LTV – The Business of Social Games and Casino

Summer highlights from The Business of Social Games and Casino

Normally, I take the summer off from writing blog posts but 2020 is anything but a normal year. Unfortunately for many, the pandemic meant you had to cancel or postpone your holidays or had more time to add to your knowledge. Thus, I continued to post but at a slightly reduced schedule. If you were lucky enough to get away, below is a summary of my posts over the summer that you may have missed, enjoy.

Lifetime Value Part 29: Increasing Retention

Key Takeaways

Retention is the strongest driver of LTV and data from Google shows the most important retention KPI is the amount of players who return on day 2 after installing your game.
The strongest driver of D2 retention is how many minutes your customers stay/play within the first ten minutes of starting the app.
To improve retention between the first and second days, make the early experience faster and more fun by improving load times (while reducing secondary loading), making your lobby intuitive, and not distracting your player with a bad tutorial or promotions.

Behavioral Economics Tips for Gaming Companies

Key Takeaways

A key lesson of behavioral economics is that less choice often drives better results. When the number of choices increases, our ability to make a decision decreases.
Consumers hate uncertainty. Questions without answers cause fear and kills the experience and sales, it is a customer experience killer.
Use AB and multi-armed bandit tests help you understand how your players will react in the context of your game, market research conversely might provide bad information as people do not know what they want.

How to avoid meetings about the trivial, aka bikeshedding

Key Takeaways

Bikeshedding is the tendency we have to spend excessive time on trivial matters in meetings, often glossing over important ones.
Bikeshedding is damaging because it wastes very valuable time and, more importantly, leads to insufficient discussion of important issues.
To avoid bikeshedding, set a clear purpose for all meetings (and eliminate conversations about other issues), only invite necessary people, appoint a decision maker and have the decision maker set clear parameters for the meeting.

Lifetime Value Part 30: Why clumpiness should be one of the KPIs you focus on

Key Takeaways

We normally focus on analyzing recency, frequency and monetization of the customer but by adding a new KPI, clumpiness, we get a much better understanding of their expected value.
Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable.
Clumpiness can help you better segment players, predict VIPs and target your reactivation efforts and spend.

Why Evo’s $2 billion+ acquisition of NetEnt is more important (to both iGaming and social casino) than you think

Key Takeaways

Evolution Gaming, the largest Live Dealer provider, recently announced a bid to acquire NetEnt, the largest slots provider for $2+ billion.
The deal, in that Evo is the acquirer, shows that Live Dealer is eclipsing slots in the casino ecosystem.
For real money operators, they need to ensure they balance resources around both Live Dealer and slots while social casino companies need to figure out the best way to embrace this opportunity.

Customer analytics tips for gaming companies

Key Takeaways

People who wander around a retail location spend more than ones who immediately find what they are looking for and retailers optimize to create this jiggliness. Online casinos and games can also build in jiggliness so players find new games and offerings rather than simply quickly go to the one they are looking to play.
While satisfaction with customer service positively impacts profitability, the relationship is not linear. Improvements have a strong impact when players are highly dissatisfied (and that is corrected) or when customers with great service make further improvements, companies in the middle often do not see a positive ROI on CS improvements.
A relationship between two variables does not show one is causing the other, to have causation there must be a relationship plus temporal antecedence plus the absence of a third variable driving both factors.

How to get your big initiatives done

Key Takeaways

Many important initiatives, from new products to operational efficiency, bog down and die in the middle phase. They initially have momentum but stall once the initial burst dies down.
To MOVE projects through this middle phase, the Middle element needs a clear and concrete strategy and you need an Organizational structure with capacity to complete the initiative.
The final keys to getting through the middle phase are Valor, making tough decisions and prioritizing the initiative, and getting Everyone involved.

How Operations Analytics can help online gaming companies

Key Takeaways

While Operational Analytics are a focus primarily in retail and traditional businesses, there are many best practices that iGaming and social game companies can leverage.
Forecasting is central to generating and earmarking resources but is often a challenge for game companies, rather than trying to create a point forecast create a range based on moving averages and looking at standard deviation. For new products, create a simulation that will show the distribution of potential outcomes and the risks and rewards possible.
You need one, and only one, distinct goal and then optimize your strategy around that goal; it’s impossible to optimize for multiple goals. Use constraints to incorporate what used to be additional goals.

