analytics – Page 3 – The Business of Social Games and Casino

Lifetime Value Part 13: Managing users and customers profitably

I have written many times about the importance of customer lifetime value (LTV), about its central role in determining a company’s success and how to impact it. In this post, In this post, I will address how to manage users (customers, players, etc.) based on LTV.

Profitability, success and LTV

The success and growth of your company comes down to one basic principle, your customer LTV needs to be greater than the cost of acquiring and servicing the customer. The larger the difference between the value you get from a user and the costs associated with that user, the greater your profits. The greater the difference, the more resources you can devote to user acquisition. The greater the difference, the more someone will pay for your company.

The key to optimize the value of a customer versus their costs is not treating all customers the same. Each customer has a different LTV, which can be estimated from everything nearly all the data you are collecting, including from how you acquired the user, their demographic, their initial behavior, etc. The first step to optimizing your business is to determine your different customer segments (VIPs, heavy spenders, occasional spenders, one time spenders, social whales, browsers, etc.). You then put all of your users (hopefully automate this process) into these segments.

Managing a customer portfolio

Once you have segmented your users based on their behavior and predicted lifetime value, you need to manage actively this portfolio of users to optimize your profitability. For each segment, you need to build a strategy that maximizes the difference between the LTV and the costs. For users who spend frequently, you may want to give them enhanced customer service and free gifts so they come back (and spend) even more often. The costs of this additional service and gifts are more than offset by the additional revenue you generate. Conversely, for the one-time purchasers, you want to minimize expenses tied to them. Since they have a low lifetime value, you do not want to devote any resources (costs) to them. By allocating resources only where it increases the difference between LTV and costs, you optimize your profitability. Continue reading “Lifetime Value Part 13: Managing users and customers profitably”

How startups should use metrics

I recently came across a fantastic presentation on startup metrics by Andreas Klinger. It is embedded below but given its length I wanted to highlight the key takeaways:

The biggest risk for a startup is not failing to create a good product with a market; it is having a competitor come up with something a little better. Great example is Lyft, which I am sure is a little envious of Uber.
There are four stages for a startup to succeed. The first is discovery, generating the product idea. The second is validation, making sure the market wants the product. The third is efficiency, being able to supply the product cost effectively in quantity. Then there is scale, delivering the product to millions.
To look at it from the user perspective, there are two key elements: finding the product the market needs and then optimizing (the former encompassing discovery and validation, the latter representing efficiency and scale). To find a product the user needs, you need to understand these needs and create something that will be sticky (i.e., that they will return to) and viral (they will talk about). To optimize, you then need to build out the right revenue model and level, and then scale.
According to Klinger, 83 percent of startups are in the discovery phase (empathy, stickiness and virality) while most analytics are around revenue and scale.
Andreas Klinger
A/B tests, funnels, referral optimization, etc., are about optimization, not innovation and cannot replace creating a great product that people want.
There is a way to get product insights from data to create that innovative product and you can do it with a much smaller number of users. They key is looking at whether people stay on your site or in your app, in other words, whether they are hooked.
Focusing on improving metrics creates a false positive, you can always improve ad conversions or funnels but what looks good for investors does not necessarily improve the product. You may be converting or funneling the wrong users.

Continue reading “How startups should use metrics”

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”

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”

My favorite posts of 2013

Happy New Year!!! Rather than make predictions, which I find as valuable as reading tarot cards, I wanted to look back at some of my favorite posts from last year. Below is a list of the ones I felt were most useful:

LTV: The Lifeblood of Your Business My first in a series of posts about lifetime value (LTV), the most important metric any company (from games to bricks and mortar) should focus on.
Using probability to build a game portfolio likely to succeed One of my first posts of 2013 but one potentially very important to game companies. It discusses how to structure your portfolio to maximize return while reducing risk.
Growth tactics for mobile game and social media companies The key tenets to building a successful growth and user acquisition plan. Continue reading “My favorite posts of 2013”

Do not mistake the unfamiliar for the improbable

One of the critical mistakes you can make when making analytics-based decisions is mistaking the unfamiliar with the improbable. A few weeks ago, I wrote about how Nate Silver influenced my understanding of how to incorporate uncertainty into your LTV calculation, Silver also did a great job of showing that we must consider contingencies we may not even have thought of. There is a tendency in our planning to confuse the unfamiliar with the improbable. The contingency we have not considered seriously looks strange; what looks strange is thought improbable; what is improbable need not be considered seriously.

Avoid anosognosia

There is a medical condition called “anosognosia,” in which a person who suffers a certain disability seems unaware of the existence of the disability. When a possibility is unfamiliar, we do not even think about it. Instead we develop a sort of mind-blindness to it. The relevant version of this syndrome for professionals in the game industry requires us to do one of the things that goes most against our nature: Admit what we do not know.

In his book, Silver used the attack at Pearl Harbor as a prime example of anosognosia within the US government. He outlined the myriad reasons why the Japanese attack had been such a surprise to our military and intelligence officers. Worse than being unprepared, we had mistaken our ignorance for knowledge and made ourselves more vulnerable as a result. In advance of Pearl Harbor we had a theory that sabotage was the most likely means by which our planes and ships would be attacked. We stacked our planes wingtip to wingtip, and our ships stern to bow, on the theory that it would be easier to monitor one big target than several smaller ones. Meanwhile we theorized that if Japan seemed to be mobilizing for an attack, it would be against Russia or perhaps against the Asian territorial possessions of the United Kingdom since Russia and the UK were already involved in the war. We had not seen the conflict through Japan’s eyes. Continue reading “Do not mistake the unfamiliar for the improbable”

How to hire for analytic positions

Given the importance of analytics to social and mobile game companies (just see all my posts about LTV, performance marketing, virality, monetization, etc.), having the best business intelligence (BI) team is of central importance. Finding that talent, however, is not easy. I have been very lucky to work with some of the best BI talent throughout my career; they have made me look much smarter than I am. Not everyone will be as lucky as I have been. A recent article in the MIT Sloan Management Review provides great advice on predicting the performance of potential analysts.

The article points out that the ideal analyst does not exist; the job description is looking for a “unicorn.” You should not be hiring for a laundry list of skills (e.g., “I need someone with R, SWRVE and Mixpanel expertise”) because the game industry is evolving so quickly most of those skills will soon be outdated. Instead, you should look for the curiosity to keep learning, rather than the skills themselves. Continue reading “How to hire for analytic positions”

Finding the influencers with influence

Although identifying and leveraging influencers is one of the fundamental strategies in social media marketing, a recent article in the Harvard Business Review (“What Would Ashton Do – And Does it Matter” by Sinan Aral) shows it is not as simple as many think. For those not familiar with the term “Influencer,” it refers to someone who has significant influence with either a niche or the mass market due to social media presence. It could be someone with two million Twitter followers or somebody whose blog is read by virtually every doctor (and thus influences the medical community). There are third-party services, such as Klout, that create scores that attempt to show how much leverage somebody has in social media.

In social media marketing, the tactics often revolve around identifying influencers and getting them to promote your game or product. The belief is that if some of these influencers are promoting your game or product, you will hit a threshold at which everyone is talking about it (and playing it). Thus, every marketer’s top goal is to get Ashton Kutcher, who has 13.7 million twitter followers, to tweet about them.

Continue reading “Finding the influencers with influence”

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Profitability, success and LTV

Managing a customer portfolio

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How deep to probe

What assets to use

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What is Bayes’ Theorem?

Some examples of Bayes’ Rule

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A brief history of analytics

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Avoid anosognosia

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