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In several product categories, such as electronics, video games, computer hardware and software, and other hi-tech products, backward compatibility feature--the property of a current generation of hardware to allow previous generation of software or accessory to work with it--is an important strategic decision for firms introducing hardware upgrades. We empirically investigate the effect of Microsoft Xbox’s decision to make its new generation console (NGC, Xbox One) backward compatible with selected games for its previous generation console (PGC, Xbox 360) on sales of video games for both PGC and NGC. We assemble a unique dataset using data from a large proprietary game retailer and data scraped from gaming sites during 2013-2017. We analyze the effects using a difference-in-differences approach that includes the use of a synthetic control group. Our results show that when a video game console firm makes its NGC compatible with PGC games, unit sales of PGC games do not change. However, dollar sales of PGC games increase due to a price increase effect. Importantly, sales (units and dollars) of NGC games increase due to a spillover effect, driven primarily by console upgrades. The increases in dollar sales of PGC games result from greater sales of classics, sequels, and first-person shooter genres than sales of non-sequel and other genres of games. Our research offers managerial insights on the backward compatibility feature decision in product upgrades.
(Complete working paper with @Venky Shankar available on request)
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Over half of all shopping journeys start with the mobile channel. In particular, the presence of a branded mobile app significantly influences shopping across channels. However, a majority of app users decrease app usage or even abandon an app, in part, due to app service failure(s). Do app failures influence purchases made within the online channel? Are there any spillover effects across other channels? What factors moderate the within and across channel effects? We leverage exogenous systemwide failure shocks in a large multichannel retailer’s mobile app and related data to examine the impact of app failures on purchases in all channels using a differences-in-differences approach. We investigate heterogeneity among shoppers using a set of moderators of these effects based on insights from prior research. Our analysis reveals that although app failures have a significant overall negative effect on shoppers’ frequency, quantity, and monetary value of purchases across channels, the effects are heterogeneous across channels and shoppers. Interestingly, the overall decreases in purchases across channels are driven by purchase reductions in brick and mortar stores and not in digital channels. Furthermore, we find that shoppers with a stronger relationship with the retailer, greater digital channel use, and who experienced failures less attributable to the retailer, are less sensitive to app failures. We outline failure preventive and recovery strategies for app providers based on the insights from this study.
Download my latest working paper with Dr. Venky Shankar and Dr. Sridhar Narayanan on this link.
1. Mobile:
2. AI and robotics
3. Search and Segmentation
4. Pro-social and gift-giving behaviors
This code uses the MatchIt package for propensity score matching to demonstrate with and without replacement Nearest Neighbor matching. The additional extension it offers is to create panel data using matched sample in both instances (particularly non-trivial for with replacement matches).
There isn't an easy way to export messy R output for regressions into a usable table. You're probably okay manually doing this for 1-2 regressions, but what if you have to estimate hundred of models for different Ys (dependent variables). This code allows export of regression results produced from regressing different Ys ('outcome list') on a set of Xs into a usable CSV file. You can flexibly incorporate clustering, fixed effects, etc. as needed in the code. Happy regressing!
 Intense retail competition has led old standbys, such as Sears, to close dozens of stores. Walmart is venturing online more. And Amazon is expanding offline, opening stores and buying Whole Foods. The fight for retail dollars is fierce, and the battleground will soon migrate into the palms of customers’ hands – via apps on their smartphones. This isn’t just happening with mega-retailers. Movie chains and pet supply stores are increasingly connecting with their customers through their own branded apps. Zumiez, a specialty clothing chain with 600 stores in the U.S., has an app. Scooter’s Coffee, an Omaha-based coffee chain with 200 stores, has one too. So does New York Pizza Oven, a single pizza parlor in Vermont. Mobile apps are becoming key ways for customers and retailers to interact. Our recent analysis of data from a large U.S. retailer of video games and electronics (whose name we agreed to keep confidential) found that apps can even affect consumers’ offline buying habits. Growth in use – and spending The number of people who have the option to use mobile apps is skyrocketing. More than 70 percent of the world population will own a smartphone by 2020. And they’ll spend more