The Role of SAS in the Analytics
Framework
Aleksandar Zajic
VP Business Analytics
aleksandar@speeddate.com
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
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• 130,000 premium subscribers
• 9M monthly unique visitors
• 17M+ users
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.
CRM/Life Cycle Management
Customer LTV
Marketing
Acquisition
Basic
Subscription
Organic
Traffic
Premium
Subscription
Retention
Micro trans.
Reactivation
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
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SpeedDate Analytics Framework
Analytics
DW
Database
SAS Datasets
Data cubes
Analytics
and
Modeling
SAS/ACCESS®
Engine
•
•
•
•
•
Customer profiling
Propensity modeling
Segmentation
Forecasting
Risk modeling
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
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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.
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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
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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
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 TP1:
Customer behavior and
performance
Time point 2:
New Traffic:
•
•
•
•
Quality
Revenue
Life Cycle
Other
variables
•
Time point 1:
•
Create time
series algorithm
to forecast LTV
and ROI
•
Algorithm
absorbs new
information and
adjusts.
Reforecast LTV
and ROI with
adjusted
algorithm
New Information from TP N - 1:
Customer behavior and
performance
Time point N:
•
•
Algorithm
absorbs new
information and
adjusts.
Reforecast LTV
and ROI with
adjusted
algorithm
End of life cycle:
•
Actual customer
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
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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 1
Life Cycle Stage 2 Life Cycle Stage 3
Life Cycle
Communication
strategy A1
Communication
strategy A2
Communication
strategy A3
Segmentation Segment B
Algorithm
Communication
strategy B1
Communication
strategy B2
Communication
strategy B3
Segment C
Communication
strategy C1
Communication
strategy C2
Communication
strategy C3
Segment A
Customer:
• Demographic variables
• Behavior
• Site utilization
•
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.
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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.
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
Proactive optimization through propensity modeling (estimating likelihood of the event) could create
competitive edge and ultimately wow factor for your product.
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• 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.
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