Credit Card Cross Sell
Company Size
1,000+
Region
- America
Country
- United States
Product
- APT’s Test & Learn software
Tech Stack
- Proprietary control-matching algorithms
Implementation Scale
- Departmental Deployment
Impact Metrics
- Cost Savings
- Customer Satisfaction
- Revenue Growth
Technology Category
- Analytics & Modeling - Predictive Analytics
- Analytics & Modeling - Real Time Analytics
Applicable Industries
- Finance & Insurance
Applicable Functions
- Business Operation
- Sales & Marketing
Use Cases
- Demand Planning & Forecasting
- Predictive Replenishment
Services
- Data Science Services
- System Integration
About The Customer
The customer is a prominent U.S. regional bank and major credit card issuer with over 500 branches. As a significant player in the financial sector, the bank has a substantial customer base and a wide array of financial products, including credit cards. The bank frequently engages in direct mail campaigns to attract new customers and promote its credit card offerings. Despite its extensive reach and resources, the bank faced challenges in optimizing its marketing strategies and understanding the true impact of its campaigns. The bank's management team was keen on leveraging data-driven insights to enhance their marketing efforts and improve customer acquisition rates. With a focus on maximizing ROI and targeting the right customer segments, the bank sought a solution that could provide actionable insights and drive better decision-making in their marketing campaigns.
The Challenge
As a major credit card issuer, the bank frequently launched direct mail campaigns to spur customer acquisition. It targeted its mailings to customers that management believed would be more receptive to various card offers, but card acquisition had plateaued in recent months. Further, the bank had a difficult time isolating the true impact of each campaign amidst numerous outside factors. Management was struggling to confidently answer the following questions: How much incremental revenue do our campaigns drive per customer? Which features of each offer are most successful? Which customers will respond best to which offers? The direct mail campaigns were a significant investment for the bank, and without a full understanding of each campaign’s true ROI, there was uncertainty about how to best allocate resources. The bank had three key cards it wanted to promote: Card A: No annual fee, 1.25 Miles; Card B: Annual fee, Double Miles; Card C: 0% APR for 12 months. Management hypothesized that each card might appeal to Money Market customers in different ways, and the bank wanted to launch direct mail campaigns for each. Before moving forward, the bank needed to understand if the offers would generate positive ROI, which Money Market customers to target each offer to, and how to tailor each offer to maximize profitability.
The Solution
The bank selected a test group of customers with Money Market accounts to send the campaigns to. APT’s Test & Learn software automatically flagged an issue with the strategy: test customers had significantly higher household incomes than the withheld control group. Using proprietary control-matching algorithms, Test & Learn eliminated this bias by selecting control customers that closely matched the test group. Overall, the software showed that the test group that received the direct mail campaigns drove a significant revenue lift per customer, resulting in positive ROI. The campaign promoting Card A generated the highest revenue per customer, while Card C’s campaign only drove a slight increase in revenue. Additionally, an accompanying mobile message issued in support of the direct mailer proved to be a differentiating tactic. Test & Learn automatically identified the strongest indicators of performance, and segmented customers accordingly. The software found that middle income customers who had experienced lower deposit growth in the past year were most receptive to the campaigns. Top performance drivers were combined to create a model that predicted which customers would respond most profitably to the direct mailers, allowing the bank to maximize revenue generation from the campaign.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Real-time In-vehicle Monitoring
The telematic solution provides this vital premium-adjusting information. The solution also helps detect and deter vehicle or trailer theft – as soon as a theft occurs, monitoring personnel can alert the appropriate authorities, providing an exact location.“With more and more insurance companies and major fleet operators interested in monitoring driver behaviour on the grounds of road safety, efficient logistics and costs, the market for this type of device and associated e-business services is growing rapidly within Italy and the rest of Europe,” says Franco.“The insurance companies are especially interested in the pay-per-use and pay-as-you-drive applications while other organisations employ the technology for road user charging.”“One million vehicles in Italy currently carry such devices and forecasts indicate that the European market will increase tenfold by 2014.However, for our technology to work effectively, we needed a highly reliable wireless data network to carry the information between the vehicles and monitoring stations.”
Case Study
Safety First with Folksam
The competitiveness of the car insurance market is driving UBI growth as a means for insurance companies to differentiate their customer propositions as well as improving operational efficiency. An insurance model - usage-based insurance ("UBI") - offers possibilities for insurers to do more efficient market segmentation and accurate risk assessment and pricing. Insurers require an IoT solution for the purpose of data collection and performance analysis
Case Study
Smooth Transition to Energy Savings
The building was equipped with four end-of-life Trane water cooled chillers, located in the basement. Johnson Controls installed four York water cooled centrifugal chillers with unit mounted variable speed drives and a total installed cooling capacity of 6,8 MW. Each chiller has a capacity of 1,6 MW (variable to 1.9MW depending upon condenser water temperatures). Johnson Controls needed to design the equipment in such way that it would fit the dimensional constraints of the existing plant area and plant access route but also the specific performance requirements of the client. Morgan Stanley required the chiller plant to match the building load profile, turn down to match the low load requirement when needed and provide an improvement in the Energy Efficiency Ratio across the entire operating range. Other requirements were a reduction in the chiller noise level to improve the working environment in the plant room and a wide operating envelope coupled with intelligent controls to allow possible variation in both flow rate and temperature. The latter was needed to leverage increased capacity from a reduced number of machines during the different installation phases and allow future enhancement to a variable primary flow system.
Case Study
Automated Pallet Labeling Solution for SPR Packaging
SPR Packaging, an American supplier of packaging solutions, was in search of an automated pallet labeling solution that could meet their immediate and future needs. They aimed to equip their lines with automatic printer applicators, but also required a solution that could interface with their accounting software. The challenge was to find a system that could read a 2D code on pallets at the stretch wrapper, track the pallet, and flag any pallets with unread barcodes for inspection. The pallets could be single or double stacked, and the system needed to be able to differentiate between the two. SPR Packaging sought a system integrator with extensive experience in advanced printing and tracking solutions to provide a complete traceability system.
Case Study
Transforming insurance pricing while improving driver safety
The Internet of Things (IoT) is revolutionizing the car insurance industry on a scale not seen since the introduction of the car itself. For decades, premiums have been calculated using proxy-based risk assessment models and historical data. Today, a growing number of innovative companies such as Quebec-based Industrielle Alliance are moving to usage-based insurance (UBI) models, driven by the advancement of telematics technologies and smart tracking devices.
Case Study
MasterCard Improves Customer Experience Through Self-Service Data Prep
Derek Madison, Leader of Business Financial Support at MasterCard, oversees the validation of transactions and cash between two systems, whether they’re MasterCard owned or not. He was charged with identifying new ways to increase efficiency and improve MasterCard processes. At the outset, the 13-person team had to manually reconcile system interfaces using reports that resided on the company’s mainframe. Their first order of business each day was to print 20-30 individual, multi-page reports. Using a ruler to keep their place within each report, they would then hand-key the relevant data, line by line, into Excel for validation. “We’re talking about a task that took 40-80 hours each week,” recalls Madison, “As a growing company with rapidly expanding product offerings, we had to find a better way to prepare this data for analysis.”