Link: ​https://www.forbes.com/sites/nicolekobie/2025/10/28/e-scooters-may-spark-crime---and-increase-emissions-study-suggests/ 

I was pleasantly surprised and humbled to see myself chosen among leading young professionals and change makers in our community. Not sure I deserve this one, but super cool to be on the front page of our local newspaper featured alongside people I admire!

Not all papers are created equal. And their fate — where they end up in the review process, how long they take, how they evolve — is not always linear or predictable.

This is another post in my “journeys of papers” series (the first was on my job market paper’s eight-year journey). My hope is to make my experiences with academic research and the publication process more transparent, especially for junior faculty and PhD students. You are not alone.

Here’s the paper:
Link: Privacy and prediction: how useful are geo-tracking data for predicting consumer visits?

And since videos can sometimes speak louder than words, here’s a short overview of the findings as I teach them in my MBA courses:
Link: Video summary

My Toggl time tracker tells me that just this paper was over 1700 hours in the making (for the first versions between 2020 and 2023). But there is more to a paper than the hours and the outcomes. This blog post is the story behind the paper — a story of pivots. 

Early ideas and a chance encounter

Spring 2018. I was a PhD student at Texas A&M, teaching an undergraduate course in market research. One rainy afternoon, during office hours, a woman walked in with boxes of pizza. Their career event on campus had been cancelled, so she stopped by instead. She turned out to be the CEO and founder of a mobile app company focused on safe driving — rewarding people for driving without looking at their phones.

We kept in touch. That summer, I began a sort of “pseudo-internship” with her team, helping shape their data strategy. At that time, they weren’t even collecting location data, though users had opted in. I nudged them to think of data not just as a by-product, but as part of the product — something that could power dashboards, partnerships, and insights for clients. Eventually, they launched a premium membership built on location intelligence, like comparing foot traffic at competing restaurants.

In parallel, I had grown fascinated with IO courses and demand estimation frameworks in my own PhD coursework. Once I had an NDA signed, had sufficient data collected, and was able to check the quality of the data, I approached and started working with my co-author Fernando Luco at Texas A&M econ. Like me, he was interested in new methods and bringing Machine Learning to demand frameworks.

Very soon, we realized that the app data alone won't suffice for a research project of the scale we were planning. So we brought in Safegraph, Yelp, Data Axle, ACS, and at one point -- even menu prices data -- to the mix. 

Very soon, we also realized that a project predicting users' visits to retail stores alone won't suffice. I am a mobile-first researcher. It wasn't until our discussions much later--when the prediction problem had taken shape--that we decided to bring in the privacy perspective. Looking back, this was the natural step.

Pivot 1: From data to research question

Initially, the project was about predicting where individual users would go — their individual visits to restaurants and retailers — over a two-month period. Privacy was framed as tracking frequency: more frequent location pings meant less privacy.

But as we gathered feedback, we realized two things:

1. Restaurants care more about aggregate demand, especially about fluctuations in demand, than individual visitors.
2. Privacy concerns aren’t just about frequency — many consumers care about being tracked at all or what data are tracked about them.

So, we pivoted. We shifted to predicting weekly demand fluctuations at the restaurant level, and expanded our privacy lens beyond tracking frequency to include restrictions on what and where data are collected. 

Pivot 2: From raw coordinates to meaningful features

Working with geo-location data was another learning curve. At first, I thought raw latitude-longitude points, fed into advanced models like LSTMs, CNNs, or Transformers, would improve predictive power. It didn’t. What mattered were the features we engineered — clusters, behavioral summaries, and patterns from movement data. Complexity alone didn’t improve predictions. Thoughtful feature engineering did. That lesson has stayed with me: conceptual clarity beats fancy models esp. when the signal is messy.

Pivot 3: Managerial decision making

We received criticism that the usefulness of geo-tracking data won't translate to managerial decisions. We were asked if there was a way we could quantify how managers will tweak their strategies and decisions in response to the use of geo-tracking data. This is one critique we perhaps couldn't fully resolve due to data limitations on not observing managerial decisions. However, we collected revenue, cost, and profit information based on industry reports and the paper now shows about 5% of restaurant revenues can potentially be "saved" with using these geo-tracking data. 

