January 2014 – The Business of Social Games and Casino

Bayes’ Theorem Part 2: How to use Bayes’ Rule when you have multiple prior data points

Last week I started my series of posts about Bayes’ Rule and why it was the foundation of good business decision making . In the next few weeks I will hit on some fun applications but today wanted to build further the foundation for using effectively Bayes’ Rule by discussing continuous parameter values (or multiple existing data points). Again, I borrow heavily from easily the best work on Bayes’ Rule, James Stone’s book Bayes Rule: A Tutorial Introduction to Bayesian Analsysis. This application will be particularly relevant when using Bayes’ Theorem to make the best decisions in your green light process, corporate development, investing or anywhere there are multiple historical results to examine.

Multiple data points are referred to as “continuous variables.” The values of a continuous variable are like densely packed points on a line, where each point corresponds to the value of a real number. The main advantage of working with continuous values is that we can usually describe a probability distribution with an equation, and because an equation is defined in terms of a few key parameters, it is said to provide a parametric description of a probability distribution.

To make the above relevant to you, think of yourself as a VC. You are looking at a potential investment. You start by looking at how similar investments over the last two years performed; this return on investment represents the points on the line. The parameters could be the space the business occupied, management team and level of investment. Rather than potential investments, to keep the analysis simple I will use a coin flip as an example. Continue reading “Bayes’ Theorem Part 2: How to use Bayes’ Rule when you have multiple prior data points”

What is the difference between machine learning and predictive analytics?

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Why your Facebook fanpage is less effective

Many social game companies use Facebook as the central point of their community strategy but changes to Facebook’s surfacing algorithm suggest you may want to focus elsewhere. A social game’s Facebook fan page has been a great medium to increase retention, virality and even monetization. Keeping the community engaged through dialogue and promotional offers, so they are regularly on the fan page, is a relatively low-cost, low-effort way of getting your players back in the game (and thus increasing retention). Running promotions that encourage your Facebook fans to share links and other information increases the virality of your app. You can even use your Facebook fan page to improve monetization, by running sales directly from the page that cater to the community.

A recent article on Business Insider, however, points to the lower reach these pages are now experiencing, and thus reduced effectiveness. According to the article, at the beginning of December, Facebook changed the algorithm it uses to select the stories that appear in users’ News Feeds. The effect of the change was to reduce the reach of Facebook posts, which some companies feel declined by as much as 80 percent. Although Facebook’s motivation for the change is uncertain (Facebook claims it is trying to surface more relevant posts, while companies feel they are trying to force them to advertise more), the impact is very clear: Less surfacing equals less effectiveness. Continue reading “Why your Facebook fanpage is less effective”

Bayes’ Theorem Part 1: Why Bayes’ Rule is the key to good decision making and success

Making the right decision, in business and in life, is the most important thing you can do. Wrong decisions can haunt you your entire life while the right decision can mean making your company worth billions, years of happiness, etc. Imagine if Travis Kalanick, CEO of Uber, had decided to focus on connecting buses with passengers and not taxis, or if Trip Hawkins would have focused 3DO on creating software and not a hardware platform. Understanding Bayes’ Theorem (also known as Bayes’ Rule, two terms I will use interchangeably) increases the chance you use data the right way to make your decisions.

This post is the first in a series I will be writing on Bayes’ Rule. This post and most of the background I discuss is based on the best book I have found about Bayes’ Rule, A Tutorial to Bayesian Analysis by James Stone. Last year, I wrote several posts on Lifetime Value (LTV), given how crucial it is to the success of any business, from the newest technology to the oldest brick and mortar enterprise. This year, we will be tackling Bayes’ Theorem. As you will see in the next few posts, by understanding Bayes’ Theorem you can then make optimal decisions about what games or projects to green light, how to staff your company, what to invest in, which technology to use, who to sell your company to, what areas of your company need to be fixed/improved, etc. Bayes’ Theorem is the single most important rule for good decision-making, both in your professional and business life.

What is Bayes’ Theorem?

