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For banks, attracting new customers is one thing, but getting them to complete the sign-up process is another. Even when users show interest, many drop off before taking action, leaving institutions struggling to hit their acquisition targets.
Quin AI has had the pleasure of working with a couple of leading European banks that faced these exact challenges. While their credit card and account offerings were compelling, two key roadblocks stood in their way:
- A gap between interest and conversion – Visitors explored financial products but didn’t always take the next step.
- Low-performing traffic channels – When major acquisition sources didn't convert, lead generation became inefficient.
To turn things around, these banks partnered with Quin AI to identify high-intent users, engage them at the right moment, and guide them seamlessly toward completing their applications.
This resulted in a 192% increase in daily account openings, a 45% application-to-sale rate, and a 52% boost in applications from underperforming traffic channels — all without relying on discounts.
Here’s how Quin AI helped these prestigious banks unlock higher conversions and attract better-quality leads:
The results:
- 192% increase in average daily account openings
- 45% of form submissions convert into credit card sales
- 52% more credit card applications from previously low-converting channels
For banks, attracting new customers is one thing, but getting them to complete the sign-up process is another. Even when users show interest, many drop off before taking action, leaving institutions struggling to hit their acquisition targets.
Quin AI has had the pleasure of working with a couple of leading European banks that faced these exact challenges. While their credit card and account offerings were compelling, two key roadblocks stood in their way:
- A gap between interest and conversion – Visitors explored financial products but didn’t always take the next step.
- Low-performing traffic channels – When major acquisition sources didn't convert, lead generation became inefficient.
To turn things around, these banks partnered with Quin AI to identify high-intent users, engage them at the right moment, and guide them seamlessly toward completing their applications.
This resulted in a 192% increase in daily account openings, a 45% application-to-sale rate, and a 52% boost in applications from underperforming traffic channels — all without relying on discounts.
Here’s how Quin AI helped these prestigious banks unlock higher conversions and attract better-quality leads:
The results:
- 192% increase in average daily account openings
- 45% of form submissions convert into credit card sales
- 52% more credit card applications from previously low-converting channels
Challenge #1: Closing the gap between interest and conversion
A leading European bank needed to drive more membership sign-ups and account openings to hit its customer acquisition targets.
However, accurately identifying its website visitor's financial interests - whether in credit cards, checking accounts, or other products - proved challenging.
Without these insights, directing users to the right application pages (and turning them into qualified leads) was a guessing game the bank wasn't winning.
On top of that, they noticed that while plenty of customers started credit card applications, far fewer followed through. Something was getting in the way between initial interest and a completed application.
To solve this, the bank needed a way to guide customers smoothly through the process, making it easier for them to take the next step.
How Quin AI helped
Quin AI made it easy for the bank to turn potential drop-offs into conversions by spotting high-intent users interested in applying for a credit card but likely to leave before finishing their application.
To keep them engaged, Quin AI's predictive AI leveraged real-time behavioral data to redirect these users to the application page at just the right moment - when they were most likely to complete the process.
The results spoke for themselves. In just one month, daily account sign-ups skyrocketed by 192%, and conversion rates saw a major boost, reaching an impressive 45% application-to-sale rate across multiple credit card products.
This success showcases the power of behavior prediction and hyper-personalization in driving conversions without relying on discounts.
Challenge #2: Increasing conversions from low-performing traffic channels
Another major bank wanted to boost applications for one of its flagship credit cards, but one of its main traffic sources wasn't delivering. Visitors were clicking through but not completing their applications, resulting in both low conversion rates and inconsistent lead quality.
To fix this, the bank needed to:
- Identify the reasons behind the drop-offs.
- Find ways to keep users engaged from the moment they show interest.
- Refine its targeting to attract higher-quality applicants likely to complete the process.
How Quin AI helped
Using Quin AI’s real-time insights, the bank identified visitors from the low-performing traffic source who had shown interest in the credit card and pinpointed the exact moment they were most likely to convert. This allowed them to strategically re-engage users before they dropped off, keeping them on the path to completion.
But, rather than relying on a one-size-fits-all approach, Quin AI stepped in at just the right time - reminding users of the card’s key benefits and seamlessly guiding them back to the application form.
The result? A 192% increase in average daily account openings and a 52% increase in credit card applications from what was once an underperforming traffic source. Despite the same product information being available site-wide, Quin AI’s precise targeting and well-timed engagement made all the difference.
Challenge #1: Closing the gap between interest and conversion
A leading European bank needed to drive more membership sign-ups and account openings to hit its customer acquisition targets.
However, accurately identifying its website visitor's financial interests - whether in credit cards, checking accounts, or other products - proved challenging.
Without these insights, directing users to the right application pages (and turning them into qualified leads) was a guessing game the bank wasn't winning.
On top of that, they noticed that while plenty of customers started credit card applications, far fewer followed through. Something was getting in the way between initial interest and a completed application.
To solve this, the bank needed a way to guide customers smoothly through the process, making it easier for them to take the next step.
How Quin AI helped
Quin AI made it easy for the bank to turn potential drop-offs into conversions by spotting high-intent users interested in applying for a credit card but likely to leave before finishing their application.
To keep them engaged, Quin AI's predictive AI leveraged real-time behavioral data to redirect these users to the application page at just the right moment - when they were most likely to complete the process.
The results spoke for themselves. In just one month, daily account sign-ups skyrocketed by 192%, and conversion rates saw a major boost, reaching an impressive 45% application-to-sale rate across multiple credit card products.
This success showcases the power of behavior prediction and hyper-personalization in driving conversions without relying on discounts.
Challenge #2: Increasing conversions from low-performing traffic channels
Another major bank wanted to boost applications for one of its flagship credit cards, but one of its main traffic sources wasn't delivering. Visitors were clicking through but not completing their applications, resulting in both low conversion rates and inconsistent lead quality.
To fix this, the bank needed to:
- Identify the reasons behind the drop-offs.
- Find ways to keep users engaged from the moment they show interest.
- Refine its targeting to attract higher-quality applicants likely to complete the process.
How Quin AI helped
Using Quin AI’s real-time insights, the bank identified visitors from the low-performing traffic source who had shown interest in the credit card and pinpointed the exact moment they were most likely to convert. This allowed them to strategically re-engage users before they dropped off, keeping them on the path to completion.
But, rather than relying on a one-size-fits-all approach, Quin AI stepped in at just the right time - reminding users of the card’s key benefits and seamlessly guiding them back to the application form.
The result? A 192% increase in average daily account openings and a 52% increase in credit card applications from what was once an underperforming traffic source. Despite the same product information being available site-wide, Quin AI’s precise targeting and well-timed engagement made all the difference.
A smarter approach to customer acquisition
Winning new customers isn’t just about attracting interest - it’s about turning that interest into action and ensuring every interaction moves customers closer to conversion.
This case study highlights how Quin AI’s real-time behavioral insights helped leading European banks do exactly that, transforming their customer acquisition strategy by engaging high-intent users at the right moment to unlock new opportunities for lead generation.
A smarter approach to customer acquisition
Winning new customers isn’t just about attracting interest - it’s about turning that interest into action and ensuring every interaction moves customers closer to conversion.
This case study highlights how Quin AI’s real-time behavioral insights helped leading European banks do exactly that, transforming their customer acquisition strategy by engaging high-intent users at the right moment to unlock new opportunities for lead generation.
