General Social Games Business – Page 16 – The Business of Social Games and Casino

Regaining churned players

With the ever increasing cost of user acquisition, keeping and reactivating churned players is critical to success. Most game companies, however, take a shotgun approach to reactivation, treating all churned players the same. There was an excellent article in the Journal of Marketing last year, Regaining “Lost” Customers by V. Kumar, Yashoda Bhagwat and Xi Zhang, that provided excellent details on how to create a strategy to reactivate churned customers.

Not only does the cost of a new customer highlight the value of reactivating a lapsed customer, there is also a competitive benefit. Given that these customers have a propensity to your game, it is likely that if you lost them they went to a competitor. Thus, they are probably providing additional profits to your competitor, who can then use that revenue to compete (bidding on the same new users, investing in new features, etc.).

Kumar and his colleagues point to three reasons to devote significant resources to winning back customers. First, these people have shown an interest or use in your product, much better than cold calls or almost random performance ads. Second, they are already familiar with you and your game, so you do not have to create brand awareness. Third, you can use data to craft the best win-back strategy for each segment since you already know how they behaved when they were a customer.

While there is little argument as to the value of reacquiring churned players, companies devote little analysis to whether and which customers are worth the cost of reacquisition. As with any acquisition program (either first time players or reactivation), there is a point where the value no longer exceeds the cost. The authors write, “firms may unnecessarily waste time and resources reacquiring customers who are unlikely to return to the firm in the first place. Even more problematically, firms may reacquire unprofitable customers or spend too many resources on winning back customers who will defect again very quickly.”

Given that we all have limited budgets and need to optimize ROI, game companies should aim to optimally allocate their resources to win back the best customers. To execute on this goal, you should look at three things:

How likely is the player to return
If the player returns, how long will they stay
How profitable will this player be each month

The other key element is using the right strategy to win back the player. While one segment of players might respond to a special offer, another segment might be interested in new content. Matching the reactivation strategy to the target customer will yield a higher return rate.

Find out why they left

The first key to an optimal customer win-back strategy is segmenting your churned users based on the reason they left. This is an area where machine learning can help you create optimal micro-segments but also one that any person or company can do by reviewing their data. Some (though by no means all) potential reasons players have left your game are:

Ran out of currency
Got blocked on a level or mini-game
Lost too frequently
Won too frequently
Found a better product
Friends stopped playing the game

Analysis can help you bucket each segment. Players with no currency left can be assumed to have left because of their wallet size. Players who reached a certain level and then could not progress for a prolonged period may have left due to getting stuck there. Players whose friends’ activity decreased and then they stopped playing may have churned because of their friends.

One additional way to understand why customers have churned is to ask them. Many will be anxious to tell you what they did not like and what drove them away.

From this data, you then create as many micro-segments that are meaningful. This will help you understand what they value and thus present them with a strong reason to return.

Decide how likely they are to stay

The next element to consider is whether you are likely to retain the reacquired player. You do not want to devote significant resources to reactivate players who will return only to churn again in a few days.

There are two keys that help understand the likelihood of a reactivated player staying. The first is whether they originally went to a competitor who offered a better value. If you have a slots game but you lost players to a competitor running a 5 X sale, you may be able to lure the player back with a 10X sale but they are then probably going to leave again when your competitor has a better offer.

If the player left because they both thought a competitor had better value and they had complaints about your product, they are even more likely to churn a second time. These are constant complainers who you can never satisfy.

Other key factors

Kumar and the other authors also identified other factors that predict how likely a churned player was to reactivate. Not only does this data help you segment churned players and prioritize which to try to reactivate, it provides insights into retaining the player initially.

The more positive the initial experience the more likely the player to return. If the player referred many friends to the product or monetized, they are more likely to come back in the future. This research indicates the importance of getting the player engaged early, even if they do churn.

Also, if the player had a problem initially but a positive service recovery, they were more likely to return post-churn. As the authors write, “sarily dissatisfactory and does not exacerbate customer frustration with the initial service failure. Rather, it shows the firm’s willingness to expend efforts to correct its mistake. This willingness of the firm to recover its failure lowers customers’ perceived risk and mitigates the uncertainty they may feel about trusting and returning to the firm.” This finding shows the importance of resolving issues and providing a good CS experience.

