Interview with Mara Jennings, Growth Analytics Lead at CapitalSpring Lending

Q1: Mara, exit-intent surveys sound straightforward—catch users as they leave and ask why, right? What’s the real challenge in designing these surveys for fintech business lenders using platforms like BigCommerce?

Absolutely, the premise is simple: catch the visitor’s attention just before they leave and understand their hesitation or objections. But the devil’s in the details—especially when your customer journey involves sensitive financial decisions and a complex approval funnel.

On BigCommerce, you’re often balancing a digital storefront vibe with heavy regulatory and underwriting workflows. That means your exit-intent survey can’t just be a generic popup asking “Why aren’t you applying?” or “What stopped you?” You have to tailor the questions to capture the signal that matters for your ROI calculations.

For example, if a user drops off after pricing shows, your survey might ask, “Did the rates meet your expectations?”—not a broad “What can we do better?” This level of specificity enables you to segment responses by funnel stage and estimate lost revenue with precision.

One gotcha: BigCommerce’s native popup tools sometimes interfere with third-party survey tools, causing load delays or conflicts. I’ve seen teams struggle with survey triggering reliability, which kills data quality. We ended up using Zigpoll integrated via custom scripts rather than the built-in BigCommerce popups because it gave us better control over timing and analytics tracking.

Q2: When it comes to measuring ROI from exit-intent surveys, which metrics should senior growth leaders in fintech focus on? How do you tie survey data back to revenue?

Great question. Tracking just raw completion rate or general feedback won’t cut it.

First, you want to measure survey participation rate—how many exit-intent opportunities convert into actual responses. If you’re getting under 2-3%, that’s a red flag your timing or messaging is off.

Second, response quality and actionability matter. You need to map qualitative reasons given into categories linked to revenue impact. For instance, "interest rates too high" is a different kind of friction than "application too complicated." Those signal different fixes and have distinct impacts on loan volume.

Third—and here’s the heart of measuring ROI—you build attribution models that connect survey insights with funnel drop-off data and ultimately loan disbursal or funded amount. Say 30% of survey respondents flagged "complex eligibility criteria." If your average loan size is $50k and your monthly drop-off at that stage is 500 users, you can estimate potential lost volume—and then calculate how much improvement in criteria clarity might lift funded loans by 5-10%.

A 2023 McKinsey report showed fintech lenders who integrated exit-intent feedback into their growth dashboards improved funding conversion by 15% in under six months. They did this by combining survey data with CRM and BigCommerce analytics, making a “closed-loop” system.

Q3: What’s the process for designing an exit-intent survey suited for BigCommerce users in business lending? Can you walk through the steps, including any technical integration tips?

Sure. Here’s what I recommend—these are hands-on, practical steps:

1. Map the user journey: Identify exactly where you want to trigger your exit-intent survey. In BigCommerce, you might want to trigger after users view loan product pages or pricing details but before they leave the site. Use BigCommerce’s event listeners or Google Tag Manager to capture exit intent signals such as mouse movements away from the viewport or closing tabs.

2. Define clear survey objectives: What specific questions about user objections or hesitations will give you signal for ROI? Narrow the questions to avoid survey fatigue. For fintech lenders, typical questions might be:

   • “What stopped you from submitting an application?”
   • “Was the loan amount or term what you expected?”
   • “Did you find all the eligibility info you needed?”

3. Choose your survey tool carefully: Zigpoll is great because it has robust targeting options and integrates well with BigCommerce through script injection. Alternatively, SurveyMonkey with its API hooks or Hotjar for qualitative feedback can work but might require more custom engineering to sync responses with your user profiles.

4. Set up event tracking and tagging: Use BigCommerce’s native Google Analytics integration or custom GTM tags to track survey views, completions, and responses. The key is to tie individual survey responses back to user sessions or cookie IDs while observing privacy rules like CCPA or GDPR.

5. Test survey timing and triggers: Exit intent can be tricky to get right. Too soon, and you annoy users who still want to browse. Too late, and they’re gone. In fintech lending, you may want to delay triggers until after certain user actions (e.g., after they click “Calculate Loan Eligibility” or visit FAQ pages). Use A/B tests on trigger timing and question order.

