With over 1.2 billion reported users and close to $200B in market capitalization, Facebook is undoubtedly the most ubiquitous social network today. For most users, the core value proposition of Facebook is simple – it is a means to stay connected with their friends (and acquaintances) and to share and learn about each others’ lives. And yet, over the years and over countless tweaks to Facebook’s NewsFeed algorithm (popularly known as EdgeRank), more and more users complain that they don’t get to see any updates from a majority of their friends. Indeed, the average user has over 300 ‘friends’ on Facebook, but thanks to Facebook’s determination of what’s relevant, they are likely seeing updates from only 20% (or less) of their network. What’s going on? Why is it that I have over 1200 ‘friends’ on Facebook, yet I never see anything from almost a 1000 of those? I used to believe they simply didn’t post as much, until I checked out several people’s profiles and saw major updates I would have liked to see, but never saw, despite logging in several times a day. Why is it that I see some stories over and over for days, and several never appear?

Keep it simple….

Several hours of tweaking Facebook settings, privacy controls and reading Facebook optimization controls told me one thing – it’s complicated by design. There is a lot on Facebook that’s simple and intuitive, but customizing your experience is definitely not. There is an option to sort your feed by ‘Most Recent’ but all it does is sort the pre-selected ‘Top Stories’ into reverse chronological order of any action taken by anyone, thus being not helpful at all as it doesn’t introduce new content and in fact increases repetition. You can unfollow or block users, you can tweak content settings for people and types of content individually, or you can organize your 1200 friends in lists you then follow (like really?). For the average user, it is too much to ask, but I’d venture to say that even for power users, it doesn’t really help much.

They have the edge

EdgeRank works in mysterious ways, and the best one can gather is that Facebook measures and ranks ‘edges’ connecting any one user to another user (or Page, Group, Brand etc) by the strength, time delay and frequency of their interaction. However, only active interactions count, i.e. liking, commenting, following or sharing. So if you passively enjoy reading someone’s updates but don’t actively ‘like’ them, chances are you’d stop seeing updates from them sooner than later. This is true for both your friends as well as pages you may have liked, unless of course they pay Facebook to promote the post. The problem arises when over time you see what you like becomes you like what you see, making your Newsfeed populated by the same subset of users and content types and effectively limiting the reach of content. And lest you figure it out, they tweak (and AB test) EdgeRank all the time. So you may not even realize that the reason some of your real world friends don’t comment on your exciting Facebook updates may be that they actually never got to see it, for no lack of intent whatsoever.

“Trust us, we know what you want to see”

Let’s face it, Facebook does know a lot more about us than we think. As long as you’re signed in, Facebook knows not just what you ‘like’ and who you stalk on their website, but also most likely what articles you’re reading and what websites you’re surfing for how long. Besides, information overload is a real problem. Between friends’ updates, activities, engagement content and brands, Facebook estimates they have thousands of news stories to show every user at any point. Surely some stories are better or more important than the other for every user. But by Facebook’s own estimate, only 0.2% of these stories are ever shown to the user. With no easy way to even access the remaining 99.8% and no straightforward explanation of how those 0.2% are determined, it is unsurprising that I see check-ins every time my dorm neighbour gets down to eat and I totally missed the news of wedding and first child of my high school best friend. And these were happy stories – considering Facebook doesn’t want users to not see many ‘negative’ emotion stories, I wonder what all I’ve missed that would have been relevant to know. Or not.

It’s all about the money, honey

All this brings me to the business of Facebook. It is not so hard to gather that the purpose of ‘optimizing’ your NewsFeed is as much to show you the most relevant updates from your friends as it is to show you ‘relevant’ sponsored stories by those that pay Facebook by creating real estate. Facebook marketing is, after all, a fast growing and rather effective (for now) channel for most brands’ marketing efforts these days. One can argue that, after all, it is a free service that Facebook is providing to the users and they deserve being compensated in some way for it by selling part of the user engagement it creates to the brands who want them. And these are brands the users want too, demonstrated if not explicitly by subscription then implicitly based on their behavior as Facebook understands. Perhaps the users shouldn’t complain so much, after all. Sure, they don’t get a perfect experience and sure, there are a few ethical questions because users don’t really understand how they are being manipulated. But what about the brands themselves?

