Analytics – Page 8 – The Business of Social Games and Casino

Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development

Most of my posts about the reasons and methodology for creating accurate customer lifetime value (LTV) predictions have focused on the numbers and metrics, but a key element to predicting accurately LTV is observational (qualitative) data. It all comes down to more data is better, so predictions with qualitative data are going to be more accurate than those that rely solely on quantitative data. A mistake that is commonly made in the analytics world, and particularly in gaming, is to disregard anything that is not a quantitative value.

Some examples of incorporating effectively qualitative data

The example that had the most impact on me is that Billy Beane and the Oakland A’s, the subject of Moneyball (and multiple blog posts by me), has one of the highest scouting budgets in baseball. Scouts provide data on variables, like mental make-up and desire to win, that are not evident in the historical metrics. So although Beane makes personnel decisions based on metrics, he has also invested large sums in getting qualitative data (scouts watch players and prospects and then report on how they perceive the player’s skills). This approach has proven successful, as Beane’s A’s again surprised people by winning their division last year. What Beane has mastered is finding a way to incorporate the scouting reports with the available quantitative metrics. Continue reading “Lifetime Value Part 10: Incorporating qualitative data into your LTV predictions and game development”

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”

Speaking at MIT on data quality and how it affects LTV

I am very proud to announce I will be speaking at MIT’s CDOIQ (Chief Data Officer and Information Quality) Symposium in July about how data affects your LTV projections, and ways to improve the quality of your metrics. It’s going to be a great conference and I would love to meet up with anyone who will be there.
MIT Logo

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”

Lifetime Value presentation to Yetizen

Below is a presentation that I gave yesterday on lifetime value (LTV) to the portfolio companies of YetiZen. It covers the importance of LTV, key variables (monetization, virality and retention) and how to affect them, importance outside gaming, cohort analysis and the predictive nature of LTV. Other than the final section on uncertainty, which echoes my blog post on Tuesday, the presentation is largely consistent with the one posted earlier that I gave at Groundwork Labs a few months ago. Here is the one from last night:

Yetizen presentation on LTV from Lloyd Melnick

Lifetime Value Part 9: Uncertainty and LTV

The key to using customer lifetime value (LTV) effectively is the understanding that it is a prediction, not a value. In my previous eight posts on LTV, I stressed the importance of LTV to the success of your game and company and the key components in determining LTV. After reading Nate Silver’s The Signal and the Noise, I realized that it is crucial to understand that LTV is a prediction and suffers the same risk as other predictions (e.g., elections, weather, sports scores).

The Uncertainty Principle

Many people mistakenly believe (and I may have inadvertently implied this in a previous post), that LTV is an exact function of virality, monetization and retention. It implies you put those variables into a formula and get out a number that shows precisely how much a player is worth. That would be the case if you did it with historical information after five years and then calculated how much that player had been worth to you. However, you are calculating how much the player will be worth, which is inherently different because you are predicting their future value.

The uncertainty principle, a key tenet of quantum mechanics (as popularized by Stephen Hawking), postulates that perfect predictions are impossible if the universe itself is random. Since you cannot have a perfect prediction, your LTV cannot be a distinctly quantified value. You are predicting future events (how much the player will monetize, how viral they will be and how long they will stay in your game) based on the available data. Your LTV model is a simplification of the world the player is in; you are looking at several variables but you cannot look at everything (e.g., chance of war, plague, everyone switching to Blackberry devices). In effect, your LTV calculation is very similar to a sportscaster’s estimate of how many home runs Albert Pujols will hit or a weatherman’s prediction on the likelihood of a hurricane to hit Cape Hatteras. Continue reading “Lifetime Value Part 9: Uncertainty and LTV”

Lifetime Value Part 8: Incorporating costs and expenses in LTV

An aspect of lifetime value that is often neglected but could mean the difference between the ability to advertise (or not), are the costs associated with your game (or product for those outside the gaming space). As I have discussed in detail in the first seven posts on customer lifetime value (LTV), your lifetime value has to exceed to cost per install of a new user (CPI) to justify advertising. The LTV is a formula incorporating retention, virality and monetization. The other areas, though, that you need to look at are costs that lower the revenue stream from the user. Continue reading “Lifetime Value Part 8: Incorporating costs and expenses in LTV”

Using analytics to optimize all of your advertising spend

A recent article in the Harvard Business Review on Advertising Analytics 2.0 shows how advanced analytic tools and concepts can improve the return from your growth efforts. The article, written by Wes Nichols of MarketShare, shows how ad channels increasingly interact with each other and you can be much more effective by understanding these interactions. What you do in performance marketing, search ads, web, YouTube, TV and PR are not independent of each other. For example, a TV advertisement may increase Google searches that are then directed to your web game by purchasing ad words.

Advanced analytics allow you to understand these interdependencies and allocate accordingly. For example, one company found 85 percent of its budget went to TV ads and six percent to YouTube ads but the YouTube ads were nearly twice as effective at driving search. They then changed their allocation of ad dollars. This adjustment increased sales nine percent without incurring any additional advertising expense.

One of the keys to using analytics more effectively is understanding what data to collect. Many in the game industry think that tracking clicks on cost-per-click (CPC) campaigns, adding some consumer surveys, focus groups and last-click attribution is enough to optimize their marketing. It is not. Continue reading “Using analytics to optimize all of your advertising spend”

How to implement A/B testing

There’s a great blog post on GamesBrief on how to get your A/B testing efforts going. Given the importance of A/B testing to optimizing both your game’s performance and user acquisition, this is a must-read article if you are not already A/B testing.

A/B testing image from blog.empowerment-group.org

To summarize the post (read the full post for a much deeper explanation of each point), the key point is that there are six steps to start successful A/B testing: Continue reading “How to implement A/B testing”

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Some examples of incorporating effectively qualitative data

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Avoid anosognosia

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The Uncertainty Principle

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