Tracking Users for Fun and Profit

Advertising in today’s world has been pretty much accepted by most Internet users. A useful rule of thumb is that if a website is not selling you anything, then you are the product. After all, many websites exist with a profit motive. However, advertising has also spawned a cottage industry in tracking users effectively on both online and offline mediums, and this has caused much concern to other people. The end goal of such tracking is to persuade and influence people to buy more of their products by analyzing their shopping habits. In this short paper, I provide a brief overview on the modes of online advertising and how they attempt to influence people to buy more of what they are peddling.

Social Media Tracking

It is indubitable that Facebook and Twitter have become ubiquitous. They also contain a treasure trove of personal data. Previously, search engines like Google had to infer the age, gender and demographics of a user based on the websites he or she visits. With Facebook, people willingly provide that info when they complete their profile or like pages and links. While there is much advice to fill up your profile with fake personal info, most do not take heed of that because Facebook is ostensibly more useful when you provide real information. After all, who would want other people to wish you happy birthday on your fake birthday? Hence, it is easier for companies like Facebook to target you with more relevant ads. Are you a 21 year old female college student? Perhaps ads for a sale for clothes could be shown. What about an unemployed 40 year old man? Ads purporting get-rich-quick schemes can be displayed. In itself, this might not be a bad thing. However, there should be some concern that such a considerable amount of personal information is concentrated in the hands of a few companies.

In one such case, Facebook intended to partner with other websites to share activities of users on external websites directly on Facebook’s news feed. This service, called Beacon, created an uproar because it did not allow users to opt out of the feature. For example, if one bought a book called Coping with Cancer on Amazon, one’s purchase will show up on other people’s news feeds as “Your friend XXX bought a book “Coping with Cancer” on Amazon. Click here to check it out”. Facebook’s intentions were clear – from persuasion theory, social proof is one way of influencing people to take action on partner websites. Simply put, people are more likely to buy something if their friends bought it. However, they implemented it in a way that third parties were privy to personal information on the Facebook platform. As can be seen, such data sharing among different websites can lead to privacy leaks and unwanted information disclosure.

More worryingly, there are companies such as Lookery that attempts to segment users into various demographics based on their website visits over participating websites. In fact, I used Lookery a few years ago and they paid $0.10 per 1000 visitors, which while seemingly very little, is quite substantial considering they place no ads on your website – only an invisible JavaScript beacon tag is placed. Although they promised that no personally identifiable information is stored and transmitted, prior incidents like the AOL search data leak leaves much doubt. In that particular case, AOL released detailed search logs to many users for research purposes. Although AOL did not specifically disclose which user queried what terms, it was possible to identify users based on their search terms. In many cases, specific individuals were even identified. It is of concern that such advertising beacons, while in itself is anonymized, can be cross-referenced with data from other providers to provide enough information to identify users. Therefore, online advertising can be used to identify users, which is an ethical problem in the broader schemes of persuasion.

The Rise of Data Brokers

The traditional role of data brokers was to provide firms with a way to get demographic data, or verify personal information in that firm’s database. For example, in the case of fraud prevention in an online shopping portal, firms can cross-reference a potential customer’s email to their shipping address. How it works is that the firm provides a one-way hash of their customer’s email and address and provides it to the data broker, and the latter returns a result indicating if they have an exact match. Either parties do not have access to the email or physical address that is being queried, since a one-way hash is being passed, with the hashing algorithm known only to those two parties at that point in time. However, some people are still spooked by the fact that personal data, even though encrypted, is still being passed around different companies in the normal course of business. With so many parties involved in a transaction, there are more points of failures in a privacy leak.

The more contemporary role of data brokers now involve bridging real-world and online actions to a person. For example, if Coca-Cola launches an online advertising campaign on Facebook, measuring its effectiveness would be hard without data brokers. These companies, via deals with multiple retailers, get transactional data of specific products in purchases made in-store, aggregate them and present them to clients to gauge advertising effectiveness. It can also work in the opposite way as well – Walmart might want to track and compare the shopping habits of those in-store vis-à-vis those online. In this case, the “bridge” is your loyalty card – if you use your card at a brick and mortar store, then enter it online to enjoy a discount, then retailers will know that the online persona is the person registered in your membership card. Data brokers can then help Walmart market products that you are more likely to buy on not just Walmart, but on other websites as well. At this point, your online identity can be associated with a physical one. An example of such a company is Datalogix, which tracks more than $1 trillion in consumer spending across more than 1400 retailers. This data is then passed to data cooperatives which act as a central clearinghouse for personal information across multiple companies, online and offline so that they can be homogenized into a machine readable format, to be resold to other advertising agencies.

As some critics rightly point out, data brokers have operated even before the Internet matured, perhaps even more unscrupulously. If you have written down your details to take part in a sweepstakes or lottery, you have effectively signed away your personal information to a company which purportedly helps facilitate the lottery. In fact, data brokers back then were subject to little regulation. Paid services existed which allow you to get someone’s address from their phone number, and vice versa, for telemarketing purposes. The modern form is encapsulated in the company Towerdata which prides itself on finding a person’s social media accounts and physical addresses based on their email addresses. Additionally, this service called Email Intelligence allows interested parties to buy “demographic, interest and purchase data for more effective list segmentation and personalization”. As can be seen, such exchange of personal information is not new, but due to increased frequency of data leaks, people are more concerned now than in the past.


