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The Role of SAS in the Analytics Framework Aleksandar Zajic VP Business Analytics

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Presentation on theme: "The Role of SAS in the Analytics Framework Aleksandar Zajic VP Business Analytics"— Presentation transcript:

1 The Role of SAS in the Analytics Framework Aleksandar Zajic VP Business Analytics

2 Company Snapshot 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 2

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

4 Subscription Based Model: Flow SpeedDate relies 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. User Experience CRM/Life Cycle Management Customer LTV Basic Subscription Premium Subscription Retention Micro trans. Reactivation Organic Traffic Marketing Acquisition Metrics to monitor: – Acquisition ROI – Basic to premium subscription conversion rate – E-Mail campaign lifts – Customer satisfaction / user experience indicators – Average customer LTV 4

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

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. 6

7 Role of Analytics 1 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 7

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

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 Traffic: Quality Revenue Life Cycle Other variables Time point 1: Create time series algorithm to forecast LTV and ROI Time point 2: Algorithm absorbs new information and adjusts. Reforecast LTV and ROI with adjusted algorithm End of life cycle: Actual customer LTV and ROI New Information from TP1: Customer behavior and performance Time point N: Algorithm absorbs new information and adjusts. Reforecast LTV and ROI with adjusted algorithm New Information from TP N - 1: Customer behavior and performance 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 9

10 Know Your Customer Customer segmentation is a key to provide optimal CRM strategy and incrementally increase customer engagement and response by customizing message. 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. Communication strategy A1 Communication strategy A2 Communication strategy A3 Communication strategy B1 Communication strategy B2 Communication strategy B3 Communication strategy C1 Communication strategy C2 Communication strategy C3 Customer: Demographic variables Behavior Site utilization Segmentation Algorithm Segment A Segment B Segment C Life Cycle Stage 1 Life Cycle Life Cycle Stage 2 Life Cycle Stage 3 10

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. Proactive optimization through propensity modeling (estimating likelihood of the event) could create competitive edge and ultimately wow factor for your product. Customer: Demographic variables Behavior Site utilization Transactions Collection Propensity Model High Moderate Low Likelihood of collecting money from a credit card Subscription Page: Show an appropriate default subscription plan based on likelihood of CC money collection 11

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. 12

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