Cycle time, Velocity, Lead time

Definitions 

In Scrum, velocity is pretty much the same as cycle time.

Velocity measures what a development team is able to deliver in terms of developed product backlog items within a sprint.

From this blog https://leanandkanban.wordpress.com/2009/04/18/lead-time-vs-cycle-time/ I got this nice definition

Lead time clock starts when the request is made and ends at delivery. Cycle time clock starts when work begins on the request and ends when the item is ready for delivery. Cycle time is a more mechanical measure of process capability. Lead time is what the customer sees.

Lead time depends on cycle time, but also depends on your willingness to keep a backlog, the customer’s patience, and the customer’s readiness for delivery.

Another way to think about it is: cycle time measures the completion rate, lead time measures the arrival rate. A producer has limited strategies to influence lead time. One is pricing (managing the arrival rate), another is managing cycle time (completing work faster/slower than the arrival rate).

Model 
Sprint Retrospectives do increase velocity over time. Knowledge, insights are shared which leads to team learning which affects the velocity in a positive manner.
Hence, retrospectives act as a moderator between developing software and output.

Which KPI's should I focus on after a mobile app launch ?

These are some additional "quick&dirty" ideas around the question: What are the KPI's that I should take a look at after launching a mobile app (free-to-play in particular. btw, see article below on "Key Metrics for App Monetization." and "Healthy Retention Rates".

(free pic under CC)

1. Rolling Retention
In my short article on "Healthy Retention Rates", I primarily focused on rolling retention. This is: Take a look at a cohort and track their life time. Averaging this figure gives you the average life time.

2. DAU Frequency Retention
By that I mean a methodology which takes a snapshot from one specific day and takes a look at that daily cohort by measuring how many of those that have been active today have been active on a daily basis over the past 7 days. This also gives you an indication of recency. I have seen top mobile games achieve a 70% DAU Frequency retention. In other words, of that daily logged in users, 70% had logged each day for the past 5 out of 7 days! Of course, the DAU basis should be reduced by the number of new daily logins, so that you really have only loyal DAU.

3. Predictive LTV and Milestone Tracking

Define Milestones which could give you an indication as to whether your mobile game will succeed or not.
These two findings are from Tapjoys research on "predicting the future LTV of your your users."

• "Reaching a critical point of 1,000 users who make > 3 purchases is a good indication that an app will ultimately top $1MM in revenue. 84% of the apps with 1,000 or more users who completed three or more in-app purchases within the first 90 days broke that $1MM threshold.
• 35% conversion rate from 1st to 3rd purchase was the critical number for breaking the $1MM revenue threshold."

4. Recalibrate your own benchmarks constantly. 

I previously mentioned that D1 (40%), D7 (20%) and D30 (10%) is a good Western benchmark for midcore games. In my experience, East-Asians take a a different approach, which is  slightly more short term on this. I remember one Japanese executive mention the following retention rates as successful.

D1 > 50%
D3 25-30% !
D7 ~ 20%

In his talk, he stressed D3 which seems to empirically work for him to predict if a game is successful or not.

Anyway, these are just preliminary thoughts. Please comment below for further exchange.

How to compute a doubling with percentages

What does it mean when someone says: We are going to grow revenues

14% every year ? 

10% every year ?

5% every year ?

There is a very simple trick that allows you to quickly translate it in terms of doubling.

(free pic under CC)

Just take 70 and divide by the percentage number:


70/14 = 5 yrs

70/10 = 7 yrs

70/ 5 = 14 yrs


So when someone says:

We are going to grow 14% every year, this person is saying that revenues will double in 5 yrs.

 in short:

14% every year ? --> 2x in 5 yrs

10% every year ? --> 2x in 7 yrs


5% every year ? --> 2x in 14 yrs

Healthy Daily Retention Rates

What are healthy retention rates ? Below is a rolling retention benchmark.

(free pic under CC)

If your midcore f2p mobile title does not match any of these, forget it:

1 Day Retention: 40%
7 Day Retention: 20%
30 Day Retention: 10%

(for iOS 5.0 or 2.3 Android and higher)

Playstore Ranking and Revenues

Here are my guestimations for the Korean Market (Play Store)

Below figures reverse engineer the current mobile gaming revenue size of 1.5 billion US$ in Korea for Play Store (2013) figures.

