis Georgiou
Customer Insight Analyst
Tesco Mobile
UK
As offerings amongst competitors become increasingly rich, operators in the UK telecom industry are continuously faced with the issue of customers churning – defecting to other suppliers. The challenge for Tesco Mobile was to build a model to identify those high-value customers with a high propensity to churn.
This session described how Tesco Mobile built the churn propensity model, how predictive analytics was used proactively to define an approach to retaining high value customers, and how this has been deployed in targeted marketing campaigns.
The presentation also demonstrated how an iterative procedure was used to arrive at the modelling definition, and how this was a key to the model’s success. It also shared practical tips and tricks based on the project.
Tesco Mobile’s predictive model delivered the capability to apply retention strategies towards customers with a high propensity to churn, and to vary the strategies according to the customer’s value. The intelligent application of predictive modelling has helped Tesco Mobile to control churn and enhance the returns from its retention programmes.
Back
|
|
Clementine
Churn modelling for Koç-Group companies
Enis Basegmez
Analytical Business Intelligence Manager
Melisa Topcu
Business Intelligence Consultant
Tanı Pazarlama ve İletişim Hizmetleri A.Ş.
Turkey
Tanı Pazarlama ve İletişim Hizmetleri A.Ş. (Tanı) is a company within Koç Holding AŞ, one of Turkey’s leading industrial conglomerates with operations in the automotive, durable goods, food, retailing, energy, financial services, tourism, construction and IT sectors. Tanı provides Customer Relations Management (CRM) services to both external companies and to the Koç Group, in particular its customer satisfaction and loyalty programme.
As markets in Turkey mature, shifting from rapid growth to near-saturation amid fierce competition, the focus of Koç Group companies shifted from building a customer base to keeping customers ‘in house’.
In order to thrive in such market conditions, Tanı produces life-stage and event-based marketing activities; creates and manages multi-channel campaigns; determines business needs and fulfils them through data mining and other predictive analytic techniques.
Tanı also leads Koç Group’s CRM projects, including segmentation modelling, loyalty programmes, Recency-Frequency-Monetary value, migration, and churn analyses, which are all designed to attract attention and improve loyalty.
This session discussed Tanı’s methods, which include:
- A digital loyalty card platform that recognises customers and transaction details across 15 companies in seven sectors
- Pre-defining customers who are more likely to churn
- Methods to keep the customer ‘in house’
- The competitive experience, which creates cost and profitability advantages, gained through thousands of campaigns.
Attendees also heard about how Tanı’s churn model has successfully identified over 100.000 customers who were likely to churn, and placed them on the loyalty programme.
Back
|
Dimensions, Text Mining for Clementine
Turning up the volume on your customers
Olivier Jouve
Vice President, Corporate Development
Heena Jethwa
Product Marketing Manager
SPSS Inc.
The Netherlands
Your customers are talking. Are you listening? Or are you missing key insights by not hearing everything they have to say?
Customers and prospects communicate in many ways – through surveys and feedback programmes, and via unsolicited comments received through blogs, call centres, and e-mail. When companies can hear everything their customers have to say, they get a complete market and consumer picture that helps focus product development, customer experience, and brand management practices and objectives.
This presentation discussed different types of feedback and how they can be combined and analysed to gain actionable and comprehensive insight into customers’ wants, needs, and preferences.
Back
|
|
Clementine, PredictiveMarketing
Promoting the third generation: optimising telco campaigns with analytics
Silvia Codogno
Marketing CB Manager
3 Italia
Enrico Cosio
Senior Manager
Deloitte Consulting
Italy
With more than eight million customers, 3 Italia is the most important UMTS mobile phone operator in Italy. The company carries out more than 30 up- and/or cross-sell campaigns each month, and has now invested in predictive analytics to improve its marketing performance.
3 Italia adopted a two-step approach, first introducing predictive models to achieve better targeting for single campaigns, and then using SPSS PredictiveMarketing to optimise campaigns to improve contact rates, maximise redemption rates and reduce ‘noise’.
Back
|
Dimensions
Know your customers: how Braun keeps in touch
Stefan Bender
Vice President, Marketing and Strategic Research
Braun GmbH
Bernhard Witt
2x4 Ltd
Germany
‘Know your customer’ is a pretty good rule in business, but really knowing consumers’ personal opinions is one of the most difficult challenges for companies such Braun, a subsidiary of Proctor & Gamble.
At Braun, they manage this task through a strategic product registration platform that allows them to get very fast feedback after the launch of new products or a change in marketing activities.
At this session, Braun described how it obtained rich consumer profiling across most of its products and across key regions.
The presentation included descriptions of its techniques for data collection, data processing and ad hoc analysis, as well as outbound direct marketing to deepen consumer insight and/or cross-selling.
Other topics included:
- How to implement a product-registration environment using multi-language platforms
- How to establish direct relationships with consumers despite independent sales channels
- The pitfalls to avoid international comparisons and their differences.
