Customer Segmentation
And Optimisation
AI Case Study


AI as a Service

Customer Segmentation and Routing AI Case Study

Before AI

Customer Segmentation Using Personas

The steps undertaken when segmenting customers using personas were:

Data collection: As much information as possible was gathered about customers through sales data, social media analytics, customer surveys, and other sources.

Data analysis: Data analysis techniques software was used to identify patterns and correlations in the data.

Persona creation: Detailed profiles for each segment were created to better understand their behaviours and needs.

Testing and Validation: Marketing campaigns were implemented targeting specific segments to test the effectiveness of the segmentation.

Review and Revision: Customer segments change over time, so the segments were reviewed and revised periodically.

With AI

Customer Segmentation Without Personas

AI is used to analyse and unobtrusively measure new customer intent during onboarding.

Under the hood K-Means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) were all tested and evaluated.

Supervised learning methods were also used on the labeled data.

Decision trees, random forests, and neural networks were used to classify customers into predefined segments.

Customer Routing

For the clustering algorithms, metrics like silhouette score, Davies-Bouldin index, etc., proved to be useful.

For the classification models, accuracy, F1-score, etc. were used.

During on boarding now high intent customers are successfully routed to light touch (lower cost) digital customer onboarding channels...

The Outcome
By routing high intent customers to purely digital channels Sales Team productivity was increased by over 30%

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