The win back offer

You also want to craft the correct offer to reactivate and retain lapsed players. While a win-back offer that offers both a price discount and a product upgrade is the most effective in reacquiring customers, it is also associated with the lowest second-lifetime duration and profitability relative to price-based and feature- based win-back offers. The win-back offers that discount price are associated with relatively higher second lifetime duration, and the win-back offers that provide a free service upgrade are associated with the highest second-lifetime duration.

The key to successful reacquisition

The key to reacquiring churned players is the first lifetime experience. Also, player experiences and behaviors (i.e., referral behavior and monetization level) are indicators of the quality of the first-lifetime experience and how the customers who had positive first-lifetime experiences were more likely to accept a win-back offer. Although the reason for defection is a good indicator of one’s experience and a good predictor of the likelihood of reacquisition, it also suggests how a reacquired customer will behave.

Key takeaways

Given the increasing costs of acquiring new users and players, more emphasis should be placed on reactivating users who have lapsed.
Lapsed users are attractive targets as they already have shown an interest in your product, they are familiar with it and you can use their historical data to craft the best win-back proposition.
To run a successful reactivation campaign, you need to segment lapsed players by how likely they are to return, their value once they return, what offer is most likely to re-engage them and what win-back strategy will be most profitable.

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.

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

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.
The reality is that it is actually the less risky approach, as the underlying odds favor success via a Blue Ocean approach.
Given the challenges of trying to win in a competitive industry, finding an uncontested market and growing it is a less risky approach.

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.

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

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.<
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).
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.

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

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.

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.

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.

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.
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.
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.
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.

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

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.
The key to advertising successfully now is creating content that is relevant, engaging and entertaining.
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.

Dealing with a changing business environment

Nobody can argue that the business environment is constantly changing, probably at a faster rate than ever before. New technologies are introduced almost daily, customer tastes change frequently as our global society exposes people to new products and the business cycle is compressed with automated trading. An article in the Harvard Business Review — The Biology of Corporate Survival by Martin Reeves, Simon Levin and Daichi Ueda – explains how companies are like biological species and need to adapt to survive.

As the authors write, “companies operate in an increasingly complex world. Business environments are more diverse, dynamic, and interconnected than ever – and far less predictable. Yet many firms pursue classic approaches to strategy that were designed for more stable times, emphasizing analysis and planning focused on maximizing short-term performance rather than long-term robustness.”

When looking at the results of this practice, the authors say that companies are disappearing faster than ever. Over 30 percent of publicly traded companies will be delisted in the next five years, due to bankruptcy, liquidation, M&A or other causes. Compared to 40 years ago (the 1970s for those who did not major in mathematics), this represents a 6 times greater likelihood that a public company disappears.

The authors believe this higher mortality rate is due to companies failing to adapt to the increasing complexity of the business environment. Many misread the environment, selected the wrong approach to strategy, or failed to support a viable approach with the right behaviors and capabilities.

Business is complex

As the article states, “in a complex adaptive system, local events and interactions among the “agents,” whether ants, trees, or people, can cascade and reshape the entire system—a property called emergence. The system’s new structure then influences the individual agents, resulting in further changes to the overall system. Thus the system continually evolves in hard-to-predict ways through a cycle of local interactions, emergence, and feedback. In nature we see this play out when ants of some species, for example, although individually following simple behavioral rules, collectively create ‘supercolonies’ of several hundred million ants covering more than a square kilometer of territory. In business we see workers and management, through their local actions and interactions, shape the overall structure, behavior, and performance of a firm. In both spheres these emergent outcomes influence individuals and create new contexts for their interactions. Whether we look at team dynamics, the evolution of strategies, or the behavior of markets, the pattern of local interactions, emergence, and feedback is apparent.

Complex adaptive systems are often nested in broader systems. A population is a CAS nested in a natural ecosystem, which itself is nested in the broader biological environment. A company is a CAS nested in a business ecosystem, which is nested in the broad societal environment. Complexity therefore exists at multiple levels, not just within organizational boundaries; and at each level there is tension between what is good for an individual agent and what is good for the larger system.”

This complexity has several implications for business leaders:

Do not overestimate what you can predict and control as much is beyond the reach of managerial influence. You need to expect unpredictable and extreme emergent outcomes will cascade from actions at the lower levels.
You must look beyond what your company owns or controls, monitoring and addressing complexity outside of your company.
You must embrace the inconvenient truth that attempts to control directly agents at lower levels of the system often create counter-intuitive outcomes at higher levels. Rather than trying to control the behavior seek to shape the context for that behavior.