Back
|
|
SPSS, SPSS Server, Clementine, SPSS Predictive Enterprise Services
Churn prediction and targeted offers in a highly competitive market
Nebahat Dönmez
Head of Customer Insights
Vodafone Netherlands
The Netherlands
The Netherlands is a highly saturated telecommunications market where there is a great deal of competition, including many mobile virtual network operators. In such an environment, post-pay customers can easily change providers at any time, and the decision is usually based on the handset without consideration for the brand.
As one of the main providers in this market, Vodafone Netherlands faces a number of challenges:
- To identify the subscribers with a propensity to churn
- To understand the reasons for churn
- To decrease the churn rate by developing targeted retention activities while controlling the retention budget with tailor-made handset or other (non‑handset) offers.
Those attending this session learnt how Vodafone Netherlands’ Customer Insights Department uses SPSS technology to produce eight different monthly churn scores for two different post-pay value segments, and how, with these models, churn decreased from between 10 to 25 percent.
This presentation also addressed the approach the company took to building the predictive models, including a definition of churn and the collection and derivation of more than 2.000 variables. It also discussed the use of models within the ‘big picture’ by focusing on the example of a specific Christmas campaign.
Back
|
Dimensions
Improving the recruitment process with predictive analytics
Dr Peter Holderegger
Organisational Psychologist
Switzerland
Helvetia is one of Switzerland’s five largest general insurance companies, with annual premiums of 5,3 billion Swiss francs (€3,4 billion). Personlisation is an important element of its sales process, and having the right people can make the difference between closing or losing a sale.
Recognising the importance of its staff’s personalities and capabilities, Helvetia used predictive analytics to develop an online tool that could pre-select job applicants. The goal was to improve hiring accuracy by setting clear criteria for potential candidates and eliminating arbitrary decision-making.
This session was about how the new tool helped cut costs and save money in the recruitment process; how managers spent less time interviewing and reviewing applications from unsuitable candidates; and how the recruitment process has become far more efficient. Attendees also heard that, as a result, the employee turnover rate has dropped from 15 to nine percent in the company’s three largest regions.
Back
|
|
SPSS Server, Clementine
Switching to automatic: how to assess credit risk in under five seconds
Edgars Peics
CRM IT Development Manager
Hansabank
Latvia
Latvia’s Hansabank Group, one of the region’s leading financial institutions, is preparing to transform its credit and pricing decision-making processes, moving from a manual procedure that takes 15 minutes to a fully automated, real-time system capable of arriving at a conclusion in between one and five seconds. The new system will comply fully with the Basel II requirements for ensuring that sufficient capital is reserved to cover possible credit defaults.
This session explained how Hansabank used SPSS Clementine to create dozens of risk monitoring and assessment models, which were then submitted to the national financial supervisory board for approval. When deployed, the models will produce credit risk scores in real time for 150.000 customers every month.
Other topics included how Hansabank met the challenge of aligning business and IT objectives; the lessons learned and the tools and practices that worked; and the bank’s vision for using modelling techniques to create business value.
Back
|
SPSS Predictive Enterprise Services
SPSS Predictive Enterprise Services: overview and roadmap
Rod Reicks
Senior Product Marketing Manager
SPSS Inc.
USA
As predictive analytics becomes a key business process, organisations need to treat analytical objects as they would any other knowledge asset. Centralised, secure, auditable storage of these assets increases their value to an organisation.
The ability to automate the deployment of analytical results for use within business processes is critical to realising the value of predictive analytics in day-to-day operations.
SPSS Predictive Enterprise Services is an enterprise-level application that addresses key issues related to the widespread use and deployment of predictive analytics. It forms the backbone of the Predictive Enterprise, enabling organisations to manage analytical assets such as Clementine streams, SPSS syntax, Dimensions questions and scripts, report definitions and SAS® code.
It also automates analytical processes such as rebuilding and evaluating models, generating reports, and scoring multiple models within an operational environment.
In this session, attendees learnt more about SPSS Predictive Enterprise Services, including what’s new in Version 3.5, and saw a demonstration of Predictive Enterprise Services in action along with a preview of what SPSS is planning for the future.
Back
|
|
Clementine
Customer loyalty programme supports business strategy at SNCF
Sébastien Le Lardic
CRM Project Manager
Aurélie Amira
CRM Project Manager
CRM Services SNCF
France
The CRM division of SNCF, the National Railway of France, provides relational marketing programmes to support the operator’s sales strategies for its high speed and regional train services.
Those attending this session discovered how data mining technology was used to analyse the traveling behaviours of three million SNCF customers, leading to the development of loyalty programmes targeted at specific groups.
In particular, they found out from two examples how data mining technology was used, first, to carry out a Recency- Frequency-Monetary value analysis to segment the customer base and reveal different classes of ‘Grand Voyagers’ (heavy travelers); and second, how predictive analytics was also used to identify those customers with a propensity to travel first class.
They also heard how specific loyalty programmes were developed for each of the customer groups.
Back
|
|