analytics – The Business of Social Games and Casino

The subscription KPIs that matter

I have written several times recently about building and running a successful subscription modelin gaming but I did not address how to measure whether it is successful. To grow any business you need to understand what to measure so you can then optimize against these KPIs. While subscriptions do share some common characteristics with the free-to-play business, driven by the in-app purchase model, there are certain KPIs unique to subscriptions that you should focus on when building your program. As Leandro Faria says in The Essential SaaS Metrics Guide, “data doesn’t do you any good unless you act on it.”

MRR

Monthly recurring revenue (MRR) is generally the first KPI that companies focus on when looking at the health of their subscription program. You calculate MRR by looking at the average revenue per subscriber by the number of subscribers. If you only have one subscription level, then it is simply the monthly subscription cost times the number of subscribers. If you have subscriptions for various terms (monthly, quarterly and annual, for example), you calculate the average monthly revenue from the different subscription (an annual subscription of $144 generates $12 per subscriber in MRR). The formula is

MRR = Average monthly revenue per subscriber (ARPS) * Total number of subscribers

The reason MRR is the first KPI that subscription businesses monitor is because it shows the value of the model. MRR effectively is how much revenue the business can count on every month. The company can then allocate this cash flow to marketing, operations, acquisitions, etc. By having a guaranteed amount of revenue (which you do not have with discrete sales or in-app purchases), you have a clear source of funds to operate your business. Most companies will extend MRR to create an Annual Run Rate (ARR), which is important both for business planning purposes and understanding the value of the subscription component of your business.

MRR Growth

In addition to looking at MRR, you should monitor MRR Growth. To analyze MRR growth, you need to break it into three components. The first is new MRR, revenue brought by newly acquired customers.

The second component of MRR growth is Expansion MRR. Expansion MRR is increases in subscription revenue from existing subscribers. This revenue is driven by up-selling and cross-selling your customers.

The third element of MRR Growth is churn. Churned MRR is the revenue that has been lost from customers cancelling or downgrading their plans.

Taking these three components into account, the overall formula for MRR Growth is:

MMR Growth = New MRR + Expansion MRR-Churned MRR

CURR

I have written before about CURR (current user return rate), and it is as valuable in subscription businesses as it is for other online models. CURR 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:

Subscribers who were active 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 between t-0 and t-6.

Measuring CURR is critical to see how engaged your subscribers are. If CURR trends downward, you are likely to experience increasing customer churn.

ARPS

As mentioned above, Average Revenue Per Subscriber (ARPS) is central for calculating MRR, but it is also an important KPI itself. Increasing ARPS shows that customers are upgrading and most likely seeing high value in your offering, thus they are willing to pay more. Conversely, declining ARPS shows that customers are not experiencing sufficient value and are either down-grading or moving to free plans

You should also monitor ARPS separately for existing players versus new players. As Faria writes, “there is a good practice of measuring the Average Revenue per [subscriber] separately for new customers. So instead of having an [ARPS] metric for all your customers, you’d have two different metrics: Average Revenue per Existing [Subscriber] and Average Revenue per New [Subscriber].” To calculate:

ARPS [existing] = Revenue from existing subscribers / # of existing subscribers
ARPS [new] = Revenue from new subscribers / # of new subscribers

Churn

Churn is the enemy of any business, but is even more troubling for subscription businesses. You never want to lose customers, but with subscription businesses churn means you are losing not simply a sale but an entire revenue stream.

You need to monitor both Customer Churn and Revenue Churn.
Customer Churn is how many players have canceled their subscription while Revenue Churn is how much those lost customers represents in revenue. There are thus three churn KPIs you should closely monitor:

Churn = # of Churned Customer / Last Month # of Customers
MRR Churn = SUM (MRR of Churned Customer)
MRR Churn % = Churned MRR / Last Month’s Ending MRR Negative

CAC

Customer Acquisition Cost (CAC) is the cost to acquire an additional customer, your marketing cost per customer. One way to calculate CAC is to consider the three variables that compose it. This method allows you to go into detail and might give you good insights about your sales process cost and conversions:

CAC = (CPL (cost per lead or cost per install) + Touch cost per customer (cost of your marketing team and any consultants)

I prefer to focus on cost per install (CPI) or cost per subscriber (CPS). As long as your lifetime value is higher than your CPS, you can continue to acquire subscribers and manage your overhead, including your marketing infrastructure.