Six principles to build complex adaptive systems

Based on the complexity of the business environment, the authors of the article propose six principals to make complex adaptive systems in business robust. Some of the principles help create a more robust structure while the others provide managerial guidance.

Avoid Homogeneity

Variety in a complex adaptive system allows the system to adapt to a changing environment. As a leader, you must ensure the company is diverse in terms of people, ideas and endeavors. This practice often starts with who you hire but you must also encourage your diverse team to express diverse ideas.

Sustain Modularity

You should build your system to consist of different nodes of loosely connected components. Highly modular systems impede the spread of shocks from one component to the next. Within a company, tight connections across regions or businesses can enhance information flows, innovation and agility. These tight connections, however, tend to make your company vulnerable to severe adverse events.

Preserve Redundancy

In systems with redundancy, multiple components play overlapping functions. When one fails, another takes over the same role. Redundancy is particularly important in highly dynamic environments, where adverse shocks occur frequently.

Expect surprises while reducing uncertainty

A key feature of complex adaptive systems is that we cannot precisely predict their future states. However, we can collect signals, detect patterns of change, and imagine plausible outcomes—and take action to minimize undesirable ones.

As the article states, “In business systems few things are harder to predict than the progress and impact of new technologies. But it can be worthwhile to actively monitor and react to the activities of maverick competitors in an effort to avoid being blindsided. Companies that do this follow a few best practices. First, if they are incumbents, they accept that their business models will be superseded at some point, and they consider how that may happen and what to do about it. Second, they understand that change often comes from an industry’s periphery—from start-ups or challengers who have no choice but to bet against incumbents’ models. Third, they collect weak signals from the smart money flows and early-stage entrepreneurial activity that constitute those bets against their models. Fourth, they practice contingent thinking: Rather than posing the unanswerable question of whether this or that company or technology will succeed, they ask, If the maverick’s idea worked, what would be the consequences for us? Finally, they take preemptive action against such threats by replicating the idea, acquiring it, or building defenses against it.”

Create feedback loops and adaptive mechanisms

Feedback loops ensure that selection occurs and improves the fitness of the system. Your company can identify quickly changes in the environment by having good feedback loops in place. As the authors write, “In nature, mutation and natural selection—the variation, selection, and propagation of genes that contribute to reproductive success—is an autonomous process. In business the analog is a predominantly “managed” activity. The variation, selection, and propagation of innovations happen only when leaders explicitly create and encourage mechanisms that promote those things. In fact, mainstream management thinking, as taught in many business schools, may actively suppress the intrinsic “variance” and “inefficiency” associated with iterative experimentation. Yet the cultivation of this adaptive capability is now essential for companies that may have managed themselves for decades using only analysis and planning.”

There are two steps you can take to adapt this principle to iterative innovation:

It must detect the correct signals from across your company. The key here is to interact with employees at all levels.
You must translate those signals into actions.

If the feedback cycle becomes too short or the response to change is too strong, the system may overshoot its targets and become unstable. As with the other principles, calibration is essential.

Foster trust and reciprocity

Complex adaptive systems require vigorous cooperation, while direct control of system participants is seldom possible. Individual interests often conflict, and when individuals pursue their own interests, the system becomes weaker and everyone suffers. Individuals lack incentives to act in ways that benefit the overall system unless they benefit in immediate ways themselves. Trust and the enforcement of reciprocity combine to provide a mechanism for organizations to overcome this predicament.

Leaders should consider how their firms contribute to other stakeholders in their ecosystem. They must ensure that they are adding value to the system even as they seek to maximize profits.

What it all means

You must build your organization and company for the reality of a constantly changing business ecosystem. To do this, you need a structure that minimizes these risks. This includes heterogeneity (so you get different ideas), modularity (so that you can isolate negative disturbances) and redundancy (so you can deal with large shocks). You also need to put in place managerial principles to deal proactively with this changing environment. These principals include expecting surprises while reducing uncertainty, creating feedback loops and implementing them and creating an atmosphere of trust and reciprocity. Only by proactively adapting to this fast changing system will you survive and thrive.

Key takeaways

Businesses face ever more diverse environments, which are harsher, less predictable and more malleable than historical environments.
To adapt to the changing system, you need to implement an adaptive structure. This includes heterogeneity (so you get different ideas), modularity (so that you can isolate negative disturbances) and redundancy (so you can deal with large shocks).
You also need to put in place three key managerial principles. These principals include expecting surprises while reducing uncertainty, creating feedback loops and implementing them and creating an atmosphere of trust and reciprocity.

