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Predictive Modelling, not magic - FOM Jam slides
- 1. Predictive and Data – How much is actually needed? Proprietary
- 3. 3 Proprietary How much data do you need? What is the optimal number of variables to use in a predictive model? What depth of web activity information do I need to include in my models? Should I include all transactional data or can I look at individual channels? I have 10 years’ worth of transactional data in a perfectly organised database. Do I need to have it all available to my models? I launched a new website. Does this mean my tag information is no longer valid in my predictive models?
- 5. 5 Proprietary How much data do you need? What is the optimal number of variables to use in a predictive model? What depth of web activity information do I need to include in my models? Should I include all transactional data or can I look at individual channels? I have 10 years’ worth of transactional data in a perfectly organised database. Do I need to have it all available to my models? I launched a new website. Does this mean my tag information is no longer valid in my predictive models?
- 6. Consider this example.. Two retailers who sell their goods through a number of channels. Their core customers are from the same demographic and they have a similar price points in their product ranges.
- 7. 7 How many peaks do you have that appear cyclically (for example consecutive Christmases)? How often do your customers purchase your core product offering? Have there been any significant changes in your business strategy, product offering or wider economic climate which need to be tracked over time? Retention Period The length of time that your data looks at depends completely on your customers and business. Some key aspects to keep in mind are repurchase windows, annual peaks and significant events Proprietary
- 9. Predictive modelling Customer ID & Cross-device tracking Orchestration Reporting & Analytics DataIntegration Personal & Demographic Onsite Behavioural Data Engagement Data Transactional Data Mobile & Device Data Data APP Data Lifestyle Data Email Direct Mail Paid Social SMS AdWords Web Push Store Multi-channel & Data Segmentation 9
- 10. 10 Proprietary Can you implement the actions from your models? How often do the models get refreshed? Does the model account for current behavioural trends? How do I use the scores to segment my customers? How do I include artificial intelligence in my day to day campaign activity? Can I set up an automated campaign based on predicted behaviour? What role does machine learning play in helping me identify my customers along their lifecycle?
- 11. Artificial Intelligence AI involves machines that can perform tasks that are characteristic of human intelligence but on a greater scale Machine Learning A process for gathering information and analysing data to provide learnings for AI to be successful. Predictive Analytics A specific set of algorithms that provides the likelihood that something will happen in the future, eg likelihood to make a purchase. 11
- 13. 13 Proprietary How do you measure impact?
- 14. 14 Is there statistical validation around how good the model is? Do you have visibility of score distributions and why someone has high or low likelihood to complete an action? Is there significance testing associated with reporting on control cells in campaigns? How do you know that the changes in behaviour you are seeing are down to your campaign and not random chance? Proprietary Reporting
- 15. Breadth of Data – Pareto Principle or the 80/20 rule Time Period – Based on the business and length of time the model is covering Do you build your own or go black box? Is it actually doing what you intended it to? How do you know it is working? Are you reporting accurately? In Conclusion….
- 16. Thank you for your time 16