Causal Analysis of Marketing Campaign
Balaji Muthukrishnan (~balaji49) |
A leading apparel chain in India needed to find the effectiveness of their new in-store campaign ran across different stores during the year 2018. The sales and campaign data for Tamilnadu circle was available to analyze the effectiveness of the campaign, causal impact analysis was used to quantify it.
The tool of choice was either r lang or python, python was chosen as we had already deployed our previous data science projects in the form of python API.
The total increase in sale due to the campaign was to be identified (i.e) cause and effect relationship to be quantified. The causal approach was choosed, various python packages like dowhy, pycausalimpact, causalinference and many more, were analyzed for the problem fitment. Endogenous and exogenous factors affecting sales like promotions and calendar data are identified and collected during the data preparation stage using python package like pandas, scikit learn etc.., last 3 years sales data was available. Based on the availability of the required data, pycausallimpact was finalized.
Basic outline of the talk:
- Apparel Business Understanding [4 - 5 minutes]
- Campaign Analysis - problem definition [4 - 5 minutes]
- How does Causal Impact work? [5 - 7 minutes]
- How did we identify and quantify the increase in sales? [10-12 minutes]
- Q/A - [2 minutes]
Who is this talk for?
- Data Scientist and Machine Learning engineers approaching retail business problems day in and day out.
- Anyone who is curious to know about causal problems and how to solve them.
- Newbies to Data Science.
- How data science is applied in business settings?
- Significance of business understanding to identify and solve data science problems.
- Basics of Apparel business(Will be briefly covered in the talk)
- Basics of python
- Basics of Causal Analysis
Balaji is a 'Machine Learning Engineer' at Pathfinder Global FZCO, Chennai. With 8+ years of working experience in building statistical, econometrics, machine learning and time series forecasting models in academia and industry, he has provided Analytical solutions to Retail, Retail Real Estate, Finance and Telecom domains.
Analytical solutions provided:
- Merchandising and Replenishment planning.
- Causal analysis of marketing campaign.
- Customer 360 - Customer propensity, Customer lifetime value, Recommendations (cross-sell and up-sell), Customer segmentation and Customer churn (contractual and non-contractual settings).
- Multi-tenant forecasting.
- Face recognition.
- Daypart and Product affinity analytics.
He has also delivered corporate training to clients (citizen data scientists) in the areas of Data Science, Statistics and Machine learning. He is passionate about speaking at data science meetups, educational institutions and writing articles.