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
Data sources
Limitation of traditional data sources:
- Survey based:
- Limited Data
- Limited contextual understanding
- Siloed Data
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
- Most commonly used technique:
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 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
- 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
- Based on
- 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
- Some of the useful data points for collaborative filtering for a platform such as Spotify:
- 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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