The first version of the paper went to the Journal of Marketing Research (JMR). It survived through three tough rounds, even an editor change, but ultimately didn’t make it. My understanding is this was mainly due to the last point of not observing managerial decisions, something we couldn't have resolved. We simply had no data on how managers used this information other than the interviews we conducted and the conversations with the app platform. 

That third-round rejection stung. The timing was awful, especially since I had just presented the paper at European schools, receiving overwhelmingly positive feedback (privacy being even more salient post-GDPR) and got the rejection on my flight home. But the truth is: some limitations cannot be resolved, and not every paper belongs everywhere.

At Quantitative Marketing and Economics (QME), the editors encouraged us to write the paper we wanted to write. The process was efficient and direct. I am deeply grateful to the editors for seeing value in the contribution as it stood i.e., predictive power of geo-tracking data, and the trade-offs introduced by privacy restrictions.

Looking back, this paper carries many “firsts” for me. My first project co-authored with someone outside of marketing and also a junior faculty at the time. My first collaboration with a company relationship I had built from scratch. My first foray into mobility data and not just where people shop, but how they move. I learned SO MUCH about modeling consumer location footprints and connecting them with online data.

The paper also carries important lessons: Relationships matter. That serendipitous meeting in 2018 shaped years of work (even as the firm went through major ownership changes in 2020). Insights matter more than raw data. Pivots are part of the process. Whether in methods, framing, or journals, flexibility keeps research alive and relevant.

Read more: ​https://www.news-gazette.com/news/the-future-of-ai-grocery-shopping-crime-scene-re-enacting-flood-forecasting-and-more/article_ff05b731-68bb-4635-9e79-77f95c78f4d0.html 

This week, the second essay of my dissertation and my job market paper was accepted for publication at the Journal of Marketing Research. I am grateful to the editors and reviewers, and to my co-authors who continued to show faith in this work and invest their energy into it.
 
I started this project as a second-year student in my PhD in 2016. I loved it from the start. We had all the elements of a good empirical paper – clean and important intervention/shock (i.e., an exogenous mobile app server failure in a large omnichannel retailer’s app), the right data (i.e., cross-channel individual purchases and mobile app usage data), and a rich and fascinating body of research we could speak to (i.e., cross-channel marketing, service failures). Coming from a retail background pretty fresh into my PhD, the prospects excited me. The project was serendipitous and came out of my raw data exploration—plotting mobile app “events” where I started to notice huge spikes in server errors. What excited me even more is that the marketing team of the company providing the data had no idea and we alerted them to this tech/business challenge they later started taking seriously.
 
So, when the paper has finally made it through the review process now in 2025, I feel compelled to share the journey of this paper. In doing so, I am hoping to share some lessons with current and future PhD students on JUST how much seeing a paper through takes. Not just the hours of analysis or writing, but the intangible inner work and growth.
 
The paper is a story, a memory, a vivid reminder of moments that went into its making. When I look at it, I remember myself as a grad student in 2016 on co-author calls trying to figure out the early evidence in the data during holidays and weekends, in town and when traveling. I remember myself presenting this paper as my second-year qualifier for my PhD after a strenuous flight back in the midst of the Hurricane Harvey in 2017 (not to mention when I was struggling in my marriage and on the brink of a divorce). My earliest memories presenting this paper are from the 2017 marketing science conference, where I still remember stalwarts and experts on mobile marketing in the audience.
 
So now that it’s “made it” in the traditional sense, it’s hard not to be emotional. It’s hard also not to be a little numb, if I am honest. It’s been a long journey.
 
It is not often that you receive positive news in academia. And I say this recognizing that I am coming from a place of privilege--from a place of having been able to get my degree at a good school, from being able to immigrate to the U.S., from having had good mentors and a supportive family, from having had the fortune to be a part of a community and environment that creates opportunities, from having had “persistence and grit” as many would say but also the fortune of getting my early work accepted when it could’ve just easily been shot down.
 