Thousands of advertisers have spent precious time and money over the years building up reach on Facebook pages, but sometime last year they realized that all of a sudden their messages weren’t being shown to all the users who had ‘Liked’ and previously engaged with their page, never mind to new users. So unless they pay for each posting, or the user is a dedicated follower who actively engages with every piece of content posted since the beginning of the change, Facebook’s reach for most brands is basically a myth and the promise of building an engaged community with two-way communication hollow. I wonder how sustainable this is, in the long run, especially as Wall Street maintains earnings pressure on Facebook and non-advertising revenue on the website continues to slip.

Bottomline, friends are not really friends on Facebook. Fans are not really fans. Don’t like the Likes too much.

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Over the past semester we have identified successful mobilization for new lines of business in tech incorporate backwards compatibility, complements that may be generated by third party developers or by the core business itself, and solutions to customer pain points with significant barriers to entry.  At the same time, core product offerings can provide stand-alone value to customers through simply streamlining the interface between software users and the data they use. While many large companies have adopted software to improve productivity of individual enterprise activities, it is the recent push towards integrating business technologies onto one platform in enterprise marketing software suites (EMSS) that unlocks the opportunities inherent in holistic marketing solutions. [1]

The beginning of the twenty-first century witnessed a revolutionary incorporation of the internet with everyday communications and transactions.  This in turn generated an unprecedented volume of information, or “big data”, encompassing how consumers make decisions and how businesses operate more efficiently.   Big data analytics now facilitate myriad entrepreneurial ventures that leverage niche markets and long-tail consumer demand into viable business models.  Consequently, traditional companies in established markets have had to redesign and streamline how they serve their customers to match changes in consumer demand and increased global competition.  Enterprise software has emerged as an answer for these established companies to utilize big data analytics to guide their business strategies.  EMSS  aim to integrate a diverse range of activities including management of ad campaigns, digital assets, web content, marketing and lead resources, as well as predictive modeling. [2]  

The complexity associated with integrating diverse marketing software solutions has left EMSS development to big software players such as Salesforce.com and Adobe. In fact, the magnitude of the opportunity to create value in this space is demonstrated by the “…$3.5 billion shopping bill as it positions Salesforce as a one-stop-shop for all its customers from the sales department to, now even more importantly, the CMO’s office.”[3]   Expected benefits from EMSS consolidation of current disparate marketing and tracking software are improved visibility and collaboration between all marketing channels resulting in clear resource efficiencies and reduced total costs of strategy implementation.  Additionally, strategy development should be of higher quality and larger impact due to improved ability to create holistic solutions aligning company offerings with customer expectations. As of yet, it seems that no one EMSS incorporates both best-in-class software and seamless integration [4].  It will be interesting  to watch how the current digital marketing integration leaders discussed above shape the convergence of real-time data analytics and holistic marketing strategies to transform online and mobile commerce as they further penetrate our global economy.

1. Teradata http://applications.teradata.com/Big-Data-Hero-eBook/Landing/.ashx (October 30, 2014)

2.  Teradata http://applications.teradata.com/Big-Data-Hero-eBook/Landing/.ashx (October 30, 2014)

3. Salagar, Serge, “Salesforce’s Reinvention as a Marketing Behemoth”, http://techcrunch.com/2014/10/29/salesforces-reinvention-as-a-marketing-behemoth/  (October 29, 2014)

4. Munbach and Warner,  “Forrester Wave: Enterprise Marketing Software Suites, Q4 2014” http://www.adobe.com/solutions/digital-marketing.html   (October 21, 2014)

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In the online era of social networks, e-commerce and the increasing culture of sharing despite concerns about privacy online, the amount of everyday personal data-points that we generate is unimaginably high. At the same time, data analytics and profiling techniques have started to catch up, enabling companies to capture, track and analyze all this data, and create sophisticated consumer profiles.

The advent of big data analytics has not been related only to the online environment. Most major retailers have for a long time focused on collecting vast amounts of data on their customers, and invested in “predictive analytics” capabilities to better understand their consumers’ preferences, lifestyle and habits and in this way target them more efficiently. The story of “how Target figured out a teen girl was pregnant before her father did” is the perfect example of how companies are able to put together different pieces of information collected directly from consumers or purchased from other sources to create profiles (in this particular example assigning a “pregnancy prediction score”) to better target sales efforts.