Rank	Daily Revenues	Annual Revenues	Total Annual
1-5	$ 300.000	$109.500.000	$ 547.500.000
6-10	$ 100.000	$ 36.500.000	$ 182.500.000
11-30	$ 20.000	$ 7.300.000	$ 146.000.000
31-50	$ 10.000	$ 3.650.000	$ 73.000.000
			$ 949.000.000
	% of Total Revenues		66%
	Total Market Size		$1.437.878.788

Sounds Big

The sentence "sounds big" has a particular meaning to me.
 When I was 26 years old, Investment Analyst at T-Venture, I had the privilege to attend a Amadeus Capital Fund Board meeting.
 Pundits like Herrmann Hauser and Frank Bonsal (Founder NEA) were participating.
Someone was mentioning a new tech start-up (don't remember which) and Frank just said:

 Souunds BIIGG. (in a slightly southern accent)

(free pic under CC)

 So what was so particular about this comment in this particular context ?
It showed to me a lot about this gentleman:

•  He is aiming for huge markets 
•  He admits that he does not exactly know about the business/industry and is not ashamed of implicitly admitting it 
•  He seems to rely on intuition (could as well have said: Let's analyze the market and the product) 
•  It showed his positive mental attitude & optimism the way he said it.

Mochi Media is shutting down its services

Before the site completely shuts down on March 31, I am copying the CEO's blog post below.

I will write about MochiMedia's model at a later point in time

(free pic under CC)

Some Final Thoughts from Mochi

First off, thank you for working with us all of these years.

Mochi Media was founded in 2005 by Jameson and Bob with the mission of fueling the creativity of indie game developers. You focus on making a great game, and we’ll take care of the rest. At that time, Flash was a platform that held a lot of potential if developers could find ways to track, monetize and build better games. Together, Flash and Mochi provided an on-ramp to a career or business in game development.

In addition to furthering our services and business, Mochi assumed a role in growing the category. We organized and hosted FGS (aka Flash Gaming Summit) for five years to get this community out from behind computer screens and in person to talk game development. We supported developer meet-ups like Mochi London. And we addressed the “state of the union” with the Flash Game Market Survey. Moving forward, we expect that others will pick up the baton in advancing the indie cause.

I think I speak for everyone who has been a part of the Mochi team over the years in saying that the innovation from you developers inspired us. We take great pride in currently seeing Ninja Kiwi’s Bloons TD5, and Flipline Studios’Papa’s Freezeria To Go among the Top Games Charts on iOS. We love that at one time we shared a desk with Casual Collective which is now known as KIXEYE.

Today, there has never been a better time to be an indie game developer in terms of the platforms and audiences one can reach: Flash, iOS, Android, XBLA, PSN, Steam, HTML5, and the list goes on. If Mochi had a more meaningful position today beyond Flash, then there may have been a different path for the company going forward.

Though we won’t (as team Mochi) be a part of your future growth, we cannot wait to see what you create next. Best of luck.

Thank you,

Josh

LTV > CPI - a quick & dirty approach to calculate LTV

This is a five minute note about how to calculate mobile app LTV of a specific cohort








Number of total acquired users = 600

Number of churned users per day. This can be found out via Churnrate = 1- retention rate
Day 1 --> 100
Day 2 --> 200
Day 3 --> 300

We assume they churn at the end of the day and that every user that does not churn is active on each day.

         (free pic under CC)

Therefore number of days active or DAU is the following
Day 1 --> 100
Day 2 --> 400
Day 3 --> 900

Total days on which users have been active is therefore 1400 days (which equals DAU)

We assume that ehe sum of total revenues on those three days 140 €

Therefore ARPDAU = Revenues / DAU = 0.10cents

Average Life Time (LT) = Total days active / total users = 1400/600 = 2.3

LTV = average LT * ARPDAU = 2.3* 0.1cents = 23 cents

If we assume that 23 cents is excluding VAT (gross revenues) we have to deduct appstore fee
the net LTV is 16 cents

LTV > CPI (cost per install) is actually not quite ok, because we have to include a certain margin based on your costs

Based on current CPI's at ~ 2-3 $ or Euro, this seems unrealistic. What are solutions?