The risks of market research

Key Takeaways

A sole reliance on customer input and feedback, traditional market research, is built on a model of human decision making that assumes humans are rational, while in practice we are not.
Not only do people provide a response inconsistent with their actions, they often do not understand the underlying causes of their behavior.
Use one or multiple tools that show actual decision making, such as ABn testing or looking at reactions to similar initiatives in adjacent industries, rather than relying on what customers believe is their preference.

Lifetime Value Part 30: Why clumpiness should be one of the KPIs you focus on

When calculating customer lifetime value (LTV),there is one KPI that is often neglected, clumpiness. Clumpiness is a term coined by Eric Bradlow, a marketing professor at Wharton. Think of clumpiness as binge-watching Ozark (or, if you must, Tiger King). Rather than an extended, constant period of consumption, a short, intense buying burst. By understanding and tracking this behavior, you have a significant weapon for improving LTV.

Defining clumpiness

Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable. When calculating customer value and segmentation, we focus on analysing recency, frequency and monetization of the customer (what Bradlow refers to as RFM). This analysis is based on customers making purchases in a regular pattern, i.e. coffee, diapers or milk. Bradlow’s analysis, however, shows that for certain products (and I would classify social and casino games here), customers actually monetize in bursts. Thus, you need to add C for clumpiness to your RFM modeling.

Why clumpiness is important

Bradlow researched how to predict the future value of customers (predicted LTV). Bradlow explains, “[l]et’s imagine you want … to predict who are going to be the valuable customers in the future. And you have four things you can use to predict it. As I mentioned: recency, frequency, monetary value and let’s say the marketing spend towards the customer. Those are the classic ways in which companies build what are called scoring models…. [T]he findings of my research suggest that higher clumpy customers are worth more out of sample, meaning in their future value, even after controlling for RFM and marketing expenditure — which means we have found another variable that firms should track [concerning] our customers and use it to predict their worth in the future.”

This finding is particularly interesting as most companies, especially in the game space, currently base their LTV projections on the RFM model. While this calculation may have worked in the traditional retail economy, consumption has evolved, especially for digital goods. Binge consumption is a fact of life in the entertainment space, and gaming sits squarely at the center of the modern entertainment environment. This analysis is consistent with Bradlow’s findings, where he says, “[i]f you look at historically purchased goods, clumpiness really isn’t there. But if you look in the new wave, the new economy, clumpiness is pervasive in every data set I’ve analyzed.”

Using clumpiness insights

Calculating clumpiness should be easy and not require tracking any new events. It is the same data you are using to calculate R, F, M and LTV. There are then several applications for this insight:

Incorporate it into your segmentation to get a better understanding of who your VIPs and high value players are, then focus your premium treatment (and benefits) on these players.
Use clumpiness to predict better what players are likely to become VIPs. This will help you concentrate your early retention efforts and reinvestment.
Focus reactivation on your clumpy players. Bradlow explains, “[i]f you reactivate them, they’ll come back and be clumpy again, and do a lot of stuff in the future. “
Do not simply rely on the KPIs you have always been trusting. Do not rely on simple theories of how your players behave, also look at things about arrival time or when people play. People who come in bursts, then go away and then come back in bursts and then go away are fundamentally different and have a different LTV.

By understanding clumpiness, you have a more accurate predictor of LTV. Once you know how to predict your LTV, you can then impact it by making changes that drive these variables.

Key takeaways

We normally focus on analyzing recency, frequency and monetization of the customer but by adding a new KPI, clumpiness, we get a much better understanding of their expected value.
Clumpiness refers to the fact that people buy in bursts and that those customers could be extremely valuable.
Clumpiness can help you better segment players, predict VIPs and target your reactivation efforts and spend.

Lifetime Value Part 29: Increasing Retention

I have written many times about LTV (customer lifetime value) being the lifeblood for a successful business, as a new customer has to have a higher LTV than the cost of acquiring the customer, and how retention is the biggest driver of LTV. Given the importance of retention, a recent article by Google’s Adam Carpenter, Why the first ten minutes are crucial if you want to keep players coming back , provides great guidance on what to measure and then improve to have the most impact on improving retention.

