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The Business of Social Games and Casino

How to succeed in the mobile game space by Lloyd Melnick

Month: March 2016

Why Blue Ocean is actually the safe route

Last year, I spoke at Casual Connect in Tel Aviv about Blue Ocean Strategy in the game space (see presentation below) and multiple people commented to me how this approach was great but too risky. The belief is that while overall Blue Ocean strategy would be the best approach, it was too risky from a career perspective to pursue. What they missed, and what I obviously failed to convey during the presentation, is that it is actually less risky to pursue a Blue Ocean strategy than a traditional strategy.

The core of Blue Ocean Strategy is that rather than trying to win against entrenched competitors you find and target uncontested market space where the competition is irrelevant. Red oceans are a known market space with many competitors where you fight for market share. In red oceans, it is all about beat the competition and exploiting existing demand. Blue oceans is an unknown market with few competitors where you are creating market share.

blue ocean

Why people think Blue Ocean is riskier

The reason many people feel that red oceans are less risky is the fact that you are competing in a known rather than unknown market space. The unknown is always scary, be it going into space or a haunted house. In many ways, it is scarier in business where people must make their own decisions rather than basing decisions on what somebody else has done successfully. People thus transfer this fear of the unknown to Blue Ocean being a riskier strategy.

The reality

The reality, however, is that it is riskier to follow a Red Ocean strategy of trying to “win” against your competitors. The Blue Ocean Institute at Insead can point you to multiple academic studies that show Blue Ocean strategy has a higher ROI than traditional Red Ocean competition. While you can be successful following a Red Ocean strategy (there are myriad examples of companies that have dominated their space by competing better than their peers, such as Disney, Exxon and GE), overall the results from pursuing a Blue Ocean strategy are likely to surpass the results of competing in a Red Ocean. Bringing it back to the risk assessment, your personal professional position is more secure the better your results. At the end of the day, the leaders who deliver the most appealing P&L are the ones who survive and advance (had to drop in that phrase given the upcoming NCAA Tournament).

Some might argue these statistics are a long term play and in the short term it is still riskier to try something new than just tried to beat your competitors on the battlefield. The long term results play out over time but a Blue Ocean strategy creates the opportunity for a quick debacle, if you launch a completely new approach and it shows no traction.

Again, the reality does not justify the fears. It is not that a Blue Ocean strategy has no risks, since it is a new approach there may not be a market for it, but Red Ocean strategies are also incredibly risky. Competitors are smart and always improving. Copying their strategies will always leave you behind them and the gap between you and your competitors is likely to widen.

The Zynga example

Zynga provides a great example of both Blue Ocean success and Red Ocean failure. When Zynga first launched in 2007, it was a Blue Ocean company. Rather than competing the game space by creating more beautiful games or spending more on advertising, they brought a new business model to the United States, free-to-play gaming (they may not have been the first but they were among the first, so let’s not get hung up on this). Moreover, rather than compete in traditional channels with other game companies, i.e. Circuit City, Egghead, Best Buy, etc., they focused on Facebook as their primary channel. The result was a company that at one point had a valuation over $10 billion and saw many leaders enjoy very appealing compensation.

In the last few years, Zynga’s strategy has apparently evolved into winning against other social game companies. They would see successful games and fast follow. Without going into too much detail, in the time I was there I saw the leadership of almost every game team (outside of our slots products) turn over at an incredible pace, with some game teams going through 2-4 General Managers in less than two years. If you compare the job security (and thus amount of risk faced) between Zynga in the Blue Ocean days and the Red Ocean days, the Blue Ocean was clearly a less risky period.

Net net

The bottom line is that business is risky. Yes, Blue Ocean strategy may fail and you can lose your job. Red Ocean strategy, however, also can fail and leave you in an equally precarious situation. Given the evidence that Blue Ocean strategy yields superior results to Red Ocean and results drive your personal professional success, Blue Ocean is actually the less risky strategy to pursue.