LTV

Speaking of customer Lifetime Value (LTV), I have written repeatedly how it is the lifeblood of a successful business. LTV is a function that shows the present value of a new customer, how much that customer is worth to your company. While the equation is effectively the same for any business (the total expected value of your customer over their lifetime), it is somewhat simpler to calculate for subscription businesses. To calculate LTV for a subscription business, the following formula captures the core elements:

LTV = ARPS * % Gross Margin / % MRR Churn Rate

The hybrid model

While these KPIs show the health of a subscription business, you need to modify how you use them in most social games as well as iGaming. As subscriptions will only be one element of your revenue stream, in-app purchases will remain a major part of social gaming while gambling revenue will drive the casino industry. The above KPIs will help understand the health of the subscription element of your business, and whether you should invest in growing it, but they need to be incorporated into your other KPIs to understand both the impact on your business and your overall financial health. Subscription revenue will only be one part of your overall LTV calculation while you may want to look separately at CURR of players customers versus those making in-app purchases. There are many different combinations of models but the core subscription KPIs need to be incorporating into your daily review of the health of your business.

Key takeaways

To run a successful business, you have to constantly monitor KPIs and optimize based on this data; the subscription model is no different but the KPIs are not the same as the ones you are used to reviewing
MRR (monthly recurring revenue) is the most important KPI for the subscription model, how much subscription revenue you generate (and can count on) each month.
Other critical metrics for the subscription model are MRR growth average revenue per subscriber (ARPS), current user return rate (CURR), cost to acquire a customer (CAC), customer lifetime value (LTV) and churn.

An often-better alternative to AB testing?

While AB testing is an integral element of mobile and social game development (as well development of most digital products), in many situations there is a better option. Several years ago, I had the opportunity to serve as an advisor to a company that had some brilliant people. Their CTO was a strong advocate of using multi-armed bandit testing as a superior alternative to AB testing. Multi-armed bandit testing is not new, there was a popular post in 2012 (http://stevehanov.ca/blog/index.php?id=132), and it is used by Google and other tech giants, but people (especially product managers) still regularly default to traditional ABn testing.

The problem with AB testing is that you leave money and performance on the table. Until the test is over, the poorer performing variant(s) will always get a significant share of your traffic. With the multi-armed bandit approach, you allocate increasingly less traffic to poorly performing variants.

What is multi-armed bandit testing

A multi-armed bandit approach allows you to dynamically allocate traffic to variations that are performing well while allocating less and less traffic to underperforming variations. Instead of two distinct periods of pure exploration and pure exploitation, bandit tests are adaptive, and simultaneously include exploration and exploitation. As Optimizely wrote recently, ” multi-armed bandit optimizations aim to maximize performance of your primary metric across all your variations. They do this by dynamically re-allocating traffic to whichever variation is currently performing best. This will help you extract as much value as possible from the leading variation during the experiment lifecycle, so you avoid the opportunity cost of showing sub-optimal experiences.”

Multi-armed bandit testing is a Bayesian approach to AB testing. As Shawn Lu writes in a post titled Beyond A/B testing, “The foundation of the multi-armed bandit experiment is Bayesian updating. Each treatment (called “arm”) has a probability of success, which is modeled as a Bernoulli process. The probability of success is unknown, and is modeled by a Beta distribution. As the experiment continues, each arm receives user traffic, and the Beta distribution is updated accordingly.”

A recap on ABn testing

To compare bandit testing with ABn testing (AB is with two variants, a test and control, n allows for additional variables), let’s quickly recap how AB testing works. Alex Atkins summarizes it succinctly, writing “in statistical terms, a/b testing consists of a short period of pure exploration, where you’re randomly assigning equal numbers of users to Version A and Version B. It then jumps into a long period of pure exploitation, where you send 100% of your users to the more successful version of your site.”