In sum, online advertising can be seen to infringe on users’ privacies, all for the ends of getting people to buy more of their products. This has very much to do with the ethics discussion we had this week. Previously, advertising was pretty much a benign medium – you search ads with a particular keyword, and was shown ads with those keywords. Now, advertising has gotten so far to the point that Walmart and Target are able to know whether you are pregnant, even before you actually conceive – all based on big data analysis of your online habits derived from a variety of online and offline sources.

What a world we live in.


Here’s an interesting discussion question that allows you to think about the future of advertising.

  1. Do you think privacy and advertising can ever be segregated? That is, can effective advertising happen without breaching users’ privacies? Do such advertising firms exist now?

United Breaks Guitars – Lessons for the Public Relations Playbook


The United Breaks Guitars case study shows that social media cannot be overlooked in a company’s customer service operations. Dissatisfaction with the company can propagate like wildfire over the Internet. In the short discussion piece that follows, this author attempts to show the significance of social media in public relations, as well as suggesting how United could have done better amidst this negative publicity.


It is indubitable that social media has changed the public relations playbook for many companies. Previously, before the user-generated content Golden Age that Web 2.0 brought about, people who felt aggrieved had little recourse against the company. Now, people are able to tweet their frustrations, write a rant on the company’s Facebook page, or in this case, make a music video. In all cases, the actions taken by them are highly visible. Contrast this to a private email complaint to the company – the latter is definitely less visible and subject to less public scrutiny. In addition to increased visibility, the complaints are also able to be propagated much more quickly. Case in point – when someone uploaded a video of a FedEx delivery person throwing his fragile packages into the porch, that video was shared thousands of times over various social media platforms. Taken together, social media is a potent force to be reckoned with in a company’s public relations playbook – one that requires a different strategy than the one of yore to counteract.

For ease of discussion, I will segment my analysis of the case to distinct time periods in the life cycle of a public relations incident – pre-incident, during incident and post-incident. This allows the reader to gain a perspective on what a company should do in a situation like United Airlines was in.

Calm before the Storm

Since social media is such a powerful voice online, the attention given to it should be commensurate to its influence. In the case of United, we see that there were glaring ineffective use of social media. In July 2009, United Airlines used Twitter to only disseminate promotional messages and flight disruptions to its followers. While some followers might like the fare deals posted on its feed, United could have taken a more proactive approach on social media.  A cursory glance shows that tweets that mention a company on Twitter are usually negative. For United, this would mean complaints about delays, rude ticket agents, inept efficiency and its ilk. There was no such effort on United’s part to address these complaints. Today, the situation on the ground (pun intended) has improved drastically with United actively responding to customers’ complaints on Twitter, inviting them to message them so that they can investigate further. In one instance, someone reported an issue with the gate number on their e-ticket. United replied telling them to message them so they can ask the Mobile Apps team to rectify this. This is a step in the right direction. Moreover, most of the replies to complaints took less than an hour (minutes in fact), which is commendable. In a time-sensitive business like flights, customers expect their concerns to be addressed swiftly [1]. Customers use social media to air their complaints because it is easy and fast to do so; likewise, they expect a response to come quickly too. As can be seen, United Airlines used social media ineffectively previously.

Next, it would seem like United did not have dedicated personnel for their social media presence. From the paper, “United employees were encouraged to monitor social media for mentions of United Airlines”. Crowdsourcing was a good step by United so that each staff member has a stake in addressing issues concerning the company. This was instrumental (pun unintended) in the early spotting of Carroll’s video, and the subsequent reach out to him by the managing director of customers solutions at United. Without this policy in place, the video could have been seen only after all the mainstream media outlets have reported  on it, which could be even more disastrous. Whilst such crowdsourcing is a not a replacement for full time social media service staff, companies that opt for this route can improve on this further by providing incentives for staff to report customer incidents which are left unaddressed to the main customer service team. All in all, companies will do well to have dedicated customer service agents to address social media issues.

The Aftermath

Of course, the United debacle would not have happened had the United agent approved his claim in the first place. In this part, we take a look at how United handled the social media firestorm. Firstly, United was vague in their Twitter reply to Carroll. While this might be an off-the-cuff reply, there were no subsequent follow up on what exactly they did to “make it right”.  It was not until after they have reached out to him did they detail their poor response to his claims. Notice how Rob Bradford reached out to him. He is the managing director of customer solutions at United. What United should have done was to get the CEO of United to reach out to him instead. Since his complaints had been seen by millions of people, the CEO should have apologized, not anyone down the corporate ladder. This shows that United is not serious about the matter. Also, it would seem that United had taken to more actively tweeting to tell people their solutions. Perhaps a better approach would be to embrace traditional media and online news sites by writing a press release.  They could also have placed a statement on their corporate website or via a shareholders’ meeting. Thirdly, the response by United was weak because they did not address any punitive actions that United would take should such an incident happen again. They only promised to use that in training materials, but failed to communicate to its customers how its customer service would be overhauled. For example, one can take a look at how Amazon does customer service. While there are customer agents at every step of the way, one can email the head of Amazon Jeff Bezos directly at his personal email – [email protected] [2] This shows sincerity in reacting to complaints. United could follow in Amazon’s footsteps if they really want to go all the way in this respect. A highly-reactive from-the-grounds-up approach would be to fire employees who lack customer service discretion. However, this has the knock-on effect of decreasing employee satisfaction so the pros and cons definitely needs to be weighed. Hence, one can see that United not only exhibited a weak response during the incident, it also failed to show its customers its sincerity in improving its services after the fact. Sometimes, from the customer’s’ point of view, education of frontline employees is not enough, they want to see a hardline stance on egregious actions by employees.



