Analytics – Page 4 – The Business of Social Games and Casino

Why you should not focus just on existing players or customers

Many companies, especially game companies, build out their products based on how early customers like the product but this approach can inhibit growth. An article in the Harvard Business Review, “Focus On the Customers You Want, Not the Ones You Have” by JP Eggers, shows that the hard part in creating a successful product or game is not appealing to users early but sustaining.

The problem with early users

Early players are by definition early adapters. They probably have a higher preference for innovation and are more likely to take risks. They may have different ways of making decisions, including whether they monetize in a game, if they invite friends or if they come back. An early player may be very enthusiastic and tell friends but then go on to the next shiny object, so basing decision on their play would suggest you should focus on getting them to bring in as many users as possible before they leave.

Companies run into problems if they base product decisions on these early adopters, which is often the case. Eggers also points to research that shows early adopters are different. According to Eggers, “early adopters are typically younger, more willing to take risks, more eager to try new things, more affluent, and substantially less numerous … First, what worked with early adopters isn’t likely to work with later adopters. In launching new products or services, start-ups tend to accumulate deep knowledge about customers at the leading edge of a technology. But that knowledge doesn’t necessarily apply to other consumer groups; that’s one reason so many new firms struggle to create second rounds of offerings. To be successful, companies need to innovate for the consumers they want, not the ones they have.”

Another issue comes up because early adopters do not want to see the product expand to the mass market. A chess app may appeal strongly to grandmasters but if it becomes something the mass-market plays, the grandmasters may no longer want to use the product. Similarly, Facebook lost some of their young users when their parents started using it, but there were hundreds of millions more adults than teens. Although it worked for Facebook, the influx of users so alienated the early users of MySpace, you may no longer even remember MySpace.

A solution

There are several strategies to combat the problem of having a product that either is not attractive to new users or alienates existing users:

Innovate for future users. Build features and new content so it appeals to new users, what people not currently using your product would want.
Engage early adopters. By engaging your customers, you ensure they feel connected to the product. Letting them have a voice, even if you cannot always listen to it, keeps them engaged and likely to cope with changes they do not consider ideal.

The key is understanding that what appealed to your first users, and their behavior, is not necessarily what will drive your growth.

Key Takeaways

Early users will not have the same preferences and consumption patterns as customers you attract later in the product’s life cycle.
If you slavishly build features and new content for existing users, it may not appeal to new users.
The key is to innovate for new users while keeping your early adopters engaged so will tolerate the changes to the product.

Trends in Analytics from BDA Conference

On Tuesday I went to the BDA Conference on Big Data Analytics. Conferences like these are always interesting to see at a high-level how analytics and its uses are evolving. This conference was no different and some of the trends that came through the various sessions suggest where future opportunities will be to leverage analytics:

A big challenge, and opportunity, is integrating data from multiple sources to get a more complete picture of your customers. Until recently, analyzing data in your product was the primary way to understand users (and play patterns in games) but now there is valuable data available from multiple sources. Data from social media (what people are saying about you and your product, sentiment, etc), data from beacons and other sensors, data from user acquisition, etc. When you integrate this data, you get a more complete understanding of your users and their motivations.
Data is connecting people and things, expanding the universe of data. There is now extensive data on how people interact with their surroundings and this will grow.
Using data is moving from the province of data scientists and analysts to everyone in the organization. This trend is driven by easier to use and manipulate tools, not by increased training. Designers and product managers and marketers are not becoming data experts but the tools now allow easy visualization, point and click charts, swipe and pinch access.
Top companies are now using the various data sources to understand holistically the customer journey and then driving activities to increase the value from the customer during their journey. The critical change is that you are using different data sources to pick up the user at different points (think of a race with cameras along the course and how the telecast switches between cameras).
People are now using, and expecting, data on a real time basis. Increasingly everyone in the organization has real time access to data and can drive actions based on this information. No longer are people waiting for the charts on yesterday’s activity.

Key takeaways

The universe of data is exploding, with multiple data sources and good analytics now blends this data to provide a complete picture of the customer.
Data is no longer being controlled by a few people in BI (business intelligence), user-friendly tools are allowing everyone in the organization to access and control data easily to enhance their decision-making
Data allows companies to see the entire customer journey, with different data sources filling in different parts of the journey.

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 importance of competitive intelligence

In most businesses, the need to know what your competitors are doing is a given. In the social gaming space, however, competitive intelligence (CI) is either an afterthought or not even considered critical. That thinking at best leads to a sub-optimal product and at worst facilitates losing your market to another product.

Why Competitive Intelligence is important

Good competitive intelligence is invaluable to all companies, including those in the mobile and casino gaming spaces.