than 80 percent of their on-phone time using task-specific apps. Is there no line because people are ordering ahead on their mobile phones? Letting buyers learn about products, discover deals, locate nearby stores and even place orders in advance is a huge business opportunity. At Starbucks, for example, an app allowing people to order and pay on the go – just swinging into the store for pickup – helped customers avoid standing in line and waiting: Over five years, 20 percent of its sales shifted to online transactions. Research has also begun to show that people who use mobile shopping apps buy more than they might otherwise. After individual shoppers started purchasing using eBay’s mobile app, their purchases from eBay’s website increased. Similarly, a tablet app from major Chinese e-tailer Alibaba led customers to spend about US$923.5 million more each yearwith the company than they would have without the app. Some of that increased spending is from shoppers using the app to buy impulsively – making one-off purchases of items they are interested in, or adding items to larger orders. Our research recently found a new dimension to this app-related spending boost. Over 18 months, customers who downloaded the branded app of the retailer we studied spent 30 percent more in stores than they would have without the app. We can infer this by looking at data on customers’ spending before and after the app was installed, and by comparing that to the spending of a random sample of customers who had similar demographics and shopping behavior before the app launched. We learned that most of the increase was because customers used the app to find out about products before buying them. For example, by closely analyzing the data on app use and purchases, we could see these customers started increasing purchases of lesser known video games when they started using the app. App users return products more While shoppers who use a retailer’s mobile app tend to buy more online and in stores, we find that they are also more prone to subsequently returning the products they purchased. In particular, customers who use a retailer’s app tend to return products most often when they purchased those products on discount, and within seven days of making the original purchase. Apps often make it easier to purchase items on impulse. When customers receive some of the items and are dissatisfied, they regret the decisions and return the items. Even taking into account the high rate of returns, app users spend more both online and in physical stores. But that’s when the apps work as customers expect them to. App failures –- and consequences Apps that load information slowly or crash frequently can deter not only online purchasing, but in-person spending, too. Surveys show that more than 60 percent of users expect an app to load within four seconds. And our ongoing research suggests that more than half of users will abandon an app that freezes or crashes frequently. App slowdowns can be costly. One estimate suggests that if each Amazon webpage took just one second longer to load, the company’s sales could drop as much as $1.6 billion a year. For smaller retailers, a similar drop of 2 to 3 percent would be a smaller dollar amount but still a significant blow. Our ongoing research with Stanford’s Sridhar Narayanan suggests that poor app performance reduces users’ in-store spending too. Specifically, we studied how shoppers react when an app is not accessible for five or six hours, due (users were told) to a server error. Our preliminary results suggest that in the following two weeks, those shoppers spent 3 to 4 percent less in stores than they would have otherwise. Less-frequent customers reduced their spending even more than the company’s more regular shoppers. Unnati Narang discusses her ongoing research on failures in mobile shopping apps.Interestingly, customers who experience app failures spend less in stores, but their online spending remains unchanged. A deeper analysis indicates that when a retailer’s app fails, shoppers often go to the retailer’s website to complete their intended transactions. But the negative experience from app failure discourages them from buying more in the retailer’s store. Our research illustrates some ways mobile apps can be a double-edged sword for customers and retailers alike. Shoppers can use apps to learn more about prospective purchases, be inspired on the fly and save time at the cash register. But if the software fails, they may be frustrated, discouraged and even spend less at physical stores. Retailers can see increased sales and faster transactions, but may have to handle more returns – though they’ll still make more money. The longer-term effects of mobile apps on the retail business have yet to be seen, of course, but in an ever-changing landscape, companies and customers alike will be exploring the options. (written with Dr. Venkatesh Shankar, Mays Business School; originally published on ConversationUS)  Let's face it. The hype around big data and machine learning (ML) can be intimidating for noobs. Particularly if you have little background in statistics and/or computer science, you may find this complex territory difficult to navigate. You may wonder how to go about building your toolkit in this space. Where do you even begin?
