Building a Targeting System from Early Adopter Signals

Goal: Given a small set of early adopters, build a scoring model to identify users most likely to adopt a product.

Git Repo: github.com/dinesh-coderepo/targetting-system

🔑 Key Concepts at a Glance

System	How It Works	Example
Recommendation	Learn from a user's own patterns → extend to similar items	"You watched X, try Y"
Targeting	Learn from early adopters' profiles → find similar non-adopters	"Users like your best customers"
Cold Start	Very few signals → traditional collaborative filtering fails	This blog's core challenge

🏗️ System Architecture

flowchart TD Data["📊 User Data
(demographics + behavior)"] --> Features["🔧 Feature Engineering"] Adopters["✅ Early Adopters
(labeled = 1)"] --> Features Features --> Model["🤖 Propensity Model
(XGBoost / LogReg)"] Model --> Scores["📈 Adoption Scores
(0.0 → 1.0)"] Scores --> TopK["🎯 Top-K Targets"] Scores --> Eval["📊 Evaluation
(AUC, Lift, Precision)"] style Adopters fill:#4caf50,color:#fff style TopK fill:#ff9800,color:#fff

🔧 Background & Prerequisites

1. Types of Recommendation Systems

graph TD RS["Recommendation Systems"] --> CF["🤝 Collaborative Filtering"] RS --> CB["📄 Content-Based"] RS --> HY["🔀 Hybrid"] RS --> DL["🧠 Deep Learning"] CF --> UserCF["User-Based CF"] CF --> ItemCF["Item-Based CF"] CF --> MF["Matrix Factorization
(SVD, ALS, NMF)"]

Approach	How It Works	Pros	Cons
User-based CF	Find similar users → recommend their preferences	Intuitive	Doesn't scale; sparse
Item-based CF	Find similar items → recommend to liking users	Stable	Needs interaction data
Matrix Factorization	Decompose user-item matrix into latent factors	Handles sparsity	Cold start problem
Content-Based	Match item features to user preferences	No cold start for items	Limited to feature quality
Hybrid	Combine CF + content-based	Best of both worlds	Complex to implement

💡 Netflix, Spotify, and YouTube all use hybrid approaches combining multiple methods.

2. The Cold Start Problem

This is the core challenge for this blog — very few adopters means extreme data sparsity.

graph LR Problem["❄️ Cold Start
Few adopters, no history"] --> S1["👤 User Cold Start"] Problem --> S2["📦 Item Cold Start"] S1 --> Sol1["🎯 Lookalike Modeling"] S1 --> Sol2["📋 Onboarding Questions"] S1 --> Sol3["📈 Popularity Fallback"] S2 --> Sol4["🏷️ Metadata Matching"] S2 --> Sol5["🆕 Exploration Boost"]

Solutions for targeting with few adopters:

🔹 Feature similarity — Match non-adopters against adopter feature profiles
🔹 Lookalike modeling — Find users who "look like" early adopters (demographics + behavior)
🔹 Propensity scoring — Binary classifier: adopter (1) vs non-adopter (0)

3. Propensity / Targeting Model

The heart of this project — scoring every user by their likelihood to adopt.

flowchart LR Features["🔧 Features"] --> Train["🏋️ Train Model"] Labels["🏷️ Labels
1 = adopter
0 = non-adopter"] --> Train Train --> Predict["🔮 Predict
P(adopt) for all users"] Predict --> Rank["📊 Rank & Select
Top targets"]

Feature Categories:

Category	Example Features
🧑 Demographic	Age, location, job title, industry
📊 Behavioral	Login frequency, feature usage, time spent, page views
🤝 Social	Connections to existing adopters, team adoption rate
⏱️ Temporal	Recency, frequency, monetary (RFM analysis)

Model Choices:

Model	When to Use
Logistic Regression	Baseline — interpretable, fast. Understand odds ratios.
Random Forest / XGBoost	Better accuracy, non-linear relationships, feature importance
Neural Networks	Large-scale datasets with many features

⚠️ Class Imbalance: If only 1% are adopters, naive models just predict "no" 99% of the time. Use SMOTE (oversampling), class weights, focal loss, or undersampling.

4. Evaluation Metrics

Metric	What It Measures	Why It Matters
AUC-ROC	Discrimination ability across thresholds	Best single metric for targeting
Precision@K	Of top K predictions, how many are actual adopters	Directly measures targeting quality
Recall@K	Of all adopters, how many are in top K	Did we find most adopters?
Lift Chart	How much better than random selection	"Top 10% scored 5x more likely than random"
NDCG	Ranking quality with position weighting	Are true adopters ranked highest?

⚠️ Never use accuracy with imbalanced data — it's misleading.

⚠️ Never random split — use time-based splits (train on past, test on future) to prevent data leakage.

5. Tools & Libraries

Library	Purpose
`scikit-learn`	LogisticRegression, RandomForest, metrics, pipelines
`xgboost` / `lightgbm`	Gradient boosting for targeting models
`surprise`	Collaborative filtering (SVD, KNN, NMF)
`lightfm`	Hybrid recommendations (collaborative + content)
`implicit`	Implicit feedback models (ALS, BPR)
`pandas` + `numpy`	Data manipulation & feature engineering
`matplotlib` + `seaborn`	Visualization (lift charts, ROC curves)

✅ TODO — Remaining Work

#	Task	Priority
1	Implement basic collaborative filtering (user-item matrix, cosine similarity)	🔴 High
2	Implement matrix factorization (SVD) with Surprise	🔴 High
3	Build propensity model with logistic regression	🔴 High
4	Feature engineering pipeline (behavioral + demographic)	🔴 High
5	Handle class imbalance (SMOTE, class weights)	🟡 Medium
6	Evaluate with AUC-ROC, lift charts, decile analysis	🟡 Medium
7	Build cold-start fallback strategy	🟡 Medium
8	Compare model approaches in a results table	🟡 Medium
9	Add Mermaid architecture diagram of full targeting pipeline	🟢 Low
10	Connect to Monolith paper learnings	🟢 Low