Four trends in social casino

I recently came across a great article, The Top Ice Takeaways, by a former colleague of mine, Barry Cottle, that highlighted four key trends in the casino space. Barry has a unique perspective, involved in online real money, land based casino as well as the social space. He’s identified several trends that will impact all three businesses. The four trends, in what I consider the most important order for social casino businesses, are:

Technology is evolving and your player’s habits are changing.We live in an age where technology changes constantly. Several years ago, Blackberries and PDAs were at the edge of technology. Then users migrated to iPhones. Then the iPad and tablets became the primary way that people consumed mobile content. Now players are leaving tablets for phablets and larger devices. It is critical to stay at the front of these trends. You need to make sure your content is available not only on the new devices people are using but also optimize the experience for these devices.

Barry uses the example of building casino apps now for portrait mode. He cites studies that show users are quickly moving to a portrait-only mentality that is driven by the speed of play ease of holding your phone in one hand while doing something else. The key is to understand how your players are consuming content and build your experience for that, rather than focusing on an old technology or any particular device.
Innovation does not have to be tech based. I frequently discuss Clay Christensen’s theory on innovation and the key is to disrupt markets by addressing users who are not served by the existing companies. A key to this strategy is not simply creating a new product but building a new business model that better serves these customers. About ten years ago, the free-to-play model disrupted the video game industry rather than a cool new technology. The innovation can be in the promotions that you offer players, the partnerships you strike with IP holders or companies in adjacent casino spaces or even your on-boarding experience.
Differentiation through IP and licensed third-party content is critical. IP allows companies to take a commoditized space and create a unique position. When Zynga launched HitItRich! slots in 2013, there were already many successful social casino titles. One problem in the space, however, was that players could play one title, use up their free chips, then play a competing product. HitItRich! pursued a strategy of integrating exclusive IP into its casino. It was the only place where you could play a Sex in the City or Wizard of Oz slot machines. This unique content allowed it to differentiate from other social casinos and be Zynga’s most successful new product in years. There is no reason that IP cannot have a similar impact on other parts of the casino space.
Omni-channels is not a fad. As Barry points out, “in the mobile age of gaming, players expect a seamless crossover in both content and experience from land-based to online.” I would add that consumers also want a seamless experience from desktop to mobile and from real money online to social. They also want the same experience if they are in Des Moines or Tel Aviv. It is thus critical that you look at your offerings holistically and build the best experience for your customers.

Key takeaways

There are four key trends impacting the casino business, from social to real money online to land based.
One of the most important trends is that technology is continually evolving and it is critical to ensure your offering creates a great experience for how your customers are consuming content today, not last year.
Other trends include the importance of innovating your business practices as well as your products, differentiating your content through IP and providing a holistic experience between land-based, social and real money on-line.

How to encourage your team to speak freely

While almost every company professes and often tries to get its employees to speak freely, most are unsuccessful. Employees either feel retribution for speaking openly or believe their efforts will be fruitless. An article in the Harvard Business Review, Can Your Employees Really Speak Freely by James Detert and Ethan Burris, shows the value of getting employees to speak up and how to do it.

Many leaders believe that by professing to have an open door policy, employees will come to them to raise issues or suggest improvements. It could be a project is going to miss its schedule or one of your manager’s is putting in a lackluster effort, Detert and Burris’ research shows your team is more likely to keep quiet than to question initiatives or suggest new ideas. When employees freely express their concerns, companies experience higher retention and better performance.

As Detert and Burris write, “leaders use a variety of tools to get people to speak up, like “climate” surveys and all-staff feedback sessions. Many of these efforts focus on improving communication up and down the hierarchy. But they usually fall short, regardless of good intentions, for two key reasons: a fear of consequences (embarrassment, isolation, low performance ratings, lost promotions, and even firing) and a sense of futility (the belief that saying something won’t make a difference, so why bother?).“

Why your team keeps quiet

There are two key reasons why employees fail to express their concerns, fear and futility. While you may think you are encouraging your team to speak up, you actually may be doing the opposite and leaving them afraid to openly discuss issues.