As a marketing major with no programming background until 3 years ago, I shared these concerns before I decided to chart out my own learning path. In this post, I will share quick and helpful tips that I have found useful for getting started in ML. Specifically, I will share my experience with three online course specializations that can be taken via Coursera.org. Exposure, exposure, exposure: What advice what you give to an aspiring swimmer who is afraid to get into the water? Get into the water! Surrounding myself with machine learning conversations and content helped break the ice. A few things to consider:
Structure, structure, structure Assuming you are now convinced that you want to be more than just an observer to exciting ML developments, it's time to add some structure to your learning. A convenient but effective way to do this is to first find resources that already have some structure. Online courses are my go-to tool. Just like any self-guided learning, they require discipline and dedication. I have been on both sides of the coin -- both an online learner and provider. Two useful tips are to (a) set aside time each week, particularly binge during weekends, and (b) get the Coursera app on your smartphone, at least to stay on top of video lessons. Finally, here are three sets of online courses that I found most useful and why. Together, when taken in the order below, you can get a lot out of these courses:
Of course, here I have assumed you have all the time in the world to take these courses for a great DIY learning experience. If you are facing a time crunch, I'd recommend the Machine learning specialization from University of Washington. Final tip from a grad student: You can apply for financial aid and save a few hundred dollars via individual course pages. This option is not available if you try to sign up for the entire specialization. Good luck! Read the full article here.  The Quantitative Marketing and Structural Econometrics Workshop was recently hosted by the Northwestern University and the Olin Business School at Washington University in St Louis, MO. Here's a quick round-up from 18+ hours of empirical grounding for the benefit of researchers, particularly grad students: The three-day workshop offered a select group of PhD students from Economics, Marketing, and related fields exposure to cutting edge quantitative research methods in causal reduced form research, structural econometrics and machine learning. These three key themes were spread over 12 different sessions of 1.5 hours each, addressed by accomplished researchers including organizers Drs. Brett Gordon and Raphael Thomadsen, Dr. Tat Chen, Dr. Peter Rossi, Dr. Avi Goldfarb, Dr. Stephen Ryan, and Dr Paul Ellickson. This post offers a summary of key topics discussed in these three areas and valuable benefits. I. Causal Inference and Identification Summary: Sessions focused on Causal Inference and Identification included: (1) Causal Effects, Experiments, and Identification, and (2) Finding Exogenous Variation in Observational Data. The focus of the first session was on conducting quasi-experimental research using methods from economics. It offered an overview of econometric techniques, such as difference-in-differences, regression discontinuity, and instrumental variable approaches to estimating causal effects. The unique focus of this session was on what makes a valid and well-written quasi-experimental paper. A highlight of this session was an overview of the latest developments in these fields including use of machine learning for estimating heterogeneous treatment effects and finding mechanisms underlying the causal effects. The focus of the second session was on finding exogenous variation in data – including the use of instruments and fixed effects, as well as the often-overlooked dangers of both approaches. Benefits:
II. Structural Econometrics Summary: Sessions focused on Structural Econometrics included: (1) Aggregate Demand Models I & II, and (2) Single-Agent Dynamics I & II, and (3) Empirical Games. These talks were broadly based on estimating demand and supply to recover the structural underpinnings and primitives of a model based on theoretical underpinnings. The discussions ranged from classic papers, models and assumptions to current cutting- edge developments and methods. Benefits:
For those who are serious about empirical IO research, these session would generate enough homework to go digging, do the groundwork, and also learn to code classic BLP models as well as dynamic models based on value- and policy- function interactions. One of my research ideas on backward compatibility of video games is based on structural methods. I have also recently taken an empirical IO course in Economics with Dr. Fernando Luco at Texas A&M University. Hence, this topic was a useful refresher for me, particularly in reviewing and gaining in- depth knowledge of those methods. III. Machine Learning Summary: Sessions focused on Machine Learning included: (1) Machine Learning to Estimate Demand, and (2) What Can Machine Learning Teach Us. These sessions discussed a broad philosophical overview of how machine-learning methods differ from econometric and traditional economics-based methods. More importantly, these sessions suggested linkages and integration between various methods. Finally, they opened up the way forward for current research in marketing and economics to gain from machine learning methods and specific ways grad students can grow in these areas. Benefits: Two key benefits from these sessions were: (1) Review of fundamental machine learning concepts, and (2) State of the field for openness to machine learning methods and specific ways to contribute All in all, an intense 3-day refresher for some concepts and fresh foundations for some others, and a great first visit to the spectacular WashU campus!  Do mobile apps influence shopper purchases and product returns? We model the effects of app adoption in the context of a large omnichannel retailer with 32 million shoppers. We leverage the launch of a mobile app by the retailer and use a difference-in-differences approach to identify and estimate the differences between app adopters and non-adopters in shopping outcomes, such as the incidence and monetary value of purchases and product returns. We find that app adopters buy 21% more often but spend 12% less per purchase occasion and return 73% more often than non-adopters in the month after adoption. Overall, app adoption results in a 24% increase in net monetary value of purchases. Our findings are robust to alternative explanations and measures. Furthermore, our analysis of the drivers of app use reveals that exposure to offers and rewards through the app plays a key role in driving shopping outcomes. Surprisingly, the number of unique app features accessed by the shopper has an inverted U-shaped relationship with shopping outcomes, suggesting managerial caution against “all-in-one” app designs.
Keywords: difference-in-differences, exponential Type II Tobit, mobile marketing, mobile apps, quasi-experiments Download paper here. |