Retaining new installs is arguably the most important driver for whether your product succeeds or fails. As Carpenter says, “retention is the primary metric, since if you can retain your new players, you can always figure out how to make money. If you can’t retain any players, you have no ability to make money.”

The metric to focus on is your day 2 retention (D2). According to Google’s data, median day 2 retention (number of users active on the day after they installed your game divided by the number of installs in your cohort) is 38 percent, while exceeding 46 percent puts your product in the top quartile (3/4 of all products would be doing worse than you if hit this target). Conversely, if your D2 is 22 percent, then 78 percent of the players you pay to acquire do not come back the next day. It is very hard to justify marketing spend if 78 percent is wasted.

What you should be looking at

While the amount of time a player spends in your game after they install it is important, the critical factor is the first ten minutes. If you divide Carpenter’s data by performance, the strongest games (top quartile) average Day 2 retention of 52 percent, with 22 percent returning the next day with virtually no gameplay the first day and then seeing D2 steadily increase with each successive minute played (see chart below). The second quartile has a similar curve but starts at a lower point. The key, though, is the impact from the first ten minutes.

The first ten minutes

In Carpenter’s analysis, performance is driven by how quickly in the first ten minutes you lose players. For games in the top quartile, retention starts out good and steadily improves. In the second quartile, retention is essentially flat across the first minute and a half, and then begins to increase steadily. In the third quartile, retention is largely flat for the first four minutes, then increases but more slowly than highly performing games. In the lowest quartile, retention declines in the first two minutes and does not start to improve until the fifth minute.

These early patterns have a strong impact on players. The worst apps lose 46 percent of their new installs by the fifth minute and this number increases to 58 percent by minute ten (so in less than ten minutes you have less than half the players you have spent money acquiring). The top games, however, only lose 17 percent of players by the fifth minute and 24 percent within ten minutes (retaining more than twice as many players as the lowest group).

How to improve your situation

Once you understand the importance of the first ten minutes, you need to focus on improving this performance. Carpenter explain this as avoiding retention flats and gorges. “The first pattern is called the ‘Flats’. This anti-pattern shows largely flat retention for up to 10 minutes, with the percentages only rising meaningfully after the 5th to 10th minute. The second is the ‘Gorge’, whereby retention appears to drop minute by minute for the first five minutes or so, and then begins to rise again.”

Once you review your data, you can understand if you are suffering flats or gorges. To alter the curves, you can then:

Improve loading times. Evaluate your loading times, keeping in mind new players are particularly sensitive to long loading times (they are have not yet decided they want to wait). In my experience, even a fraction of a second will surprisingly have a bigger impact than many sexy features.
Avoid large secondary download. You may improve loading times by creating a long secondary download but the impact can be equally damaging. Players, particularly those on a poor wireless connection, may abandon your game.
Make the lobby intuitive. Allow players to find quickly the action (and the action they want) in the game. If you are using a tutorial, ensure a smooth transition from the tutorial to where your player wants to go.
Enhance or eliminate the tutorial. Speaking of tutorials, they are largely a reason to leave a game; rather than enjoying the product customers are being sent to school. See if you really need a tutorial and, if you do, how you can make it more fun.
Do not overload player with promotions. Your goal on D1 is to get your player back on D2, monetization is a bonus on top of this goal. If the player returns, you will have many opportunities to monetize them. Do not sacrifice D2 retention by distracting players with sales and offers, have them focus on enjoying the game. I prefer to have none or at most one monetization promotion on day one.

By focusing on a quick, fun experience, you are more likely to get your customer to return on Day 2. As the Google data shows, if you get them back the next day, you will enjoy greater long-term success. By increasing this one KPI, you will experience a disproportionate positive impact on your LTV.

Key takeaways

Retention is the strongest driver of LTV and data from Google shows the most important retention KPI is the amount of players who return on day 2 after installing your game.
The strongest driver of D2 retention is how many minutes your customers stay/play within the first ten minutes of starting the app.
To improve retention between the first and second days, make the early experience faster and more fun by improving load times (while reducing secondary loading), making your lobby intuitive, and not distracting your player with a bad tutorial or promotions.

