The Role of SAS in the Analytics Framework

1. The Role of SAS in the Analytics Framework Aleksandar Zajic VP Business Analytics aleksandar@speeddate.com

2. CompanySnapshot • Founded in 2007 • 38 employees • headquarters - Millbrae, CA • Facilitates real-time interaction • Cross Platform Reach • Site, Facebook, iOS, Android • Established technology platform • Strong customer acquisition funnel • Critical mass of users

3. 130,000 premium subscribers • 9M monthly unique visitors • 17M+ users

4. Subscription Based Model: Flow • SpeedDaterelies on two tier subscription model: • Basic subscription with limited functionality and user experience. It is free for all subscribers. • Premium subscription with full functionality and enhanced user experience. Customers pay subscription fee for this level of service. • Goal is to convert as many customers as possible to premium subscription status and offer them full user experience. CRM/Life Cycle Management Customer LTV Marketing Acquisition Premium Subscription Basic Subscription Retention Micro trans. Reactivation Organic Traffic User Experience • Metrics to monitor: • Acquisition ROI • Basic to premium subscription conversion rate • E-Mail campaign lifts • Customer satisfaction / user experience indicators • Average customer LTV

5. SpeedDate Analytics Framework Analytics DW SAS Datasets Data cubes External data sources ETL • Customer profiling • Propensity modeling • Segmentation • Forecasting • Risk modeling Analytics and Modeling ETL Engine SAS/ACCESS® Database Captures user profile, preferences and all activities on the site/device/app • Reporting: • ODS HTML • VB Excel • Acquisition effectiveness report • Mobile report • UE analysis • Risk and Fraud analysis

6. Why SAS? • Multiple Analytics/Modeling and BI programs and tools available, some for free, most cheaper than SAS: • R • SPSS • Statistica • Excel • SAS is the only package that even in basic configuration can on its own play central, integrative role in the enterprise level analytics / bi framework. • It combines decent ETL and reporting capabilities with excellent analytics/modeling capabilities.

7. Role of Analytics1 • Find the most profitable growth opportunities • Manage data • Profile customers • Predict customer behavior • Indicate best actions • Optimize strategies • Customer engagement • User experience • Maximize Cross-Business Impact • Optimize ROI • Continuous learning from customer behavior 1 Competing on Customer Intelligence,SAS Whitepaper

8. SpeedDate – The Future is Real-time • Meeting people should be instant, easy, and fun • -Core feature is instant SpeedDates • -Location-based matching • -Critical mass • -High engagement • SpeedDate has the #1 mobile dating app

9. Marketing Acquisition Analytics Platform • Key objective is to create near to real time ability to asses success and ROI from each new acquisition campaign New Information from TP N - 1: Customer behavior and performance New Information from TP1: Customer behavior and performance • Time point N: • Algorithm absorbs new information and adjusts. • Reforecast LTV and ROI with adjusted algorithm • Time point 2: • Algorithm absorbs new information and adjusts. • Reforecast LTV and ROI with adjusted algorithm • New Traffic: • Quality • Revenue • Life Cycle • Other variables • End of life cycle: • Actual customer LTV and ROI • Time point 1: • Create time series algorithm to forecast LTV and ROI • With each algorithm adjustment forecast becomes more accurate increasing level of significance • Ability to have customer LTV and ROI at any time point enables apples to apples comparison between different campaigns and publishers. As a result, marketing function can simultaneously optimize marketing budget while maximizing $ net profit from advertising. • In addition to LTV and ROI forecast, platform delivers additional information on acquisition quality and efficiency, customer profile mix, and some other KPIs

10. Know Your Customer • Customer segmentation is a key to provide optimal CRM strategy and incrementally increase customer engagement and response by customizing message. Life Cycle Stage 3 Life Cycle Stage 2 Life Cycle Stage 1 Life Cycle Segment A • Customer: • Demographic variables • Behavior • Site utilization Segmentation Algorithm Segment B Segment C • Departure from classic RFM model, where one size fits all, and acceptance of new two dimensional model provides much better ability to customize message and adapt it to real interest and life cycle stages of each customer.

11. Proactive Optimization of User Experience • Proactive assessment of the probability of cc collection enables us to offer appropriate subscription plan as a default. In this way we increase number of customers who achieve premium status while increasing revenue. Collection Propensity Model High Subscription Page: Show an appropriate default subscription plan based on likelihood of CC money collection Likelihood of collecting money from a credit card • Customer: • Demographic variables • Behavior • Site utilization • Transactions Moderate Low • Proactive optimization through propensity modeling (estimating likelihood of the event) could create competitive edge and ultimately wow factor for your product.

12. For small businesses with limited analytics and IT resources, placing SAS platform in the central integrative position enables flexible, proactive analytics function. • In this case expert SAS skillset can offset limited body count and provide insight and information equivalent to that of much larger analytics team.