Profitable cross-sell, and up-sell with existing customers using behavioural segmentation

With overflowing offers for customers across all media, its the most difficult time in history to keep a customer engaged with relevant offers. The primary challenge for businesses today is identifying evolving customer needs and hyper customization. Understanding these preferences is crucial for developing effective sales and marketing strategies. Given the high volume of data, decisions must be driven by data science and advanced analytics to be optimal.

Failing to do so results in marketing the wrong products to the wrong customer segments, creating the impression that the company does not understand their preferences. This leads to reduced customer visits and lower sales over time, while costs increase without optimal returns.

This solution helps businesses understand the preferences of various customer segments, including their purchase frequency, spending patterns, and category preferences. It optimizes marketing costs by targeting the right segments, improving customer satisfaction, and ultimately enhancing long-term revenue.

Classify your customer base into targeted personas according to their revenue potential and engagement with the bank.

Categories:

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 methodology emphasizes maximum separation between categories and high similarity within each category. This approach facilitates targeting customers in clusters while enabling customization. Categories can be further refined to increase customization based on specific needs.

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

1. Achieve a competitive advantage through data: When customers receive tailored offers and services, they tend to become loyal, giving the company a competitive edge.
2. Improve business planning with a data strategy: Transition business planning from a broad level to a customer segment level, enhancing productivity and providing more precise forecasts of revenue and costs.

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: Transition business planning from a broad level to a customer segment level, enhancing productivity and providing more precise forecasts of revenue and costs.

Revenue: Boost revenue with higher conversion rates for offers. Customers who respond to offers once tend to respond more frequently. Developing customized, behavior-based offers increases the likelihood of response and long-term revenue.

Cost: Lower costs for campaigns through targeted efforts. Achieving similar or higher conversion rates by focusing on a smaller customer base can result in a more efficient cost structure. The saved costs can be redirected to enhance offers for customers.

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
   For a more comprehensive approach, additional data requirements include:
4. Daily transaction data with detailed category mapping
5. Digital usage metrics from web applications
6. Contact center interaction data
7. Customer sentiment analysis data at the individual level

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. 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.