In conclusion, United’s social media action plan seemed to have improved since the article was written. The key points to take away from this is that companies should use social media to not only produce content, but also consume feedback from its social media followers. Also, they need to address such feedback promptly, with dedicated personnel to handle such matters, rather than delegating it to everyone. If a public relations disaster would occur online, companies should get their top guy to address the issue personally, especially after it has blown up to such proportion. In communicating to the public, companies should demonstrate sincerity in changing their practices by thinking from the customer’s perspective. The best solutions are always to involve the customer experience personally – for example, the ability for customers to contact the CEO directly. Strategies like educating customer service employees are likely to be seen as impersonal since customers are not really privy to any improvements behind the scenes.



Is Crowd Wisdom Really Wise?

With all these talk in recent years about crowd sourcing and big data, I wanted to delve a little bit deeper on the costs of crowd wisdom. It is easy to see how crowd wisdom can be beneficial to society as a whole; for example, there are many crowd volunteering websites that aim to aggregate crowd decisions for social good. One of them is Tomnod which rose to fame when they used satellite imagery to crowd source data on where the missing MH370 plane is. However, in subtle ways, crowd wisdom might not be all that good.

In one of the more telling problems of crowd wisdom (herd mentality in particular), Sunil Tripathi was implicated as a suspect in the Boston marathon bombings because a Reddit user posted in the forums that they looked alike. Soon after, more people started agreeing with the first poster, and spread like wildfire over Twitter. It went into mainstream notoriety when mainstream media reported it over TV and newspaper. The lack of fact-checking by the latter was disturbing. However, this shows one thing – that crowd wisdom can turn mad. Critics of this point might point out that crowd wisdom works best if people are independent – they have no knowledge of other’s ideas. While this can be replicated in a laboratory setting, the fact is that it is difficult to ensure this online. People invariably will use search engines or share it on Facebook, which leads to a decoupling between reality and ‘reelity’. It is what makes crowd wisdom a dangerous idea to wholeheartedly believe in.

Secondly, the issue of privacy comes in. In recent years, due to the proliferation of smartphones with embedded sensors, app developers have been using the sensors to predict weather or traffic. Dark Sky, a weather app on the Apple App Store, uses pressure sensors on the latest iPhones to crowdsource data on whether it is going to rain in a particular region. Waze, a traffic directions app, uses your phone’s speed and location to crowdsource data on traffic conditions. If you are moving slower along a particular road, the app may intelligently tell other road users that a traffic jam might be imminent, and to reroute road users accordingly. It seems beneficial until you realized the same app can be used for nefarious purposes. Previously, Tinder, a dating app, uses one’s locations to tell potential dates where you are. Whilst other people see you as “3 miles away” in a vague sense of distance, some tech-savvy people figured out a way to perform deep-packet inspection of the traffic to find out your exact coordinates. It turns out that the actual coordinates are actually being sent to your phone, which then processes the data in conjunction with your location to provide you with the distance. Hence, it is possible to know where you potential ‘dates’ live, and possibly stalk them. This is a design flaw more than anything else though, but it still shows the flaw of crowd wisdom – when companies attempt to collect everything that it is possible to gather from a particular user, the possibility for irresponsible disclosure is heightened.

Thirdly, it might not be even accurate at all. As seen in class, the more variables there are in a particular prediction, the less accurate it becomes. For example, predicting how many leagues in the depth of the Marianas Trench requires people to have some notion of a league, in addition to predicting the depth of the trench. A more popular example which deals in big data was Google Flu Trends, which was recently shut down. Although it wowed researchers by predicting flu trends ahead of a full-scale epidemic, it constantly overstated the severity of it. People were too prepared, it seems. Whilst this might not be a bad thing, I can see the flaws of trying to extract information from crowd data – it might be fairly accurate now, but might not be a few years down the road.

Finally, I would like to end off with some food for thought. How do we ensure that crowd wisdom is indeed accurate? We might take a look at the intricate system Wikipedia has in place for its editing – talk pages, a system of editorial processes and its ilk. Is it possible to generalize for online communities and spaces?

The Future of OPOWER


The idea of using social norms and behavioral science in environmental conservation is not new; however, the uniqueness of OPOWER’s approach is that they combined actionable strategies with subtle cues in their monthly energy reports. This being said, the paper surmises that there might be significant challenges in the future in trying to make OPOWER’s strategies more prevalent. In the short discussion piece that follows, this author attempts to elucidate these challenges, positing a nuanced basis for said issues and suggesting possible solutions.


