Predictive Conversion Modeling - Lifting Web Analytics to the next level
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Annalect presentation at Superweek 2017: Predictive Conversion Modeling - Lifting Web Analytics to the next level. Presented by Petri Mertanen, Director of Digital Analytics and Ron Luhtanen, Data Science Analyst. #SPWK
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Predictive Conversion Modeling - Lifting Web Analytics to the next level
- 1. Predictive Conversion Modeling Lifting Web Analytics to the next level Superweek, Hungary | February 1, 2017 @mertanen & @ronluhtanen
- 2. Regional “Centres of Excellence” alongside agency embedded experts 1200 employees globally NA Offices MENA Offices EMEA Offices APAC Offices It’s our people that make the difference Our approach to data-driven marketing – a global focus
- 3. Using machine learning for explaining and predicting user behavior from web analytics data. 3
- 4. Universal Analytics made the platform more open.
- 5. Tag Management was a game changer! 5
- 6. Insights based on aggregated data. 6
- 7. Advanced methods limited to different regression models. 7
- 8. Traditional Web Analytics is dying...
- 9. • Time consuming • Not cost effective • Human brains are not able to work with large amount of complex data • Outputs depends too much on the analyst • Insights are too simple • Predicting in a very rough level What are the problems with traditional Web Analytics? 9
- 10. Web Analytics Data Science
- 11. Easy & fast implementation for the Modeling.
- 12. • Tag website features and elements like never before, more is more in this case! • Collect session ID • Save browser ID • Think about User ID • Adform cookie ID or similar Setup for the Modeling 12
- 13. This means we see every interaction that each user has during each visit. The granularity of the data greatly increases the possible model selection as well as the accuracy of the models.
- 15. • The bulk of the modeling is done by Extreme Gradient Boosting • The method is a decision tree based algorithm • Gradient Boosting can handle regresssion as well as multiclass classification • We have great flexibility with selecting the KPIs that we want to model and predict, without having to change the core modeling algorithm About the modeling 15 odor=none Cover: 1628,25 Gain: 4000,53 spore-print-color = green Cover: 703,75 Gain: 198,174 < -9,53674e-07 < -9,53674e-07 < -9,53674e-07 stalk-root=club Cover: 924,5 Gain: 1158,21 Leaf Cover: 13,25 Gain: 1,85965 Leaf Cover: 690,5 Gain: -1,94071 Leaf Cover: 112,5 Gain: -1,70044 Leaf Cover: 812 Gain: 1,7128 Leaf Cover: 309,453 Gain: -0,96853 < -9,53674e-07 < -9,53674e-07 Leaf Cover: 458,937 Gain: 0,784718 Leaf Cover: 20,4624 Gain: -6,23624 odor=none Cover: 768,39 Gain: 569,725 stalk-root=rooted Cover: 788,852 Gain: 832,545
- 16. • Incredibly accurate, hard to overfit and very fast • Ability to extract complicated non-linear relationships from very varied data • The Algorithm uses only the relevant data from all the data that is available to it • Huge improvement over some other regression models that break if they are fed with irrelevant data About the modeling 16 https://github.com/dmlc/xgboost
- 17. Outputs from the Predictive Conversion Modeling 17
- 18. Outputs from Predictive Conversion Modeling • Generally the output of the analysis is a predictive model that gives a predictions for the measurement we are modeling against. • The predictions can be used by themselves or further analysis can be done on the model to further explain the dependencies in the user interactions. • The model will be available for digital marketers and analysts. • Following are 4 example uses for the modeling. 18
- 19. Data-to-output in Predictive Conversion Model application Input Output Enhanced Web Analytics data Profiling by clustering customers based on on-site behavior Retargeting based on predicted responses Twinning to expand reach to the most prospective customer profiles Conversion optimization 19 Machine learning based predictive modelling
- 20. The predictions can be used in more effective retargeting. Instead of bombarding all the past site visitors with advertisements we can target the advertisements based on the specific interactions as well as the likelihood of having converted. For instance we can create a rule that targets people who have over 20% probability of purchase and have visited the promotion page of a specific product. Output Application 1: Enhanced Retargeting IF THEN Probability of purchase>20% Visited product page Target advertisement to specific people Recipe Trigger Action 20
- 21. The modeling process can also be used in acquiring valuable information on the behavioral differences of the users. Uncovering certain dependencies in their interactions allows the marketers to design (and later automate) their marketing messages differently and more effectively to each of their visitor groups (segments). Output Application 2: Clustering and Profiling Person A Person B WEB BEHAVIOR On-site behavior Off-site behavior Likes gambling sites Buys clothes online Has visited booking page twice Has visited promotion page three times Visits homepage regularly Has read product description page for three minutes Reads gardening blogs Watches regularly movie trailers online 21