Another example is how insurers are increasingly using big data to help improve their risk profiling and thus pricing, like car insurers monitoring driving data collected to help augment consumers risk profile. While still small at only about 2% of the US car insurance market now, analysts expect this insurance segment to grow to 10-15% of the market by 2017. At the same time, most insurers seem to be using social data in claims (like the infamous case of Nathalie Blanchard who was denied health insurance benefits based on pictures she posted on her private Facebook account while she was on long-term leave for depression) and underwriting, to evaluate customer health.

Online has been a game changer in terms of the data sets that companies can capture and analyze, and thus what they know about us. We willingly give away personal information to be able to use certain services or get personalized offers and experiences – according to a study conducted by Accenture, while 86% of respondents were concerned about their data being tracked, 85% understood that this information enabled online retailers to present them with more relevant, targeted content; almost half said they accepted to have their data tracked by trusted in exchange of a personalized shopping experience.

But are we really aware of the amount of our personal information is captured and used (directly or sold) by different entities? Take Google’s decision to integrate user information across various applications and services (around 60 in total). Think about it, if you are a Google user, it means that the company knows about every single search you’ve conducted from your computer or mobile phone, every single e-mail you have received or sent from your Gmail account, every single video you’ve watched on YouTube, every single calendar appointment, location data, contact, personal and political views that you’ve ever discussed on Google hangouts and so much more. And while I think this is a great business idea to integrate information and use it across services, and that it can translate in a more intuitive Google user experience, I do not think the vast majority of users really understand the implications of Google owning this data across platforms.

In the digital world dominated by Google, Facebook, Amazon etc. and as predictive analytics has made it easier to piece together information about individuals, national and international regulators are struggling with the challenge of how to deal with personal data protection. The European Commission drafted a new General Data Protection Regulation (GDPR), to help individuals get more control over their personal data online. Key changes include a “right to be forgotten” to ensure that unwanted data is no longer processed or retained, but deleted upon request. Also, whenever data processing requires consent it will have to be explicitly requested. Companies will also have to guarantee easy access to one’s own data. But even if these regulations get enforced, does it really protect the individual from the personal information derived from big data analytics – like the Target pregnancy example, or the health benefits example? And this is only the private sector perspective, but there are plenty of recent examples to indicate that our personal digital information can also be easily tracked and accessed by the government.

Someone told me recently that Google can supposedly tell who you are based on 5 google searches you make – if this is anywhere close to reality, the reality is pretty scary. I personally am not too excited about giving up that much privacy for “personalization” or better experience, unless I am convinced that personal data protection is in place, and right now that does not seem to be the case. I see this as a tension point between the advances in customer experiences that personalization and digitalization bring, and the lag of development of personal data protection approaches.









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This summer, I worked at McKinsey & Company, serving a small specialty consumer finance company that sought to enter the personal loans space. The company was the leader in the “structured settlements market” (aka – they are in the market of giving lump sum cash at a discount to people who have won a personal injury tort lawsuit but whose payments are staggered over a period of time). However, this was a stagnating market and they were seeking ways to grow. They used direct marketing to target customers, often through commercials aired during daytime television shows such as Jerry Springer.

Early on, we identified that personal loans would be a good space for our client to consider, given that they received a vast number of calls from people who had no idea what a structured settlement was but were looking for a loan. These clients actually had pretty good credit (FICO scores of above 660 or “prime”) but were shut out by the traditional credit markets because they often did not have collateral to back up their need for credit. We felt that our client could very easily broker leads to a personal lender and in a few years do an acquisition in this space. Further, we felt that online personal lenders represented an interesting acquisition opportunity, given that our client did not operate any brick & mortar stores and wished to limit their geographical footprint.

I was tasked with scouring the universe for online personal lenders and identifying companies that that had a high quality business model, were led by strong management, and also fit with our client’s caller base. In the process, I discovered some fascinating things.