Lifetime Value Part 27: How to know if your advertising is working (with benchmarks)

The key to any successful product, particularly a mobile game, is for your CPI (cost per install) to be less than your LTV (lifetime value of a customer). As long as it costs less to acquire a new customer than they are worth, you have a healthy business. Once the cost of acquiring a new customer exceeds the value of that customer, your product or company will languish and eventually die.

The challenge of computing LTV

While virtually nobody would argue the logic behind using LTV to drive your marketing, implementing it is not always easy. Many companies do not have a reliable LTV formula for all of their products or a data scientist (or team) to create one. New products also do not have enough data to calculate LTV.

Even when you have a reliable LTV calculation for your product, every cohort of user will have a different LTV. Players acquired one month will not have the same LTV as those acquired a different month. Players acquired through one marketing channel will not have the same LTV as players acquired through a different channel. Same can be said for country, marketing creative, platform and many other factors.

You will also not have enough data early in a campaign to calculate LTV reliably. However, you do not want to spend on unprofitable campaigns and you want to support good campaigns with more resources.

The answer to measuring campaigns reliably

The best proxy for understanding if your CPI is below your LTV is ROAS (return on ad spend). ROAS measures how much revenue a certain cohort of users generates over the first X days of acquiring those players (with X normally measured after 3, 7 and 30 days).

While it sounds overly simplistic, short term ROAS tracks very closely with your long-term return, thus whether CPI < LTV. You can be very certain if your 30 day ROAS is performing ahead of target, your acquisition is profitable. Moreover, I have found that even 3 day ROAS is very indicative of long term profitably. I have not yet come across a situation where ROAS has indicated a campaign is profitable for it to falter when analyzed after months or years. I am now very comfortable relying on 3 day and 7 day ROAS metrics to decide whether to continue, increase or decrease spend for a product, specific vendor or campaign.

Benchmarks to target

Without benchmarks, ROAS numbers would be useless. You need to know what numbers to target. As I have been in the social casino space for about five years, I am only comfortable providing social casino benchmarks. When evaluating a product or campaign, I target a 3 day ROAS of 5 percent, 7 day ROAS of 10 percent and 30 day ROAS of 20 percent. For other genres, such as hyper-casual (where you generate most of your return early), your targets would be very different so understand your space before making decisions (actually before launching a product).

What to do if you are missing your benchmarks

As with all metrics, ROAS provides guidance, not black and white answers. If your ROAS is slightly below the benchmarks, you may not have a problem, it could be noise. If one agency is outperforming another by 1 or 2 percent, again it may not be indicative, it may be luck (one caught an extra VIP). Conversely, if you are missing even by a little, it could be indicative of a deeper issue. A small miss also means that you should adjust conservatively until you see your 30 day ROAS numbers.

If performance is significantly behind ROAS benchmarks, then you need to find the cause. If it is across the board (all campaigns), then your product has issues. You either need to address these issues and improve retention or monetization (the key drivers of LTV and return) or not invest in the product. If the performance is worse on a particular platform, you need to dive deeper into your technical performance on that platform versus other platforms and how the user experience (CX) differs on the underperforming platform. If the underperformance is with a partner or channel, you should adjust your spend to focus where you have a stronger ROAS.

Key takeaways

While the success of any product or company long term is ensuring its cost for acquiring a customer is lower than the value of the customer, it is difficult to calculate in the early days of a product or marketing campaign. ROAS (return on ad spend) based on the first 3, 7 or 30 days of a campaign provides a good proxy for whether the campaign is successful.
In the social casino space (other genres may be different), the benchmark target ROAS is a 3 day ROAS of 5 percent, a 7 day ROAS of 10 percent and a 30-day ROAS of 20 percent.
If your ROAS is underperforming significantly, you need to evaluate if you should continue investing in the product overall or in the specific marketing channels, while adjusting your marketing mix to optimize ROAS.

Lifetime Value Part 26: My most valuable retention KPIs

I have written many times about customer lifetime value (LTV)and how it is the critical determinant of a company’s success (any company, from mobile games to retailers). A user’s lifetime value has to exceed the cost of acquiring the customer, otherwise companies cannot grow and will eventually die.

