Reducing customer churn matters. Banks and payment processors can’t just chase new sign-ups—retaining existing users is less expensive and builds stronger businesses. According to a 2024 Bain & Company study, improving retention by just 5% can increase profits by 25% to 95%. So, knowing exactly why people stick around—or leave—can make or break your product’s success.

User research for customer retention isn’t just a buzzword for big design teams. Entry-level data science teams have a huge role to play, especially when your focus is on customer retention and engagement in payments and fintech. Here’s how you can shape your user research, with hands-on tools, workflows, and real data examples—all tuned for the payment-processing world.

1. Start with Churn Analysis: Find Out Who’s Leaving (Customer Churn in Payments)

Definition:
Churn rate is the percentage of users who stop transacting in a set period.

Not every customer who stops using your payment service is “lost” for the same reason. Start by calculating your churn rate using the formula:
Churn Rate = (Number of users lost during period) / (Total users at start of period) * 100

How to do it:

• Pull user IDs and their last transaction dates from your database (e.g., via SQL).
• Filter for those with no activity in the last 90 days (or whatever makes sense for your business model).
• Compare the group that left to those who stayed, segmenting by account type (personal, merchant) or transaction volume.

Pro tip:
Segment users by account type (personal, merchant) or transaction volume. Sometimes the biggest churners are small but noisy accounts.

Example:
A European payment app (2023, internal case study) noticed churn among new merchants spiked after their first three failed chargebacks. Adding targeted support emails after a disputed transaction dropped early churn by 14% in one quarter.

Limitation:
Churn analysis can miss “silent churners” who log in but don’t transact—see section 11.

2. Map the Customer Journey: Where Are Drop-Offs Happening? (Journey Mapping for Payments)

Definition:
Customer journey mapping is a framework for visualizing every step a user takes, from sign-up to support.

It’s easy to assume users leave after a bad experience, but where? Journey mapping visualizes every step—sign-up, funding wallets, making a payment, customer support calls.

How to do it:

• Sketch out major steps with your product team using frameworks like the Service Blueprint or CJM (Customer Journey Map).
• Overlay event data (like page views, button clicks, time spent) from your analytics platform.
• Look for sharp drops in activity at specific steps.

Gotcha:
Be wary of “happy paths”—real users don’t always act logically or in the order you expect.

Tool tip:
Heatmap tools like Hotjar, Mixpanel, or even basic SQL queries for “last action taken” can be eye-opening.

Implementation Example:
If you see a 30% drop-off after the KYC upload step, consider simplifying document requirements or adding tooltips.

3. Run Short, Focused Surveys With Zigpoll, Typeform, or Google Forms (Survey Tools for Payment User Feedback)

FAQ:
What’s the best tool for quick user surveys in payments?
Zigpoll, Typeform, and Google Forms are all strong options. Zigpoll integrates easily into web flows and offers real-time analytics, making it ideal for in-app feedback.

You don’t need a massive survey to get useful feedback. Ask just one or two questions at key moments—after a failed payment, or on an account closure page.

Implementation Steps:

• Embed a Zigpoll or Typeform widget on your payment failure or account closure page.
• Ask targeted questions, e.g., “What’s the main reason for not using us again?” with options like ‘Fees’, ‘Declined transactions’, ‘Too complicated’, etc.
• Use branching logic to follow up based on answers.

Pros:

• High response rates if kept short.
• Easy to segment by user type.
• Zigpoll offers seamless integration with web and mobile apps.

Cons:

• Self-reported data can be biased—people may say what they think you want to hear.

Limitation:
Surveys may underrepresent users who churn silently or are disengaged.

4. Combine Quant and Qual: Match Transaction Data to Feedback (Mixed Methods in Payments Research)

Definition:
Mixed-methods research combines quantitative (transaction logs) and qualitative (user feedback) data.

You’ll get a richer picture if you connect what users do with what they say.

How to do it:

• Link survey responses (from Zigpoll, Typeform, etc.) to user IDs in your analytics database.
• Analyze transaction patterns before and after feedback using cohort analysis.

Example:
One team found users complaining about “slow transfers” had, on average, 2.4x more failed top-ups in the month before quitting (2023, fintech case study).

Limitation:
Privacy rules (e.g., GDPR) mean you have to anonymize data or secure special permissions.

5. Schedule Exit Interviews for Closed Accounts (Qualitative Interviews in Payments)

FAQ:
How do I get users to agree to an exit interview?
Offer a small incentive (e.g., $10 Amazon voucher) and keep the interview under 15 minutes.

Nothing beats hearing straight from the source. Reach out to users within a week of closing their accounts. Even a 10-minute call or chat can yield insights you’d never spot in logs.

How to organize:

• Use CRM tools to flag recently closed accounts.
• Offer an incentive for their time.
• Use a script, but stay open to tangents.

Downside:
You’ll get selection bias—people with the strongest feelings (good or bad) are more likely to reply.

6. A/B Test Retention Features—And Track Who Stays (A/B Testing for Payment Retention)

Definition:
A/B testing is a controlled experiment comparing two variants to measure impact.

Want to know if a new onboarding tip or fee reduction works? A/B testing gives you a controlled way to see what actually impacts retention.

Implementation Steps:

• Randomly assign new users to control and test groups.
• Roll out the new feature (e.g., onboarding tip) to the test group only.
• Track retention and engagement over 30+ days.

Example:
A payment processor tested sending “Welcome, here’s how to avoid fees” emails to half their new users. That group had 12% higher retention after 30 days (2023, internal data).