Key takeaways

  1. While most agree that a Blue Ocean strategy has the highest long-term returns, many fear it is too risky to pursue from a personal career perspective.
  2. The reality is that it is actually the less risky approach, as the underlying odds favor success via a Blue Ocean approach.
  3. Given the challenges of trying to win in a competitive industry, finding an uncontested market and growing it is a less risky approach.

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Author Lloyd MelnickPosted on March 30, 2016April 11, 2020Categories blue ocean strategy, General Social Games Business, General Tech BusinessTags blue ocean, blue ocean strategy, zynga4 Comments on Why Blue Ocean is actually the safe route

How to manage your algorithms

While everyone is focused on creating the most advanced algorithms for their predictive analytics and optimizing your team’s performance, I have not seen anything on how to manage your algorithms. A great article in Harvard Business Review – Algorithms Need Managers, Too by Michael Luca, Jon Kleinberg and Sandhil Mullainathan – does a great job of combining the two issues and providing a solution.

The authors begin by pointing out most businesses rely on predictions throughout their organization. The decisions can range from predicting a candidate’s performance and whether to hire them, what initiatives will have the highest ROI and what distribution channels will yield the most sales. Companies increasingly are using computational algorithms to make these predictions more accurate.

The issue is, if the predictions are inaccurate (and although they are computer generated, they are still predictions not facts) they can lead you into bad decisions. Netflix learned this the hard way when its algorithms for recommending movies to DVD customers did not hold when its users moved to streaming. More relevant to digital marketers, algorithms that generate high click through rates may actually bring in poor users not interested in your underlying game or product. As the authors write, “to avoid missteps, managers need to understand what algorithms do well – what questions they answer and what questions they do not.”

How algorithms can lead you amiss

An underlying issue when using algorithms is that they are different than people. They behave quite differently in two key ways:

  • Algorithms are extremely literal, they do exactly what they are told and ignore any other information. While a human would understand quickly that an algorithm that gets users that generate no revenue is useless, if the algorithms was just built to maximize the number of users acquired it would continue attracting worthless users.
  • Algorithms are often black boxes, they may predict accurately but not what is causing the action or why. The problem here is that you do not know when there is incomplete information or what information may be missing.

Once you realize these two limitations of algorithms, you can then develop strategies to combat these problems. The authors then provide a plan for managing algorithms better.

Slide1

Be explicit about all of your goals

When initiating the creation of an algorithm, you need to understand and state everything you want the algorithm to achieve. Unlike people, algorithms do not understand the implied needs and trade-offs necessary often to optimize performance. People understand the end goal and then backward process how to best achieve that eventual goal. There are also soft goals to most initiatives, and these goals are often difficult to measure (and thus input into your algorithms). There could also be a goal of fairness, for example a bank using an algorithm to optimize loan behavior may not provide enough loans in areas where it feels a moral obligation to do so. Another example could be where you may want to optimize your business units sales but the behavior could negatively impact overall sales of your company.

The key is to be explicit about everything you hope to achieve. Ask everyone involved to list their soft goals as well as the primary objective. Ask people to be candid and up-front. With a core objective and a list of concerns in front of them, the algorithm’s designer can then build trade-offs into the algorithm. This process may entail extending the objective to include multiple outcomes, weighted by importance.

Minimize myopia

Algorithms tend to be myopic, they focus on the data at hand and that data often pertains to short-term outcomes. There can be a tension between short-term success and long-term profits and broader corporate goals. People understand this, computer algorithms do not.

The authors use the example of a consumer goods company that used an algorithm to decide to sell a fast-moving product from China in the US. While initial sales were great, they ended up suffering a high level of returns and negative customer satisfaction that impacted the brand and overall company sales. I often see this problem in the game industry, where product managers find a way to increase in-app purchases short term but it breaks player’s connection with the game and long-term results in losses.

The authors suggest that this problem can be solved at the objective-setting phase by identifying and specifying long-term goals. But when acting on an algorithm’s predictions, managers should also adjust for the extent to which the algorithm is consistent with long-term aims.