Last year, I discussed that out of the three key components of LTV – monetization, virality and retention – retention was the one most critical for success. While people sometimes focus on monetization, its impact on the long-term value of a customer is limited. Think of a retail store. Would they rather have a customer who comes in, makes a $100 purchase but never returns or somebody who comes in every week and makes a purchase ranging from $10 to $25? Obviously, they would prefer the latter. Successful businesses, games, apps, have great retention, thus creating high LTVs and allowing for more marketing spend.

While the mathematical case for focusing on retention is incontrovertible, many companies have not perfected how to measure retention effectively. Most social game companies, among the most sophisticated users of analytics, rely on measuring retention by D1/D7/D30 retention (how many players who installed on Day 0 are play after one day, seven days and thirty days, respectively). While this method is an acceptable (and sometimes powerful) way of tracking how new users are performing, even D30 retention only reflects behavior of customers acquired in the last month. It does not show how well the game or company is retaining its existing customer base.

When I was at Zynga, I came across a metric that perfectly captures how well you are performing with your existing customers, CURR (current user return rate). CURR is complemented by NURR (new user return rate) and RURR (returning user return rate). Since leaving Zynga, not only have I taken these KPIs with me, I have used them as a key focus for optimizing products. A post by Nathan Williams, SaaS Retention Metrics: Lessons from Free-to-Play Games, reminded me how important these KPIs are and how to best use them.

CURR

CURR (current user return rate) is the most important KPI to track (or at least a tie with NPS). It shows how loyal your existing customers are; you should consider CURR the inverse of churn. If your CURR increases, it means you have improved your product’s appeal to existing players or customers, if CURR declines you have made your game worse. CURR is also an excellent way of looking at how your game is performing among different segments, VIPs versus payers versus never-spenders.

To calculate CURR, you start with all the users who played the game between t-14 (14 days before today, today minus 14) and t-20 and who used the product between t-7 and t-13, what percentage returned to play between t-0 and t-6. The benchmark for a good, but not great, game is 80 percent.

NURR

NURR (new user return rate) is a great metric for understanding how appealing your game is to players you have just acquired. A low NURR shows you have a bad initial experience (or a bad traffic source), turning off many users. It is virtually impossible to acquire players profitably with a low NURR.

To calculate NURR, take all the players who used the game for the first time between t-7 and t-13 and look at what percentage returned to the game between t-0 and t-6. You can benchmark NURR at about 30 percent, though it is dependent on the type of game and platform. There is much higher variance in NURR than CURR among successful games (a game on desktop could succeed with a much lower NURR than a game on Google Play).

RURR

RURR (return user return rate) shows how many people who had churned and returned to your game stay active. It is a great way of measuring how well your game can capitalize on CRM and paid reactivation campaigns. If the number is low, you are doing a great job of bringing players back but the product is still not compelling to these players.

You can calculate RURR by taking all the players who were active at some point but did not use the product between t-14 and t-20, and did use the product between t-7 and t-13, what percentage returned to play the game between t-0 and t-6. There is also significant variance in this benchmark but I usually target 40 percent for social casino games.

Use *URR to track product performance

Once you start monitoring CURR/RURR/NURR, you should use them to understand what is working and where there are issues. If you see a significant change in CURR, it is almost certainly caused by recent product changes. Low NURR indicates either you have broken your FTUE or you have added weak sources of traffic. A low RURR indicates your CRM or reactivation team is doing a good job but you need to add product features to keep the players you are brining back.

Key takeaways

Retention is the key driver of customer lifetime value (LTV), and CURR/NURR/RURR are the most accurate metrics to track retention.
CURR (current user return rate) is your most valuable metric, the percent of your current players who are staying active. It shows whether changes in your product are appealing to or deterring your player base.
NURR (new user return rate) shows if your initial user experience is strong while RURR (return user return rate) shows if your game is appealing to players who have churned but decide to try it again.

Why Super Mario Run was destined to fail: Lifetime Value Part 24

I am surprised that people are surprised about the lack of success for Super Mario Run from Nintendo. While I do not claim that I can predict the future, and this time of year I scoff at everyone’s predictions because if the so-called experts actually could foresee develops they would be much wealthier than they are, in January 2012 I laid out the reasons traditional gaming companies were unlikely to succeed in the free to play space . The reasons I stated nearly five years ago were also the headwinds that doomed Super Mario Run.

