Serendipity in Research: From Coding an App to Writing Privacy Theory

Written by Carolina Cuervo-Robert, Ph.D. candidate at Toulouse School of Management (France)

Source: Devlhon consulting

This paper started with a simple itch: Simon Blanchard wanted to verify what survey respondents told him. His problem and his motivation came from one key observation common to social science research: if you’re studying socially undesirable behaviours (like getting into debt) participants are not always reliable reporting agents. Maybe they don’t want to admit to their missteps, maybe it’s just memory lapses, or simply lack of recognition. To address this issue, researchers can change their approach, the questions they ask, or look for other ways to verify what people say. This is sometimes called research triangulation. Simon took the latter approach when it came to getting a view into consumers’ financial pictures: he thought, if someone claims to spend five times a week at Uber, why not check it against real transaction data? His background in coding brought the idea within reach. So, he began building an app that would let people link their bank accounts so researchers could identify unique, relevant studies for them safely, and pay them more for their surveys. 

The technical pieces were there: he built the infrastructure. But the deeper he dug, the more he realised he was stumbling into a much bigger question: how does consumer financial data move between companies, and who in the marketing community understands that process? That realisation, and the conversations it sparked with co‑authors Kelly Martin and Linda Salisbury, turned a coding project into a conceptual paper.

From code to concept

Simon’s original goal was to build a platform where researchers could pay participants to link their financial accounts, verify their self-reported spending, and then survey them, all this while protecting participant anonymity. But he realized the research challenge was greater: first, people where very iffy about linking their accounts – it didn’t feel safe. Second, with newer and more stringent privacy regulations, it was important to reassure potential users on the app’s compliance with relevant regulations. And third, he realized marketing scholars didn’t understand very well the fintech aspect behind his app idea – he needed to make things clearer. So, he teamed-up with Kelly Martin, a privacy expert, and Linda Salisbury, whose work had just taken her though the US Consumer Financial Protection Bureau.  

“My logic was: if I'm going to do this app [about linking financial data to consumer survey responses], I can handle the technology. Kelly knows better than anyone about privacy, which I would need to tackle. And then Linda would know better about regulations” 

-Simon Blanchard

They came up with an idea: to help marketing scholars understand the nuts and bolts of how intermediaries exchange data (in this case via Simon’s app), they would create a set of case-studies showcasing the logistics and tensions in the financial data-sharing ecosystem. As the cases took the lead, the app became a behind‑the‑scenes tool that helped them understand the security they were writing about. The app never made it into the final paper.

The three use cases

To make the abstract topic of open banking and financial intermediation tangible, the paper presents three real-world scenarios, each revealing a distinct layer of complexity in data exchanges between consumers, companies, banks, and emerging financial apps.

The first – Buddy, a budgeting app – illustrates how service providers secure data flows between firms, offering useful insight into the behind-the-scenes aspects for data‑driven products that provide personalized insights.

The second – Marriott’s loyalty program – shows the logistics of affiliate programs, where consumers link their payment card to earn points, and restaurants gain new customers by being part of the program. This illustrates how such programs can be designed to benefit both restaurants and consumers, relying on financial data and intermediaries.

The third – a buy now, pay later (BNPL) application – shows a tool through which consumers pay a portion of the product or service’s price upfront and the rest in instalments, reducing friction in payments, but unfortunately leaving consumers unclear about who holds and protects their financial information.

What does it mean to be "Marketing Enough" ?

One of the biggest surprises for the authors was the challenge of positioning the work squarely within marketing, despite their efforts to go beyond budgeting apps and blockchain. Even with distinctly marketing use cases, loyalty programs, affiliate models, checkout experiences, the team kept getting questions about whether the paper “fits” a marketing journal.

“There was always this tension of like, why is this marketing? And I thought that was going to be fairly obvious, particularly with the use cases, but we still had a constant pushback.”

-Simon Blanchard

So, the question remains: what does this mean for marketing? Essentially, real financial data creates real opportunities for research on product development, loyalty programs, and checkout experiences. But to take advantage of it, marketers need to understand the ins and outs of dealing with financial intermediaries and tools to protect and link financial data to consumer behaviours. This paper makes those ins and outs much easier to grasp for anyone interested in this line of work.

Read the paper

“Consumer financial data exchange: marketing meets digital finance and open banking.” Read the paper here.

Want to cite the paper?

Blanchard, S. J., Martin, K. D., & Salisbury, L. C. (2026). Consumer financial data exchange: marketing meets digital finance and open banking. International Journal of Research in Marketing.

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Meet Simon Blanchard

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What made you want to become a marketing scholar?

I was never really planning to be a marketing scholar. I came from information systems and programming, originally wanted to be a computer engineer, but I really struggled with chemistry (oddly required) and couldn’t get past that class. So, I took the easy out and switched to Information Systems, then to a Master’s in data science at HEC in Montreal. There, while thinking about my PhD options, since I knew I liked research, a professor suggested I look into quantitative marketing. The more I learned, the more that just seemed interesting: I could get a five-year funded PhD to develop algorithms to study how consumers perceive stuff? That just sounded fun. I was never driven by the goal of being a professor; I was much too short-term focused. So, I didn’t have a grand master plan; it kind of happened by accident. That’s how I got into marketing.

What is the number one question you hope to answer in your career? If there is one?

I think of myself as a broad marketing scholar. I realize that marketing scholarship has people who are probably more creative with theories and data analysis than I am, but I’ve recognized that my skills are usually about how to do things better in our research approach. So, I'm spending a lot of time thinking about that. For instance, recently, I think about questions such as: how do we use AI carefully in our research? How do we deal with the fact that in some of these AI experiments, the AI doesn't always do what we want? All in all, I want to see the marketing field get better, get more prominent, and contribute in ways they couldn't before.

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This article was written by

Carolina Cuervo-Robert

Ph.D. candidate at Toulouse School of Management (France)