Benefits of multi-armed bandit testing

Bandit algorithms try to minimize opportunity costs and regret (the difference between your actual return and the return you would have collected had you deployed the optimal options at every opportunity). Rather than letting an AB test run until it is statistically significant, a bandit test moves subjects into the best performing group faster, allowing you to capture more gains. Matt Gershoff writes, ““Some like to call it earning while learning. You need to both learn in order to figure out what works and what doesn’t, but to earn; you take advantage of what you have learned. This is what I really like about the Bandit way of looking at the problem, it highlights that collecting data has a real cost, in terms of opportunities lost.”

A related advantage of multi-armed bandit testing is you make fewer mistakes. An A/B test will always send a significant portion of traffic to the sub-optimal group.

Also, as Shawn Lu writes, “[an] advantage of bandit experiment is that it terminates earlier than A/B test because it requires much smaller sample. In a two-armed experiment with click-through rate 4% and 5%, traditional A/B testing requires 11,165 in each treatment group at 95% significance level. With 100 users a day, the experiment will take 223 days. In the bandit experiment, however, simulation ended after 31 days, at the above termination criterion.” if the treatment group is clearly superior, we still have to spend lots of traffic on the control group, in order to obtain statistical significance.”

Finally, while not mathematically an advantage, bandit testing relieves the pressure to end a test too early. With ABn testing, frequently you will see one option perform better “directionally” and decide, or be forced to decide, to terminate the test and move everyone to the higher performing bucket before you get significant results. Unfortunately, this sometimes leads to picking an option that would be reversed once there is more data.

Why multi-armed bandit is not always the correct approach

The value of bandit testing does not mean you should abandon completely ABn testing. In Lu’s post, he writes “the convenience of smaller sample size comes at a cost of a larger false positive rate.” That is, you end up sometimes gravitating to the sub-optimal solution.

Alex Atkins also writes, “in essence, there shouldn’t be an ‘a/b testing vs. bandit testing, which is better?’ debate, because it’s comparing apples to oranges. These two methodologies serve two different needs.”
A/B testing is a better option when the company has large enough user base, when it’s important to control for type I error (false positives), and when there are few enough variants that we can test each one of them against the control group one at a time.”

The Bandit Option

While multi-armed bandit testing is not always a better option than ABn testing, you should look closely at using bandit testing when possible. It can reduce the opportunity cost of your testing and relieve pressure to terminate tests prematurely.

Key takeaways

While AB testing is the most common method of optimizing between alternatives, in many situations the multi-armed bandit approach is optimal.
A multi-armed bandit approach allows you to dynamically allocate traffic to variations that are performing well while allocating less and less traffic to underperforming variations.
Multi-armed bandit testing reduces regret (the loss pursing multiple options rather than the best option), is faster and lowers the risk of pressure to end the test prematurely.

How to overcome survivorship bias

A few months ago I shared a story and post on Facebook about survivorship bias and was amazed how often it was liked and shared. It also highlights the risk of survivorship bias in the gaming and gambling space. The image and blurb told the story how the navy analyzed aircraft that had been damaged and based future armament decisions on where they had received battle damage, thus they were going to increase the armor on the wingtips, central body and elevators. These were the areas that showed the most bullet holes.

One statistician, Abraham Wald, the founder of statistical sequential analysis, however fortuitously stopped this misguided effort. According to Wikipedia, “ Wald made the assumption that damage must be more uniformly distributed and that the aircraft that did return or show up in the samples were hit in the less vulnerable parts. Wald noted that the study only considered the aircraft that had survived their missions—the bombers that had been shot down were not present for the damage assessment. The holes in the returning aircraft, then, represented areas where a bomber could take damage and still return home safely. Wald proposed that the Navy instead reinforce the areas where the returning aircraft were unscathed, since those were the areas that, if hit, would cause the plane to be lost.”

Survivorship bias is universal

Survivorship bias occurs everywhere. If you are a poker player, you may have a hand of three of clubs, eight of clubs, eight of diamonds, queen of hearts and ace of spades. The odds of that particular configuration are about three million to one, but as economist Gary Smith writes in Standard Deviations, “after I look at the cards, the probability of having these five cards is 1, not 1 in 3 million.”

