AI and 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 a 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

K-means
- Define the number of clusters (K)
- Forms clusters after computing sum of a mean of squared distances between centroids and the data points
- Optimize the position of centroids to get this sum to a minimum
- Computationally less expensive and easier to build
DBscan
- Density-based clustering
- No need to define the number of clusters (unlike K-means)
- Handles outliers quite well
- Computationally expensive; Takes a lot of time for execution
- If the density varies across the data, then the clusters may not form properly
- Can’t handle data with higher dimensionality
GMM
- 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 the 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 study