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Data Science Behind Display Ads in Digital Marketing

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Know 'Data Science Behind Display Ads in Digital Marketing'. Gain insights from the webinar led by Kushal Wadhwani, Senior Data Scientist, Vizury.

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Data Science Behind Display Ads in Digital Marketing

  1. 1. Data Science behind Display Ads in Digital Marketing Kushal Wadhwani Senior Data Scientist
  2. 2. We Help Marketers Increase Digital Share of Business $30M FUNDING Singapore, South East Asia Bangalore, India Dubai, UAE Dallas, USA CERTIFICATIONS FOCUS
  3. 3. Clients INDIA & UAE
  4. 4. Use Case: Bring back a prospective user 1) User visits hdfc website , browsed for personal loan 2) Drops off without submitting lead 3) Visits our publisher network 4) Vizury shows add with personalized banners and quotes 5) User Clicks banner 6) Reaches back to hdfc website
  5. 5. Some of the Channels Powered by Vizury Programmatic Mobile Push Browser Push / InstagFacebookram
  6. 6. Programmatic flow
  7. 7. Optimization problem behind Programmatic Pays for impression Maximize clicks Publishers Clients
  8. 8. Parameters to Optimize 1. What to bid • Depends upon probability of click of that user • Depends upon probability of click of that ad slot bidValue ∝ P( click / ad slot, user) ctr (click through rate) = 100* P( click / ad slot, user) 2. What to Show • Products visited by the user • Products and message suggested by the client
  9. 9. Data : Collection and processing
  10. 10. Data Collection Bids DB Impressions DB Clicks DB User activity DB
  11. 11. User variables and Ad slot variables User variables 1) Time spent on website 2) Products visited 3) Number of impression’s shown 4) Number of clicks Ad slot variables 1) Size of banner 2) Url of the ad slot
  12. 12. Problem formulation • Classification problem • 50 – 100 variables • Both Numerical and categorical variables • Massive amount of data to train Id Categorical variable 1 Categorical variable 2 Numerical variable 1 Numerical variable 2 - - - - Click flag 1 xyz abc 1 0 0 2 - - - - 1 3 - - - - 0 xyz abc ? ? ? Ad slot variables User level variables Historical data New bid request
  13. 13. ML Algorithms for classification
  14. 14. Logistic Regression Pros: • Handles all linear interactions between variables • There are established scalable algorithms for training • Handles High cardinality categorical variables Cons: • Assumes that variables are linearly related to the log odds ratio • Does not handles non linear interactions well ln[p/(1-p)] =  + WTX • p is the probability that the event Y occurs, p(Y=1) • p/(1-p) is the "odds ratio" • ln[p/(1-p)] is the log odds ratio, or "logit" p = 1/[1 + exp(- - WTX)]
  15. 15. Decision tree based Models Pros: • Handles non liner correlation of input variables with output variable • Handles non linear interactions • Models are intuitive, easy to understand and explain Cons: • Challenges in handling high cardinality categorical variables Random Forrest XGBoost
  16. 16. Neural Networks Pros: • Handles non liner correlation of input variables with output variable • Handles non linear interactions of variables • Handles High cardinality categorical variables • Works well for large data sets Cons: • Models are not readable
  17. 17. Variable Insights and triage 1. Visualize variables • Plot distributions • Variable Vs ctr - visually try to see the nature of correlation • Cardinality of categorical variables 2. How to preprocess variable 3. Evaluate variable against ML techniques
  18. 18. Variable Insights : Numerical variable’s Skewed Distribution Non linear correlation var1var2 Distribution Correlation
  19. 19. Handling Skew and non linearity Non Linear correlation Skewed Distribution Logistic regression N N Decision tree based models Y Y Neural networks Y Y • In general it is better to preprocess variables with skew • Log transformation newvalue = log (oldvalue) • Bucketization
  20. 20. Handling Skew and non linearity : Log transformationBeforeAfter Distribution Correlation
  21. 21. Handling Skew and non linearity : Bucketization Bucketized var1 Distribution within buckets
  22. 22. Variable Insights : Interaction of variables Non linear interaction Logistic regression N Decision tree based models Y Neural networks Y var1 vs var2 with size of circle representing ctr
  23. 23. Variable Insights : Categorical variables Cardinality 104 Cardinality 10
  24. 24. Categorical variables Neural network and logistic regression doesn’t handle categorical variables out of the box, variable have to be converted into numerical variables 1. One hot encoding – creates one new variable for each categorical value 2. Replace categorical value with its class weigh in our case ctr. Interactions with other variables cannot be captured High cardinality categorical variables Interaction between categorical variables Logistic regression Y N Decision tree based models N Y Neural networks Y Y
  25. 25. Evaluation Metrics AUC (Area under curve) : 2 D plot of False positive rate Vs True positive rate obtained by changing threshold • Random probability will give auc of 0.5 • More the AUC better is the classification • Quantifies how well model has ranked test data but doesn’t consider magnitude of response Log Loss
  26. 26. Q & A My Coordinates LinkedIn : https://www.linkedin.com/in/kushal-wadhwani-02109a1a/ Email : kushal.wadhwani@vizury.com To know more about Vizury visit : https://www.vizury.com/

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