Another example would be professional basketball. If you look at the best professional basketball players, a high percentage never went to university for more than one year. From this information, you (or your teen son) may infer the best path to the NBA is going to university for one year or less. The reality is that there are millions (if not billions) of people who went to university for less than a year and never played in the NBA (or even the G League). The LeBron Jameses and DeAndre Aytons are likely in the NBA despite playing less than a year in college due to their great skill, not because they did not go to university for more than a year.

As an investor, survivorship bias is the tendency to view the fund performance of existing funds in the market as a representative comprehensive sample. Survivorship bias can result in the overestimation of historical performance and general attributes of a fund.

In the business world, you may go to a Crossfit gym that is packed with the owner making a great living. You decide to leave your day job and replicate his success. What you did not see is the hundreds of Crossfit gyms that are not profitable and have closed.

The problem exists in gaming

You often see survivorship bias in the gaming and gambling space. People will look at a successful product and select a couple of features or mechanics they believe have driven the success. They then try to replicate it and fail miserably, only to then wonder why the strategy did not work for them. What they fail to analyze is the many failed games (for every success there are at least 8-10 failures) because they do not even know they exist. The failed games may have had more of the feature you are replicating. Getting a star like Kim Kardashian is a great idea if you only look at Kim Kardashian: Hollywood, but if you look at the hundreds of other IPs that have failed your course of action might be very different.

Survivorship bias can also lend its ugly head when building a VIP program. You talk to your VIPs and analyze their behavior, thus building a program that reinforces what they like about the game. What you neglect, however, is that other non-existent features might have created even more VIPs.

In the gambling space, you may look at a new blackjack variant that is doing great and build a strategy around creating new variants of classic games. What you did not see is all the games based on new variants that have failed.

Avoiding survivorship bias

Looking simply at successes, or even failures, leads to bad decision making. When looking at examples in your industry or other industries, you need to seek out both the successes and failures. With the failures, you need to make sure they are the failures (not the airplanes that returned shot up but the ones that were destroyed). You also should not use others successes or failures as a short cut to robust strategy decisions. You need to analyze the market, understand your strengths-weaknesses-opportunities-threats (SWOT) and do a blue ocean analysis. Only then will you build a strategy that optimizes your likelihood for success.

Key takeaways

In WW2, by analyzing surviving aircraft the US navy almost made a critical mistake in adding armor to future airplanes. The planes that returned were actually survivors, while it was the planes that were destroyed that showed where on the plane was the greatest need for new armor. This phenomenon is called survivorship bias.
This bias extends into the gaming and gambling space, as companies analyze what has worked in successful games but do not know if it also failed (perhaps to a greater degree) in products that no longer exist.
Rather than just looking at survivors or winners to drive your strategy, you should do a full SWOT and Blue Ocean analysis, that is the strongest long-term recipe to optimize your odds of success.

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.

How to avoid misleading data, aka Fake Analytics

Someone I respect recently posted an article from a news source that I also respect, but the article actually highlighted how data can mislead, either intentionally or not. An article on the Guardian.com, Amazon Prime Video’s growth outpaces Netflix in UK, tells the story of how Prime Video is growing at a faster rate than Netflix. The sub-title stresses that, “cross-promotion to Amazon shoppers and new on-demand series rank it top in 2017.”

The article goes on to point out several reasons why Amazon is top in 2017:

New series of the Grand Tour and Transparent are fueling growth
Hefty cross promotion of Prime Video to regular Amazon shoppers is also contributing
Prime Video increased its subscribers to 4.3 million in 2017, representing 41% year-on-year growth
Netflix only grew 25% in the same period.

If you stopped reading the article there, and who reads an article until the end these days, you would think Amazon is doing a great job in the video market and Netflix should be very worried. If you worked at Amazon and get a similar report from your analytics team, you might high-five the head of Amazon Prime in the UK. If you were at Netflix and got a similar report from your analytics team, you might panic a little and divert resources to the UK.

The problem is that although the data is accurate it is misleading. The key figure is that Netflix added 1.6 million new subscribers in 2017, while Amazon added 1.3 million new subscribers for Prime Video in the UK. Thus Netflix actually extended its lead over Amazon by 300,000 customers in 2017. Netflix is in 8.2 million UK households (at the end of 2017), versus 3 million for Amazon.

