Lippo Group BIG DATA. How to Develop a Deep Understanding of Your Customers by Connecting All of Your Data Sources. Pg. 1

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1 Lippo Group BIG DATA How to Develop a Deep Understanding of Your Customers by Connecting All of Your s Benny Riadi Singapore, 24 th October 2017 Pg. 1

2 Our touch points are serving Indonesian consumers Wholesale store Supermarket Pharmacy Store Loyalty, Payment, Finance Convenient store Hospital Coffee Shop Department Store E-commerce Wireless Broadband Book Store Hotels Cable TV Satellite TV Housing & Office Cinema TV Media News Malls Center System Integrator Parking system EDC Operator Bank Insurance Education Entertainment Asset Management Venture Capital We bring technology and new experience to customer lifestyle and influence their behavior, and tune with customer mind to meet their expectation and improving their Life Pg. 2

3 OVO is a digital lifestyle concierge platform An ecosystem that allowing customer to have single loyalty conversion across brands, do payment and financial services Lippo Group Assets OVO Merchant Partners Partner Partner Partner Partner Thousands of outlets have committed to join OVO loyalty and accept OVO payments Pg. 3

4 OVO connects customer transactions across merchants. When we transform data as 360 degree customer profile and actionable insights thus we can engage with Customer with new and better experience Slice and dice customer profile per individual or as aggregated insight: Gender Loyalty Segment Who Information Brand Image, sentiment, social, customer relations Age Churn Score Emotional and Attitudinal Profile Purchase Behavior Age Group Product Tendency Cross Channel Behavior Social Media, video, audio, digital, viewership and interaction Loyalty Segment Total Purchase Value CRM, Demographic, psychographic, behavioral, geographic E-commerce, e-wallets, loyalty SES (Social Economic Status) Brand Tendency Spending Class Most Visited Merchant Pg. 4

5 360 degree of Customer Profile a multi dimensions of customer attributes to describe their behavior Face Recognition : The same person appear in many of our premises Customer Profile : Age 27 Single Young Professional Medium Income High Spender Promo Seeker Visit Behaviour Visit 7 times this month. Favorite time of visit: 3-8 PM Parking time avg: 1 hrs 30 min Likelihood to visit in next 7 days: 45% Merchants visited at this location: Dailybread spend IDR Ace Hardware spend IDR Maxx Coffee spend IDR Purchase Behaviour Buy milk 2x per week Has cat as pets Favorite product is instant noodle Lifestyle Sporty Healthy Lifestyle Brand-minded Face Recognition: Tensorflow, Keras Basket Fingerprint: Clustering Socio Economic Status: Clustering Pg. 5

6 HDFS Analytic API on top of Hadoop (Initial plan) Big Cluster API Consumer REST API YARN Spark API Cluster Dashboard Visualization base Connection Hive API Aggregate Impala Mobile Apps E-Commerce AND MANY MORE {;} </> Stream JSON XML FTP Log Files E T L Tools HBase Vibe Stream Intelligent Lake Impala Kafka Big Quality Enterprise Information Catalog Hive API Granular HBase Point of sales Campaign Channel Backend System Lippo Group data source Big Platform (data lake) Big API Layer API for Internal & External Lippo Group Pg. 6

7 HDFS Analytic API on top GPU and Kinetica Big Cluster API Consumer REST API YARN Spark API analytic Dashboard Visualization AND MANY MORE {;} </> base Connection Stream JSON XML FTP Log Files E T L Tools HBase Vibe Stream Intelligent Lake Hive Impala Kafka Big Quality Enterprise Information Catalog Aggregate Granular Event trigger Campaign Fraud Detection Modeling Machine learning Mobile Apps E-Commerce Point of sales Campaign Channel Backend System Lippo Group data source Big Platform GPU Platform API for Internal & External Lippo Group Pg. 7

8 Thank you Pg. 8