Virality
In case the game is viral or gets featured by Google / Apple by a factor of 6 , then LTV in theory goes to 1.40€. If you are able to meet a net LTV of 1€ then fine

Getting featured
This is a no brainer

Push Campaigns
Has been discussed many times. Problem is the incentivized installs f*ck your stats

Buying targeted
This means you are able to target those users which have a high LTV. There are certain companies that label this user acquisition strategy as "predictive LTV". We will see how it pans out

Key Mobile App KPI's to watch

Android Monetization still behind Apple

Apologies for the brevity of the post but that chart says it all:

Source: looks like Appannie (?)

Simple financial analysis

David An

e-commerce wonderworld

In a perfect scenario, contribution margin 1 (after COGS will be 50) leading to a contribution margin 2 of 25. If you are able to replicate that 4 times a period you run at a profit after 2 times, since customer acquisition costs are 50.

DNA of Goals

When you set goals they have to be:

Strategic thinking

This is linear business school stuff but helpful at times

Revenue Split of an iTunes Album

Below is an interesting chart I found on Neue Züricher Zeitung (NZZ - a leading Swiss national newspaper) on how revenues of an  iTunes album are split. 

NZZ researched a 17 Swiss Francs (sFR) download album. The title "Krümel für den Künstler" translates as "crumbs for the artist".

Here are the figures translated and in %:

Artist	SFr. 1,30	8%
Management	SFr. 1,50	9%
iTunes	SFr. 9,50	56%
Label/Producer/Distributor	SFr. 4,70	28%
	SFr. 17,00

Short Commentary:

• I was shocked when I saw these numbers. The artist only makes 8%. Compared to 30% fee of any app dollar that is made on the iOS Store, Apple takes 56%
• Label/Producer/Distributor would have to be specified. My guess is that the producer will get something between 2-3%. But check some more insights here: "Lessons from the music industry"
• Nobody remembers download services like Pressplay, Real, etc. anymore. Apple has a clear monopoly in the download sector which is currently being cannibalized by celestial jukebox streaming services like Rdio and Spotify. But as we already all know Apple is working on a music streaming service too. This space is really getting crowded with YouTube, Nokia, Sony, Samsung soon launching their own service. 

Oishii Karlsruhe - a new sushi business model

Sushi circle restaurants owners shiver everytime I enter their restaurant for the second or n time. That is because on "all you can eat days" nothing is left over. Especially, when I go there with another sushi aficionado, we almost abuse the right to order a la carte - Sashimi in particular, which does not stuff your stomach as much as normal sushi does.

Yesterday, I went to a new place called "Oishii Karlsruhe" - an "all you can eat sushi restaurant in Karlsruhe. Since there are not many good restaurants in Karlsruhe let alone Asian restaurants - this one caught my attention.

And since this blog is about innovation, I thought their business model is worthwhile discussing shortly. Disclaimer: I am not a restaurant expert, and the following thoughts are really a quick&dirty analysis.

Here is how it goes:

• You sit at a table, equipped with an iPad + an app that serves as a mobile-order machine. 
• You pay 22.90 Euro in total, excluding beverages. So you will end up at about 30 Euros. 
• You are are allowed to order 10 times x 5 dishes / 10 minutes. Each dish has one piece of sushi, e.g. Nigiri etc. 
• Each order can be made any time, but once an order is transmitted, you need to wait 10 minutes until the next order. In theory if you make use of 10 rounds, you sit there for 100 minutes continually eating.
• Lets say, you are 4 people. This allows you to order 20 pieces max per round  of order and you need to wait 10 minutes to order the next 20 pieces. 
• There is no price differentiation between sushi pieces. So an Amaebi, or Unagi will be the same price as a Tamago or a green tea ice cream. What matters is the number of units per order which is limited to 5 dishes per order per person.
• So you could potentially end up eating 50 dishes of sushi for a price of 22.9 Euro
• There is a penalty involved in case you do not finish ordered dishes. 

Pro/Cons for the user

• Some customers prefer eating less than 22.9 Euro. However, even sushi circle restaurants a la carte will soon hit 23 Euros unless you just want to go for a mini-snack for lunch. So 22.9. Euros is quite deal. And if you order entire menus a la carte, they often actually start at 20 Euros for a decent meal.
• You enjoy the freedom of choosing without calculating the effect of a sushi dish (grey??) on your maximum "meal-budget"  
• You can order a la carte and do not have to stare at the circling sushi dishes giving your friend full attention.
• You do not have to push the cook who is usually busy as hell with a begging expression on your hungry face for an a la carte order ("Can I have some salmon sashimi please"? "really, you already had 3"!)