Part of the problem comes from relying on anonymous feedback. This policy has multiple problems:

Encouraging anonymous feedback implicitly says it is not safe to raise issues publicly. If you were truly an open organization, people would not have to comment anonymously.
Rather than focus on the content of the anonymous comment, the focus often becomes a hunt for who made or could have made the comment.
It can be difficult to address issues while protecting the identify of the people who raised them.

Another element that contributes to the fear factor is sending signals that you are in charge. While you may be encouraging open conversation, forcing someone to sit in a small chair in your large office or set up an appointment can create a power situation. In this atmosphere, people may not be willing to tell you the unvarnished truth.

The other enemy of open conversation is futility, people feel it just will not make a difference to raise issues. This is referred to as the why-bother attitude. One contributor to this attitude is when leaders fail to convey issues to their superiors. If your team members raise concerns but you fail to address them with your superior, they feel they have wasted their time.

Another contributor to this problem is leaders who are unclear about the input they want. They may encourage “all” feedback but you have your own priorities and will only act on input that can help you advance these priorities (it could be improving efficiency, making marketing more user driven, etc.). By telling your team to bring you all issues but then not acting on all issues, and we all need to prioritize, they will feel their input is fruitless.

The most frustrating area to me that leads to people suppressing comments is a lack of resources to address the issues raised. You may spend excessive time getting ideas for new products or software that could improve efficiency, but you then do not have the resources to create the product or license the software. Devoting resources to collecting ideas without making commitments, financial and otherwise, to see some of them to fruition can lead your team to feel their input will have no impact.

How to create a more vocal culture

While saying be open and not always closing your door probably will not generate open conversation, there are ways to get strong feedback from your team.

Make feedback a regular and casual exchange. Try to schedule regular one-to-one meetings with your team, this will make it less of a special even when they have something to share. The meetings also should not be with only your direct reports, but try to reach out to all the units you oversee, the ideas are not limited to your direct reports. Even when there is nothing to discuss, still hold the meeting and see what comes out. Often, if there is not a set agenda, the true issues will rise to the top.
Be transparent. As Detert and Burris write, “transparency about feedback processes can reduce anxiety and increase participation…. Spelling out guidelines and commitements up front made contributing feel less daunting and futile to employees.”
Reach out. When you want to understand what your team thinks of something, ask them. Call people in and ask them what are the company’s biggest problems or where they feel costs can be reduced or what is wrong with your product. Soliciting feedback proactively is more effective than just listening to it when someone brings an issue to you. First, it allows you to focus on your biggest problems. Second, since it is an issue you are already trying to address, there is a higher likelihood you will act on the employee’s feedback.
Soften the power cues. To get earnest feedback, play down the power relationship. Walk over to your employee to talk to them rather than asking them to your office. Sit with them at a table rather than having them sit across from your desk. Join your employees for lunch or a drink. The key is to interact with your team as peers rather than subordinates.
Avoid sending mixed messages. Do not encourage ideas and then cut them apart. Make sure not to focus the employee on process, it does not matter if their Powerpoint is awful, but instead pull the ideas out of the conversation and see which ideas will help the company.
Be the example. Employees feel inspired when they see you advocating for them. It is important to help move the ideas along. Unless you feel they will hurt the company, and provide visibility to the team on your efforts in getting their ideas implemented. As Detert and Burris write, “Employees feel inspired when they see you advocating for them. While it’s great when your subordinates can see you speaking up, in many cases that’s not possible, because they aren’t present when you interact with your own boss. But you can tell them what happened and involve them directly in any follow-up steps.”
Close the loop.The most critical element in creating a culture where people are truly open with their feedback and suggestions is telling them what you did with their idea and what they can expect as a result. Even in cases where the idea was not implemented, tell them why it was not moved forward so that they both see it was taken seriously and can adjust future suggestions to the reality of the business. Also, where possible, tell the entire team about the feedback you received and how it was acted upon.

While it is much easier to say you have an open door policy than follow through on the seven suggestions above, these suggestions will prompt your team to raise important issues to improve your company. You will find the effort is well worth it both in increased morale and a better business.

Key takeaways

Open feedback and suggestions improve retention and the company’s performance, yet just having an “open door policy is not effective at soliciting feedback.
Most people do not speak openly out of fear that there will be negative consequences or a belief that nothing will happen if they do.
To counteract this reluctance, make feedback regular, be transparent, proactively reach out to employees, soften the power position, set an example by bringing ideas up the corporate chain and provide feedback on what action came from the suggestion or criticism.