How different would the story have been if the headline was Netflix extends lead by another 300,000. How different would the reception be at Amazon and Netflix respective headquarters if their analytics team presented data in this way.

The mistake

The mistake in this case (and I will be generous and assume the Guardian was not click-baiting) is comparing growth rates (or any other rates) while neglecting the size of the relative base. It would be the same in football if you looked at Messi’s goal scoring versus a second year player. The latter may be scoring twice as many goals as he did as a rookie while Messi may have been flat or added a few. Thus the young player is growing his goal scoring 100% while Messi is adding only a few percent to his lifetime numbers. That does not mean that the second year player is either having as good a season as Messi or closing the gap.

The same happens in the mobile game world. Your slot game may be growing 100% month on month while Slotomania is growing 10% (not real numbers), but because their base is so high they are adding millions in revenue while you are still not profitable.

The key is only comparing trends when you are comparing apples to apples. Trends mean something if you are looking at two products or companies of comparable size in the same stage of their lifecycle. Looking at two auto companies who launched an SUV the same year in the same market makes sense, comparing growth rates of two automakers, one who is new and has no dealer network with one that has been around 100 years is worthless.

The answer

You need to look deeper into the numbers. Look at the absolute numbers. Look at the pricing. Look at the target market. Look at percent usage (in the Amazon case, how engaged are Prime users who may have bought it just to get free shipping versus Netflix users). The key to using data effectively is look deeply at the data and understand what is driving the results. You also need to make sure your analytics team does the same. It is very easy to make conclusions based on obvious trends. Avoid superficial analysis and, more importantly, superficial conclusions.

Key takeaways

A recent article implied Amazon Prime Video was doing better than Netflix in the UK as it grew 41% versus 25% by Netflix.
The article is misleading as Netflix actually added 300,000 more customers than Amazon. This obfuscation shows how data can mislead if you focus on trends but are not comparing comparable companies or products.
The key to using data effectively is look deeply at the data and understand what is driving the results.

How to manage your algorithms

While everyone is focused on creating the most advanced algorithms for their predictive analytics and optimizing your team’s performance, I have not seen anything on how to manage your algorithms. A great article in Harvard Business Review – Algorithms Need Managers, Too by Michael Luca, Jon Kleinberg and Sandhil Mullainathan – does a great job of combining the two issues and providing a solution.

The authors begin by pointing out most businesses rely on predictions throughout their organization. The decisions can range from predicting a candidate’s performance and whether to hire them, what initiatives will have the highest ROI and what distribution channels will yield the most sales. Companies increasingly are using computational algorithms to make these predictions more accurate.

The issue is, if the predictions are inaccurate (and although they are computer generated, they are still predictions not facts) they can lead you into bad decisions. Netflix learned this the hard way when its algorithms for recommending movies to DVD customers did not hold when its users moved to streaming. More relevant to digital marketers, algorithms that generate high click through rates may actually bring in poor users not interested in your underlying game or product. As the authors write, “to avoid missteps, managers need to understand what algorithms do well – what questions they answer and what questions they do not.”

How algorithms can lead you amiss

An underlying issue when using algorithms is that they are different than people. They behave quite differently in two key ways:

Algorithms are extremely literal, they do exactly what they are told and ignore any other information. While a human would understand quickly that an algorithm that gets users that generate no revenue is useless, if the algorithms was just built to maximize the number of users acquired it would continue attracting worthless users.
Algorithms are often black boxes, they may predict accurately but not what is causing the action or why. The problem here is that you do not know when there is incomplete information or what information may be missing.

Once you realize these two limitations of algorithms, you can then develop strategies to combat these problems. The authors then provide a plan for managing algorithms better.

Be explicit about all of your goals

When initiating the creation of an algorithm, you need to understand and state everything you want the algorithm to achieve. Unlike people, algorithms do not understand the implied needs and trade-offs necessary often to optimize performance. People understand the end goal and then backward process how to best achieve that eventual goal. There are also soft goals to most initiatives, and these goals are often difficult to measure (and thus input into your algorithms). There could also be a goal of fairness, for example a bank using an algorithm to optimize loan behavior may not provide enough loans in areas where it feels a moral obligation to do so. Another example could be where you may want to optimize your business units sales but the behavior could negatively impact overall sales of your company.

