Increase the profitability of new customer acquisitions by using behavioral segmentation in marketing campaigns

As the credit card business becomes more and more competitive, the primary challenge for businesses today is acquiring customers

1. Who are attracted to the card’s value proposition so that they stay loyal to the product
2. Meet risk criteria so that they don’t add to NCL
3. Consistently utilize their credit limits so that they generate revenue consistently

Intelligent, data-driven targeting is essential for identifying these customers, as mass campaigns are ineffective. Given the high volume of data, decisions must be based on data science and advanced analytics to be optimal.

Failing to do so results in acquiring customers who may not be profitable in the long run. These customers might exploit short-term acquisition offers without developing brand loyalty, leading to increased costs without optimal returns.

This solution predicts customer behavior post-acquisition, providing insights for a 2-5 year span. It helps optimize acquisition costs by targeting the right segments, improving customer satisfaction, and ultimately enhancing long-term revenue

Utilize the bureau data of prospects to identify similar customers within your existing customer base. Segment your customer base into targeted personas based on their revenue potential and engagement with the bank, and leverage this information to predict the behavior of potential customers.

The categories can be broadly defined as follows:

1. Customer Profitability: 2-year/5-year revenue
2. Future Potential: Off-us balance/spend potential, debt-to-income ratio
3. Customer Purchase Behaviour: On-us total spend, category-level spend, ticket size, frequency
4. Customer Contact Behaviour: Digital propensity, contact frequency, customer sentiment

This segmentation approach emphasizes maximum separation between categories and high similarity within each category. This strategy facilitates targeting customers in clusters while allowing for broad-level customization. Depending on the requirements, the categories can be refined to enhance customization.

This solution is designed for business leaders or teams who want to

1. Move from mass targeting to decision engine-based targeting
2. Want to refine their existing targeting framework by introducing enhanced customization

Increase conversion of offers: Customers are more inclined to respond to offers that feel personalized rather than generic. Detailed knowledge of customer preferences results in the creation of the right product features.

Indirectly impact customer loyalty: Tailored offers and services foster customer loyalty, giving the company a competitive edge over others.

Enhance business planning using data strategy: Utilize data intelligence to make informed decisions for customer acquisition, thereby reducing future losses.

Revenue: Boost short-term revenue with higher conversion rates for offers and increase long-term revenue by attracting customers who align well with the product.

Cost: Lower acquisition costs for campaigns through targeted efforts and reduce expected future losses based on P&L projections.

This solution can be constructed using both fundamental and sophisticated data sets.

Essential data requirements are:

1. Customer-level Profit and Loss (PnL) data
2. Bureau data
3. Monthly aggregated transaction data

Customer segmentation can be achieved using various objective and non-objective algorithms. An objective approach segments customers based on a specific metric, whereas a non-objective approach uses a combination of metrics.

The choice of approach depends on the customer’s requirements. For identifying populations based on multiple metrics, a non-objective segmentation is appropriate. If a particular metric is of greater importance, an objective approach should be taken.

The approach will be tailored according to business needs and data complexity.

The most effective algorithms used are:

1. K-means clustering
2. Decision trees
3. Random forests

1. On-prem ( on customer systems or on DeepQ-AI Environment)
2. On hosted cloud space ( Customer or DeepQ-AI Environment)
   ( deployment is subject to data availability in the same environment, or feasibility of seamless data transfers within secured environments)

1. The final outputs may consist of lead lists with customer segment tagging and definitions.
2. Batch files, including Excel files or flat files.
3. Outputs can be delivered via API requests. By sending an API request with inputs, you can receive a response. This can be integrated into an existing application or a separate web application can be developed.