Automating Customer Analytics. DynaMine Data Mining Automation powered by KNIME.

Transcription

1 Automating Customer Analytics. DynaMine Data Mining Automation powered by KNIME.

2 Outline Overview DYMATRIX CONSULTING GROUP Automating customer analytics DynaMine & KNIME Framework Case Studies 2

3 Outline Overview DYMATRIX CONSULTING GROUP Automating customer analytics DynaMine & KNIME Framework Case Studies 3

4 DYMATRIX - The Analytical CRM Company Advanced Analytics and Customer Intelligence Solution Provider located in Stuttgart, Germany Since 2000 successfully managed more than 600 projects Consulting focus on Analytical CRM, Campaign Management & Optimization and Data Mining Automation Owner-managed and independent 4

5 Consulting Expertise Business Intelligence Advanced Analytics Data Warehousing Reporting und Dashboards Business Performance Management Data Quality Management Data Cleansing Integration of Webtracking Data Integration of Social Media Data Analytical CRM Risk Management Web Analytics Social Media Analytics Text Mining Forecasting Campaign Optimization CRM Consulting Trainings Feasibility Studies Software Evaluation Business Process Reengineering Project Management Customer Experience Management Customer Journey Data Mining Trainings Business Intelligence Trainings Partner Trainings (Microsoft, SAS, KNIME) 5

6 Solution Expertise Customer Insight Suite 6

7 Multi-Touchpoint Campaign Management Established closed loop solution for successful multi-touchpoint Campaign Management DynaCampaign covers the complete campaign process from planning & resource management up to campaign execution. Parallell processing of multi-step and event-triggered campaigns across all touchpoints. Open and flexible architecture guarantees rapid solution deployment. Modular design ensures scalability and extensibility. Noticeable raise of marketing performance due to higher conversion rates. High degree of automation guarantees cost savings. More than 40 Customer installations all over Europe across all industries. 7

8 Data Mining Automation for adaptive model training & scoring Award-winning Data Mining Automation framework for adaptive self-learning model training and scoring Advanced analytic framework for automated self-learning model optimization, if changes occour in customer or market behaviour. Seamless integration into inbound and outbound campaign processes. Model Manager for realtime Next-Best-Offer/-Activity to deliver integrated and consistent realtime recommendations across all customer touchpoints. Customer individual best offers and contact dates lead to verifiable growth in sales. Remarkable reduction in support and maintenance of data mining processes (verifiable up to 200 man-day per year). 8

9 Realtime Decision Management Innovative solution for intelligent Realtime Decision Management (Next Best Offer, Credit Limit Optimization etc.) Benefits: Decisions with great accuracy guarantee sales increases. DynaCision combines predictive models, business score cards and business rules via a user-friendly frontend. Competitive advantage by combining historical and realtime behavioural data (e.g. market basket) in realtime. Plug&Play-Integration into existing architectures (e.g. Web Shops) via Web Service Standard-Interfaces. USP: High-quality realtime Next Best Activities based on intelligent and up-to-date decision models running on online- and offline data. 9

10 Outline Overview DYMATRIX CONSULTING GROUP Automating customer analytics DynaMine & KNIME Framework Case Studies 10

11 Gartner Hype Cycle 2011 Predictive Analytics on its way to productivity 11

12 Customer Experience* and Customer Intelligence Customer needs Usage Delivery Purchase Approach Decision Possibilities Traditional CRMapproaches focus on analysis of product usage. Valuable information along the entire customer experience chain is not considered and is not integrated with others touchpoints information. Intelligent integration of all customer touchpoints along the customer experience chain allows the identification of customer needs. 12 * Source: Customer Experience, Phil Winters, 2011

13 Challenge: Intelligent Integration of all Customer Touchpoints Customer Master Data Social Media Data Transactional & Usage Data Online Tracking Data Integrated Analytical Data Mart Product Information Sociodemographic Data External Data Campaign & Contact Data Mobile & Context Data 13

14 Automation of Customer Analytics to identify customer needs Learn from the past and detect valuable correlations to predict customer behaviour in the future. Decisions Customer Behaviour Predictive Model 14

15 Automation of Customer Analytics to identify customer needs Credit Scoring Product Affinities 15 The demand for multiple predictive models will increase continuously to predict up-to-date and precise customer needs in the future. Demand Forecasting Churn Prevention Aktion Verhalten Aktion Verhalten Aktion Verhalten Aktion Verhalten Aktion Verhalten Aktion Verhalten Customer Decisions Behaviour Predictive Model Predictive Model Predictive Model Predictive Model Predictive Model Predictive Model Predictive Model Social Media Analytics Credit Limit Optimization Text Mining Fraud Detection

16 Automation of Customer Analytics to identify customer needs Best Product Offer? Best Contact Time? Best Touchpoint? Best Context? The intelligent interaction of different predictive models serves as decision basis for the next best activity. 16

