yogurt : predicting the unpredictable with 95% accuracy
TRANSCRIPT
Predicting the Unpredictable with 95% accuracy
Why of Prediction• Campaign ROI Estimation.
• Seasonality/Holiday effect assessment.
• Upgrade/Breakdown Impact.
• Marketing Health Check.
• Above all, know the future…
Problems of Prediction• Less accurate results.
• Data is never clean. Major time spent in cleaning and preparation.
• No fully automated Data Science. You need to get your hands dirty.
• Complicated results. Academia and Businesses are two different world.
Prediction with • Less accurate results. Predict the unpredictable with 95%
accuracy.
• Data is never clean. Major time spent in cleaning and preparation. No Data Preparation required.
• No fully automated Data Science. You need to get your hands dirty. Fully automated Software.
• Complicated results. Academia and Businesses are two different world. Clear actionable results.
Introducing
Weekly Predictions – SimpleNumbers in a click
Introducing
• No limitations on number of KPIs’(5<Range<1000).
• Absolute Data Privacy(Simple and Secure).
• Yogurt connects with all analytics platform(Google Analytics, Adobe Analytics, Mixpanel) and databases.
Case Study 1: Ecommerce AnalyticsCampaign ROI Estimation
Challenges for ACME Ecommerce
• Spent X Millions in Diwali/Christmas Sale but not sure about the right ROI.
• Different methods throws different results.
• Numerous factors affecting the growth at that time. Difficult to isolate the campaign factor.
Solution
• Yogurt helped them to predict the results with and without Diwali effect.
• Diwali Sale Effect identified @ Y%.• Prediction was done with single
variable only, hence easy and fast.
Achievements
• Achieved ~90% accuracy in predicting their KPIs in less than 20 Mins.• Campaign Effect was identified in minutes without data messaging or preparation.
Case Study 2: Ecommerce App AnalyticsSeasonality/Holiday Effect
Challenges for Ecommerce App
• How much Long weekend had affected their conversion and other KPIs’.
• How much decrease/increase in App install and conversion will happen due to seasonality.
• Absence/Non-relevant previous year data for Mobile App makes difficult for data science to predict.
Solution
• Yogurt helped them to predict the results with and without Long Weekend effect.
• Seasonality Effect identified @ X% and lasted Y days.
• Prediction was done with single variable only, hence easy and fast.
Achievements
• Achieved ~85% accuracy in predicting their KPIs in less than 30 Mins.• Holiday & Seasonality Effect was identified and passed to the Strategy Team.
Case Study 3: Ecommerce App AnalyticsApp Upgrade Impact
Challenges for Ecommerce App
• ACME Inc wants to perform the major upgrade of their App.
• How quickly the impact could be calculated before its too late.
• How much installs and conversions are going to be affected.
Solution
• Yogurt helped them to predict the results with and without Long Weekend effect.
• Within a week Upgrade increase impact identified @ Z%.
• Prediction was done with single variable only, hence easy and fast.
Achievements• Achieved ~88% accuracy in predicting their KPIs in less than 20 Mins.• App Upgrade Impact was identified for the coming week and feedback was passed to
tech team.