CI shows you the minimum quality level acceptable for your game. In the mobile space, most users will try multiple applications and then settle on one, a winner take all environment, though in casino they may play two or three. Thus, your potential customers are also playing your competitors’ products and deciding which one to invest their time in the future. If your game is clearly inferior, weaker graphics, slower tech, etc., you have lost.
Your competitors are not stupid and you should learn from them. Internally, they are looking at the same opportunities and problems you are trying to tackle. By understanding features and initiatives they are taking to improve, they can inspire you on ways to manage the situation. Not that you want to copy everything they are doing, but understand how they are approach problems and if you have a different approach make sure your solution is better before deploying it.
You can learn from their mistakes. It is great to make mistakes because it means you are trying unique initiatives; it is not great to repeat mistakes as that has no value. What is even better is if somebody else makes the mistake to learn from them without having the cost.
You ensure you are value competitive. A car company would not never release a new model without understanding how its price and features compare with other cars. It would base the price on the competitive feature set, including branding, and then price their car so it is a reasonable option for consumers. Very few people will purchase an auto when they can get a comparable one for half the price.In the game space, companies mistakenly believe users are price inelastic. Many players, particularly those likely to monetize, understand what they are spending money for and how much they will get for it. The value is often in play time (e.g., I will spend $20 in a bingo app to play an extra hour). If the player feels your game is much more expensive than comparable games, they are less likely to spend in your app and will shift their spending to competitors (and you will see lower revenue from higher prices).

Continue reading “The importance of competitive intelligence”

Creating that Aha moment

There was a great blog post on the Mode Blog, “Facebook’s Aha Moment is Simpler Than You Think,” which provided a straightforward strategy to creating Aha moments. Aha moments when a player or user understands the value of your product are widely considered the key to growth.

The key point of the post was that you create the aha moment through simple math and strong messaging; it is not a complex task that requires advanced analytics. Take Facebook’s self-defined aha moment, acquiring seven friends in ten days. In practice, this is not a binary. Some people may fall in love with Facebook after getting three friends in a week; others may need to get twenty friends in thirty days. This fact does not take away from the aha moment, it is actually the point of the aha moment. The blog post states, “Facebook’s decision to define their ‘aha moment’ in such simple terms suggests they weren’t trying to optimize it to be precise as possible. Other “aha moments”—30 follows, 1 file upload, 2,000 messages—follow the same pattern: they emphasize simplicity over science…. Because “aha moments” aren’t about precision, but about defining a core principle and a quotable rally cry for the entire company. “

Defining the aha moment

To create a useful aha moment, you need to tie it to a metric that defines customer value. Keep in mind that it is not one “moment,” Facebook’s aha moment is over ten days and requires seven different actions (friending seven people).

The most useful metrics for quantifying your aha moment are based on retention. Customers who find value come back. If you identify which actions separate retained customers from lost ones, you will know what drives customer value and then have your “aha moment.” Continue reading “Creating that Aha moment”

Ignorance is a competitive advantage

I recently had a conversation with a gaming industry CEO whom I deeply respect that reinforced a MIT Sloan Management Review article, “Embrace Your Ignorance” by Michael Schrage, about how the savviest leaders promote and embrace ignorance. The thesis for both Schrage and the CEO was that you cannot accurately predict what your customers will want, like or need. Thus, you need to embrace this ignorance and run experiments to get the data.

Moneyball and The Innovator’s Dilemma

I have seen many companies where the leadership “felt” they understood the customer and would develop new products for these customers. It leads to project green light meetings very similar to the draft room in Moneyball, where people argue based on their experience which initiatives have the most potential. It is also one of the biggest contributors to the huge number of failed projects, particularly in the gaming space where we typically see more than 8 out of 10 new games fail.

This issue is actually often a bigger problem with executives who have had past successes. Even if they knew their existing or past customers very well, they do not necessarily know what a broader or new market wants. Even their existing data can skew innovation effort, which is the core point of the Innovator’s Dilemma: Companies that have been leap-frogged often create innovations for existing markets rather than new markets.

You already are ignorant—accept it

In Schrage’s article, he discusses how Microsoft’s Ronny Kohavi (a pioneer in online experimentation) challenges tech-savvy audiences when he speaks. Kohavi shows screenshots of actual A/B tests that Microsoft has run for website design. He then asks his audience to predict the outcome of the tests. Although the audience is sophisticated, they almost always fragment with different opinions. Kohavi then advises, “stop debating…it’s easier to get data.” Continue reading “Ignorance is a competitive advantage”

S	M	T	W	T	F	S
				1	2	3
4	5	6	7	8	9	10
11	12	13	14	15	16	17
18	19	20	21	22	23	24
25	26	27	28	29	30	31

The problem with early users

A solution

Key Takeaways

Key takeaways

New features won’t change the key metrics

It all depends on the source

Trends can be misleading

The only metric that counts

What it means

Key takeaways

Why Competitive Intelligence is important

Sustaining competitive advantage

Defining the aha moment

Do not surprise your customers

Moneyball and The Innovator’s Dilemma

You already are ignorant—accept it

Analytics emerge across the organization

Everything integrates