Different businesses

As I wrote then, the biggest challenge traditional game companies face when moving to a free-to-play model (at the time it was more Facebook than mobile) is that they are different businesses. Old school game companies, be it Nintendo or EA to Take2, are skilled at creating a great product that someone buys, enjoys and finishes. Free-to-play companies are creating a service (hence the now relatively old term software as a service), something that customers use over time and becomes part of their life (like Netflix or Amazon Prime).

Building and running a service requires a different skill set than building a great game. The former needs

Analysts to look at customer behavior and suggest changes to continually improve the product
Product managers to create new features and elements that keep existing players engaged and ensure strong elder gameplay for the most committed players
User acquisition specialists who not only can bring in new players but can reactivate the most valuable players
CRM experts who can communicate with existing players both inside and outside of the product through multiple channels to increase their engagement and loyalty.

Conversely, traditional game companies succeed with a very different skill set

Great game designers who can create a fantastic experience, though usually of a limited nature
Marketing gurus who can get people to purchase a product
Distribution experts who can ensure the product is placed prominently in front of potential customers.

I have no privileged information in how Nintendo structured its Super Mario Run team but based on the product and how they are managing it, I would bet the focused more on the skills needed by traditional game companies than those creating a free-to-play game service experience. The game clearly has great designers and their promotion by Apple shows they still know how to manage channels.

They do not understand free-to-play LTV

What they are lacking is an understanding of free-to-play economics, primarily how to optimize player lifetime value. I have written way too many times about customer lifetime value (even wrote a book on it) but it is still a concept that traditional game companies like Nintendo fail to grasp. In summary, lifetime value is the monetary value to your company of a new player and it is a function of monetization (how much they spend), retention (how long they remain a customer) and virality (how many other customers they bring in).

In the traditional game space, this equation is quite easy. For Nintendo, LTV is largely how much a payer pays for a DS and Wii game. It does increase by downloadable content (DLC), whether they buy additional Nintendo products and if they encourage friends to buy but it is largely driven by that retail purchase of a game.

This experience is evident in how Nintendo approached Super Mario Run. The product is built so that the free element is a teaser to get a $10 purchase. It is not built to create a long-term relationship between players and the game, where they return (and often spend) daily for months or years (no exaggeration, take a look at Mobile Strike or Clash of Clans or most of the games on the top grossing charts). They are still focused on the discrete purchase, selling the razor and not the blades.

This approached doomed Super Mario Run (and by the doomed, I meant compared to expectations and potential, as it will still generate millions), even if they were seeing more traction getting the initial $10 purchase. In the world of free-to-play, $10 is largely an irrelevant transaction. Supercell was acquired by Tencent for $8.6 billion because players are spending hundreds or thousands of dollars in the game. By focusing on the first ten dollars, Nintendo missed where the bulk of free to play revenue comes from and largely capped what most players would have to spend. Thus, a player who Nintendo could have built a relationship with that would generate $20,000 is now spending $10 and moving back to a Supercell or King or Zynga game.

Core game companies will continue to flail in the free-to-play business

I said it almost five years ago and it has largely held true, traditional game companies won’t succeed in free to play. Since I wrote that article a handful of game companies have seen some free-to-play success (most notable Hearthstone) or acquired respectable free-to-play businesses but most have either failed or gone bankrupt. We still have not seen EA turn their core games (FIFA, Madden, Battlefield) into free-to-play franchises. There is nothing from Microsoft or Sony on any mobile chart. Take2, not a player in the mobile space. You get the point. It’s no longer a question of when the core game companies will successfully move into free to play (and mobile) but when they will just give up (and investors will stop expecting it) and focus on what they understand.

Key takeaways

The failure of Super Mario Run by Nintendo was very predictable, as traditional game companies face many structural issues in creating free to play mobile apps.
The biggest hurdle is their team structure, as they are built to create a product and not run a successful service.
Core game companies also do not truly understand free to play economics, that customer lifetime value is driven by long-term retention and monetization and not a discrete purchase.

