Strategic Review on Big Data Application Hurdles and How to Overcome. Ashley Jeong T 13 Center for Digital Strategies

Transcription

1 Strategic Review on Big Data Application Hurdles and How to Overcome Ashley Jeong T 13 Center for Digital Strategies

2 Agenda Leanings from primitive big data ebay and H-card Big data in current consumer markets Application hurdles and risks, and how to overcome

3 Learning from primitive big data ebay.com Once a dot-com bubble hero, one of the garage legends Incredible scaling of growth* 0.5mm users, $4.7mm in revenue (1998) 112mm users, $6.0bn in revenue (2012) Fanatic about number driven decisions w/ affluent data However ebay Inc. stock price ( ) $50 $40 $30 $20 $ * marketplace only. i.e. paypal and other businesses are excluded

4 Learning from primitive big data ebay.com (cont d) Business decisions based on dashboard (simplified example) Restricting more collection of DATA Monetize first, then service #Listings $$$ #Sold listings $$$ #Registered users #Visits Listing fee Additional photos Special fonts (bold, color, etc.) $Avg. sold item Disconnected between steps throughout #Page views #Biddings Track the metrics momentarily/daily/weekly/ Allocate resources based on weakness on the dashboard Monetize at two steps (when listing and when sold)

5 Learning from primitive big data H-card Financial arm of Hyundai group Frontier in lifestyle based segmentation Basic availability of data, but highly utilized Latest mover, and one of the top three players Overcame weak sales force, no offline office presence 25% 20% 15% 10% 5% Market share of major card companies Shinhan Hyundai Card Samsung Lotte

6 Learning from primitive big data H-card (cont d) Connected the dots and applied data interpretations to diverse marketing activities Membership info Age, sex, occupation, income level, address, credit rating, Transaction info Business location, transaction amount, type of business, time of transaction, + = Answers meaningful facts about a customer Favorite neighborhood and activities Purchasing power and price sensitivity Brand preference Expenditure allocation New product development bundling discount offers based on lifestyles Events invitations based on target group s interests

7 Agenda Leanings from primitive big data ebay and H-card Big data in current consumer markets Application hurdles and risks, and how to overcome

8 Bigger data available in consumer business The questions is how to create value, monetize and be better off Retail / e-commerce Communication Search engines Commercial activities including item categories, amount, frequencies, buyer/user locations Mobile, fixed-line, Internet based communications including communication length, time, participants, medium Search activities including keywords, clicks, behavioral sequence Social networks Healthcare Financial institutions Social relations and activities including communication, photos, videos, relationships, check-ins, reviews Health conditions and activities including physical profiles, treatment histories, visits, medicines prescribed Financial transaction activities including income, loans, savings, withdrawals, transfers, bill payments

9 Reality as a consumer Gmail Ad sample I m invited to a wedding, not getting married Dresses for a wedding guest Thus, I don t pay attention to Ads I exchanged an about making a pie Dessert recipe site I m already insured with Geico A competing/cheaper offer Very low bank balance, can t afford Outlet malls information

10 Agenda Leanings from primitive big data ebay and H-card Big data in current consumer markets Application hurdles and risks, and how to overcome

11 Application hurdles and risks Data abundance doesn t guarantee effective applications Incomplete models Partial observation Return on big data Privacy concerns Limited offers Although it s about probabilities, current predictive models frequently throw in random displays Depending on the data collector s business, the information is not comprehensive Collecting, storing and analyzing big data cost money, yet how to monetize is unclear Users concern about the usage of the information they feed to businesses Data analytics implications can be irrelevant to the business and it doesn t always own what it should offer

12 Tackling the hurdles Data exchange/pooling Filling the gap by exchanging complementary data from different businesses/data sources By active data exchanges, big data gives more comprehensive and dynamic views to understand user behaviors Can help improving Partial observation and incomplete models Example Foursquare (where a user went/did) + Facebook (with whom for what) Log-in w/ Facebook, gmail or etc. Mint.com w/ various financial institutions

13 Tackling the hurdles Capitalizing data asset Less than sufficient volume of data and unproven analytics models can harm the effectiveness of application Not every business will need a big data strategy Trade, barter and/or sale of data can help financially, while saving on unnecessary investment in implementing big data initiatives Can help improving Return on data and what to offer Example Many of retailers sell the point-of-sales data to its suppliers and business partners

14 Tackling the hurdles Partnerships w/ SMBs Big data companies can partner w/ small-medium businesses for facing consumers SMBs have more personal service capabilities, which meets sophisticated data analytics outcome Application of long tail marketing Can help improving Return on big data and what to offer Example Insurance companies connecting its customers to local repair shops

15 Tackling the hurdles Data stewardship Maintaining, updating and internally/externally communicating the organization s data governance policies Not a cost center, but helps regain lost business opportunities Can help improving Return on big data and privacy concerns Example 44% of US online adults report having abandoned a transaction because of something they read in a privacy policy or terms or use disconnect.me ( Microsoft committed to shipping Do Not Track as a default setting

16 Summary Issues and directions to tackle Incomplete models Partial observation Return on big data Privacy concerns Limited offers Data Exchange/Pooling Capitalizing data assets Partnerships w/ SMBs Data stewardship