Marketing Analytics

How to marketer do Analysis

Transcript

1. DATA SCIENCE MARKETING ANALYTICS 1- Data Analysis 2- Model Building 3- Performance Estimation Data Analysis Model Building Performance Analytics IN
2. DATA ANALYSIS
3. MODEL BUILDING ONCE THE DATA HAD BEEN ANALYZED, DEVELOP THE PREDICTIVE MODEL TO IDENTIFY CUSTOMER WHO ARE LIKELY TO PURCHASE OR PERFORM AN EVENT. TESTING OF VARIOUS MODELING TYPES AND APPLICATION PLATFORM TO HELP CLIENT TO DRIVE BETTER RESULTS BY TRANSITIONING THEM FROM A HEURISTIC MARKETING CAMPAIGN MODEL TO DATA DRIVEN STATISTICAL MODEL. THROUGH THESE EXPERIMENTS, THE DATA DISCOVER THE BEST MODELING ALTERNATIVE FOR USE CASE. ENALABING THE CLIENTS TO ADOPT MORE CUTTING EDGE APPLICATIONS INSTEAD OF TRADITIONAL MODELS.
4. PERFORMANCE ANALYSIS ONCE THE MODEL WAS IN PLACE, GAUGED THE CAMPAIGN EFFECTIVENESS (SPECIFICALLY FOR DIRECT MAILS) USING TEST – CONTROL METHODOLOGY. TO FACILITATE EASY CONSUMPTION OF THE DATA BY VARIOUS STAKEHOLDERS, DASHBOARDS WERE DESIGNED AND USED TO CONTINUOUSLY MONITOR THE PERFORMANCE OF MODELS. TARGET AUDIENCE AND CONTENT RECOMMENDATIONS WERE CREATED BY COMPILING DATA FROM SEVERAL MODELS AND SHARED ON A REGULAR CADENCE WHICH WAS LATER USED TO MEASURE THE EFFECTIVENESS OF THE CAMPAIGNS.
5. THE IMPACT 1- HELPED THE CLIENT DELIVER THE RIGHT CONTENT TO THE RIGHT AUDIENCE FOR MULTIPLE SUBSCRIPTION MAILS AND CAMPAIGNS BEING SENT OUT REGULARLY TO CONSUMERS AND PROSPECTS. 2- GLOBAL SCORING TABLES SET-UP WITH REGULAR UPDATE CADENCE FOR USAGE IN VARIOUS AD-HOC CAMPAIGNS BY EMAIL & SOCIAL MEDIA TEAMS 3- REVAMPING EXISTING MODELS USING ADVANCED MODELLING TECHNIQUES TO IMPROVE AUDIENCE SELECTION FOR CAMPAIGNS
6. ADVANTAGE