The Future of Social Networking at Singularity U

Last week, I was asked to give a guest lecture at Singularity University on the topic “The Future of Social Networking

To frame the discussion, I chose to walk through the following structure:

  • Web 1.0 vs. Web 2.0
  • Social Networking as a disruptive platform
  • LinkedIn as an example of a social platform
  • Mobile as a disruptive accelerator for social platforms
  • Thoughts on future disruptions

On a personal note, I hadn’t actually been back to visit NASA Ames Research Center since my internship during my senior year in high school (21 years ago).  Back then, I was helping develop simulation software for fluid dynamics simulations in Fortran.  Thankfully, no one asked me to code in Fortran during the Q&A.

The team at Singularity U was incredibly gracious, and I appreciated the opportunity to talk to the class.

User Acquisition: Mobile Applications and the Mobile Web

This is the third post in a three post series on user acquisition.

In the first two posts in this series, we covered the basics of the five sources of traffic to a web-based product and the fundamentals of viral factors.  This final post covers applying these insights to the current edge of product innovation: mobile applications and the mobile web.

Bar Fight: Native Apps vs. Mobile Web

For the last few years, the debate between building native applications vs. mobile web sites has raged.  (In Silicon Valley, bar fights break out over things like this.) Developers love the web as a platform.  As a community, we have spent the last fifteen years on standards, technologies, environments and processes to produce great web-based software.  A vast majority of developers don’t want to go back to the days of desktop application development.

Makes you wonder why we have more than a million native applications out there across platforms.

Native Apps Work

If you are religious about the web as a platform, the most upsetting thing about native applications is that they work.  The fact is, in almost every case, the product manager who pushes to launch a native application is rewarded with metrics that go up and to the right.  As long as that fact is true, we’re going to continue to see a growing number of native applications.

But why do they work?

There are actually quite a few aspects to the native application ecoystem that make it explosively more effective than the desktop application ecosystem of the 1990s.  Covering them all would be a blog post in itself.  But in the context of user acquisition, I’ll posit a dominant, simple insight:

Native applications generate organic traffic, at scale.

Yes, I know this sounds like a contradiction.  In my first blog post on the five sources of traffic, I wrote:

The problem with organic traffic is that no one really knows how to generate more of it.  Put a product manager in charge of “moving organic traffic up” and you’ll see the fear in their eyes.

That was true… until recently.  On the web, no one knows how to grow organic traffic in an effective, measurable way.  However, launch a native application, and suddenly you start seeing a large number of organic visits.  Organic traffic is often the most engaged traffic.  Organic traffic has strong intent.  On the web, they typed in your domain for a reason.  They want you to give them something to do.  They are open to suggestions.  They care about your service enough to engage voluntarily.  It’s not completely apples-to-apples, but from a metrics standpoint, the usage you get when someone taps your application icon behaves like organic traffic.

Giving a great product designer organic traffic on tap is like giving a hamster a little pedal that delivers pure bliss.  And the metrics don’t lie.

Revenge of the Web: Viral Distribution

OK. So despite fifteen years of innovation, we as a greater web community failed to deliver a mechanism that reliably generates the most engaged and valuable source of traffic to an application.  No need to despair and pack up quite yet, because the web community has delivered on something equally (if not more) valuable.

Viral distribution favors the web.

Web pages can be optimized across all screens – desktop, tablet, phone.  When there are viral loops that include the television, you can bet the web will work there too.

We describe content using URLs, and universally, when you open a URL they go to the web.  We know how to carry metadata in links, allowing experiences to be optimized based on the content, the mechanism that it was shared, who shared it, and who received it.  We can multivariate test it in ways that border on the supernatural.

To be honest, after years of conversations with different mobile platform providers, I’m still somewhat shocked that in 2012 the user experience for designing a seamless way for URLs to appropriately resolve to either the web or a native application are as poor as they are.  (Ironically, Apple solved this issue in 2007 for Youtube and Google Maps, and yet for some reason has failed to open up that registry of domains to the developer community.)  Facebook is taking the best crack at solving this problem today, but it’s limited to their channel.