 Pro/Cons for the restaurant

• The place was full and I could only see a few tables being empty. Enormously high table fill rate.
• People on average will not order more than 5 rounds which affects the the table turnover. I guess it will be quite predictable and have the shape of a normal curve with something like 1.5 hours in the middle scewing to 2 hours to the right and 1 hour to the left. 
• Same goes for average revenue per customer which will range between [ 26 to 30] including beverages. 
• I guess one could sum it up by concluding: high predictability regarding ARPU (average revenues per user) and table turnover. 
• What I would be worried about is material cost. Sushi is expensive. To do the analysis, one would have to look at the distribution of ordered sushi (stuff like amebi, unagi is really expensive)
• However as long as the number of restaurant visitors remains stable you have a good prediction of total revenues, thus can optimize profitability in a quite controlled manner. 

Conclusion

• Great advantage for the user who love to eat a lot of sushi at a fixed budget
• Therefore high fill rate, predictable table turnover rate and therefore stable total revenues
• Profitability to be optimized in purchase and production costs. The portfolio can be optimized upon that (for instance taking away super expensive sushi won't hurt the majority, maybe sushi freaks like me)
• Overall great idea which proves that innovation nowadays is about business model innovation.

Are mobile game developers overpaid ? Lessons from the music industry

What is a fair deal between a mobile game publisher and a developer? Of course, there are many ways to approach this question. So I thought why not compare numbers with adjacent industries, like the music business?

From: http://www.flickr.com/photos/jensview/ (CC licensed)

So I came across this book "All you need to know about the music business" from Donald Passman which seems to be some sort of industry standard literature. 


Passman takes the example of 500'000 units sold (gold album) and calculates revenues for the artist: 
 

Profit Statement Music Artist

(From Donald Passman, All you need to know about the music business, p.102)

• The artist makes about 34% of royalty revenues and 20% after paying his personal and business manager, that is $170'250 or $100'000 respectively
• However, there are additional costs to be covered by the artist. He/she has to pay a personal manager and a business manager (for managing financials). Normally, PM alone will get ~15% of gross revenues. Sometimes PM fee is capped at 50% of artists net income. So the figure is rather conservative.
• Recording costs are usually pre-financed by the record company, and is deducted in the form of a recoupment. 
• The producer gets about 3%, a star producer will get more 
• Free goods for physical CDs are a form of discount.

Conclusion

• The situation in the music industry is quite comparable to the mobile or online gaming industry
• Recording costs can be compared to development fee of a mobile game developer. These monies are paid upfront and recouped from the artists revenues but risk is carried by the publisher. In any case, the more is paid upfront to the artist/developer the better of course because of NPV (net present value)
• Overall, roughly 25-30% share for the content creator (artist, game developer) seems to be industry norm for creative industries. 

Any thoughts or comments? please comment below:

How to forecast viral growth of your mobile app

Ever since Hotmail, viral marketing has been the summum bonum ("highest good" so to speak) in the information economy and is still the most effective way to grow an online business. Since users do the marketing job as product invitors and invitees, customer acquisition costs virtually become zero.

A good job in explaining the key metrics behind viral distribution has been done by David Skok, a VC from Matrix Partners. He derived the following formula:



K = viral coefficient which is number of successful invitations per active user
t = time in days
ct = cycle time which is the time interval between an invitation and a successful invitation
Customers (0) = the first active users Customers (t) = number of cumulative active users at a certain point in time t


As David Skok extensively explains the findings for the viral growth formula, K does have to be bigger than 1 and the shorter ct the steeper the upward sloping curve.

Example: if you take a cycle time of 2 which means it takes two days from the discovery of a an app to sending out an invitation which then leads to a successful download by the invitee, and K = 2 (each invitor sucessfully invites 2 invitees), then the result will be 20470 cumulative users. Half the CT and you could end up with ~ 21 million users.





This sounds very much like the Hal Varian teachings which state that information industries with strong network externalities often can end up in a "winner-takes-it-all" scenario.