I recommend using NPS to balance out short term objectives with the long-term health of the product and company. I have written before about NPS, Net Promoter Score, which is probably the most powerful tool to measure customer satisfaction. It is also highly correlated with growth and success. By ensuring you keep your NPS high, you are providing a great way to look holistically at the success of specific initiatives.

Chose the right data inputs

Using the right data can make your algorithms much more effective. When looking at a game like Candy Crush, you can create levels by looking at when people abandon the game and decompose the levels before abandonment. However, by adding social media posts to the your data, you could get a more holistic view of which levels players are enjoying and thus build a more compelling product.

The authors also point to an example with the City of Boston. By adding Yelp reviews to what health inspectors use to determine what restaurants to inspect, they were able to maintain their exact same performance but with 40 percent fewer inspectors. Thus, the new data source had a huge impact on productivity.

The authors point to two areas of data that can improve your algorithms:

    • Wider is better. Rather than focusing on more data, the amount of data you know about each customer determines the width. Leveraging comprehensive data is at the heart of prediction. As the authors write, “every additional detail you learn about an outcome is like one more clue, and it can be combined with clues you’ve already collected. Text documents are a great source of wide data, for instance; each word is a clue.”
    • Diversity matters. Similar to your investment strategy, you should use data sources that are largely uncorrelated. If you use data that moves closely to your data sources, you will have the illusion of using multiple data sources but really only be looking at one angle of the data. If each data set has a unique perspective, it creates much more value and accuracy.

Understand the limitations

As with anything, it is also critical to understand the limitations of algorithms. Knowing what your algorithm will not do is equally important as understanding how it helps. Algorithms use existing data to make predictions about what might happen with a slightly different setting, population, time, or question. “In essence, you are transferring an insight from one context to another. It’s a wise practice, therefore, to list the reasons why the algorithm might not be transferable to a new problem and assess their significance,” according to the authors.

As the authors point out, “ remember that correlation still doesn’t mean causation. Suppose that an algorithm predicts that short tweets will get retweeted more often than longer ones. This does not in any way suggest that you should shorten your tweets. This is a prediction, not advice. It works as a prediction because there are many other factors that correlate with short tweets that make them effective. This is also why it fails as advice: Shortening your tweets will not necessarily change those other factors.”

Use algorithms, just use them smartly

This post is not intended for you to avoid using algorithms, it is actually the opposite goal. Algorithms are increasingly powerful and central to business success. Whether you are predicting how consumers will react with a feature, where to launch your product or who to hire, algorithms are necessary to get great results. Given the central importance of these algorithms, however, it is even more crucial to use them correctly and optimize their benefit to your company.

Key takeaways

  1. Algorithms are increasingly powerful and central to business success. Given the central importance of these algorithms it is even more crucial to use them correctly and optimize their benefit to your company.<
  2. Problems with algorithms result from them being literal (they do exactly what you ask) and are largely a black box (they do not explain why they are offering certain recommendations).
  3. When building algorithms, be explicit about all your goals, consider the long-term implications and make sure you are using as broad data as possible.

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Author Lloyd MelnickPosted on March 23, 2016February 28, 2016Categories Analytics, General Social Games Business, General Tech Business, Machine LearningTags algorithms, analytics, goals, Machine learning, Net Promoter Score, NPSLeave a comment on How to manage your algorithms

The big opportunity in gaming (and tech) that nobody is talking about

While everyone in the game industry always seems to be chasing the next big thing, what looks like the next big thing is actually being neglected by most game companies. Game companies are quick to chase what they think will be hot, be it a new platform, a new genre or these days VR (virtual reality) and AR (augmented reality).

The problem with this strategy is that it rarely creates a competitive advantage, as not only are you creating games for this technology but your competitors are too. Rather than creating a new marketplace, you are shifting the battlefield.

VR (and to a degree AR) is a great example of this situation. Everywhere I look, I see people talking about how it will change gaming and trying to pick the winning hardware, with the foregone conclusion that it will change the face of gaming. Additionally, virtually everyone has now started VR or AR projects, either full scale development or tests.

