Uplift Modelling on Marketing Promotion Campaign

In this project, I developed an Uplift Model, using CausalML and Supervised Learning Classification algorithms, to maximize the impact of marketing promotion campaign by targeting the right customers.

🔍 Objective:

I am using Customer Retention data to prescribe actions to a business for maximizing marketing campaign and reducing campaign cost.

💡 Uplift modeling refers to the set of techniques used to model the incremental impact of an action or treatment on a customer outcome. Therefore, it is both a Causal Inference problem and a Machine Learning one. It is a causal inference problem because one needs to estimate the difference between two outcomes that are mutually exclusive for an individual. Uplift modeling is also a machine learning problem as one needs to train different models and select the one that yields the most reliable uplift prediction according to some performance metrics. This requires sensible cross-validation strategies along with potential feature engineering.

🌐 About Dataset:

^{❗This is a fictional dataset for practicing purpose.}

Marketing Promotion Campaign with a total of 64,000 customers data.

• recency: months since last purchase
• history: $value of the historical purchases
• used_discount: indicates if the customer used a discount before
• used_bogo: indicates if the customer used a buy one get one before
• zip_code: class of the zip code as Suburban/Urban/Rural
• is_referral: indicates if the customer was acquired from referral channel
• channel: channels that the customer using, Phone/Web/Multichannel
• offer: the offers sent to the customers, Discount/But One Get One/No Offer
• conversion: customer conversion (buy or not)

⚖️ Methodology:

Causal Inference Model using CausalML library.

🛠️ Approach:

I intend to calculate Average Treatment Effect (ATE) on different models and learners, along with their feature importances. For experimentation purposes, I will consider conversion as 'Target' feature, and 'offer' as Treatment feature.

🎉 Outcome Interpretation

S-Learner

1. XGBClassifier:

• ATE: 0.14
• From the method= 'auto' output, we can see that history is the most important feature, and zip_code_urban is the least important feature.
• From the method='permutation' output, we can see that history is the most importance feature, and channel_multichannel is the least important feature.
• used_bogo is the most important feature, followed by history, is_referral, and recency.

2. LGBMClassifier:

• ATE: 0.13
• From the method= 'auto' output, we can see that history is the most important feature, and zip_code_urban is the least important feature.
• From the method='permutation' output, we can see that used_bogo is the most importance feature, and zip_code_urban is the least important feature. This is different from the auto method results.
• used_bogo is the most important feature, followed by is_referral, history, and recency. \

T-Learner

1. XGBClassifier:

• ATE: 0.17
• From methods= 'auto' and 'permutation' output, we can see that history is the most importance feature, and channel_multichannel is the least important feature.
• history is the most important feature, followed by used_bogo, recency, and is_referral.

2. LGBMClassifier:

• ATE: 0.14
• From methods= 'auto' and 'permutation', we can see that history is the most important feature, and channel_multichannel is the least important feature.
• used_bogo is the most important feature, followed by history, is_referral, and recency.

🎯 Key Takeaways

Exploring the causal relationship between the 'offer' treatment variable and 'conversion' target variable showed three important relationships which were common throughout most learners:

• For a majority of customers using 'web' channel, we observe a failed conversion (i.e., no buy decision).
• For customers who used a discount and used a buy one get one before, we observe a success on conversion (i.e., buy decision).
• For customers with more passed months since last purchase, there is a success on conversion (i.e., buy decision).

🔗 Supporting files

• 👩‍💻 Python script for clustering models
• 📁 Entire dataset and Data Dictionary