lifetime value – Page 3 – The Business of Social Games and Casino

Lifetime Value Part 14: Machine learning and LTV

Last week I discussed how you need to manage your customers based on their expected lifetime value, and machine learning technology is a powerful tool to execute this strategy. When applied effectively, machine learning can reduce your service and support costs while increasing your profit margin. Machine learning lets you tailor virtually any part of your business to each individual customer so you optimize the value of that customer.

Predicting and placing users in the right bucket

The first key in maximizing profitability is segmenting your customers based on expected lifetime value. You can accomplish this manually by examining their past behaviors and key metrics, but most analytic teams do not have the resources to analyze how all the metrics and data interact. Machine learning algorithms, however, can not only incorporate hundreds of variables, but also predict future behavior based on these patterns. Thus, two customers who may have both spent $500 may warrant very different treatment, if the first one is likely to spend $20,000 while the latter is projected to spend $750. Machine learning is a strong tool for making these projections and optimizing the segmentation of your users. Continue reading “Lifetime Value Part 14: Machine learning and LTV”

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”

Lifetime Value Part 11: How to calculate LTV

Last year, I published a series of posts on the importance of knowing your users’ or players’ lifetime value, the key components and how to impact them and techniques to increase the accuracy of your customer lifetime value (LTV) predictions. I intentionally did not publish a formula for calculating LTV—while it is always a factor of retention, monetization and virality—as it is different by product and there are many alternative ways to get to an accurate customer lifetime value. Prompted by an infographic that I came across (see below) I did want to go into some details of the mechanics of calculating LTV.

The first step is to obtain your key variable metrics as averages across all users. The ones I prefer are ARPDAU (average revenue per daily active user), day 1 retention (how many people who use or install your website, app or game come back the next day), day 30 retention and k-score (how many free/organic users does a user bring in). Continue reading “Lifetime Value Part 11: How to calculate LTV”

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”

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 2: Virality’s role in LTV and how to impact it

Virality is one of three components that you use to determine lifetime value of a customer (LTV) and is the one where small changes can have the greatest impact. Last week I wrote about the central importance of LTV to the success of your game and your company. This week I want to discuss one of the key elements of LTV, its importance and how you can improve it.

How to define virality

Virality can be defined as the number of new players an existing player recruits (for free), and is often referred to as a “K-score” or “K-coefficient.” The viral coefficient K is usually calculated as K=i*conv% (conversion percentage), where “i” is the number of invites sent out by each new customer and “conv%” is the percentage of invites that convert into costumers.

Although calculating the K-score seems pretty straightforward, there are many different measures you can use in your LTV calculation. In a future post, I will detail how lifetime value is calculated, but I can only provide a framework, as companies calculate it differently based on their priorities and needs. There is no perfect formula and you will always be adjusting your formula to get a more accurate picture of your player’s lifetime value.

The K-score is a time- and cohort-dependent variable. Thus, based on the earlier equation, you may measure i*conv% over the life of the game, over a month, over a week or simply in one day. You may also want to use a combination of these metrics, for example both 1-week K-score and lifetime K-score. A related measure to track that could be incorporated into your LTV is Viral Cycle Time, that is how long it takes for a user to bring in another player (and another and another); the shorter the cycle time, the quicker your growth. Continue reading “Lifetime Value Part 2: Virality’s role in LTV and how to impact it”

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Predicting and placing users in the right bucket

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Profitability, success and LTV

Managing a customer portfolio

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The Uncertainty Principle

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How to define retention

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How to define virality

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What LTV consists of

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