I am still undecided whether VR will redefine gaming or have an equivalent impact as 3D did on television but am surprised that people are neglecting an evolving technology that is more likely to be adopted by the mainstream and have a greater impact on games, voice recognition.

The Voice Recognition landscape

Many strong, and forward looking, companies are making major pushes into voice recognition. In the VR world, the battle between Facebook (Oculus Rift), Sony (Playstation VR), Samsung (Gear VR), Microsoft (HoloLens) and HTC (Vive) has generated excitement and helped push the technology forward.

The battle is no less pronounced in the area of voice recognition. Apple was the first company with a major initiative, when it added Siri to its devices. Apple acquired Siri Inc in 2010 and released its first devices with Siri in 2012. Since then it has been a staple of all new products.

Microsoft was the second major technology company to make voice recognition a key part of its mobile product strategy, with its Cortana intelligent personal assistant. Cortana was first shown in 2014 and is now integrating not only in Microsoft’s mobile products but has been added to Windows 10 as well as new products for both Android and iOS.

The latest entrant in the field is Alexa, Amazon’s voice recognition personal assistant. Alexa is available on the Amazon Echo speaker and voice command device, originally offered to some Amazon customers in June 2015. Amazon has now expanded the Alexa offering to the Tap wireless speaker and Echo Dot.

The first big thing

Amazon Echo

A Forrester analyst recently said, “The Echo is a sleeper hit.” While Siri and Cortana benefitted Apple and Microsoft, the success of Alexa points to the opportunity for all tech companies, particularly game companies, with voice recognition. According to an article in the New York Times, The Echo from Amazon Brims with Groundbreaking Promise, Alexa is on a path to become Amazon’s next $1 billion business. Understating this demand is the fact that while the Echo sells for $180 on Amazon, because of supply shortages the same product sells for $200-$300 on eBay.

The article points out that the Echo is evolving from a device with fixed functionality (like the original iPhone where people initially used it as a phone) to a platform with unlimited functionality. “But the Echo has a way of sneaking into your routines. When Alexa reorders popcorn for you, or calls an Uber car for you, when your children start asking Alexa to add Popsicles to the grocery list, you start to want pretty much everything else in life to be Alexa-enabled, too.”

Amazon has also started to turn the Echo into the center of a new ecosystem, again like Apple did with the iPhone. Many developers are using the technology to create voice-controlled apps for the device, or skills, as Amazon calls them. There are now more than 300 skills for the Echo, from the trivial — there is one to make Alexa produce rude body sounds on command — to the pretty handy. Other tech companies, like Nest, are also making their products compatible with the Echo. Alexa can control Internet-connected lights, home thermostats and a variety of other devices.

The parallels between the opportunities with Smartphones and now with Alexa are impossible to ignore. From demand exceeding supply to developers creating a myriad of applications that even Amazon does not anticipate, it is hard to argue against voice recognition having the same impact as Apple’s iPhone.

Why voice recognition will become ubiquitious

Not only does the data (the sales and third party applications) point to Alexa’s success, but the dynamics of the opportunity also show why it is a much more powerful force than the current hot technologies.

First, voice is already how almost everyone prefers to communicate. It’s what people learn from the day they are born. It is already how people give commands and they react in emergencies (if you are in the passenger seat of a car and see another car about to hit your vehicle, you do not email the driver, you scream). Rather than asking people to change the way they behave, voice recognition amplifies this power.

Second, the equipment is also natural. Very few people wear a headset from birth (except maybe in some science fictions stories). Most people even find those little cardboard 3D glasses you use at cinemas annoying as they are not what users are used to. Echo, and now Tap, masquerade as speakers that just sit in a room and then you talk naturally. The key here is that you do not do anything differently than your instincts tell you to behave.