The simple truth is that the people out there that you need to grow do not have your application.  They have the web.  That’s how you’re going to reach them at scale.

Focus on Experience, Not Technology

In the last blog post on viral factors, I pointed out that growth is based on features that let a user of your product reach out and connect with a non-user.

In the mobile world of 2012, that may largely look like highly engaged organic users (app) pushing content out that leads to a mobile web experience (links).

As a product designer, you need to think carefully about the end-to-end experience across your native application and the mobile web.  Most likely, a potential user’s first experience with your product or service will be a transactional web page, delivered through a viral channel.  They may open that URL on a desktop computer, a tablet, or a phone.  That will be your opportunity not only to convert them over to an engaged user, in many cases by encouraging them to download your native application.

You need to design a delightful and optimized experience across that entire flow if you want to see maximized self-distribution of your product and service.

Think carefully about how Instagram exploded in such a short time period, and you can see the power of even just one optimized experience that cuts across a native application and a web-based vector.

Now go build a billion dollar company.

User Acquisition: Viral Factor Basics

This is the second post in a three post series on user acquisition.

In the first post in this series, we covered the basics of the five sources of traffic to a web-based product.  This next post covers one of the most important, albeit trendy, aspects of user acquisition: virality.


It’s About Users Touching Non-Users

Look at your product and ask yourself a simple question: which features actually let a user of your product reach out and connect with a non-user?   The answer might surprise you.

At LinkedIn, we did this simple evaluation and discovered that out of thousands of features on the site, only about a half-dozen would actually let a user create content that would reach a non-user. (In fact, only a couple of these were used in high volume.)

I continue to be surprised at how many sites and applications are launched without having given careful thought to this exactproblem.  Virality cannot easily be grafted onto a service – outsized results tend to be reserved for products that design it into the core of the experience.

Useful questions to ask, from a product & design perspective:

  • How can a user create content that reaches another user?
  • How does a users experience get better the more people they are connected to on it?
  • How does a user benefit from reaching out to a non-user?

Understanding Viral Factors

One of the most useful types of metrics to come out of the last five years of social software is the viral factor.  Popularized by the boom of development on the Facebook platform in 2007, a viral factor is a number, typically between 0.0 and 1.0.  It describes a basic business problem that affects literally every business in the world:

“Given that I get a new customer today, how many new customers will they bring in over the next N days?”

“N” is a placeholder for a cycle time that makes sense for your business.  Some companies literally track this in hours, others 3 days, or even 30.  Let’s assume for now that 7 is a good number, since it tells you given a new customer today, how many new customers will they bring in over the next week.

Basic Viral Math

The good news is, once you identify the specific product flows that allow users to reach non-users, it’s fairly easy to instrument and calculate a viral factor for a feature or even a site.  But what does the number really mean?

Let’s assume a viral factor of 0.5, and an N of 7.  If I get a new user today, then my user acquisition will look like this over the next few weeks:

1 + 0.5 + 0.25 + 0.125 ….

It’s an infinite series that adds up to 2.  By getting a new user, the virality of this feature will generate a second user over time.

Two obvious epiphanies here:

  • A viral factor is a multiplier for existing sources of user acquisition.  0.5 is a 2x, 0.66 is a 3x, etc.
  • Anything below 0.5 looks like a percentage multiplier at best.

What about a viral factor of 1.1?

One of the memes that started to circulate broadly in 2008 was getting your viral factor to “1.1”.  This was just a proxy for saying that your product or service would explode.  If you do the math, you can easily see that any viral factor or 1.0 or higher will lead to exponential growth resulting in quickly having every human on the planet on your service.

I don’t want to get into a Warp 10 debate, but products can in fact have viral factors above 1.0 for short periods of time, particularly when coming off a small base.

Learning from Rabbits

The key to understanding viral math is to remember a basic truth about rabbits.  Rabbits don’t have a lot of rabbits  because they have big litters.  Rabbits have a lot of rabbits because they breed frequently.