From a Hal Varian paper

Although there are examples like that in the Internet, like Facebook or Twitter, for apps especially gaming apps this might be rarely the case. As David Skok himself writes his model has two drawbacks: 1. user attrition which I would call "churn", 2. user saturation which I think becomes more of a important issue in this discussion. Why ? Because viral distribution usually follow an S-curve pattern like viral diseases do. In other words, infectors at a certain point in time start to meet more and more infectees as time goes by. I remember disproportionately more people telling me about Gangnam Style a few months after it hit Europe. Another discounting factor is app consumer taste. E.g. 30% probably never play gaming apps.

Though I prefer the above bottom-up approach because of its major two keen findings, it needs to be combined with a top-down approach that takes into consideration the overall addressable sizable market and viral saturation.

I found this cool article about s-shaped marked adoption curve by M.Brandwinder which I think can be combined with the viral growth formula. This page also offers the excel tool as download for those of you who are excel ninjas. Brandwinder makes use of the sigmoid function which has two values

• long term market share: By this % you project how much you will be able to cover in the long run 
• Midterm share: This is a point in time between peak and start on the way to the peak share

Further the sigmoid function gives T0 (the tipping point of the curve) which shifts the timeline and alpha stretches or compresses time.

For instance the green curve starts to saturate at 1.0 taking into account the saturation effect of viral infections. The website provides an xls tool to download as well which allows you to play around with input and output variables. Basically you need to define the following values

• Maximum number of users 
• two dates (time) by which X% is reached 

 In my previous blog post "App Retention follows App discovery" I mentioned a game called "Everybodys Game" for the Kakao-Gaming Platform. This game got around 5 Million downloads in ~2 weeks. So my goal was to reverse-engineer their growth by using the following parameters:

• Max Number of Users is limited by the total number of smartphone users in Korea. In this case 30 Million 
• Value 2: On the 26th day after launch the 75% of the maximum number of users should be reached 
• Value 1: The growth dynamic should be reached on day 11 with 6% leading to 5 Million downloads (which actually happened)
• Peak Value = 75% meaning it is expected that a quarter will never be reached 

The result is a sigmoid curve as follows: 

The alpha value is 1.03 (quite steep) and T0 (tipping point) is at day 22. Note that after the tipping point the curve flatens due to the redundancy effect of viral distribution.

So what I did was basically take the two models and tweak the numbers: 

• So that I have reasonable but aggressive input variables in the Skok model 
• Take the number of cumulative numbers that have been achieved on day 18-20 (ca.) which was 5 Million downloads 
• Then use that value as a target goal for the Brandwinder model (taking into account saturation, attrition effects and natural limitations of the addressable market. Of course a 500 M growth is way too inflated as the excel shows:

 borrowed from forentrepreneurs

Conclusion and Recommendation

• Virality often was never planned but just happened. However, two levers from the Skok model (cycle time, viral coefficient) can be used for beta testings (A/B) prior and after launch so that you optimize the overall virality on a continuous basis.
• Take the Brandwinder algorithm with a target value and get a feeling for how much is actually possible by limiting the inflated values via the inputs addressable market, total peak value and growth trajectory through value 1 and 2.
• This article does not cover actual tips. I will do this in a future post. 

Ok, this article is actually not really app specific :) but I hope you are taking some insights away

Key Metrics for App Monetization, App Retention and User Acquisition

Here are the key metrics any app manager for apps in particular for freemium apps should look at:
Please let me know in case I have forgotten some important metrics

Customer Acquisition

• CPI = Cost per install  
• CTR = Click through rates. Can vary from 0.1% to 15% depending on the invasive character of the ad 
• Conversion rates 
• Click to install 
• Install to active user 
• active user to paid 

• CAC Customer acquisition costs = sum of direct marketing expenses of all channels 
• Weighted CAC = In case you are boosting a game from zero to the top charts, you might want to factor in the zero CAC through organic installs.

