Digital Marketing Mix Modelling
This is a classical marketing mix model applied to the digital world. The objective is to find the optimal mix of budget allocations to various digital media viz. paid search, display ads, affiliate marketing, video, and comparison shopping. Unlike classical media, the digital media presents with a challenge of higher cross-interaction between the various participating vehicles and thus masking their true contribution to the final sale. Typically the analytics tools report revenue based on last click attribution. However, the marketers know they cannot use this data to make spending decisions. Moreover, some vehicles are more influential at the top of the funnel and some at the bottom. For example, someone clicking on a banner ad today may come back from a paid search tomorrow, and eventually convert using a coupon from an affiliate. This project takes into account the cross-vehicle interaction and helps arrive at the true contribution of each vehicle.
O mně
I have 18 yoe in ML, Big Data, BI and related data science works. Focused on helping a few niche technology companies in bringing their (mostly disruptive) ideas to life. A design thinker, responsive and collaborative individual with strong background in data science. My clients engage me to architect and develop solutions that have Big Data and/or Machine Learning as differentiating components. - Predictive Analytics & ML - Big Data Backends - Streaming Data Pipelines - Good Old ETL & BI Machine Learning: Regression, Association (apriori), Classification (decision trees, random forest, logit), Clustering (k-means) Python, Scikit-learn Dataiku, RapidMiner, Azure ML Studio, SageMaker Data Visualisation: Power BI, Tableau, Qlik, Klipfolio, Kibana DW and ETL: Snowflake, BigQuery, Athena, AirFlow, SSIS Stream Processing: Spark, Flume, Kafka, Kafka Streams, Kinesis Elasticsearch (ELK) I believe in simple and future-proof design. Putting trust and satisfaction before money.