When trying to “spread” to other users, most developers just focus on branching factor – how many people they can get invited into their new system.  However, cycle time can be much more important than branching factor.

Think of a basic exponential equation: X to the Y power.

  • X is the branching factor, in each cycle how many new people do you spread to.
  • Y is the number of cycles you can execute in a given time period.

If you have a cycle that spreads to 10 people, but takes 7 days to replicate, in 4 weeks you’ll have something that looks like 10^3.  However, if you have a cycle that takes a day to replicate, even with a branching factor of 3 you’ll have 3^27.  Which would you rather have?

In real life, there is decay of different viral messages.  Branching factors can drop below 1.  The path to success is typically the combination of a high branching factor combined with a fast cycle time.

As per the last blog post, different platforms and traffic channels have different engagement patterns and implicit cycle times.  The fact that people check email and social feeds multiple times per day makes them excellent vectors for viral messages.  Unfortunately, the channels with the fastest cycle times also tend to have the fastest decay rates.  Fast cycle times plus temporary viral factors above 1 are how sites and features explode out of no where.

Executing on Product Virality

To design virality into your product, there really is a three step process:

  1. Clearly articulate and design out the features where members can touch non-members.  Wireframes and flows are sufficient.  Personally, I also recommend producing a simple mathematical model with some assumptions at each conversion point to sanity check that your product will produce a strong viral factor, layered over other traffic sources (the multiplier).
  2. Instrument those flows with the detailed metrics necessary for each step of the viral cycle to match your model.
  3. Develop, release, measure, iterate.  You may hit success your first time, but it’s not unusual to have to iterate 6-8 times to really get a strong viral factor under the best of conditions.  This is the place where the length of your product cycles matter.  Release an iteration every 2 days, and you might have success in 2 weeks.  Take 3-4 weeks per iteration, and it could be half a year before you nail your cycle.  Speed matters.

You don’t need hundreds of viral features to succeed.  In fact, most great social products only have a few that matter.

What about mobile?

Now that we’ve covered the five scalable sources of web traffic and the basics of viral factors, we’ll conclude next week with an analysis of what this framework implies for driving distribution for mobile web sites vs. native applications.

User Acquisition: The Five Sources of Traffic

This is the first post in a three post series on user acquisition.

The topic of this blog post may seem simplistic to those of you who have been in the trenches, working hard to grow visits and visitors to your site or application.  As basic as it sounds, however, it’s always surprising to me how valuable it is to think critically about exactly how people will discover your product.

In fact, it’s really quite simple.  There are only really five ways that people will visit your site on the web.

The Five Sources of Traffic

With all due apologies to Michael Porter, knowing the five sources of traffic to your site will likely be more important to your survival than the traditional five forces.  They are:

  1. Organic
  2. Email
  3. Search (SEO)
  4. Ads / Partnerships (SEM)
  5. Social (Feeds)

That’s  it.  If someone found your site, you can bet it happened in those five ways.

The fact that there are so few ways for traffic to reach your site at scale is both terrifying and exhilarating.  It’s terrifying because it makes you realize how few bullets there really are in your gun.  It’s exhilarating, however, because it can focus a small team on exactly which battles they need to win the war.

Organic Traffic

Organic traffic is generally the most valuable type of traffic you can acquire.  It is defined as visits that come straight to your site, with full intent.  Literally, people have bookmarked you or type your domain into their browser.  That full intent comes through in almost every produto metric.  They do more, click more, buy more, visit more, etc.  This traffic has the fewest dependencies on other sites or services?

The problem with organic traffic is that no one really knows how to generate more of it.  Put a product manager in charge of “moving organic traffic up” and you’ll see the fear in their eyes.  The truth is, organic traffic is a mix of brand, exposure, repetition, and precious space in the very limited space called “top of mind”.  I love word of mouth, and it’s amazing when it happens, but Don Draper has been convincing people that he knows how to generate it for half a century.

(I will note that native mobile applications have changed this dynamic, but will leave the detail for the third post in this series.)