17 Outline Overview DYMATRIX CONSULTING GROUP Automating customer analytics DynaMine & KNIME Framework Case Studies 17

18 DynaMine Framework for data mining automation DynaMine is a revolutionary Advanced Analytics Framework for automated, adaptive model training and model management. DynaMine enables streamlined, costeffective and fully-integrated data mining processes and supports real-time model training and scoring for any KNIME predictive modeling algorithm. DynaMine automates time- and resource-consuming data mining tasks (data preparation, creation, re-training, assessment and deployment of data mining models) Data Mining Models are always up-to-date and are easily administrated and adjusted by the DynaMine closed-loop control. DynaMine offers best-practice set-ups for data mining processes gathered, combined and optimized from numerous data mining projects. DynaMine overcomes the limitations imposed by existing modeling approaches - train as many data mining models with as many input variables as you want without any limits. 18

19 DynaMine Advanced Analytics Loop powered by KNIME DynaMine Data Mining Automation Customer Touchpoints Model Deployment Models & Scores Model Assessment Evaluation Data Preparation Training & Recalibration Pre- Processing Customer Behaviour Operational & packaged applications Parts Date Planning Values Actual Values Values Car Configurator Market Data KPIs Cruise Control Week Cruise Control Week Cruise Control Week Cruise Control Navigation System Week Navigation System Week Navigation System Week Navigation System Car Audio Week Car Audio Week Car Audio Week Car Audio Data Integration CRM Social Media Website Other 19

20 DynaMine in a distributed analytical environment: Plug & Score! Model Assessment Model Upload Model Deployment Evaluation Cloud based Model Repository Requests Scores & Decisions Plattform independent Scoring on the cloud On-site Scoring via DynaMine Mobile Applications Web Applications CRM, Sales & Service Applications Training & Recalibration Pre- Processing Requests ERP & SCM Applications Scores & Decisions Batch / Real-time Business Intelligence Hub Social Media ERP SCM CRM Legacy other 20

21 DynaMine Fields of application in Customer Analytics Next Best Offer / Next Best Activity Affinity modeling (Up Selling / Cross Selling) Churn-/Retention Modeling Credit Scoring / Credit Limit Allocation Fraud Detection Social Media Analytics & Text Mining any other application of Predictive Modeling 21

22 Outline Overview DYMATRIX CONSULTING GROUP Automating customer analytics DynaMine & KNIME Framework Case Studies 22

23 Case Study Utilities: Advanced Analytics Enterprise Platform Business Scenario One standard platform for all customer analytics szenarios Churn Prevention, Uplift Modeling Fraud & Application Scoring, Forecasting & Simulation E2E-Integration into SAP environment Constraints Budget Manpower Lack of data mining skills Time to market Solution DynaMine Data Mining Automation powered by KNIME Enterprise Server Industrialization of analytical model management (adaptive training & retraining, evaluation & scoring of models) Model- & Score-Integration in Campaign Management and SAP Applications 23

24 Case Study Utilities: Advanced Analytics Enterprise Platform Benefits Cost-effective implementation of a next generation advanced analytics closed loop platform, which ensures a continuous ROI. Easy to use DynaMine-Frontend encapsulates data mining complexity. KNIME backend offers an unrivaled range of data mining algorithms (KNIMEnative, R, WEKA, user written nodes etc) all being parametrized via DynaMine Web Services. Scalability and easy extensibility for new models and business scenarios. 24

25 Case Studies: Social Media Analytics & Customer Touchpoint Integration Business Scenario Automation of intelligent Social Media Content Parsing & Sentiment Detection Integration with other touchpoint information Enrichment of existing channel driven predicitve models Early Warning System Constraints Social Media Data access & availability Sentiment Parser for German language Methodology Solution DynaMine Data Mining Automation powered by KNIME Text Mining Utility for Sentiment Detection & Classification DynaMine-API for realtime access to Social Media Platforms (Facebook, Twitter & others) 25

26 Case Study Car Rental Tag Cloud as Early Warning System In June 2011 an online campaign of German car rental company Sixt was revealed. The ad is both insensitive and provocative as it invites Greeks to take advantage of the low Sixt rates and spend their drachmas. 26

27 Case Study Car Rental Tag Cloud as Early Warning System Text Mining of Twitter Communications: Tag Cloud as Early Warning System 27

28 Case Study ecommerce Social Media Monitoring Integration of KNIME Text Mining in DYMATRIX SocialMediaAnalytics Management Dashboard Sentiment Visualization 28

29 Case Study ecommerce Social Media Monitoring Integration of KNIME Text Mining in DYMATRIX SocialMediaAnalytics Facebook Details Sentiment Visualization 29

30 Case Study Telecommunication Identification of customer needs Identification of customer needs along the customer lifecycle Classify Messages into need-oriented messages Map & identify product/service-oriented needs 30

31 CONTACT DYMATRIX CONSULTING GROUP GmbH Ansprechpartner: Stefan Weingaertner Zeppelin Carré Lautenschlagerstrasse 2 D Stuttgart, Germany Telefon Telefax Web s.weingaertner@dymatrix.de