The key is to be explicit about everything you hope to achieve. Ask everyone involved to list their soft goals as well as the primary objective. Ask people to be candid and up-front. With a core objective and a list of concerns in front of them, the algorithm’s designer can then build trade-offs into the algorithm. This process may entail extending the objective to include multiple outcomes, weighted by importance.

Minimize myopia

Algorithms tend to be myopic, they focus on the data at hand and that data often pertains to short-term outcomes. There can be a tension between short-term success and long-term profits and broader corporate goals. People understand this, computer algorithms do not.

The authors use the example of a consumer goods company that used an algorithm to decide to sell a fast-moving product from China in the US. While initial sales were great, they ended up suffering a high level of returns and negative customer satisfaction that impacted the brand and overall company sales. I often see this problem in the game industry, where product managers find a way to increase in-app purchases short term but it breaks player’s connection with the game and long-term results in losses.

The authors suggest that this problem can be solved at the objective-setting phase by identifying and specifying long-term goals. But when acting on an algorithm’s predictions, managers should also adjust for the extent to which the algorithm is consistent with long-term aims.

I recommend using NPS to balance out short term objectives with the long-term health of the product and company. I have written before about NPS, Net Promoter Score, which is probably the most powerful tool to measure customer satisfaction. It is also highly correlated with growth and success. By ensuring you keep your NPS high, you are providing a great way to look holistically at the success of specific initiatives.

Chose the right data inputs

Using the right data can make your algorithms much more effective. When looking at a game like Candy Crush, you can create levels by looking at when people abandon the game and decompose the levels before abandonment. However, by adding social media posts to the your data, you could get a more holistic view of which levels players are enjoying and thus build a more compelling product.

The authors also point to an example with the City of Boston. By adding Yelp reviews to what health inspectors use to determine what restaurants to inspect, they were able to maintain their exact same performance but with 40 percent fewer inspectors. Thus, the new data source had a huge impact on productivity.

The authors point to two areas of data that can improve your algorithms:

Wider is better. Rather than focusing on more data, the amount of data you know about each customer determines the width. Leveraging comprehensive data is at the heart of prediction. As the authors write, “every additional detail you learn about an outcome is like one more clue, and it can be combined with clues you’ve already collected. Text documents are a great source of wide data, for instance; each word is a clue.”
Diversity matters. Similar to your investment strategy, you should use data sources that are largely uncorrelated. If you use data that moves closely to your data sources, you will have the illusion of using multiple data sources but really only be looking at one angle of the data. If each data set has a unique perspective, it creates much more value and accuracy.

Understand the limitations

As with anything, it is also critical to understand the limitations of algorithms. Knowing what your algorithm will not do is equally important as understanding how it helps. Algorithms use existing data to make predictions about what might happen with a slightly different setting, population, time, or question. “In essence, you are transferring an insight from one context to another. It’s a wise practice, therefore, to list the reasons why the algorithm might not be transferable to a new problem and assess their significance,” according to the authors.

As the authors point out, “ remember that correlation still doesn’t mean causation. Suppose that an algorithm predicts that short tweets will get retweeted more often than longer ones. This does not in any way suggest that you should shorten your tweets. This is a prediction, not advice. It works as a prediction because there are many other factors that correlate with short tweets that make them effective. This is also why it fails as advice: Shortening your tweets will not necessarily change those other factors.”

Use algorithms, just use them smartly

This post is not intended for you to avoid using algorithms, it is actually the opposite goal. Algorithms are increasingly powerful and central to business success. Whether you are predicting how consumers will react with a feature, where to launch your product or who to hire, algorithms are necessary to get great results. Given the central importance of these algorithms, however, it is even more crucial to use them correctly and optimize their benefit to your company.

Key takeaways

Algorithms are increasingly powerful and central to business success. Given the central importance of these algorithms it is even more crucial to use them correctly and optimize their benefit to your company.<
Problems with algorithms result from them being literal (they do exactly what you ask) and are largely a black box (they do not explain why they are offering certain recommendations).
When building algorithms, be explicit about all your goals, consider the long-term implications and make sure you are using as broad data as possible.