Third, this is closer to a mature technology than some of the hot ones (i.e. VR). As mentioned above, Siri launched four years ago and in those years the technology continues to be refined. It is now much more natural, you can talk to Echo as you would talk to your mate. It is also much quicker, rather than waiting seconds (which again is unnatural as you usually do not have to wait ten seconds for a friend to respond), new voice recognition can process and act as quickly as a human.

Overall, the beauty of voice recognition is what keeps it from being the sexy new thing. It feels natural, just an extension of what people are already doing (communicating to each other by voice). You cannot create viral YouTube videos of somebody talking in their living room to a speaker like you can by creating a 3D universe. My philosophy, though, is that the strongest opportunities are usually the least sexy. Rather than invest in a MySpace or Ouya, I always prefer to invest in a Waste Management type company, where people have a clear need that is being solved.

What it means for games

Neglecting the emergence of voice recognition for a game company would be akin to neglecting the emergence of mobile as a gaming platform after Apple launched the iPhone, and we see how that turned out for many one-time great Facebook game companies. Rather than control your experience with a keyboard, game controller or even gesture controls, the next generation of gamers is likely to want to control their experience with voice. Why click on a slot machine when you can just say spin. Why go onto a monetization page and purchase a currency package when you can simply say “buy 1 million chips”. The gaming experience will become more natural and fluid. Most importantly, customers will start abandoning products and games that are not voice controlled for ones that are.

Key takeaways

  • The biggest paradigm shift that will hit the game industry is voice recognition, not VR, AR or any new platform.
  • Voice recognition is already exploding, with Amazon’s Echo its surprise hit with some predicting it is Amazon’s next billion dollar product.
  • Games that leverage voice recognition early will be the big winners while companies that miss this shift will join those that missed the shift to mobile.

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Author Lloyd MelnickPosted on March 16, 2016March 21, 2016Categories General Social Games Business, General Tech Business, Growth, Lloyd's favorite postsTags amazon, Amazon Echo, Echo, innovation, new technology, VR5 Comments on The big opportunity in gaming (and tech) that nobody is talking about

The key to breakthroughs is not ideation

Most leaders are constantly bombarded with new ideas for products, services and business models but we often have trouble capturing the most promising ideas. An article in the Harvard Business Review, The Innovative Power of Criticism, shows how to judge and prioritize these ideas.

In my experience, you get great ideas from your team, your leadership, your customers and even friends. The problem is not enough ideas, it’s how to identify the ones you want to execute on. What most companies do is end up gravitating to the familiar ideas, whether or not they actually are the best.

Roberto Verganti, the author of the article, suggests a process rooted in the art of criticism.

The Art of Criticism

First, Verganti explains there are two types of innovation, improvements and new directions. Improvements are novel approaches that improve existing definitions of value. They address problems that you and your competitors have already identified.

New directions arise from reinterpreting the problems worth tackling. They redefine what customers consider important. Given that customers themselves do not know what WILL be important, they cannot generate this type of innovation.

A great example is the iPad. Customers who were happy with phones and laptops had no idea they would prefer to use a tablet. Apple would not have pursued the iPad if it relied on currently popular methods of innovation. Generating lots of ideas works well for improvements but it does not help to spot new directions. Companies tend to pick customers and other outsiders who back current directions and reject ideas that are untraditional.

Slide1

Verganti writes, “to find and exploit the opportunities made possible by big changes in technology or society, we need to explicitly question existing assumptions about what is good or valuable and what is not—and then, through reflection, come up with a new lens to examine innovation ideas. Such questioning and reflection characterize the art of criticism.” Verganti has built a four step process to come up with new readings of customer issues and come up with innovative solutions.