Email Traffic

Everyone complains about the flood of email, but unfortunately, it seems unlikely to get better anytime soon.  Why?  Because it works.

One of the most scalable ways for traffic to find your site is through email.  Please note, I’m not talking about direct marketing emails.  I’m referring to product emails, email built into the interaction of a site.  A great example is the original “You’ve been outbid!” email that brought (and still brings) millions back to the eBay site every day.

Email scales, and it’s inherently personal in its best form.  It’s asynchronous, it can support rich content, and it can be rapidly A/B tested and optimized across an amazing number of dimensions.  The best product emails get excellent conversion rates, in fact, the social web has led to the discovery that person to person communication gets conversion person over 10x higher than traditional product emails.  The Year In Review email at LinkedIn actually received clickthroughs so high, it was better described as clicks-per-email!

The problem with email traffic generally is that it’s highly transactional, so converting that visit to something more than a one-action stop is significant. However, because you control the user experience of the origination the visit, you have a lot of opportunity to make it great.

Search Traffic

The realization that natural search can drive traffic to a website dates back to the 90s.  However, it really has been in the past decade in the shadow of Google that search engine optimization scaled to its massive current footprint.

Search clearly scales.  The problem really is that everyone figured this out a long time ago.  First, that means that you are competing with trillions of web pages across billions of queries.  You need to have unique, valuable content measured in the millions of pages to reach scale.  SEO has become a product and technical discipline all it’s own. Second, the platform you are optimizing for (Google, Microsoft) is unstable, as they constantly are in an arms race with the thousands of businesses trying to hijack that traffic. (I’m not even going to get into their own conflicts of interest.)

Search is big, and when you hit it, it will put an inflection point in your curve.  But there is rarely anysuch thing as “low hanging fruit” in this domain.

Advertising (SEM)

The fourth source of traffic is paid traffic, most commonly now ads purchased on Google or Facebook.  Companies spend billions every year on these ads, and those dollars drive billions of visits.  When I left eBay, they were spending nearly $250M a year on search advertising, so you can’t say it doesn’t scale.

The problem with advertising is really around two key economic negatives.  The first is cash flow.  In most cases, you’ll be forced to pay for your ads long before you realize the economic gains on your site.  Take something cash flow negative and scale it, and you will have problems.  Second, you have solid economics.  Most sites conjure a “lifetime value of a user” long before they have definitive proof of that value, let alone evidence that users acquired through advertising will behave the same way. It’s a hyper-competitive market, armed with weapons of mass destruction.  A dangerous cocktail, indeed.

While ads are generally the wrong way to source traffic for a modern social service, there are exceptions when the economics are solid and a certain volume of traffic is needed in a short time span to catalyze a network effect.  Zynga exemplified this thinking best when it used Facebook ads to turbocharge adoption and virality of their earlier games like FarmVille.

Social Traffic

The newest source of scalable traffic, social platforms like Facebook, LinkedIn and Twitter can be great way to reach users.  Each platform is different in content expectations, clickthrough and intent, but there is no question that social platforms are massively valuable as potential sources of traffic.

Social feeds have a number of elements in common with email, when done properly.  However, there are two key differences that make social still very difficult for most product teams to effectively use at scale.  The first is permission.  On social platforms, your application is always speaking through a user.  As a result, their intent, their voice, and their identity on the platform is incredibly important.  Unlike email, scaling social feed interactions means hitting a mixture of emotion and timing.  The second issue is one of conversion.  With email, you control an incredible number of variables: content, timing, frequency.  You also have a relatively high metrics around open rates, conversion, etc.  With social feeds, the dynamics around timing and graph density really matter, and in general it always feels harder to control.

The Power of Five

Eventually, at scale, your site will likely need to leverage all of the above traffic sources to hit its potential.  However, in the beginning, it’s often a thoughtful, deep success with just one of these that will represent your first inflection point.

The key to exponential, scalable distribution across these sources of traffic is often linked to virality, which is why that will be the topic of my next post.