But I also say this from a place of working really really hard. And I don’t mean the mechanics of it, of collecting and cleaning data, or of running regressions, or of rewriting hundreds of times, of updating my thinking and conceptualization. I mean the inner work--of finding comfort in discomfort and in creative destruction (i.e., being willing to throw out old ideas that are not working and implement new changes through the process), of putting myself out there—even if it means harsh rejections, of getting personal and distraught when I (or the paper, since I’m told your research is not YOU) am rejected, but then getting back up on my feet to work through the clarity of what needs to be done next.
 
I also say this from a place of managing with care and kindness your co-author and advisor relationships. People before projects. Always. This is a valuable life lesson for me. And I appreciate my co-authors for their patience and encouragement throughout—now that I am an advisor and working with students, I realize how much work it takes even to review the findings and come back to the drawing board several times! I am grateful to them for navigating a messy multi-journal multi-year review process with me.
 
Importantly, I am thankful that this is one of those cases where the paper improved through the review process. The editor, AE, and the reviewers pushed us on the right elements. This is not something I take lightly, having been through brutal and unfair review processes a fair amount.
 
A key lesson I learned with this paper was to evolve with the review process. In the early revisions, we expanded from one failure event to two even though getting the additional data was hard. In the second experience, we dived much deeper into the consumer search process and underlying explanations. The paper is completely different from where it started—and much richer. I always give the example from this paper to students how I have a running document with 125+ figures to explore and visualize the data to fully understand the robust patterns. Even if only 10 make it to the final paper, it’s worth the groundwork. You need the groundwork.
 
So why am I both emotional and numb at the same time now that it’s done? Because by the time projects reach the closure they deserve (which in many cases, they may not at all), it’s natural for a part of your starry-eyed dream, especially if it was your dissertation, job market paper, and one of the first few projects of your life, to die a little. The grit lies in not letting them die. The grit is in getting as excited about the work today as you were when you started. Like I always say, fortunately, I chose topics I truly cared about and was interested in. How miserable would this journey have been otherwise!?
 
Rough timelines across journals from memory of this paper for a “curious” reader
 
Summer 2018 - First submission
Fall 2018 – Reject and resubmit
Summer 2019 –  Second submission (revision)
Fall 2019 – Major revision
Spring 2020 – Third submission (revision)
Fall 2020 – Reject
Summer 2021 - First submission
Fall 2021 – Received Major revision
Fall 2022 – Second submission (revision)
Spring 2023 – Rejected
Fall 2023 - First submission
Fall 2023 – Risky revision
Summer 2024 - Second submission (revision)
Summer 2024 – Revision
Fall 2024 – Third submission
Fall 2024 – Conditional accept
Spring 2024 – Unconditional accept
 
Examples of key review concerns through the various journals and how the paper evolved:

1. Cross-channel effects seemed interesting to the team, but they did not like the fact that most sales take place offline i.e., that the data came from an offline-dominant multichannel retailer like Walmart, or Macys. We better explained the context and incorporated more data from the online channel along with power calculations.
2. Did not like the fact that the sample was from holiday periods, e.g., Nov 2014. We collected more data on another failure in April 2018 that became our main sample.
3. Some journals stated small effect sizes as ground for rejections because it doesn’t matter (it’s 3-7%). Effect size has not changed in 8 years. We better explained the economic effects of app failures and owned up to the short-term nature of effects.
4. Mechanisms are a hard sell. Our initial mechanisms included brand dilution and a brand damage effect. We moved away from this explanation and dived much deeper into search and channel switching. We added completely new data and new analyses to add theoretical richness to the paper and by exploring different mechanisms empirically.

 
All in all, it is a completely different (and in my biased view, much stronger paper) in 2025. The JMR review team was phenomenal in guiding us and focusing our attention on the most interesting aspects of the paper.

​Research and reviewing (and the synergies)
Junior faculty are often given the advice to review selectively, or only review for journals they hope to be on the ERBs of. It takes too much time.