OPOWER is a publicly listed energy-efficiency software company that aims to reduce energy usage through social cues derived from behavioral science. Their results work on the premise that normative influence runs deep in people’s predilection to energy conservation.  Such normative beliefs mean that people tend to be more greatly affected by the conservation efforts of similar people around them. This was a milestone in energy conservation because energy reduction is the only method which provides a net positive value in benefits accrued to society, and OPOWER made it a cinch to integrate into existing utility billing systems. Contrast this to solutions like replacing incandescent bulbs with fluorescent lights, or installing photovoltaic panels – both require a significant sunk cost, the value of which can only be recouped over a long run. OPOWER’s solution, on the other hand, saw an almost immediate reduction in energy usage without any significant costs.

However, there are significant roadblocks to OPOWER’s widespread implementation. Firstly, there is the issue of boomerang effects (8) which might cause people who use less than average electricity to regress to the mean. Next, there is skepticism among utility companies that their methods will work on their customer base due to demographic differences. Finally, there have been questions on the sustainability and extensibility of OPOWER to other countries – would it work overseas without much modifications?

Mean Reversion

The boomerang effect describes a social phenomena where attempting to persuade people to do things in a certain way (such as conserving energy) unexpectedly results in them doing just the opposite. In this case, OPOWER knew its strategies would cause higher energy-usage households to consumer less energy, but it would also cause lower energy-usage households to use more energy. To counteract this, OPOWER used injunctive messaging such as smiley faces to signal approval and disapproval for their current energy usage. Subconsciously, the smiley faces serve as a reinforcement and approval indicator for people to continue with their above average performance. However, as seen in Exhibit 4, the boomerang effect still played its hand, albeit to a smaller extent.

One possible solution is to introduce subtle language cues into the energy report, as well as a revision of the presentation of the statistics. In figure B, we see that the Social Comparison Module (SCM) provides absolute statistics on energy usage comparison. (ie. I used 1033 kWh vs 784 kWh for my efficient neighbours) Instead of making absolute figures the central to the SCM, relative figures could be provided instead. For instance, one can place the user on a bar chart on a percentile basis. If I used 32% more energy than my efficient neighbors, then I would be placed in the 90th percentile, for example. Instead of saying “You used 32% more than your efficient neighbors”, the language could be revised to “90% of your efficient neighbours use less energy than you. What this serves is to provide reinforcement that a substantial number of people are ahead of the user when it comes to energy usage. Even if one is below average in energy usage, there will still be a percentage of households that are still using less than you. In this case, the optimal energy usage to strive for is the one that used the least. Hence, the boomerang effect can be mitigated to a certain extent.

Slow Adoption by Utilities

As mentioned in the case study, there has been less than favorable responses from utilities about the adoption of OPOWER strategies. One point that is noteworthy that most utility companies are for-profit. If they promote energy reduction, less profits accrue to them due to transmission and distribution revenue. To offset this, most companies depend on tax breaks or grants from governments in achieving green objectives. In Pittsburgh, Duquesne Light charges all customers a small sum of money in each monthly bill to recover the costs of implementing a program to educating customers on energy usage, in their WattChoices program. Without some kind of financial incentive by government stakeholders, there is a conflict of interest in power generation companies – both to their shareholders as well as energy conservation groups. Moreover, those companies believe that the demographics of each state mean that people would not be receptive to the idea at all.

It is difficult to tackle this problem due to said conflict of interest. Hence, an effective solution would call for some form of government intervention, one that does not shackle electricity generation companies, but rather rewards people for reduced energy usage. To take a leaf out of health care, some insurance companies pay people to keep fit – they provide complimentary gym passes  and even reward you based off the activity that you do [1]. The premise is that insurance companies would have a positive expectancy due to increased health that their customers have after keeping fit, even after the costs incurred in such perks. This can hopefully be applied to energy conservation, except that there is no clear stakeholder involved here – the environment benefits, but a clean environment is a public good. From economic theory, such a public good will not be funded at all. Hence a solution would have to involve the government. One way to go about this is to continue the healthcare approach – the government can offer tax breaks or refunds for reduction in energy usage, either in absolute or relative terms. Such a measure would be funded by discounting the fact that due to the reduces energy usage, less money would be needed to maintain existing power generators, or build new power plants.

To a Sustainable Future

With all these strategies being bandied about, one thing is almost certain if OPOWER sticks to its current method. It might be useful to consider the fact that people will tend to be numbed to such messages after some time. Energy conservation is not a Boolean entity. Consider the fact that some people might use more energy than other households, simply because there are more people living in it. How would we prevent such users from being discouraged and not care about energy conservation? To take a leaf out of obesity studies, most dieters successfully lose weight. However, when their weight loss flattens out, they inevitably get discouraged and start to eat more. To bring in the energy usage perspective, the most avid energy reduction users would, in the near future, start using more energy. What is needed then is a way to engage them over a long run.

One possible idea is the use of dynamic messaging. Instead of saying the same “You use xx% less energy than other households”, it might be possible to present the information in a different layout every time, like in infographics. Appealing to people’s emotions is also a good way. For example, “From the electricity you reduced this month compared to last month, you have provided enough electricity to power a school in less developed countries for 24 days.” Instead of bandering about with terms like kWh which most users do not understand, this strategy actively engages people with the real-world consequences of their actions.