Why Zynga is a Great Business

With the Zynga IPO filing rumored to be hours away, I thought a light hearted blog post might be in order.

There are many aspects to economics behind video games that have been largely unchanged over the past two decades.  Fundamentally, Zynga lept to an opportunity to take advantage of a social platform (Facebook) to challenge some of the fundamental limitations of distribution and monetization that plagued the software giants who dominated desktop and platform gaming.

Obviously, I am a fan of the company.  The number of blog posts here about Zynga games should tell you that.  But when people ask me in real life why I’m such a big fan of Zynga, I give them a simple tongue-in-cheek thesis.

Selling Things You Don’t Need

It’s a well know fact that selling people things they don’t need is a great business.   Some might say it’s when retailers and/or products rise higher in the Maslow hierarchy of needs.  By definition, when items rise up that motivation chain, more powerful emotions come into play.  Fundamentally, no one needs a cotton candy tree.  But Zynga gets to the emotions of why you might want one.

In the end, the willingness to pay for things you don’t need is shockingly high in an economy where people have disposable income.

Selling Things You Don’t Need that Don’t Exist

Hundreds of years ago, this was what selling “snake oil” was all about.  Selling something that you don’t need, and that doesn’t exist has always been a great way to make money.  Unfortunately, it also used to be a sure fire path to getting run out of town (and perhaps tarred & feathered in the process).

A little computer icon of a purple cow does not exist, and you don’t need it.  But that doesn’t change the fact that Zynga has found a way not only to make you want it, but deliver it to you with an effective cost of goods sold of approximately zero.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold.

Selling Things You Don’t Need, That Don’t Exist, and That Are Addictive

This might be called the holy trinity of virtual goods, but in the end, this is the most amazing part of the Zynga model.  Certain types of social interaction are clearly pleasurable to people at a fundamental level.  We love the inherent stimulation in getting a response, recognition or even just insight into another human being.  Once we find a path for these interactions, we want more of it.  By leveraging a social platform for its games, Zynga has integrated social stimulation into their economics with outstanding results.

So now we have a high willingness to pay, combined with low friction and low cost of goods sold, with relatively low distribution costs and a high propensity for repeat activity.

Any wonder that I wish I owned Zynga stock?

Congratulations (in advance) to all of my great friends on the Zynga team.

Café World Economics: Spiceonomics

I really didn’t think I was going to write another blog post about the economics of Café World.  However, the rollout of the spice rack was just begging for some financial analysis, and so here we are.


Since I’ve written three previous articles on the topic:

The Economics of the Spice Rack

The “Spice Rack” is a concept I have advocated previously for Farmville.   A mechanism to purchase items that would accelerate / change the equations for existing actions.  (My original request was for increased levels in Farmville to actually accelerate the length of time it would take you to harvest any crop, like a 10% cut in time, etc.)

Café World has rolled out 7 spices:

  • Mystery Spice – Random improvement (reduce time by 1,2,5 min, +5 or +20 CP, +5% or +10% servings)
  • Super Salt – Increase the number of servings by 5%
  • Power Pepper – Increase the number of servings by 10%
  • One hour Thyme – Speed a dish by one hour
  • Six Hour Thyme – Speed a dish by six hours
  • Instant Thyme – Make a dish ready immediately
  • Salvage Sage – Rescue a spoiled dish

For this analysis, I’ve started with the simplest spices: Super Salt and Power Pepper.

For each dish, I calculated the increase (or decrease) in profit for buying the spice and applying it to one dish for the cycle.  I assume that Café World rounds down when you apply the 5% or 10% increase in number of servings. I express the number as an “Return on Investment” percentage (ROI) on the cost of the spice.

So, for example, if spending 600 coins on Power Pepper yield an extra 150 coins of profit after subtracting the cost of the pepper, I describe that as a “25% ROI” for Pepper for that dish.