Reviewing makes you a better researcher in my view. Others can perhaps give a ton more reviewing advice on how to write good reviews, but I will share 3 things that have helped me use reviewing feedback as a way to improve my own research and reviews:

1. Pay attention to the decision letter: The decision letters sent out by the Editor with the AE and review team's feedback can be used to gauge the quality of your review. Ken Wilbur, someone I consider a mentor, once shared with me that the alignment between your recommendation and the editor's decision on a paper should be going up over time. Another good indicator is how often the editor/AE may refer to your comments in their letters, and if they found it useful.
2. Get direct feedback: Sometimes, I have also reached out to the editors for direct feedback and/or thanked them for the opportunity to review. I recently ALSO had an in-person discussion with an AE I reviewed for. Sometimes you can learn very specific things they liked in your review--which is your cue to keep doing it. Not to pat-myself-on-the-back too much but one editor said: "On this one, you caught a critical confound, and gave them a way out. I appreciate your tendencies to be both constructive and critical, and this is very crucial to the development of papers."
3. Gain insights on processes and people: Reviewing is a service to others but also a service to yourself. As an author, you could be better informed now about how various editors and/or AE's write their letters and what's important to them. Otherwise, there is a huge information asymmetry especially starting out as a PhD student and junior faculty. Making the review process (and personalities!) transparent helps. I often wonder why reviewer comments and process cannot be published once the papers are out -- as a way for readers to get insight into what the papers went through. In this aspect, I highly recommend the How I wrote this podcast!

Similarly, I learned a lot from giving others feedback on their papers through tangible and intangible ways including discussing my peer and friend's Shrabastee and her co-authors work on Goodreads at UCSD recently (video, paper).

Teaching (and synergies with research and mentoring)
Junior faculty are often given the advice to not to take on too many new preps. It takes too much time.

While I have been lucky to be able to stack my teaching and get an extended 2-0 teaching load (3-0 is more common), I have never shied away from a new prep. I have taught marketing analytics to undergrads (BADM 361), marketing analytics to masters (BADM 591), two different online iMBA courses (that also have a Coursera version with over 67k learners here and here), and an advanced marketing management course to undergrads (BADM 420) that earned me the Poets&Quants' Best Undergrad Professor honor.

Again, others can offer much more teaching advice and great content (e.g., I often refer to Ken and Dan's course materials here and Avi's quant marketing PhD seminar list here among others), I want to emphasize synergies between research and teaching that have helped me:

1. Make your research known to students: In my marketing analytics course, I discuss causal and predictive models. I often devote time during class to share my (and others') research with my students and allow them to apply class concepts to the research topics. For example, I discussed Brand, Ayelet et al.'s new GPT work in my iMBA session on sources of data. Ayelet was gracious enough to share her slides with me! Similarly, I discussed political consumerism research and retailing research. I even invited Nathan to my class to speak to the students and he graciously agreed to do this.
2. Make your class topical and current: If I read an interesting paper or attend an interesting seminar in a particular week, I bring it to my students. Last month at the Haring Symposium, PK made an excellent presentation on technology's role in marketing. He had several cases on AI and influencer marketing, which were topics I was also covering in my course. Some of my MS students in their course project were studying ChatGPT's impact on youtube content. Again, PK was gracious enough to let me use some of his slides and the students loved examples he had on TikTok's role in product development, e.g., in case of Chipotle creating a Philly Cheesesteak quesadilla.
3. Raise the bar for your students: My last reflection from my teaching this semester is that we need to ask more of our students and to show them how much more they can accomplish compared to what they think they can do. When they say "I can't do it" there is a way to break it down into small chunks of tasks and ask them to get interim feedback from you or their peers to make the bigger goals less overwhelming and more attainable.

Finally, across all domains, it's helped me to SHOW UP to things, be part of a community, and help propel others as you have been helped along the way. 

​Coming soon: Next week, I will post about the exciting projects my MS students put together this semester including framing questions about collecting data to analyze topics like  how do Boeing crashes impact its stock prices and order cancellations, and how Taylor Swift's appearances in NFL games impacts the social media following of Travis Kelce and related influencers. More soon!​

The American Marketing Association's (AMA) Retail & Pricing Special Interest Group (RAPSIG) interviewed me about my research and teaching, as their incoming Vice President of Outreach. Read more here: amarapsig.org/?page_id=2324.