Caveat:
Statistical significance takes time and volume—if your user base is small, results may not be reliable.

Comparison Table: Survey vs. A/B Test

7. Leverage Support Ticket Analysis: Find Hidden Friction (Support Analytics in Payments)

Every time a user contacts support, that’s a mini user research session. Analyze support tickets (with text analysis or just tags) to spot recurring themes.

How:

• Export ticket summaries from Zendesk, Intercom, or your CRM.
• Use keyword searches for “refund”, “declined”, “can’t login”.

Example:
A 2024 Forrester report found that payment apps resolving issues in under 1 hour retained 19% more users than those taking 24 hours or longer.

Limitation:
Support data may miss users who churn without ever contacting support.

8. Observe Real Users (with Consent) in Usability Sessions (Usability Testing for Payment Apps)

Sometimes, watch and learn. Invite users (especially those who’ve lapsed or complained) to try your app—on Zoom, or in a lab.

How to run:

• Ask them to complete a real task (e.g., send $10 to a friend).
• Record where they get stuck.
• Don’t intervene; just watch.

Tip:
Recruit a mix of new and experienced users. Their struggles can be very different.

Limitation:
Lab settings may not reflect real-world distractions or device issues.

9. Set Up Early Warning Alerts: Watch for Retention Predictors (Predictive Analytics for Churn)

Don’t wait for churn. Find patterns—like users who stop using a card for 14 days, or get multiple transaction declines.

How to implement:

• Identify key predictors in your data (e.g., no logins for 2 weeks, 3+ failed transactions).
• Trigger emails or app notifications (“Is everything okay?”) using automation tools.

One team’s result:
A payments start-up cut churn by 7% by reaching out to merchants after the first week of inactivity (2023, industry report).

10. Tap into App Store and Social Media Reviews (Sentiment Analysis for Payment Apps)

Your public ratings are a goldmine. Scrape or monitor reviews on app stores and social channels like Twitter or Reddit.

How:

• Use sentiment analysis libraries (like TextBlob in Python).
• Bucket complaints and praise by theme (e.g., “fees”, “support”, “app crashes”).

Gotcha:
Noise is high—many reviews unrelated to retention, and some are spam or competitors’ plants.

Limitation:
Public reviews may not represent your core user base.

11. Track “Silent Churn”: Dormant Accounts Still Logging In (Detecting Silent Churn in Payments)

Definition:
Silent churners are users who log in but don’t transact.

Not every at-risk user disappears. Some log in, check balances, but don’t transact. These “silent churners” are easy to miss in standard churn metrics.

Steps:

• Segment users with no transactions but recent logins.
• Use Zigpoll or in-app nudges: “Need help making your next payment?”

Example:
After finding 8,000 “silent churners”, a payment app ran a campaign offering free instant transfers. 17% reactivated within the month (2023, internal data).

12. Analyze Onboarding Drop-Offs (Onboarding Funnel Analysis in Payments)

If users don’t make it past the first steps, retention is impossible. Look closely at the onboarding funnel:

• Where do people abandon sign-up?
• Which KYC (know-your-customer) checks cause frustration?

Tip:
Time how long each step takes. If step 3 (e.g., ID upload) averages 4+ minutes, that’s a red flag.

Implementation:
Use funnel analysis in Mixpanel or Amplitude to visualize drop-offs.

13. Use Cohort Analysis to Spot Patterns Over Time (Cohort Analysis for Payment Retention)

Definition:
Cohort analysis groups users by signup date to track retention patterns.

Group users by the month or week they joined. Track how each cohort behaves—do users from December 2023 stick around longer than those from January 2024?

How-to:

• In SQL or your BI tool, create a cohort column based on signup date.
• Graph retention curves to compare.

What to look for:
If one month’s users churn faster, what changed? New fees, a system bug, or a marketing campaign that attracted the wrong audience?

14. Interview High-Value, Long-Stay Users (Retaining Power Users in Payments)

Don’t just focus on lost users—find out why your best customers stay.

• Identify users with consistent, high-volume transactions.
• Offer them early access to features or beta programs in return for feedback.

What you’ll learn:
Often, it’s not flashy features, but reliability or a single time-saving function that keeps them loyal.

Industry Insight:
In B2B payments, long-term retention is often tied to API reliability and customer support responsiveness (2024, McKinsey Payments Report).

15. Close the Loop: Test Changes and Measure Again (Iterative Retention Improvement in Payments)

User research is cyclical. After making changes (new FAQ, improved onboarding), check if retention metrics move.

How:

• Set your baseline before launching changes.
• Compare churn rate and engagement over 1-3 months after rollout.

Caveat:
External events (holidays, new regulations) can muddy the results. Always annotate big events in your data timeline.

Prioritization Advice: Where Should You Start? (Prioritizing Retention Research in Payments)

FAQ:
What’s the fastest way to start user research for retention?
Begin with short Zigpoll or Typeform surveys and support ticket reviews—they require minimal setup and yield actionable insights quickly.

Entry-level teams often have limited time and tools. Prioritize by:

• Biggest impact: Target where most churn occurs (onboarding, failed payments).
• Fastest wins: Short surveys and support ticket reviews can yield insights in days.
• Available data: If you don’t have deep event tracking, start with what’s accessible (e.g., CRM logs, support tickets).

Mini Comparison Table: Zigpoll vs. Typeform for Payments

Remember, user research isn’t about perfection. It’s about building an ongoing feedback loop. Even basic steps—like matching support complaints to churn, or reaching out after a customer closes their account—can have outsized effects on loyalty and engagement in the payments world.