In this short write-up, I considered the future of OPOWER as the authors of the Harvard Business School case study raised in the prologue and epilogue of their case study. I argued the need for the government to intervene because of a conflict of interest in energy conservation, as well as suggested possible amendments in data representation to avoid the dreaded boomerang effect. Finally, I also explored the possibility of changing how data is represented – not only to keep people engaged, but to allow them to see the real-world effects of their energy reduction (or increase).

Auspicium Melioris Aevi

Future Discussion

  • Do you think OPOWER should consider telling everyone that they are above average energy usage, even though they are not, thus doing away with the boomerang effect? The advantages of such a strategy is obvious, but is the key issue here ethics?
  • For those statistically inclined, would a percentile based approach really do away with the boomerang effect.




Nudge Effects and Government Intervention in Public Health – Oped #1

My interest was piqued on the concept of nudge theory when I read about a study which successfully got high school students to make healthier food choices in their school canteen, simply by making the choice to choose fruits and vegetables more convenient, attractive and normative [1]. It got me thinking about the potential of nudging in public governance and regulation, a field that I am particularly interested in. It is plausible to extend the application of nudge theory from a hyperlocal space (like a school tuck shop) to a neighbourhood or even the entire country. Indeed, this concept was broached albeit briefly in class when we discussed how we can use nudge effects to persuade people to increase voter participation in the recent judicial elections. Amongst the points brought up were getting people to like the candidates’ Facebook pages, or telling people the importance of the elections. In this post, I explore the use of nudge theory as a substitute for public regulation in the health domain. I will describe each strategy in turn and end off with a nuanced evaluation of their effectiveness.

On Government Regulation

There is no lack of literature in the use of nudge effects in government regulation. In fact, Cass Sunstein who posited the idea of nudges used to head the Office of Information and Regulatory Affairs. However, to the casual observer, it seems that most government interventions work via explicit law and regulation, rather than subtle nudges. There are a few reasons why this is so. Taking a leaf out of economic theory, it is known that government regulation has both demand-side and supply-side portions. An example of the former is an outright ban on smoking in public spaces, hence making consumers smoke less. In contrary, a supply-side decision would entail imposing a hard limit on the number of cigarettes all producers can produce, hence reducing the amount in the market. Both serve the same purpose – to reduce public smoking. To this end, they work very well. Every 10% increase in tobacco prices result in a 4% fall in demand from consumers (2). Further, in America, such policies have been so effective that tobacco companies have looked to other developing markets to increase their revenue since growth locally have been stagnating. As can be seen, government regulation seems to be an effective measure. This arises from the irrationality of most people. Given perfect information about the health risks of smoking, people still tend not to reduce consumption. Advertising is one possible cause of this irrationality – smoking is portrayed to be hip or cool. In this respect, the heavy hand of the law such as adopting public smoking bans is required. This moves people away from accepting public smoking as a social norm. Hence, government regulation is seen as effective by many.

However, social psychologists have pointed out that government regulation may not be effective. For example, the problem of boomerang effects. Boomerang effects occur when a measure is used to bring about a specific intent, but the opposite of that actually happens. In essence, government regulation can backfire and worsen the situation. Continuing the case on smoking, in Asia, there have been massive spikes in the consumption of illegal cigarettes due to tax duties imposed by the government (3). Illegal cigarettes do not have the same quality control as legitimate ones and could contain more poisonous chemicals in their manufacture, hence it brings more public harm. Whilst government regulation is intended to reduce the consumption of cigarettes, the nett effect was to steer consumers to illicit sources where the government can control less effectively. Secondly, such regulation may not be cost-effective. It takes effort and money to properly enforce policies. Policy without enforcement is ineffective. With these disadvantages, it is not surprising that scholars have been considering nudge theory in getting people to make personally beneficial decisions.

On Nudges

Nudges were first mooted by Sunstein. He defined it as ‘any aspect of the choice architecture that alters people’s behaviour in a predictable way without forbidding any options or significantly changing their economic incentives’ (4). By choice architecture, he meant the environment in which allows people to more easily make decisions that maximise their well being. In his research paper, Marteau notes that nudges are comparatively lower in cost, and appeals to governments who do not subscribe to the in loco parentis doctrine. For example, we can make smoking less appealing by reducing smoking cues – these can include removing public cigarette stubs and ashtrays in dustbins, or getting people to sign a form acknowledging health risks each time they want to buy a pack of tobacco. By doing so, these measures attempt to nudge people into accepting non public smoking as a social norm. This can lead to cascade effects as well – if there is no second hand smoke in the surroundings, smokers will be less inclined to start smoking as well, leading to positive externalities. Moreover, by constantly reminding them of the health risks, we might possibly get them to be constantly aware of its dangers. Of course, there is the possibility that people are numbed to such a measure. This can be seen by a law in some countries forcing cigarette manufacturers to put images of people having diseases arising from smoking. Most smokers ignore the photos on the pack.

However, there are detractors who, while conceding that nudging can be effective, they can only be truly effective when combined in concert with some form of legislation. For example, how do we get shopkeepers to make buyers to sign a health risk form? It is indubitable that it needs some kind of local law to enforce it. Secondly, nudging can also be done by the private sector to further their interests as well, if we further the definition of a nudge originally proposed by Sunstein. Their strategies can be inimical to the effectiveness of government nudging; this possibly calls for some kind of intervention to render their strategies moot.