Why data is more important than hair: The Donald Trump story

There is a great article on Politico, How Trump Let Himself Get Out-Organized, that explains how Trump’s Iowa debacle was a result of a failed analytics strategy. Trump made the same mistake many companies commit, he felt a strong brand and what he believe compelling product allowed him to under-invest in analytics. This issue was compounded by the aggressive use of analytics by competitors. Although this occurred in the political arena, there are lessons for all businesses.

The article explains that despite Trump’s strength in the polls, he did not have “the tools they needed, which is why they overpromised and underperformed.”

Penny wise and pound foolish

While Ted Cruz and Marco Rubio spent millions building sophisticated voter targeting machines, Trump did not start building a data operation to target voters until mid-October. It did not even start buying data (i.e. voter lists, etc) until November and waited to December to start using the Republican National Committee’s (RNC’s) voter file.

The Trump campaign declined to use Cambridge Analytica, a behavioral modeling company with political expertise, due to cost. Cruz, however, retained Cambridge Analytica’s services and the firm is now widely credited with engineering Cruz’s cutting-edge targeting operation. Rubio, who also over delivered on expectations, spent $750,000 for an outside company to assist in its data operations. Trump overall spent $560,000 on data services in 2015, compared to $3.6 million by the Cruz campaign. It is also about $700,000 less than Trump spent on hats.

You also need the analytics team

The Iowa caucas also showed the value of having a strong analytics team, not simply software. Cruz’s data team, which they call the Oorlog (the Afrikaner word for ‘war’) project, includes four full-time data scientists and embedded talent from Cambridge Analytics.

The Rubio campaign, which also exceeded expectations, has also invested heavily in its analytics team. It has a 22-person data war room in DC.

The Cruz campaign also hired ten canvassers (and recruited many volunteers) to go door-to-door to contact people the analytics suggested were supportive or could be persuaded. Traditionally, these so-called match rate initiatives are 50 percent successful but with Cruz’s advanced analytics the success rate reached 70 percent. The Cruz campaign also used the voter profiles to shape its strategies for most marketing activities, from television ad buys to telephone banks.

Micro-segmentation

Micro-segmentation, or creating very small customer segments and treating them uniquely, is another area where Trump fell down compared to Cruz. As Politico wrote, the Cruz campaign, “built a list of more than 9,000 Iowans who were still on the fence between their candidate and Trump. The team divided the undecided voters ― who were heavily evangelical and 91 percent male ― into more than 150 different subgroups based off ideology, religion and personality type, Wilson said. It used Facebook experiments to determine which issues jazzed up their voters the most.”

The lesson

No matter how strong you feel your product is, or how well it has performed in the past, you are vulnerable to competitors who may have a superior analytics solution. To combat this risk, you not only need to match the investment your competitor’s are making in analytics and look at micro-segmentation but also build a world class data team.

Key Takeaways

Donald Trump’s loss to Ted Cruz in Iowa can be attributed to Cruz’s superior use of analytics to build a competitive advantage.
Cruz invested much more in both analytic products and a great data team and it helped him get pro-Cruz people to caucus.
Cruz also did a great job of micro-segmenting potential voters into more than 150 different subgroups based off ideology, religion and personality type and used Facebook experiments to determine which issues were most relevant for each subgroup.

Find out why they left

Decide how likely they are to stay

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The win back offer

The key to successful reacquisition

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Why people think Blue Ocean is riskier

The reality

The Zynga example

Net net

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How algorithms can lead you amiss

Be explicit about all of your goals

Minimize myopia

Chose the right data inputs

Understand the limitations

Use algorithms, just use them smartly

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The Voice Recognition landscape

The first big thing

Why voice recognition will become ubiquitious

What it means for games

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The Art of Criticism

Coming up with the strongest innovation

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High search ranking

Relevant

Engaging

Entertaining

Brand marketing 2.0

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Business is complex

Six principles to build complex adaptive systems

Avoid Homogeneity

Sustain Modularity

Preserve Redundancy

Expect surprises while reducing uncertainty

Create feedback loops and adaptive mechanisms

Foster trust and reciprocity

What it all means

Key takeaways

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Key takeaways

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Why your team keeps quiet

How to create a more vocal culture

Key takeaways

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Penny wise and pound foolish

You also need the analytics team

Micro-segmentation

The lesson

Key Takeaways

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