Data Science and Experimentation for Growth Hacking Case Study
Explore a real-world case study on applying data science and experimentation for growth hacking, focusing on validating models and customer propensity, with insights on effective strategies for maximizing impact in marketing.
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Presentation Transcript
Applied Growth Hacking An E2E approach of how to apply data science and experimentation in a real world case study. Ayesha Ghaffar
Experiment Validation Premises to Validate: (1) P2B SMB Propensity Model accurately reflects likelihood to buy (2) P2B SMB Propensity Model is better lead scoring model than the current lead scoring model (champion vs. challenger) (3) Leads with a higher propensity score should hold a higher priority to be called by the Tele team (linear relationship vs. parabolic relationship) What is the threshold between high and low propensity? Prioritization Prioritization vs Probability Score Probability Score 3
Experiment Design Who? Trial Customer Propensity to Azure Low Propensity Medium Propensity High Propensity 3,831 3,812 2,792 Control Call Immediately Treatment 1 Delayed Call (t=+15 days) 764 703 577 1,286 1,393 1,097 Treatment 2 No Call
TL;DR TL;DR Calling is only effective for high propensity customers but needs to be delayed. Calling Immediately = Not Calling at All
It Takes a Village Data Science / EXP Marketing Data Engineering
Data Science turn Questions into Insights Economics turns Insights into Impact