Results of Spiceonomics

There are a few very interesting takeaways from the table below:

  • Spices are rarely worth it. Salt & Pepper have negative ROIs for almost all dishes.  In fact, in the history of the game, only 9 dishes are profitable when using the spices.  Interestingly, Grand Tandoori Chicken is net neutral (ROI = 0%).
  • Spices help more advanced players. Almost all the dishes with positive ROI are at the higher levels.
  • Spices help infrequent players more. The way the numbers work out, all the dishes where spices help are longer cooking time dishes.  This is good for players that might only play the game once a day (say, in the evening).

The Spiceonomics Table

Here is the summary table.  As usual, you can find all the supporting data in my Café World Economics spreadsheet on Google Docs.

Dish Salt ROI Pepper ROI
Chinese Candy Box 200.00% 200.00%
Impossible Quiche 153.33% 153.33%
Gingerbread House 124.00% 133.33%
Chicken Pot Pie 84.00% 85.00%
Giant Dino Egg 80.00% 80.00%
V.I.P. Dinner 32.00% 48.50%
Martian Brain Bake 30.00% 30.00%
Ginger Plum Pork Chops 30.00% 30.00%
King Crab Bisque 9.67% 10.83%
Grand Tandoori Chicken 0.00% 0.00%
Steak Dinner -4.00% -2.50%
Homestyle Pot Roast -5.00% -4.17%
Seafood Paella -6.67% -6.67%
Mystical Pizza -8.33% -8.33%
Veggie Lasagne -10.00% -10.00%
Chicken Adobo -18.33% -18.33%
Delicious Chocolate Cake -21.67% -20.83%
Herbed Halibut -25.00% -25.00%
Overstuffed Peppers -28.33% -28.33%
Loco Moco -30.67% -30.00%
Savory Stuffed Turkey -40.00% -40.00%
Crackling Peking Duck -40.00% -40.00%
Lavish Lamb Curry -45.33% -45.33%
Spitfire Roasted Chicken -46.67% -46.67%
Dino Drumstick -50.00% -50.00%
Lemon Butter Lobster -55.00% -55.00%
Voodoo Chicken Salad -56.67% -55.83%
Rackasaurus Ribs -57.33% -56.67%
Stardust Stew -58.00% -58.00%
Bacon and Eggs -58.00% -58.00%
Smoked Salmon Latkes -60.00% -60.00%
Tostada de Carne Asada -60.00% -60.00%
Valentine Cake -60.00% -60.00%
Sweet Seasonal Ham -60.00% -60.00%
Shu Mai Dumplings -61.33% -61.33%
Corned Beef -63.33% -62.50%
Fish n Chips -67.00% -67.00%
White Raddish Cake -68.00% -67.00%
Vampire Staked Steak -68.00% -67.00%
Triple Berry Cheesecake -73.00% -72.50%
Kung Pao Stir Fry -73.33% -73.33%
Tony’s Classic Pizza -78.33% -78.33%
Spaghetti and Meatballs -78.33% -77.50%
Fiery Fish Tacos -80.00% -80.00%
Eggs Benedict -82.00% -81.00%
Pumpkin Pie -82.67% -82.67%
Atomic Buffalo Wings -84.00% -84.00%
Crème Fraiche Caviar -89.33% -89.33%
French Onion Soup -90.00% -90.00%
Belgian Waffles -90.67% -90.00%
Macaroni and Cheese -92.00% -91.50%
Buttermilk Pancakes -93.33% -93.33%
Tikka Masala Kabobs -94.67% -94.00%
Caramel Apples -95.00% -95.00%
Hotdog and Garlic Fries -98.00% -98.00%
Powdered French Toast -98.00% -97.00%
Jammin’ Jelly Donuts -98.00% -98.00%
Super Chunk Fruit Salad -98.33% -98.33%
Chicken Gyro and Fries -98.67% -98.67%
Jumbo Shrimp Cocktail -98.67% -98.00%
Bacon Cheeseburger -100.00% -99.33%
Chips and Guacamole -100.00% -99.50%

Updated Tables for Profits, Café Points, and Real Hourly Wages

Have trouble figuring out whether Mystical Pizza is a good dish?  Deciding on whether to make the Dino Egg or Rackasaurus Ribs?  My Google Doc is now updated with tables for all 62 Cafe World dishes for data, and color coded based the cooking time of each dish, to help make picking the right dish easy.  Rather than cut & paste everything here, I’m going to just link to the doc.