- 22. The machine learning models can help in conversion optimization. We are not restricted with just A/B testing, but instead we can create rules that change the site in order to maximize the likelihood of purchase or conversion of each and every user. By leveraging the trained model we can direct the user towards the interactions that are most effective in increasing the likelihood of conversion. Output Application 3: Conversion Optimization WEBSITE CONTENT RULES Activated rule Not actived rule 22
- 23. Once we have identified the most beneficial behavioral patterns, we can use the cookie data of the most prospective visitors in order to build larger target groups out of similar web users. The groups can then be used in programmatic buying of advertisements. Output Application 4: Twinning BUYING RULES for different target groups 23
- 25. How to target marketing so that it maximizes users likelihood to convert? 25
- 26. • Finland’s largest shipyard – builds and operates cruise ships • Operates in a very competitive online environment • High maturity with online optimization and data-driven marketing • Large portion of sales through online Case Tallink Silja 26 9 mil. Passengers * Annually 945 mil. Turnover *
- 27. • Very accurate predictions for non- converting visitors • Possibility to adjust prediction treshold for different actions The model 27 ROC CurveAccuracy 98% • Sensitivity 99% • Specificity 75%
- 28. • Previously possible only to create custom segments • Now clustering using unsupervised machine learning over 240 dimensions • Four distinct behavioral groups • Heavy users • Intermediate users • Reactivated • Just visiting Clustering using on-site behavioral data 28 Mean Conversion % - Indexed 1 9,5 8,4 8,8
- 29. Exploring differences time spent on site 29 Mean Duration from past Session* - IndexedMean Session Duration - Indexed 10,5 10 27 *Calculated as a cumulative sum with 50% daily decay 1 21 1,6 2,4
- 30. Not limited to averages 30 3 1 4,3 Session Duration – Just Visting ConvertedNo convertion 1,5 0,28 ConvertedNo convertion Session Duration – Heavy User 240 280 213 315 250 216
- 31. Proportional differences depending on source 31 Proportion of visitors from DisplayProportion of visitors from Direct *Calculated as a cumulative sum with 50% daily decay
- 33. • Partial dependencies • Change inputs • Observe outputs • Automate • Can be applied to advertisement messages, channels, or on-site elements • Possible to use smart optimization algorithms to identify actions that maximize conversion probability Gaining insight from a complex model 33 https://github.com/fmfn/BayesianOptimization
- 34. Discount campaign’s effect on mean propability for conversion 34 Heavy Users Intermediate Users Reactivated Just visiting
- 35. Future development with the Predictive Conversion Modeling
- 36. Machine learning leveraged analytics and real time predictive modelling Input Output Enchanced Web Analytics data Basic Web Analytics data Client’s Customer Data Semantic data ID’s from ad serving platforms Organic clustering based on off- and on-site data Immediate onsite adaptations based on off-site data AI driven marketing: test and modify content based on predicted behaviours Retarget to increase conversion percentage 36 Future Development Streams Automated optimization of online advertising spending
- 37. • Spend less time on manual analysis • No more headache with complex data and pressure for outputs • Think more about the business questions • The model will do the counting and give answers with a high confidelity level • You will interpret results for the business and edit the model for more in-depth analysis • You are able to enable analysts with tools previously available only to data scientists • Shift the focus from simple metrics to the actual business objects • Set up automatically optimizing feedback loops in order to continiously increase conversion rates How this is changing our work? 37
- 39. • No more time consuming, labor heavy and expensive manual analysis • Enable analysts with machine learning • Fast to implement and quick to show results • Ask another question • Continiously improve marketing efficiency and ROI • Get real competitive edge with analytics Executive Summary 39 Petri Mertanen Director, Digital Analytics petri.mertanen@annalect.com +358 400 792 616 Ron Luhtanen Analyst, Data Science ron.luhtanen@annalect.com +358 50 431 8166
- 40. Q&A Petri Mertanen Director, Digital Analytics petri.mertanen@annalect.com +358 400 792 616 Annalect Finland is a part of Omnicom Media Group. Ron Luhtanen Analyst, Data Science ron.luhtanen@annalect.com +358 50 431 8166 Annalect Finland www.annalect.fi info.finland@annalect.fi @annalect_fi