Our client, the largest bank in Indonesia in terms of assets, loans, and deposits, aimed to boost card spending among its customers by leveraging data science to create personalized marketing campaigns that deliver highly relevant and targeted messages.
Overview
Approach
Spend Stimulation Campaigns:
- We began by segmenting active customers (those who had made at least one transaction in the previous 90 days) based on their frequency and monetary behavior scores.
- These segments were further refined using clustering algorithms, which analyzed transactional and behavioral data.
- Within each cluster, we identified categories that constituted at least 5% of the cluster’s average spend.
- For each customer, we calculated the percentage spend deviation from the average cluster spending on low-spend categories.
- The category with the highest spend potential, as indicated by the maximum difference, was selected for the campaign.
Loyalty Campaigns:
- For each active customer, we introduced special offers tailored to the highest spend merchant within the highest spend category.
Outcome
- Loyalty Campaign Results:
- The loyalty campaign witnessed an impressive 11% increase in the response rate for mass campaigns.
- Spend Campaign Results:
- The spend campaign contributed to a 2% increase in revenue, exclusively from spend-related campaigns.
This success story highlights how a data-driven approach to marketing not only increased customer engagement but also had a positive impact on the bank’s revenue.