Bayes’ Theorem is a rigorous method for interpreting evidence in the context of previous experience or knowledge. Bayes’ Theorem transforms the probabilities that look useful (but are often not), into probabilities that are useful. It is important to note that it is not a matter of conjecture; by definition a theorem is a mathematical statement has been proven true. Denying Bayes’ Theorem is like denying the theory of relativity.

Some examples of Bayes’ Rule

The best way to understand Bayes’ Rule is by example (I will touch on the math later). Again, much of this is based on Stone’s tutorial on Bayesian analysis. First, look at probability as the informal notion based on the frequency with which particular events occur. If a bag has 100 M&Ms, and 60 are green and 40 are red, the probability of reaching into the bag and grabbing a green M&M is the same proportion as green M&Ms in the bag (i.e., 60/100=0.6). From this, it follows that any event can adopt a value between zero and one, with zero meaning it definitely will not occur and one that it definitely will occur. Thus, given a series of mutually exclusive events, such as the outcome of choosing an M&M, the probabilities of those events must add up to one. Continue reading “Bayes’ Theorem Part 1: Why Bayes’ Rule is the key to good decision making and success”

Data and analytics: the enemy of innovation

One issue that is likely to haunt some of the high-flying tech and game companies that are currently doing wonderfully is their reliance on data and analytics can inhibit innovation. Clayton Christensen, the esteemed Harvard professor who wrote the seminal work on innovation, The Innovator’s Dilemma: When New Technologies Cause Great Firms to Fail, explains how deep customer understanding works against strong firms keeping up with innovators in their space. The use of data and analytics to understand and anticipate customer needs is now the driving force behind most of the exciting tech and gaming companies. This strength, however, could leave these companies vulnerable to new competition and turn today’s stars into tomorrow’s duds.

The Innovator’s Dilemma

To understand the situation data reliant companies will find themselves in it is important to understand first the innovator’s dilemma. Although I will blog about it in more detail this year, (I will not try to capture all the nuances of Christensen’s book; I strongly recommend you read it for yourself), the underlying thesis is that often great companies fail to be the disruptive innovative force in their industry because they have such a deep understanding of their customer’s needs they do not see disruptive opportunities that initially cater to other users. Instead, they continually create better products for their customers, which sustains technology but cannot disrupt. Disruptive companies come in with a simpler technology that aims at a different market, gains traction there and then encroaches on (and eventually destroys) the established firms. This cycle has been repeated in multiple industries. As Christensen writes, “blindly following the maxim that good managers should keep close to their customers can sometimes be a fatal mistake.” Continue reading “Data and analytics: the enemy of innovation”

Analytics 3.0

As many know, I believe end-of-year predictions have zero value and I prefer to look at important trends that are already unfolding and will impact readers next year. The most important trend right now for people in the social media and gaming spaces, as well as almost anyone in the tech space, is the evolution of analytics. Thomas Davenport, author of the seminal work Competing on Analytics, recently wrote an article in the Harvard Business Review about Analytics 3.0. Just as Analytics 2.0 transformed the gaming space, allowing companies like Zynga, Playfish and Disney to leap over established competitors, Analytics 3.0 can reshape as dramatically the gaming ecosystem. Analytics 3.0 is a new resolve to apply powerful data gathering and analysis methods not just to a company’s operations but also to its offerings—to embed data smartness into the products, services and games that customers buy.

A brief history of analytics

To understand best the impact of Analytics 3.0, it is helpful to understand 1.0 and 2.0 and their impact. Analytics 1.0 ushered in an objective, deep understanding of important business phenomena and gave managers and leaders the fact-based comprehension to go beyond intuition when making decisions. Data about sales, customer interactions, production processes, etc., were recorded, aggregated and analyzed. For the first time, analytics were used to compete by creating greater efficiency: making better decisions on key issues to improve performance. Continue reading “Analytics 3.0”

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Hire adults and treat them like adults

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What is Bayes’ Theorem?

Some examples of Bayes’ Rule

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The Innovator’s Dilemma

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A brief history of analytics

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