Why your business model should not rely solely on in-app purchases

I have seen many disparate data points recently that when looked at holistically show how the free to play business model needs to evolve from relying on in-app purchases. While previously a strong in-game economy that generated significant revenue from in-app purchases was sufficient to create large profits for successful games, changes in the economics of the industry suggest in-app purchases will not be enough to support even successful games. The obvious answer is the advertising model, but even this model needs evolution to keep up with the times.

The data points

There are multiple data points that suggest CPIs (cost-per-installs) for performance marketing are not only increasing, but are likely to increase at the rate where they will surpass the lifetime value of acquired users for most games.

More apps, more spend

Most of the major game publishers are planning to grow through the launch of multiple new apps, particularly in the social casino space (where user acquisition costs are already high). While speaking colleagues recently, I learned that two of the major social slot companies have three new SKUs each planned for 2016, with planned spend of over $250,000 per day for each company already budgeted. They also expect their major competitors to each launch 1-2 new SKUs next year (and all in the first half of next year).

CPIs are simply a matter of supply and demand, and given that the advertising inventory is not increasing, it is inevitable that the cost of installs will increase. Even if the new apps are not targeted in your space, there is a limited number of people who will download a game and these companies will start to target your potential customers also because they will dry up their demographic.

Companies scapegoating user acquisition teams and agencies

The second data point is how many companies, including some of the biggest in the space, are replacing the leadership of their UA (user acquisition) teams and firing UA agencies they have worked with for several years. Many people I know have recently been approached for senior UA positions, and many of these people do not have significant UA experience. What is occurring is publishers are seeing a lower return on their advertising investment, or a negative return, and believe the problem is their UA team or advertising agency. Continue reading “Why your business model should not rely solely on in-app purchases”

Why your game’s metrics mean nothing

From the beginning of time (or at least 2009), companies have been basing decisions on game metrics, investors have used performance metrics to base investment decisions and outside analysts have used game metrics to estimate the health of a company. Unfortunately, all of these applications of metrics are deeply flawed and often mislead the decision-makers, investors and analysts (sometimes intentionally, sometimes not).

The charts below from Appsflyer show the critical flaw in looking at a game’s metrics:

Retention performance on Android by source:

It all depends on the source

The first lesson from these charts is that performance depends largely on the source of users. Although these charts show retention metrics, I am certain monetization and virality performance shows similar behavior. Thus, your performance metrics are incredibly biased by the source of traffic and metrics for your game overall only reflect the quality of the various user sources.

As an example, assume your Bingo game has D7 (Day 7) retention of 20% and an ARPDAU (average revenue per daily active user) of $0.25. Your main competitor’s game had D7 retention of 10% and an ARPDAU of $0.15. Based on this information, you may decide to cancel other projects and focus on this game. As a corporate development expert, you may aggressively try to acquire this company. The problem, however, is that the competitive game may be three years old and the company is using a very weak user acquisition channel that does not have a high cost because they have gotten all potential users from the better channels. Conversely, you may have acquired users through a Facebook mobile ad campaign targeting high-value Bingo players. Thus, your new users are of much higher quality than those from competitive Bingo game.

Even if your game is significantly worse, its metrics would look better at this stage. If you decide to focus on this game, the new users you bring in end up performing much worse than when you made the decision. This deterioration is not due to the game getting worse; it’s just based on a different set of users. If you end up acquiring the company for $100 million, you will then find out that as you try to grow it, you have trouble getting new profitable users.

Trends can be misleading

Trends one of are the most powerful analytics tools in your arsenal, as they show how your product or game is performing over time and if it is improving or deteriorating. Looking at the Appsflyer charts, however, it’s easy to see that that trends can largely be created by changes in the user mix. As the user acquisition team changes the mix of players, the new mix could create upward trends even if the game has not improved or deteriorated. The changes in behavior will be caused by the changes in user mix.

The only metric that counts

I have written many times about customer lifetime value (LTV), and the variance in metrics by source shows the central importance of focusing on the lifetime value to CPI (cost-per-install) equation. To summarize, LTV is how much profit a new player will generate for you in total and is a function of retention, monetization and virality. As long as your LTV is higher than the cost of acquiring that customer (CPI for paid user acquisition on mobile or web), you want to continue acquiring players.

