The importance of prediction in customer experience management

Transcription

1 The importance of prediction in customer experience management

2 TRANSIRIS Corporation Marketing Automation Company Campaign Management (IBM Unica) Customer Predictive Analytics (IBM SPSS) Real-time Marketing (IBM Unica Interact) Global presence: Romania, Canada, US, UK 30+ Enterprise Marketing Management certified consultants Strong track record

3 If you torture the data long enough, it will confess. Ronald Coase, Economist

4 Important? What customer wants: More of Comfort to be a customer Value Solutions Proactivity Responsiveness and involvement Flexibility Less of Time to complete interactions Rigidity Cost Bureaucracy Excuses Lack of integration What companies wants: More of Customers Sales Revenue Margin Control Less of Personnel Facilities Cost Churn Operational Risks

5 Predictive Analytics (Data Mining) defined Predictive Analytics is generating operative actions from data, by drawing reliable and accurate conclusions on the current situation and predicting future events enabling organizations to make decisions in real-time and at the point of impact

6 Predictive Analytics helps answer the most important question!

7 IBM SPSS Predictive Analytics Optimized decisions made possible by pervasive, predictive, real-time decisions at the point of contact Predictive Customer Analytics Acquire Grow Retain Predictive Operational Analytics Manage Maintain Maximize Predictive Threat & Fraud Analytics Monitor Detect Control Data Collection Data Transf. Statistics Modeler Decision Management Collaboration and Deployment Services 7

8 What Does Predictive Analytics Do? Customer Predictive Analytics uses existing data to predict: Buy a product Increase the usage/up-sell need Churn Likelihood of default 8

9 Predict the Behavior A Universe of Data A Predictive Model Attributes: Married Lives in the Corbeanca VP R&D Owns a BMW 47 years old + Transactional data Interactions data = Predicted Behavior High probability to buy a credit card in the next month.

10 IT S A FACT Results in Marketing Campaigns The lift chart shows how much more likely we are to receive positive responses than if we contact a random sample of customers. For example, by contacting only 10% of customers based on the predictive model we will reach 3 times as many respondents, as if we use no model.

11 Apply Predictions at the Point of Interaction Help People Take the Best Course of Action What should I do Now??? Next Best Product Next Best Offer Next Best Activity SPSS Predictive Modeling

12 Important? What customer wants: More of Comfort to be a customer Value Solutions Proactivity Responsiveness and involvement Flexibility Less of Time to complete interactions Rigidity Cost Bureaucracy Excuses Lack of integration What companies wants: More of Customers Sales Revenue Margin Control Less of Personnel Facilities Cost Churn Operational Risks

13 IT S A FACT Analytically sophisticated companies outperform their competition 2.2x more likely to outperform industry peers 260% more likely to be top performers 1.6x Revenue Growth 2.5x Stock Price Appreciation Source: The New Intelligent Enterprise, a joint MIT Sloan Management Review and IBM Institute of Business Value analytics research partnership. Copyright Massachusetts Institute of Technology Outperforming in a data-rich, hyper-connected world, IBM Center for Applied Insights study conducted in cooperation with the Economist Intelligence Unit and the IBM Institute of Business Value. 2012

14 Conclusions Predictions positive impact on customer experience. Predictions are driving profits. We can do it with IBM SPSS (excellent tool for prediction).

15 Thank you!