One thing is certain is that nudging has sparked interest in the wider field of behavioral theory that can ameliorate public health problems. Obesity alone costs $200 billion in health costs a year. The UK Cabinet has a Behavioral Insights Team dedicated to such issues and published a very interesting paper on the various issues. While no concrete government policy arose out of this, the paper shows how nudging is designed to still improve public health while respecting consumer sovereignty.


In conclusion, I believe the public policy challenge is clear. While nudging can be very effective, two forms of government intervention is still required – legislation to implement the nudging strategy nationwide, as well as regulation to prevent other parties from counteracting said strategy. It is also clear that government regulation is effective in itself; however, because it disrupts free market dynamics, governments have to take note on where to draw the line.

Possible Discussion Questions

  • In government regulation, there are limits as to what the government can or cannot regulate, such as free speech. Do you think the same limits apply to nudging?
  • I mainly discussed about nudging on the consumer-side in this post. Is it possible to extend nudge theory to the producer sector as well or can only government regulation help? For example, how would we nudge food companies to not use hormones and fake additives in their food, other than outright banning them?
  • What are your viewpoints on this issue? Do you think nudging or regulation is more effective?

(1) Smarter Lunchrooms Can Address New School Lunchroom Guidelines and Childhood Obesity (



(4) Nudge: Improving Decisions About Health, Wealth, and Happiness Thaler and Sunstein

(5) Judging nudging: can nudging improve population health?

(6) The Healthcare Costs of Obesity

Penned with ❤️ for 08-624 and 17-704

Influence and Persuasion – Commentary #1

Just some of the opinions I have regarding the readings this week

Before the year 2000, organizations like Apple, Pepsi and Coke used television and newspaper as the primary medium for advertising their products. They relied heavily on the 30 second commercial for creating the brand awareness. Ever since the advent of Internet, these companies have migrated to different channels for advertising. They have started employing nudges to attract more customers online. In this blog, I would be focusing on how nudges created and used by companies like Amazon, Google, and Facebook to generate revenue are invading the privacy of the users.

In 2004, Google launched its email service called ‘Gmail’. Gmail became one of the most popular and widely used email services across the world. Though it is a free service, Gmail became one of the most important apps for Google as it generated a lot of revenue through ads. This email service gathers data regarding the sites that you have visited. They also display ads that are extremely relevant to your current needs. For instance, if you just now received a mail from your friend regarding vacation in California, they display hotel’s ad as a text. This text will be placed right on the top of your inbox so that it is visible to you when you are checking your emails. By placing a relevant ad on the top, you are most likely to click on the link [1]. How do they display relevant ads in Gmail? Google reads all the emails present in your inbox and then displays an ad based on the email’s content. When you click on the ad, Google will receive the money from the advertiser.

Apart from Google, e-commerce websites like Amazon track the websites and products that you have searched so far. When a person logs into Amazon, it will recommend products based on the data they have collected relevant to search preferences. Apart from the shopping history, these companies also recommend products that are specific to your location [2]. Social networking websites like Facebook employ a different type of tactic. Apart from gathering location details, they also keep track of the pages that your friends like. If your friends like a particular product on Facebook say a restaurant in your location, Facebook will display ‘ABC and 5 others like this’ on news feed where most of your attention would be. By displaying that, you are most likely to check out what your friends have bought (social proof). If they have liked a restaurant, you are most likely to try it out.

After collecting the data, these organizations are not going to delete them. For instance, Google or Amazon will not delete your history from their servers. They are going to build on top of that and learn more about you using latest technologies. Is it safe? I think the data will be stored safely. However, there have been many instances of security breach. For instance, TJX, a shopping store, had all of the data about their users stored insecurely in their servers. Anonymous hackers hacked their store’s account and made them public [3]. Is it ethical to collect information about a user? I personally feel that it’s not ethical to collect data about you without your permission and use them for creating nudges. As discussed in the ‘Privacy Nudging’ lecture, a person can use the App Permission manager in Android to prevent apps from collecting information about you. The users can also make use of ad blockers or incognito mode for browsing using desktop. So what do you guys think? Do you think this is an ethical way of creating nudges? Please feel free to comment on this topic.


You mentioned that “nudges created and used by companies like Amazon, Google, and Facebook to generate revenue are invading the privacy of the users”. Can you elaborate on the nudges used by said companies? In the case of Gmail, you mentioned Google “places a relevant on the top”, but I think that would be tough to qualify as nudging since it lacks a user objective.

What I think would be more evident is Facebook. Every so often, Facebook will notify you on the site to do a Privacy Checkup[1] which prevents users from sharing information that they don’t intend to. Before, the privacy settings were deeply embedded in the bowels of Facebook that people do not make sure they are correctly set the way they want it to be. With this tool, more Facebook users are nudged into adjusting their privacy settings, which is a step in the right direction privacy-wise after their privacy brouhaha a year ago.

You mentioned in your views that “it’s not ethical to collect data about you without your permission and use them for creating nudges”. You gave the example of Android apps collecting unnecessary information. At this point, I am pretty confused about what your a nudge entails. Does showing an advertisement to a user constitutes a nudge? Personally I don’t think so. Sunstein defines it as anything that influences our choices that leads to *improvements* in the rationality of decisions people make. Such nudges are informed by the findings of behavioural economics.