The mobile gaming space has turned from a blue to a dark red ocean where publishers with big pockets are outspending competitors. In general the best monetizing games will justify a CPI of 70 cents. Not more. General rule of thumb: Don't spend more than 30 cents depending on the marketing channel. Users coming from an offer like Tapjoy usually will have a lousy monetization with unofficial deinstallation rates of ~80%

Retention 

• DAU = daily active users 
• Retention (7days, 30 days) = Logins in a time interval and time usage thereof

Most mobile games level off after a couple months. Look at appannie data and you will be astonished. Mobile social games like Rule the Sky can have a life time of 18months. But this is largely due to 2 updates per month keeping users busy. Big hits like Anipang in the casual space seem to last at least 6months.

Monetization

• ARPU = average revenue per user per month
• ARPPU = average revenue per paying user per month
• ARPDAU = average revenue per daily active user
• % paying users (to cumulative users) 
• ARPDAU / DAU = ratio between average revenues per daily active user and daily active users
• Life Time (Free and Paid) --> LTV Life Time Value
• Time from free to paid user t{free --> paid}
• eCPM = (revenues / total ad impressions) x 1000  --> In case you are in-app ads driven

 What I noticed are definitely some cultural differences as to how steep the balancing between free and pay can go. In Germany, users can turn the best made game launch into a total shitstorm because game balancing quite frankly started to suck after 1h of game play, forcing users to pay more than they would have ever paid for a premium game.

Profitability (Contribution margin 1) metrics

• DAU x ARPDAU > Costs (--> Target margin)
• LTV - CAC = Profit 
• (LTV - CAC) / LTV = ROI 
• NPV {LTV} Net present value of LTV --> would only make sense if you had a very long LT over two years, so that you would have to discount the cash flows. 

In case you are not having enough budget or inherent virality with the game that the average CAC per user becomes less than the total LTV, you need to control ROI right from start, especially when you have run some initial tests and now want to grow. So when you are able to boost yourself up to the top 10, a direct contribution margin might be lousy but offset by the free installations you get from organic store users.

 If you have more feedback, please add me on my David An Gplus account


Update note: 18March2014
I have found a good article called " Monetization-based Game Design: ARPDAU Contribution" 
http://quarterview.com/?p=228 with a good benchmark data

App Marketing Best Practices

I cannot think of any market in the internet economy that has the same degree of competitiveness than the app economy - in particular the mobile gaming space.

As I previously stated, the top 10 apps make of more than 44% app usage, half of revenues on iOS and Android go to 25 top publishers. This is equivalent to about 0.008% of app developers.

So I thought we need a little bit of best practice for app developers as to how they can successfully grow their user base:


Paid App Promotions 

1. In-App Cross Promotion (using platforms like Chartboost, Applifier)
2. In-App Advertisement (Admob, Inmobi)
3. Incentivized Downloads (Tapjoy, Sponsorpay, Supersonicads)
4. Special Deal Apps (FAAD, App gratis)
5. Mobile Banner Campaigns (Adwords)
6. Facebook Marketing (Buying fans or installations)
7. Influencer Marketing

Public Relations 

1. Special Interest app online magazines (Playandroid Magazine, Fettspielen Magazine)
2. Online news magazines (like heise.de, or Golem.de
3. Tech bloggers  (Techcrunch, Gigaom, Mashable)

Social Media 

1. Twitter (especially working with promo codes)
2. Weibo (leading chinese site)
3. G-Plus (likely that it affects Google rankings)
4. YouTube (Video trailer)

  App Store Marketing 

1. Carrier Stores (there are 5-7 in China, 3 Stores in Korea with T-Store, Olleh, LG Uplus) 
2. Portals (Opera, Getjar, Dolphin, Playandroid.com, Androidpit)
3. OEM Stores (Samsung Apps)
4. App Store Marketing Aggregators (there are companies who will do everything for you)

App Store SEO

1. Downloads
2. Number of reviews (stars) 
3. Comments 
4. Description, Name , Screenshots, Keywords

Social Graph Viral

1. Messengers (like Kakao Talk, Line, Whatsapp APÌ)
2. Social Networks (Facebook API, QQ)

Friends at Google and Apple 

1. Can you bring you up to 50-60k per day per country per category 
2. Only works if you have a top quality game

Channelling 

1. Find app or website publishers who are willing to advertise your app for a revshare deal
2. TinyCo has built the first internal program 
3. Mobile-PS, a mobile game publishing solution and platform (not launched yet)

But most important: Have a great game that the world has not seen before!

Please comment below if you have further suggestions!