In particular, I discovered a lot of extremely shady, underhanded companies that will exploit every loophole in the law to charge usurious interest rates to customers. Generally, regulatory bodies are quite strict on how much a lender can charge in the form of interest rates. However, there are some online personal lenders that have formed partnerships with Indian tribal lenders (who do not fall under traditional state jurisdiction) in order to charge ridiculous interest rates and do business in states that they would otherwise not be able allowed to touch. We normally consider doing business online as being more transparent and straightforward. Unfortunately, my research showed that there are a lot of lenders that have intentionally used an online business model in order to hide information and mislead customers.

On the plus side, there are a lot of extremely innovative companies that are using creative ways to both underwrite customers as well as advance personal loans to markets that did not historically have access to credit. For example, Zest Finance (founded by the ex-CIO of Google) uses big data to re-invent underwriting and target the underbanked. They look beyond FICO scores to everything from technology usage and social media presence to determine the creditworthiness of an applicant. According to their website, their “machine learning-techniques and large scale data analysis” has led to higher approval rates and lower default rates.

Another company I found was interesting was a California based online lender called LendUp. Their website is designed to help borrowers rebuild their credit profiles. LendUp advances loans at pretty high interest rates to first time borrowers but rewards them for timely repayment and for completing credit education courses. In time, these borrowers become eligible for longer term loans at lower interest rates.

Finally, my research led me to discover a niche space within the world of online personal loans called “peer to peer lending.” Several online platforms, such as Lending Club act as an intermediary between lenders such as you and I seeking to make a return, and borrowers who do not want to take a loan from a bank. These companies also rely on a strong underwriting model and enable lenders to split up their loan into small amounts (for e.g. giving a $10 loan to 10 different individuals) order to diversify risk. I found it interesting that these companies were democratizing the process of acquiring a personal loan, and believe companies such as Lending Club could disrupt traditional financial institutions in the near future.

I feel my summer internship only hit the tip of the iceberg of this fascinating space and would love to see how the market evolves as more people look online as a means by which to consume more financial goods and services.


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Big Data in the Online Economy

Everyone is talking about “Big Data” and the amount of interest in this area from investors and even the government is currently huge. Online/internet companies like Google, Amazon, Facebook have be

en the lead adoptors of this technology and are also in the forefront of innovations in this space. It would not be too far-fetched to claim that “Big Data” is one of the engines driving the online economy .

What benefits does this massive data processing capability provide?

For companies, one of the primary benefit is improved operational efficiencies in targetting the right customer base – whether it is Google and Facebook using your browsing/searching history and online activity to sell targeted ads, Amazon providing better recommendations for product purchases, or Target using your purchase history to send you coupons. In the semi-online world, “big data” is enabling new discoveries in the health sciences field and other scientific endeavors, helping intelligence agencies with better analyses, driving more operational efficiencies in supply chain management, Khan Academy is using it to better evaluate students, insurance companies are using massive data sets to predict risk better and quicker, hubway is using it to provide improve locations and capacity to best serve cyclists, sports teams (money ball!) are using it to evaluate players, and even presidential election are using it to win elections! There is also a push towards using big data for more data oriented decision within organizations with the hope that it will result in better business outcomes.

For customers, these advances in big data processing have made the online browsing experience more customized. Imagine LinkedIn without its “Jobs You may be interested In” feature or Amazon, YouTube, New York Times etc. without its recommendation engine. New frontiers are also being explored. A startup travel company in the Boston area is exploring the idea of using individual browsing and search histories and online social activity to provide you with travel destination recommendations which you most likely to enjoy. Big data analytics combined with “crowd sourced” intelligence is another approach leading to some very interesting products. A blog post earlier in this semester dealt with

The Downsides of Big Data
There is also a potential downside to this advance – the social and legal implications of governments and organizations posessing so much information about individuals is not completely clear yet. There is literally a “data grab” going on and there have been some obvious cases of invasion of privacy. A retail chain knowing when women are pregnant before their families do, or a group of students working on a class project being able to predict a person’s sexual preferences based on the person’s facebook activity are just two instances of this. If enterprises – with their primary objective of profit making – know so much about you, how will they use this information? In the best case scenario, people will get information really relevant to them. In the worst case scenario, it could lead to some kind of “online discrimination” based on your online activity or even something insidious as information control – companies or governments deciding what information people see and when. Whichever way it plays out, it is definitely going to be an interesting ride in the near future.

References and Articles:


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