“There is an app for everything”, so much so that Apple has trademarked the quote for their advertising campaigns. If one looks at majority of the apps developed, especially the ones using the open data from the US government, one can see that they are variations of a standard set of applications that help with credit processing, medical aid, tax filing, bill payments and so forth. This is understandable as these applications are used a lot by the public and there is the incentive for a third party to monetize through ads. Seeing the success story of one such company motivates other players to enter the domain. But how does one persuade someone to use your data for the public good?

The flaw in this model is that it takes a very long time for some player to enter into the niche markets (by niche, I mean the agencies that are not targeted by most of the third parties – Center for Dietary Studies for example), and more often than not, it does not address what the niche community might want. To encourage diversity of applications for government agencies, the US government spawned the idea of is a technical platform and list of challenge and prize competitions, all of which are run by more than 80 agencies across federal government. These include technical, scientific, ideation, and creative competitions where the U.S. government seeks innovative solutions from the public, bringing the best ideas and talent together to solve mission-centric problems. This platform is available at no cost to all federal agencies to help them list their challenge and prize competitions and learn how to engage the public through this innovative approach. To read more on this –

This approach succeeded in persuading and motivating people to create solutions that leveraged the organization’s data and create solutions specific to the target audience. Crowdsourcing the solution helped in alleviating costs for the organization and incentives persuaded the public to respond. Since this is backed by the US government, it prompted a lot of response to every government agency posting a challenge on the platform –

We can see some of the techniques we learnt in class at play. We can see Authority at play here. Since this backed by the US Government and not just any other organization, people tend to take notice and participate in the challenges more than what a standalone organization could have achieved. Also we see the role of Reciprocity at play. Incentives for creating the solution outweigh the efforts needed to put in and thus people are motivated to contribute.

I believe that is a great example of influencing and persuading third party to contribute more to projects without looking for monetization through the market. A non – profit organization for example can tie up with a well- known commercial company and organize hackathons to get the solution that they want – check out –, where this organization partnered with Harvard Med School and other big players to come up with innovative solutions for their problems.

So what do you guys think? Is latching on to big fry to promote our interests a good way to persuade someone? Looking forward to seeing your comments.


You mentioned that the name cachet afforded by the US Government led to more active participation. Perhaps the monetary prizes have a significant role in motivating involvement? Just something to think about.

As to your question if getting ‘big fry’ to promote your interests is effective or not, it all depends on what you mean by effective. If you mean performance-per-cost effectiveness, there have been studies that show otherwise. [1] This paper can be distilled into a few main points: 1) influence to difficult to measure and predict; 2) ordinary influencers might be more cost-effective than ‘big’ influencers; 3) influence works best via quantity (number of influencers) rather than quality (fame of influencers) For example, in a case study, Lady Gaga and Michelle Phan were given brand endorsements for a cosmetic product on Twitter. Even though Lady Gaga had almost 100 times more followers than Michelle Phan, it was the latter that got more clicks on the Twitter link. A possible explanation is that when your social network is smaller, it becomes more personable and hence followers are more likely to engage with your content more. As can be seen, how popular the person or company is might not be a determinant of effectiveness in influence and persuasion.



Penned with ❤️ for 08-624 and 17-704

An Expanding and Expansive View of Computing

I attended Jim Kurose’s talk on the advances in computer and information science and engineering primarily because I wanted to gain some insights on the state of technology today, as well as get his novel interpretations of the future of the field, being at the crossroads of both industry and academic in his role as Assistant Director at the Computer and Information Science and Engineering (CISE) Directorate at National Science Foundation (NSF).

During the first half of the talk I learnt that the National Science Foundation mission was more than just advancing science without a specific aim. I had always thought that grants were given out as long as the idea seems plausible and if it succeeds, would boost the development of science and technology to a certain degree. It turns out that NSF’s aims to advance science so as to boost national health, economic wealth and social welfare as well as defense. There are 5 main arms that CISE is focusing on – these 5 are part of the White House Initiatives. These include data science, smart systems, understanding the brain, smart cities, and a nationwide strategic computing initiative. Of those facets, I am most familiar with the one on brain science with the creation of the BrainHub to look into brain science at CMU. I believe this was done after President Obama announced an initiative to research into brain science so as to find new ways to treat disorders such as Alzheimer’s or autism. I think these are great domains to research into, though we should not neglect the security aspect of it. Sometimes, trying to network anything leaves us more susceptible to attacks via side channels. I believe that an engineer can add value to brain research. For example, in 18-220, we learnt about Telegrapher’s Equations in a coaxial transmission line which incidentally can be used to model the transmission of an action potential, an electrical impulse through the axon of a neuron in our nervous system. After all, both science and engineering are cross and multi-disciplinary – both fields are not mutually exclusive.

He later talks about the details of CISE such as its organization chart and finances, which aren’t that all interesting. However, the partnerships that NSF has forged are essential to their success at each cycle of development – from discovery to innovation. This can be seen at present as many companies license patents from universities to be used in consumer-facing products. This way, researchers can leverage their resources and increase the speed at which R&D is being conducted.