Click here to view the Google Doc

Café World Economics: Alien Invasion & Google Docs

So I take the time to create a whole new post for Café World in 2010, and what does Zynga do?  They roll out some new crazy dishes based on an alien invasion, and now I’m 1.6M Café coins poorer.  Oh well.


Since I’ve written three previous articles on the topic:

I find it fairly interesting that Zynga is clearly mapping the same thematic variants from Farmville to their other games.  I remember when they did the space theme for Farmville (I still have 5 alien cows that produce Milktonium as proof…)

I won’t repeat the previous analysis. As a reminder, all of these numbers assume:

  • The numbers are per dish, per stove
  • The numbers assume the cost (15 coins) and experience (+1) of cleaning the stove each cycle
  • Profit & Cafe Points tables assume “instant” cleaning time.
  • Real World Hourly Wages assumes a cleaning time of 1 minute per stove.

You can read my previous posts for the rational behind these assumptions.

Profit per Dish

Here are the dishes, sorted by profitability as measured by profit per dish per day.

Dish Profit / Cycle Cycle Time Profit / Day
V.I.P. Dinner 9,786.00 1,080.00 13,048.00
Bacon Cheeseburger 22.00 5.00 6,336.00
Overstuffed Peppers 2,985.00 720.00 5,970.00
Kung Pao Stir Fry 985.00 240.00 5,910.00
Delicious Chocolate Cake 3,435.00 840.00 5,888.57
Fiery Fish Tacos 490.00 120.00 5,880.00
Lemon Butter Lobster 485.00 120.00 5,820.00
Martian Brain Bake 5,585.00 1,440.00 5,585.00
Shu Mai Dumplings 1,355.00 360.00 5,420.00
King Crab Bisque 5,370.00 1,440.00 5,370.00
Lavish Lamb Curry 1,785.00 480.00 5,355.00
Chips and Guacamole 11.00 3.00 5,280.00
Impossible Quiche 10,185.00 2,880.00 5,092.50
Powdered French Toast 67.00 20.00 4,824.00
Super Chunk Fruit Salad 50.00 15.00 4,800.00
Atomic Buffalo Wings 595.00 180.00 4,760.00
Jammin’ Jelly Donuts 65.00 20.00 4,680.00
Smoked Salmon Latkes 385.00 120.00 4,620.00
Tostada de Carne Asada 1,485.00 480.00 4,455.00
Buttermilk Pancakes 135.00 45.00 4,320.00
Tony’s Classic Pizza 885.00 300.00 4,248.00
Stardust Stew 1,535.00 540.00 4,093.33
Chicken Gyro and Fries 28.00 10.00 4,032.00
Grand Tandoori Chicken 3,985.00 1,440.00 3,985.00
Voodoo Chicken Salad 1,960.00 720.00 3,920.00
Chicken Pot Pie 7,585.00 2,880.00 3,792.50
Herbed Halibut 3,785.00 1,440.00 3,785.00
Sweet Seasonal Ham 1,885.00 720.00 3,770.00
Crackling Peking Duck 2,685.00 1,080.00 3,580.00
Jumbo Shrimp Cocktail 68.00 30.00 3,264.00
Savory Stuffed Turkey 2,885.00 1,320.00 3,147.27
Tikka Masala Kabobs 130.00 60.00 3,120.00
Macaroni and Cheese 245.00 120.00 2,940.00
Crème Fraiche Caviar 57.00 30.00 2,736.00
Spaghetti and Meatballs 910.00 480.00 2,730.00
Gingerbread House 13,485.00 7,200.00 2,697.00
Spitfire Roasted Chicken 2,585.00 1,440.00 2,585.00
French Onion Soup 425.00 240.00 2,550.00
Triple Berry Cheesecake 1,235.00 720.00 2,470.00
Caramel Apples 195.00 120.00 2,340.00
Homestyle Pot Roast 3,935.00 2,880.00 1,967.50
Vampire Staked Steak 1,695.00 1,440.00 1,695.00
Pumpkin Pie 845.00 720.00 1,690.00

Café Points per Dish

Here are the dishes, sorted by café points per dish per day.

