2021-10-21 –, Data
Live broadcast: https://www.youtube.com/watch?v=cVEkJqcmivQ
Marketing attribution is one of the trickiest problems to crack for data scientists working with marketers. To reach potential customers one needs to measure the value of campaigns and channels
that the customers interact with. It's easier said than done. One solution to this problem is through the Markov chain. We will see how we can implement the markov chain for channel attribution.
For any organization, measuring the value of the campaigns and channels that are reaching their potential customers is very important but not very straightforward. Data scientists/ Analysts can help in these scenarios . Data-driven attribution models can eliminate the biases associated with traditional attribution mechanisms, and understand how various messages influence potential customers and the variances by geography and revenue type.
- Introduction to marketing attribution (3 Minutes)
* Overview
* importance
* Challenges - Introduction to Markov chain model (8 Minutes)
* Overview
* States of the Markov Chain Model
* Transition Probabilities - Implementation of markov chain model (12 Minutes)
* Data Preprocessing
* Calculate the Transition Probabilities
* Calculate removal effects
* Interpretation and Prediction
* Assumptions and Limitations of Markov Model - Conclusion and final remarks (2 Minutes)
* Summarizing what we discussed and discuss other sources to increase the knowledge - Q&A (5 Minutes)
I am a Data Scientist at HBOMax EMEA WarnerMedia developing predictive models and influencing and driving the way the marketing team consumes data and insights through highly usable and visual data analysis products. I have spoken at PyCon Sweden 2019 and 2020 on the topics
- Making sense of ML Black Box: Interpreting ML Models Using SHAP.
- Getting grip of handling imbalanced dataset
Apart from that I moderated ML panel discussion during Pycon Sweden 2020.