Kurose then talks about each emerging field of CISE. He recognizes that a national-scale experimental infrastructure would benefit research so that parallel computation data can be exchanged across institutions from both coasts quickly. He proposes the use of a public cloud infrastructure to achieve this. He also goes into the central theme of his talk, which is that computing should focus more on societal applications, while not neglecting the human aspect of it. It is also beneficial to let computing be pervasive around us, to the point that it does not get in the way of everyday life (ie. it just ‘knows’). Finally, he notes that investment should not solely be poured into upstream research, but also into its most basic tenets – quality computer science courses for students. With that, they have developed pedagogies and curricula for a PreAP as well as Advanced Placement course in Computer Science Principles. I think this is a far-sighted move on their part since there is likely to be more positive externalities in quality computer science education. One proposal to think about for engineering is whether our pedagogy can transition to a more application-based, hands-on education, rather than the systematic, theory-based one we have now.

In sum, I feel that I gained some insights into the role of NSF and what they have done in improving the quality of research and education. However, he did not cover some of the points he promised to make in the questions the audience posed in the middle of the session, such as the one on Power and Water sustainability – how much research has been done in ensuring such natural resources remain available into the future? Also, I would actually like to learn more about the NSF funding process – how grants are given and what the process is like. Last year, I went for a talk on patent registration and I found the whole process very enlightening – I was hoping this talk would shed some light on this process.

Thoughts on Yelp Tech Talk

I attended the Yelp Tech Talk, organized in partnership with Society of Women Engineers. It was held at Margaret Morrison Room A14 on the evening of 29th September. What appealed to me about the event was the topic on Yelp online advertising and how Yelp optimizes it so that everyone benefits – the advertiser gets a steady stream of customers, customers get to discover great new food, and Yelp earns a cut from the advertising fees. Personally, I have been keeping abreast of the pay per click advertising industry because it is a fascinating field. From advertising optimization to fraud detection to machine learning analytics, there are many domains in advertising which interest me. Hence, I wanted to explore how Yelp does things differently from Google, if they are.

The first half of the talk was more of a pitch to tell students how great Yelp is, and how each Yelp employee can look under the hood and work on a feature based on feedback from customers. They gave the case study of a person asking Yelp why they could not order take-out directly on the Yelp website, when they were already searching for what to eat. Subsequently, he discussed how his team made an API, or an interface, for other companies to integrate their services on the website in a tighter way. For example, OpenTable is able to use this special API link to display the number of free reservations on Yelp, while other on-demand services can choose to provide deliveries for a particular food place.
What I garnered from this segment of the talk is that it does not pay to go all-in to every segment in your market. Rather than creating a whole new subsidiary like Yelp On Tap to provide food delivery or reservations, Yelp chose instead to provide the necessary infrastructure for other companies to more tightly integrate their services on Yelp’s website. This is counterintuitive to many business people – after all, why lead people away from your site when you could be the one profiteering off this new segment? However, I believe Yelp might have reasons for doing this even though it was not discussed. Firstly, they might not have the necessary knowledge to succeed in that niche. OpenTable probably knows how to do it right; why reinvent the wheel and try to compete with them? Secondly, Yelp is a lean company and they might not want to waste resources on things that do not add any innovation to the industry. After all, integrating services is not innovation. Finally, they might be emphasizing Yelp as a platform, much like how Facebook is. When Facebook tightly integrates features like embedded YouTube videos on their site, they are not leading users away; rather, they actually gave more reasons for users to remain on the website since they can watch the video on Facebook. Similarly, Yelp users do not need to navigate to another app to make a reservation – they can do it all from within the app.

The latter part of the talk discusses the Yelp Auto Bid Genius, which is an internal algorithm which determines what ad to show to customers. She demonstrated that the highest paying ad does not necessarily get the top spot – a combination of click through rates as well as bidding rates do. She discussed about Yelp ad formats, including click-to-call where the advertiser pays each time a call is made to the business from Yelp. I was disappointed that she didn’t go in depth in exploring conversion tracking – how effective advertising on Yelp can be when measured quantitatively. Yelp advertising can cost up to $600 for every 1000 times an ad is shown, which makes it insanely expensive. There are many occurrences of competitors viewing a particular ad many times to drain the advertising budget of their opponents. How does the business know that the customer came via an ad on Yelp? Some of the methods off my head include using special coupon codes which can be assigned to the viewer of the ad. However, I was wondering if there is anything Yelp can do to further increase the tracking and engagement of its users. For example, if I view a listing on Yelp and I go to that place to have a meal, paying for it using my Yelp-branded credit card, then Yelp can perhaps give me a 5% loyalty rebate for using Yelp. Although Yelp technically loses money, it will gain a lot of insights into the users and activities of Yelp. Of course, the privacy concerns must be looked into, but as it is, Yelp engagement is practically non-existent. Users only fire up the app or website only when they need to, which can be a good thing too, since this means each visitor on the website is a ready paying customer.

It was an interesting evening to think about advertising on a ‘new’ platform like Yelp. I was previously more acquainted to text ads on Google and those intrusive display banners on the web, but I have gained some appreciation of what it takes to make advertising on Yelp more effective, and the technology to make it work effectively.

Just an ordinary guy, living in an extraordinary world