  1. Step 1: Individual reflection. The first step is to have members of your team reflect on how your company can solve an underlying issue (i.e. the aging of your customer base). One key to managing this step successfully is creating a heterogenous group to reflect on the position. It should include people of different seniority within the organization, different backgrounds, different departments, different approaches (analytic versus qualitative) and different personalities. Instruct the team members to consider, individually, how your company can create brand new concepts of value.The key here is to have the team members try to come up with their own ideas not asking customers, reflect alone rather than as a team and provide enough time to think ideas through thoroughly.
  2. Step 2: Sparring partners. The second step entails each person having a trusted peer critique their vision. The colleague acts like a sparring partner, delivering a safe environment where the person can share a wild or half-baked hypothesis without fear of it being scorned.Verganti suggests you help team members find a sparring partner by using a near speed-dating methodology. After step one, in which individuals reflect independently on possible directions, invite them to a meeting and ask them to briefly illustrate their ideas, which can be posted on a wall. Then have each person choose another’s idea that he or she would like to explore. If more than one person chooses the same direction, ask them to indicate a second and, if necessary, a third choice.
  3. Step 3: Radical Circles The third step is to gather a group of 10-20 people to discuss the ideas, which Verganti calls a radical circle. The group’s goal is not to decide which ideas are correct or wrong but how or why they are different, what underlying insights may have been missed and whether there may be a value proposition more formidable than all the hypothesizes.
  4. Step 4: Outsiders The fourth step is to take the directions identified by the Radical Circle and subject it to criticism from outsiders. The outsiders’ goal is to challenge the ideas from the radical circle, not to create new ideas. By challenging the ideas, they will help strengthen them. This is also an area where analytics can help, as your data analytics team (or an external one or both) can assemble data to both support and oppose the hypothesizes and determine which results are more compelling.

Coming up with the strongest innovation

Verganti’s methodology allows you to build the strongest solutions to your key business problems. These solutions, moreover, can be truly innovative rather than incrementally improving your offering. The key is to focus your internal resources and then have multiple layers of criticism direct you to the strongest options.

Key takeaways

  • You probably have a virtually unlimited list of ideas to improve your business. The key to success, though, is not generating millions of ideas but finding the best ones to deal to prepare you for the future
  • To identify the best innovation opportunities, create a process that allows you to look internally for ideas then critically evaluate them.
  • You first identify a diverse group of employees and have them come up with potential solutions, you then pair them up to critique these solutions, then form a group that looks at the ideas and sees if there are even better underlying options and finally subject the ideas to external evaluation.

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Author Lloyd MelnickPosted on March 9, 2016February 15, 2016Categories General Social Games Business, General Tech BusinessTags criticism, innovationLeave a comment on The key to breakthroughs is not ideation

How Pitbull shows us the future of advertising

I recently searched Pitbull on YouTube (don’t ask why I was searching for Pitbull) and I came across a great example of the future of advertising. One of the videos that showed up high in the search was Pitbull – Freedom. The video turned out to be an original song that featured the Norwegian Cruise Line’s ship Norwegian Escape. After watching this video, I realized that for several reasons it shows how advertising will look in the next ten years rather than how it has worked for the past one hundred (and how it works currently even in the performance marketing space).

By deconstructing the video advertising campaign, you can learn how to market effectively in 2016.

High search ranking

The first key to the success of this ad is that if you search for Pitbull on YouTube, it is one of the top-five results. Pitbull is a popular celebrity so will generate more searches than the Norwegian Cruise Lines. Thus, he provides exposure to a broader range of potential customers. In its first two weeks, the video generated more than 3.2 million views.

Relevant

Many advertisers, especially in performance marketing, make the mistake that once they get you into the ad their job is done. By getting the click, they can point to a high CTR or a low CPI. The problem is that they often drop you into an advertisement that has nothing to do with the reason you clicked. While in a rare case they still might convert you to a customer, more likely you will leave quickly, generating no value to the brand. In the Pitbull video, you actually get a song and video consistent with Pitbull’s non-sponsored offerings. The fact that the video is consistent with your expectations makes it much more likely to engage potential customers and create the value the brand is pursuing.

Engaging

Outside of Super Bowl ads, how many people really want to watch or experience an advertisement. This problem does not exist only in television but just look at the success of (and fear of) ad blocking software online. Rather than creating another advertisement that people want to avoid. Norwegian Cruise Lines created an advertisement people want to consume (40,000 upvotes versus less than 5,000 downvotes).

