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Digital space marketing
- 1. DIGITAL SPACE MARKETINGWITH DATA FACEBOOK DEVELOPER SUMMIT Vidhya Chandrasekaran
- 2. 4 P’S OFMARKETING Goods Services Online content Email SMS Mobile App Social media … Email campaigns Facebook ads Google display ads adverts Discounts Additional promotion Additional features Free premium period Product Promotion Place Price
- 3. INTENT OF MARKETING Acquirenew customers Retain and increase value ( to and from ) customers Bring back Churned customers
- 4. Customer Customer Valuable Customer Churn Prospect Lead Scoring Market Research MarketSegment Cross Sell/Upsell Customer Segmentation Channel Attribution Send Time Optimization CLTV Offer optimization RFM Segmentation Win back Market Research Customer/Market Research Product Research Share of Wallet Competitor Research
- 5. DATA Market Research Customer Acquisition Customer Retention Survey Questionnaire Third party data ThirdParty Data Market Research Data Web traffic Click stream Purchases Social Media Campaign Responses Third Party data
- 6. MARKET RESEARCH –SURVEY FOOD DELIVERY SERVICE Person Free Delivery Time to Deliver Tasty options Discounts 1Not important Important Imporant Very Important 2Very Important Not important Very Important Not at all important 3Important Imporant Imporant Imporant 4Very Important Imporant Imporant Very Important 5Not at all important Very Important Very Important Not at all important Attribute Level Part-worth Utility Attribute Utility Range Attribute Importance Free Delivery Important 30 Very Important 60 40 27% Not Important 20 Not at all Important 10 Time to Deliver Important 90 Very Important 50 90 33% Not Important 20 Not at all Important 0 Tasty Options Important 20 Very Important 10 20 10% Not Important 5 Not at all Important 0 Discounts Important 30 Very Important 20 50 30% Not Important 20 Not at all Important 10 Customer Survey Conjoint Analysis 33% 30% 10% 27% Chart Title Time to Deliver Discount Tasty Options Free Delivery
- 7. MARKET RESEARCH KNOW YOURPRODUCT, CUSTOMER AND MARKET Region Sales Packaging Quality Price Range 1 23478 2 1 2 23489 1 4 3 54525 3 2 4 45778 4 3 5 23456 5 4 Does the price range and the packaging quality influence the sales One Way ANOVA – Statistical Significance of influence Significant different – Concentrate on packaging
- 8. CUSTOMER RETENTION ▪ “Who”,“ What”, “When”, “Where” ▪ Consistent messages and content ▪ Proactive actions – Knowing the intent
- 9. CROSS-SELL/UPSELL Add to Cart Page/Abandon Cartcampaigns Upsell Apriori( Market Basket ) AlgorithmIntentWherePeople who bought also bought Product Detail Page/Post Purchase campaigns Cross Sell Collaborative Filtering Singular Value Decomposition Product Detail Page/Post Purchase campaigns Cross Sell Product-Customer Collaborative Filtering Singular Value Decomposition
- 10. COLLABORATIVE FILTERING Customer 12 3 4 a 0.2 0.4 0.7 0.9 b 0.9 0.3 0.4 0.8 c 0.7 0.2 0.6 0.1 d 0.2 0 1 0.2 Product 1 2 3 4 a 0.2 0.4 0.7 0.9 b 0.9 0.3 0.4 0.8 c 0.7 0.2 0.6 0.1 d 0.2 0 1 0.2 • Matches the Implicit (Clicks and purchases ) and Explicit rating of customers • Marches the Attribute values of products • Identify similarity of customers and Products • Recommend similar products or products rated high by similar customers
- 11. SINGULAR VALUE DECOMPOSITION •Recommendation based on similarity of customers/products based on Implicit ratings like clicks, purchases • Matrix Factorization: Decomposes customer-product implicit/explicit rating matrix into 3 matrices • Identifies latent features of Customers and products • Compute similarity of customers and products and recommend Customer Prod1 Prod2 Prod3 Prod4 a 1 0 0 1 b 0 1 1 1 c 1 0 0 0 d 0 0 0 1 Customer-Product Implicit Rating Customer Matrix Product Matrix
- 12. APRIORI • Identifies frequentproducts in the same order ( basket ) • Association Rule mining • Uncovers association between items • Support – Number of times item A and B are bought together • Confidence – Number of transactions with Item A
- 13. ENGAGEMENT RFM/PURCHASE RFM ▪Not all customers are same ▪ Different Strategy for different segment of customers ▪ Purchase RFM – RFM Score computed from purchases ▪ Engagement RFM – RFM Score computed from clickstreams ▪ Recency – A score that indicates retention ▪ Frequency – A score that indicates Engagement ▪ Monetary – A Score that indicates the Life time value Win back Campaign Welcome seriesDon’t worry now Sneak peak campaigns
- 14. WE KNOW WHATYOU DID LAST SUMMER ☺ • Java tag based cookie tracking for customer acquisition and customer tracking • No. 1 in digital customer acquisition • Surpassed AdWords and search digital spend for acquisition • Personalize customer experience with click history • Consistent content and messages across channels and devices
- 15. CHURN PREDICTION ▪ ChurnAnalytics Customer acquisition is 3 times more costlier than retention Identifies customers with Churn risk with the purchase and behavioral data Past churned customers with similar patterns Survival analysis, Classification to identify potential customers with Churn Risk Marketing automation tool runs Churn save series for customers who enter this life cycle Logistic Regression Survival Analysis
- 16. MODERN MARKETERS TOOLKIT ▪Tag manager ( Google tags ) ▪ Third party DMP ( Bluekai, Axiom , Audience manager ) ▪ Big data processing ( Hive, Hbase, Mongo, Cassandra ) ▪ Cloud Computing ( AWS, GCP, Azure ) ▪ Marketing automation ( Responsys, Clevertap, Mailchimp, Pardot, Marketo ) ▪ Analytics ( Google Analytics, Webanalytics, Omniture )
- 17. LIST VS MARKETINGAUTOMATION List Marketing Marketing Automation Manual selection of customer segments Customers enter the series as they become eligible More dependency on the campaign development team More dependency on Data Engineers and Data science Teams Dependent on the availability of team No dependency on team, runs 24x 7 Potential Compliance risk. No central repository of responses, opts outs for compliance All the responses, opt outs are central No data connectedness of Channels Total data connectedness of behavior and responses across channels
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