6. Analyze and categorize responses: Don’t just dump survey answers into a spreadsheet. Build categories that align with your funnel metrics—price sensitivity, product fit, trust/credibility, or usability issues. Feed these into your BI dashboards for visibility by product and marketing teams.

7. Close the feedback loop: Assign owners in product, underwriting, or marketing to act on survey insights. Implement changes and measure lift in loan application rates and funded amounts post-iteration.

Q4: Can you share an example where a business-lending fintech improved ROI using exit-intent surveys? What were the concrete results, and how did they avoid common pitfalls?

Sure, take FinGrowth Capital (fictional but realistic). They used BigCommerce to showcase their lending products and had a 5% cart abandonment rate on their loan application start pages.

They implemented an exit-intent survey that triggered when users moved their mouse toward the browser’s close button after viewing loan terms. The survey was two questions max, targeting reasons for abandonment.

Initially, the participation rate was low, around 1%. The team realized their popup was interfering with the built-in BigCommerce checkout flow; they switched to Zigpoll with a custom script that respected page load and session events.

Results after tweaking:

• Survey participation climbed to 7%
• 43% of respondents cited “unclear eligibility requirements”
• They revamped the eligibility presentation with clearer loan calculator messaging and FAQ videos
• Within three months, application starts increased by 12%, funded loans grew 9%
• The marketing team calculated a $125k monthly increase in funded loan volume attributing 35% of lift directly to survey-driven changes.

Pitfalls avoided included survey fatigue—by keeping it short, and technical conflicts by carefully integrating the survey tool outside of BigCommerce’s native popup system.

Q5: What are some nuanced considerations or edge cases senior growth leaders should watch out for when using exit-intent surveys to measure ROI in fintech lending?

Several come to mind:

• Regulatory sensitivity: Asking users about loan rejections or creditworthiness in exit-intent surveys can trigger compliance issues. Phrase questions carefully to avoid suggesting pre-approval or guarantees.

• Survey bias: Often, only frustrated users respond, skewing data toward negative feedback. Counterbalance with other feedback sources or incentivize neutral users to answer.

• Multi-device journeys: Many business borrowers start on desktop but finish applications on mobile or offline. Exit intent triggers might miss users who drop off because they switch devices. Syncing survey data across channels is tricky but crucial.

• Data privacy: Always align with fintech data policies. Don’t capture personal data unnecessarily; anonymize responses where possible.

• Attribution complexity: Improvements in funded loan volume can result from many simultaneous initiatives (ads, pricing changes, product tweaks). Create controlled experiments where exit-intent survey insights inform a specific change with measurable outcomes.

Q6: What reporting frameworks or dashboards do you recommend for making exit-intent survey data actionable and visible to stakeholders in fintech business lending?

You want dashboards that combine qualitative exit-intent insights with funnel and revenue metrics, ideally updated in near real-time.

A good setup looks like this:

Tools like Looker, Tableau, or Power BI work well here. I’ve also seen senior leaders create custom dashboards in Mixpanel or Amplitude for behavioral data, then overlay survey tags.

One fintech I worked with made a “feedback loop health score” combining survey data freshness, response quality, and funnel improvements. That kept leadership informed whether the exit-intent survey was driving real business outcomes, not just vanity metrics.

Final advice from Mara

Start small but precise. Design exit-intent surveys that map directly to funnel leakage points you care about. Don’t drown your users in questions, and pick tools that play nicely with BigCommerce’s ecosystem—Zigpoll has saved my team multiple times.

Make sure your data team can connect survey responses back to revenue metrics—without this, you’re collecting feedback but not proving value.

Finally, treat exit-intent surveys as part of a continuous experimentation program. Iterate on your questions, timing, and triggers based on data, and share results openly with your teams to maintain momentum.

Understanding why your business borrowers leave just before applying is game-changing only if you can prove how fixing those issues impacts your bottom line.