Not only do people want to consume the content, they want to engage with it. The video, again in its first two weeks, generated more than 2,100 comments. For comparison, I searched for Ford and came to a video of a Ford F-150 adalso released about two weeks ago, which was a traditional brand video. As opposed to Pitbull’s 3 million plus views, the Ford ad had slightly over 5,000 views. Rather than Pitbull’s 40,000 upvotes and 2,100 comments, Ford had 96 upvotes and 3 comments.

These numbers show the importance is not the distribution channel (both pieces of content are offered on YouTube) but the content. Ford simply used the same formula it has for almost 100 years in creating ads. Norwegian Cruise Line, however, rewrote the rulebook and created content for 2016. These numbers clearly show the Norwegian Cruise Line ad will have orders of magnitude more impact than Ford’s traditional ad on a modern channel.

Entertaining

The key difference in advertising today versus the past hundred years, or at least advertising successfully, is you need to create content that is truly entertaining. Consumers have thousands or millions of options of entertainment and they will not consume your ad when they can find something they like quickly and for free. Why watch the Ford video when you can watch an Adele video. Your ad has to be just as good and compelling as a pure entertainment product.

Brand marketing 2.0

It is easy for me to say television commercials are history but just as easy for someone else to say they will always remain predominant. Rather than just make the claim, I decided that the best way to compare the effectiveness of new age digital marketing versus traditional television brand marketing is to look at the companies creating the most shareholder value. As a shortcut to doing a full analysis, I searched for all the so-called “Unicorns”, private companies whose market value exceeds over $1 billion. The list, which can be found here, is striking in how few of the companies have had television ads (or at least ones that are memorable). There are 174 companies on the list but out of the 174, I have only remembered seven advertising on TV (Shazam, DraftKings, FanDuel, Machine Zone, Jawbone, Credit Karma and Airbnb). Among the ones who have not advertised (or at least enough that I have seen it), who I would say are doing pretty well:

  • Uber
  • Palantir
  • Snapchat
  • Pinterest
  • SpaceX
  • WeWork
  • Lyft
  • Zenefits
  • Docusign
  • SurveyMonkey

Most companies would like to show the same growth that the 167 companies that do not advertise on television have shown. The answer is to understand how to market to the consumer in 2016, and Pitbull is helping to show the way.

Key Takeaways

  1. Pitbull’s promotional video for the Norwegian Escape cruise ship shows the future of advertising, as it is as much a music video as an ad.
  2. The key to advertising successfully now is creating content that is relevant, engaging and entertaining.
  3. Television is no longer a driver of success, as shown by less than ten of 174 Unicorns (private companies valued over $1 billion) are using it.

Pitbull freedom

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Author Lloyd MelnickPosted on March 2, 2016February 27, 2016Categories General Social Games Business, General Tech Business, Growth, Social Games MarketingTags brand marketing, marketing, norwegian cruise lines, pitbull, television, YouTubeLeave a comment on How Pitbull shows us the future of advertising

Get my book on LTV

The definitive book on customer lifetime value, Understanding the Predictable, is now available in both print and Kindle formats on Amazon.

Understanding the Predictable delves into the world of Customer Lifetime Value (LTV), a metric that shows how much each customer is worth to your business. By understanding this metric, you can predict how changes to your product will impact the value of each customer. You will also learn how to apply this simple yet powerful method of predictive analytics to optimize your marketing and user acquisition.

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Lloyd Melnick

This is Lloyd Melnick’s personal blog.  All views and opinions expressed on this website are mine alone and do not represent those of people, institutions or organizations that I may or may not be associated with in professional or personal capacity.

I am a serial builder of businesses (senior leadership on three exits worth over $700 million), successful in big (Disney, Stars Group/PokerStars, Zynga) and small companies (Merscom, Spooky Cool Labs) with over 20 years experience in the gaming and casino space.  Currently, I am on the Board of Directors of Murka and GM of VGW’s Chumba Casino

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by Lloyd Melnick

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