AI/ML to achieve Customer Segmentation

Modern customer segmentation strategies/techniques

Personalization in Recommendation
Using patterns of activity to define segments
Tying multiple traditional methodologies together for better customer segmentation
Building a customer persona
Online customer segmentation

Limitation of traditional data sources:

Newer data sources:

Internal Data Warehouses
Social Media Platforms
- Social Media Advertising
- Social Media Analytics
External Web Tracking Tools (Pendo and similar tools)
Service/Product Usage (Internal database/warehouses)
Web Scraping (Post Segmentation – eg. Spotify)
Freely available data (Government provided et. al.):
- Demographics
- Profession
- Industry Type
- (Helpful in the starting phase of company to understand the market)

Clustering:

When we talk about segmentation, the very first thing that comes to our minds is AI based clustering
AI models enable clustering based on similar traits/similar characteristics but with softer boundaries as opposed to the rule based models
Clustering forms the first step to personalization
Ecommerce example:
- Most commonly used technique:
  - RFM clustering ( recency/frequency/monetary value based segments created)
- Limitations of RFM:
  - RFM technique is used mostly on transactional data
  - Parameters such as age, gender, region etc. of the customer are not tied to the transactional data
  - Customer based data points like Usage / trend / seasonality / calendar and impulsive buys not taken into consideration
- Churn/Engagement scores as an additional input to the clustering algorithm
  - Identify accounts with high propensity of churning (score them)
  - Behavior (usage) – understand the engagement of a customer wrt platform and provide scores
  - These Propensities/probabilities can be used as features for Segmentation
  - Usage – data source examples GA, Pendo, Internal Data warehouses

ML Techniques used for clustering

Unsupervised learning is generally used for clustering

- - - Define the number of clusters (K)
    - Forms clusters after computing sum of mean of squared distances between centroids and the data points
    - Optimize the position of centroids to get this sum to minimum
    - Computationally less expensive and easier to build

- - Based on
    - Mean – defines the centre
    - Covariance – defines the width
    - Mixing probability – defines how big or small the cluster will be
  - Can form clusters of different shapes, and not just circular like K means
SOM/PCA
- - - Techniques used for dimensionality reduction
    - The output from these techniques can be subsequently used as input to the clustering algorithms

Use Cases for clustering

- Once clusters are created, you can devise growth/marketing strategies around each cluster based on their common characteristics
- Types: Retention /Growth/ Upsell/ Xsell/ Ad-targeting
  - Retention – High value customers
  - Growth – Low level engagement + prospects – push them to the other side of the fence – eg: Ad targeting
  - Upsell/Xsell – Customers (medium engagement but scope for increased revenue) with better recommendation (Recommendation System)

Recommendation Systems – Various techniques/approaches

Collaborative filtering (Cross referencing – pick favourites from one user and recommend to another user belonging to the same cluster)
- Some of the useful data points for collaborative filtering for a platform such as Spotify:
  - Genre
  - Artist
  - Preferred Language
Content Based filtering using NLP for a platform like Quora
- Word embeddings – convert words to vectors
- Similarity between words (distance between the vectors)
- Doc2vec (Finding similarity between documents)
- Topic modelling on the document
- Contextual similarity – similarity of the content falling within a certain topic

Case Study:Spotify B2C case case study

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