Why data is more important than hair: The Donald Trump story

There is a great article on Politico, How Trump Let Himself Get Out-Organized, that explains how Trump’s Iowa debacle was a result of a failed analytics strategy. Trump made the same mistake many companies commit, he felt a strong brand and what he believe compelling product allowed him to under-invest in analytics. This issue was compounded by the aggressive use of analytics by competitors. Although this occurred in the political arena, there are lessons for all businesses.

The article explains that despite Trump’s strength in the polls, he did not have “the tools they needed, which is why they overpromised and underperformed.”

Penny wise and pound foolish

While Ted Cruz and Marco Rubio spent millions building sophisticated voter targeting machines, Trump did not start building a data operation to target voters until mid-October. It did not even start buying data (i.e. voter lists, etc) until November and waited to December to start using the Republican National Committee’s (RNC’s) voter file.

The Trump campaign declined to use Cambridge Analytica, a behavioral modeling company with political expertise, due to cost. Cruz, however, retained Cambridge Analytica’s services and the firm is now widely credited with engineering Cruz’s cutting-edge targeting operation. Rubio, who also over delivered on expectations, spent $750,000 for an outside company to assist in its data operations. Trump overall spent $560,000 on data services in 2015, compared to $3.6 million by the Cruz campaign. It is also about $700,000 less than Trump spent on hats.

You also need the analytics team

The Iowa caucas also showed the value of having a strong analytics team, not simply software. Cruz’s data team, which they call the Oorlog (the Afrikaner word for ‘war’) project, includes four full-time data scientists and embedded talent from Cambridge Analytics.

The Rubio campaign, which also exceeded expectations, has also invested heavily in its analytics team. It has a 22-person data war room in DC.

The Cruz campaign also hired ten canvassers (and recruited many volunteers) to go door-to-door to contact people the analytics suggested were supportive or could be persuaded. Traditionally, these so-called match rate initiatives are 50 percent successful but with Cruz’s advanced analytics the success rate reached 70 percent. The Cruz campaign also used the voter profiles to shape its strategies for most marketing activities, from television ad buys to telephone banks.

Micro-segmentation

Micro-segmentation, or creating very small customer segments and treating them uniquely, is another area where Trump fell down compared to Cruz. As Politico wrote, the Cruz campaign, “built a list of more than 9,000 Iowans who were still on the fence between their candidate and Trump. The team divided the undecided voters ― who were heavily evangelical and 91 percent male ― into more than 150 different subgroups based off ideology, religion and personality type, Wilson said. It used Facebook experiments to determine which issues jazzed up their voters the most.”

The lesson

No matter how strong you feel your product is, or how well it has performed in the past, you are vulnerable to competitors who may have a superior analytics solution. To combat this risk, you not only need to match the investment your competitor’s are making in analytics and look at micro-segmentation but also build a world class data team.

Key Takeaways

Donald Trump’s loss to Ted Cruz in Iowa can be attributed to Cruz’s superior use of analytics to build a competitive advantage.
Cruz invested much more in both analytic products and a great data team and it helped him get pro-Cruz people to caucus.
Cruz also did a great job of micro-segmenting potential voters into more than 150 different subgroups based off ideology, religion and personality type and used Facebook experiments to determine which issues were most relevant for each subgroup.

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).

MRR

MRR Growth

CURR

ARPS

Churn

CAC

The hybrid model

Key takeaways

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What is multi-armed bandit testing

A recap on ABn testing

Benefits of multi-armed bandit testing

Why multi-armed bandit is not always the correct approach

The Bandit Option

Key takeaways

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Survivorship bias is universal

The problem exists in gaming

Avoiding survivorship bias

Key takeaways

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CURR

NURR

RURR

Use *URR to track product performance

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The mistake

The answer

Key takeaways

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How algorithms can lead you amiss

Be explicit about all of your goals

Minimize myopia

Chose the right data inputs

Understand the limitations

Use algorithms, just use them smartly

Key takeaways

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Penny wise and pound foolish

You also need the analytics team

Micro-segmentation

The lesson

Key Takeaways

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New features won’t change the key metrics

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It all depends on the source

Trends can be misleading

The only metric that counts

What it means

Key takeaways

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