Advance Analytic Game Changer in FSI Industry. Greg Wong, Director Analytics Centre of Excellence BI & PA

Advance Analytic Game Changer in FSI Industry Greg Wong, Director Analytics Centre of Excellence BI & PA

How mbank used SAP Advance Analytics to drive their business?

mbank: Delivering a Personalized Banking Experience for 4.5 Million Customers with SAP Predictive Analytics Company mbank S.A. Headquarters Warsaw, Poland Industry Banking Products and Services Retail and corporate banking products and services, wealth management Employees 6,318 Web Site www.mbank.pl Objectives Respond to customer needs as quickly as possible Gain insights into customer preferences to support a customer-centric banking experience across all channels Optimize the company s discount program (mdeals) by providing better service to partners and more targeted offers to customers Improve the performance of marketing campaigns by better understanding customer behavior and anticipating future demand Why SAP Ability of SAP Predictive Analytics software to drive a close, personal connection to clients by providing a data and analytical modeling tool Pragmatic, user-friendly software, allowing quick user adoption and rollout Easy integration with the existing IT infrastructure Resolution Predictive modeling based on transactional and demographic data Precise segmentation, advanced reporting, and execution of real-time, multichannel marketing campaigns Context-specific offers across all channels based on customer profiles Benefits Reduce churn and grow revenue by automating campaigns and offer activation Increase sales efficiency and reduce the cost of sales Minimize the effort to train employees and identify targeted customers Empower mbank s discount program by personalizing discounts for each customer based on predictive analytic SAP Predictive Analytics has allowed mbank to discover individual customer preferences and identify the next best activity for our marketing efforts. Now we are able to initiate more direct conversations, resulting in a better understanding of our clients on a personal level. Bartosz Witorzenc, Strategic Initiatives Manager, Retail Banking Department, mbank S.A. Rapid Increase in response rates to marketing campaigns 400% Higher hit rate for nonmortgage loans 200% Increased hit rate for insurance products 250% Higher hit rate for savings products 2016 SAP SE or an SAP affiliate company. All rights reserved. 41826 (15/12) This content is approved by the customer and may not be altered under Internal any circumstances. 4

Aviva: Building Predictive Models with Ease Using SAP Predictive Analytic Company Aviva plc Headquarters London Industry Insurance Products and Services Life and general insurance Customer Base 31.4 million customers in over 15 countries Employees 27,700 worldwide Operating Profit 2.05 billion ( 2.5 billion) Web Site www.aviva.co.uk Objectives Leverage predictive analytics to build propensity models for individual customer groups rather than build generic models for all customers Avoid contacting customers too frequently, while also improving campaign response rates Increase return on marketing and campaign response rates by identifying customers most likely to respond Why the SAP Predictive Analytic solution Charts that help marketing experts visualize the anticipated business impact of models Significantly better modeling automation that allows many models to be built with ease Automatic analysis of the individual contributions of hundreds of variables to a model, rather than manual inspection of a limited number of variables Future plans Further improve return on marketing with uplift modeling that predicts the impact of marketing activities on specific target groups Build predictive models to analyze customer acquisition and win-back Personalized Further improve return on marketing with uplift modeling that predicts the impact of marketing activities on specific target groups Efficient Significant increase in the number of propensity models used within the company, with more than 30 models in production Current Ability to use the freshest data to keep models up-to-date and capture the latest trends "Modeling made easy thanks to SAP Predictive Analytic. Dr. Margaret Robins, Statistical Analyst, Data Analytics and Insight, Aviva plc 2016 SAP SE or an SAP affiliate company. All rights reserved. 30599 (14/05) This content is approved by the customer and may not be altered under any circumstances. Internal 6

Business Value of an Integrated and Fully Evolved BI Solution 36% Lower BI expenditures More Companies that consolidate multiple BI tools into a standardized and enterprise-wide portfolio, have 36% lower BI expenditures 10% timely information Companies that have enhanced business user access to information, have 10% more timely information to create actionable insights 24% Higher usage Companies with flexible information systems have 24% higher usage of business intelligence to manage in their business processes Greater than 10x 1 More ROI 13.01 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 9

Greatly Improve your Time to Value 3 Months Data Connections Data Manipulation Variable Reduction & Sampling Predictive model creation Scoring & Validation Model Interpreta -tion Application to business Automated and simplified by SAP Predictive Analytics Optimal model selected Simple GUI Automated Automated Simplified automatically Application to business 1 Week 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 10

What is SAP Predictive Analytics? https://www.youtube.com/watch?v=sibuavro70a&list=pl4b_3kgkzmui7hu74ldkjculp6ff3orr9

Applying Predictive to Real Business Problems Types of Business Problems Solved with Predictive Sales and Marketing Operations Fraud and Risk Finance and HR Others Churn Reduction Predictive Fraud and Abuse Cash Flow and Life Sciences Customer Maintenance Detection Forecasting Health Care Acquisition Lead Scoring Product Recommendation Campaign Optimization Customer Segmentation Next Best Offer/ Action Load Forecasting Inventory/demand Optimization Product Recommendation Price Optimization Manufacturing Process Opt. Quality Management Yield Management Claim Analysis Collection and Delinquency Credit Scoring Operational Risk Modeling Crime Threat Revenue and Loss Analysis Budgeting Simulation Profitability and Margin Analysis Financial Risk Modeling Employee Retention Modeling Succession Planning Media High Education Public Sector/ Social Sciences Construction and Mining Travel and Hospitality Big Data and IoT 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 13

Predictive Analytic help corporation bring in high ROI Retail Sales increased by 8.5%, ROI increased 14% Sales Predictability Increased 10% Campaign Response Rate increased 220% Bank Telco Each year because of fraud caused lost has decreased USD $ 1B Campaign response rate increased 260% Response Rate increased 5 times Monthly Churn Rate decreased 3% Compared with the control group,7 times increase in response rate Customer Churn & Cross-Sell Models over 700 Others Every year over 400 incidents, fuel and tires Costs reduction 3% Covering 20,000 different social communities Integration of more than 5 billion raw data from diff. State agencies 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 14

Success With Over 500 Leaders Around the World Telcom Financial Retails e-comm Credit Investigation Media Public Sector Energy & Manufacturing 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 15

Predictive Future It will embed into any application to make the application become more intelligence Predictive will become more generic and easy to use by anyone BI + Predictive + Business Domain is the key of success (analytic cycle) Predictive will make people getting smarter Whoever can leverage Predictive into their business whoever can gradually out perform in the market! Just like automation. No matter what, technology will become useful if it can be manage by business people. 2016 SAP SE or an SAP affiliate company. All rights reserved. Internal 16

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