实例探究 > Credit Card Cross Sell

Credit Card Cross Sell

公司规模

1,000+

地区

America

国家

United States

产品

APT’s Test & Learn software

技术栈

Proprietary control-matching algorithms

实施规模

Departmental Deployment

影响指标

Cost Savings
Customer Satisfaction
Revenue Growth

技术

分析与建模 - 预测分析
分析与建模 - 实时分析

适用行业

金融与保险

适用功能

商业运营
销售与市场营销

用例

需求计划与预测
补货预测

服务

数据科学服务
系统集成

关于客户

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.

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挑战

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.

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解决方案

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.

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运营影响

Using the analysis from APT’s Test & Learn solution, management was able to optimize the campaign and discern which customers should be contacted with which offers.
By targeting the highest value customers with appropriate offers, the company was able to generate 80% of the revenue benefit of a broad rollout at less than half the cost.
The software identified that middle income customers who had experienced lower deposit growth in the past year were most receptive to the campaigns.
An accompanying mobile message issued in support of the direct mailer proved to be a differentiating tactic.
The bank was able to create a model that predicted which customers would respond most profitably to the direct mailers, allowing for maximized revenue generation.

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数量效益