Given the variance in performance by source that the AppsFlyer charts show, the LTV by source will also have huge variance. Thus, the goal is to have an LTV higher than the cost of user for that source. The LTV on a global scale is not important, it is finding pockets where LTV is greater than CPI and then acquiring against those targets. Any target KPIs (key performance indicators) outside of LTV being greater than CPI are worthless because this last equation will determine the success of your game. Anything else can be manipulated (e.g., buying users from certain sources who generate your “goals”). As long as LTV exceeds CPI for a source, you can acquire users from that source and grow your game. Once there are no sources where the LTV for that source are higher than the CPI (even if overall LTV is higher than many sources but not higher by source), you cannot buy users and the game will stop growing and start withering.

What it means

The high variance in performance of players by source has several important implications:

You must track performance of users on a cohort/source basis. Only by comparing similar users can you understand how your game is performing.
The sophistication of your user acquisition/growth team is critical. Managing CPIs is meaningless if it is not tied to the performance of the users; you need growth experts who can get you users with the highest yield (delta between LTV and CPI). You also may otherwise miss opportunities where your metrics may not appear to support buying expensive traffic, but that traffic will perform so well that it is a good value.
Holistic game metrics (sometimes referred to as vanity metrics) are meaningless or even misleading. You need to focus on keeping LTV higher than CPI for your sources of traffic.

The critical point is not to focus on vanity metrics that can be manipulated and are not actionable. Instead, focus on the performance of your product for specific cohorts of customers.

Key takeaways

There is an extremely high variance in performance of users based on the source of the users. Thus, it is misleading to look at overall metrics as they are largely a result of your user sources.
You must track performance of users on a cohort/source basis. Only by comparing similar users can you understand how your game is performing.
It is critical to build a strong growth or user acquisition team. Managing CPIs is meaningless if it is not tied to the performance of the users, you need growth experts who can get you users with the highest yield (delta between LTV and CPI).

The power of the podcast

One channel largely neglected in social media marketing conversations is the growing importance of podcasts. While not as sexy as SnapChat or Secret, this relatively old channel is becoming a critical component of effective social media marketing. Podcasts were originally built for the iPod (hence the name) but are now listened to not only via iTunes but also multiple IOS and Android apps and even via the good old Internet.

Why podcasts now?

Looking at the numbers, the growth of consumer interests in podcasts is clear. A Washington Post story reported that podcast subscriptions on iTunes reached 1 billion. An Edison Research report shows that over 39 million people listen to a podcast last monthly and that 20 percent of podcast users consumer six or more podcasts weekly.

The accessibility of podcasts has led to their growth. As the Washington Post writes, “despite some early enthusiasm, podcasts faded in popularity in the early 2000s, partly because of the many steps required to download them and play them in a vehicle. The introduction of the iPhone in 2007 changed that, making podcasts as convenient to access as a Netflix show. It’s easier to play them in cars, too, as automakers build wireless media functions into more and more models. And faster WiFi and mobile data speeds have made podcasts a snap to stream.”

Podcasting increase retention

There are multiple benefits for integrating podcasting into your social media marketing mix. First, they increase engagement. Rather than a few seconds to share a message with your customer or player, you have minutes or more to share your narrative. Rather than a superficial message, you can go in-depth on the value your product has to the user, how to get the most out of your product, the background of how it was made, etc. These messages can have a strong impact on users, if they get more value out of the features they are more likely to keep using it. Continue reading “The power of the podcast”

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Defining clumpiness

Why clumpiness is important

Using clumpiness insights

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What you should be looking at

The first ten minutes

How to improve your situation

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The challenge of computing LTV

The answer to measuring campaigns reliably

Benchmarks to target

What to do if you are missing your benchmarks

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CURR

NURR

RURR

Use *URR to track product performance

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Different businesses

They do not understand free-to-play LTV

Core game companies will continue to flail in the free-to-play business

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The data points

More apps, more spend

Companies scapegoating user acquisition teams and agencies

It all depends on the source

Trends can be misleading

The only metric that counts

What it means

Key takeaways

Reward your customers

Recognize customers from their online footprint

Why podcasts now?

Podcasting increase retention