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.
Archives For Analytics
The use of analytics in the marketing, development or deployment of social games
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R, SWRVE and Mixpanel expertise”) because the game industry is evolving so quickly most of those skills will soon be outdated.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.
The research discussed in the MIT article points to several traits that are crucial to finding great analysts:
- They have a cognitive “attitude” and will search for deeper knowledge about everything.
- They are driven to be creative and will want to create not only solutions, but also elegant solutions.
- They have a strong desire to “do things the right way,” and will encourage others to do the same.
- They have an extremely high sense of quality, standards, and detail orientation, often evaluating others by these same traits.
- They tend to be somewhat restrained and reticent in showing emotions, and may be less verbal at team or organizational meetings unless asked for input or if the topic is one of high importance from their perspective.
- They may take calculated, educated risks, but only after a thoughtful analysis of facts, data, and potential outcomes. They persuade others on the team by careful attention to detail, and through facts, data, and logic—not emotion.
Overall, when looking for members of your BI team, focus on their curiosity and creativity (also a good idea when hiring for any position); not their resume or existing skillset. Finally, do not put too much emphasis on the interview process. Analytic professionals are analytic by definition, and thus may not be charismatic or present well in an interview even though they have the traits you need. Moreover, an interview is only one small piece of data in the picture of a candidate; do not put too much emphasis on this single data point and look at the body of work.
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.
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:
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…
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…
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…
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.
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…
In previous posts, I discussed the importance of customer lifetime value (LTV), its key elements (monetization, retention and virality) and how to calculate LTV; but it is important to also understand that there is not a monolithic LTV for your game (or product). You may remember that the practical value of LTV is to use it as a metric to determine whether or not an ad spend has a positive return. If the LTV is higher than the cost per install (CPI), it is profitable to advertise (and vice versa).
The key to success, though, is understanding the LTV of the customer you will be acquiring as opposed to the general LTV for the game. Some low cost user acquisition channels may bring in players who are effectively worthless (they leave your game right after they click on the ad) even in a game that has a high overall LTV, so understanding the lifetime value of these users would save you from wasting your money. Conversely, there may be a very expensive advertising channel that brings in great players who all monetize well and have a much higher lifetime value than their CPI.
There are four factors that you should use to calculate separate LTVs (and in different combinations): Continue Reading…
The big buzz phrase in the Bay Area the last year or so has been “growth hacking,” and the ideas behind it can help significantly game companies. The underlying principle in the phrase is that modern start-ups should be focused on using the new tools available via technology to grow rapidly their user base rather than relying on older, sometimes outdated, marketing techniques. Growth—unlike marketing—usually encompasses multiple aspects of an organization, with the growth team not only bringing in users but also working with the product team to optimize the product for growth. It stresses the importance of product to growth and how the two should work together rather than having marketing set aside in a corner. The phrase itself was coined by Sean Ellis, CEO of Qualaroo and the first marketer at many great tech companies including Dropbox and LogMeIn.
What is a growth team?
A quora post from Andy Johns (currently on Quora’s growth team and one of the early members of Facebook’s growth team) described the typical people an early stage company would put on its growth team: Continue Reading…