Dish Café Points / Cycle Cycle Time Café Points / Day
Bacon Cheeseburger 7.00 5.00 2,016.00
Chicken Gyro and Fries 14.00 10.00 2,016.00
Chips and Guacamole 4.00 3.00 1,920.00
Powdered French Toast 21.00 20.00 1,512.00
Super Chunk Fruit Salad 14.00 15.00 1,344.00
Jammin’ Jelly Donuts 15.00 20.00 1,080.00
Crème Fraiche Caviar 22.00 30.00 1,056.00
Jumbo Shrimp Cocktail 21.00 30.00 1,008.00
Buttermilk Pancakes 31.00 45.00 992.00
Lemon Butter Lobster 68.00 120.00 816.00
Smoked Salmon Latkes 63.00 120.00 756.00
Shu Mai Dumplings 156.00 360.00 624.00
Lavish Lamb Curry 200.00 480.00 600.00
Fiery Fish Tacos 49.00 120.00 588.00
Atomic Buffalo Wings 68.00 180.00 544.00
Tikka Masala Kabobs 22.00 60.00 528.00
Macaroni and Cheese 41.00 120.00 492.00
Delicious Chocolate Cake 273.00 840.00 468.00
Kung Pao Stir Fry 75.00 240.00 450.00
Savory Stuffed Turkey 403.00 1,320.00 439.64
Caramel Apples 35.00 120.00 420.00
Overstuffed Peppers 206.00 720.00 412.00
Grand Tandoori Chicken 403.00 1,440.00 403.00
Stardust Stew 139.00 540.00 370.67
Tostada de Carne Asada 123.00 480.00 369.00
French Onion Soup 61.00 240.00 366.00
Voodoo Chicken Salad 168.00 720.00 336.00
Tony’s Classic Pizza 68.00 300.00 326.40
Martian Brain Bake 314.00 1,440.00 314.00
Spaghetti and Meatballs 100.00 480.00 300.00
Triple Berry Cheesecake 140.00 720.00 280.00
King Crab Bisque 252.00 1,440.00 252.00
V.I.P. Dinner 175.00 1,080.00 233.33
Herbed Halibut 225.00 1,440.00 225.00
Crackling Peking Duck 166.00 1,080.00 221.33
Gingerbread House 1,063.00 7,200.00 212.60
Spitfire Roasted Chicken 210.00 1,440.00 210.00
Sweet Seasonal Ham 102.00 720.00 204.00
Impossible Quiche 351.00 2,880.00 175.50
Chicken Pot Pie 307.00 2,880.00 153.50
Pumpkin Pie 76.00 720.00 152.00
Homestyle Pot Roast 279.00 2,880.00 139.50
Vampire Staked Steak 113.00 1,440.00 113.00

Real World Hourly Wage per Dish

Here are the dishes, sorted by the real world hourly wage for each dish per day, in US dollars.

Dish $ / Hour (Low) $ / Hour (High)
Gingerbread House 121.35 264.23
Impossible Quiche 91.66 199.57
V.I.P. Dinner 88.07 191.75
Chicken Pot Pie 68.26 148.62
Martian Brain Bake 50.26 109.43
King Crab Bisque 48.33 105.22
Grand Tandoori Chicken 35.86 78.08
Homestyle Pot Roast 35.41 77.10
Herbed Halibut 34.06 74.16
Delicious Chocolate Cake 30.91 67.31
Overstuffed Peppers 26.86 58.49
Savory Stuffed Turkey 25.96 56.53
Crackling Peking Duck 24.16 52.61
Spitfire Roasted Chicken 23.26 50.65
Voodoo Chicken Salad 17.64 38.40
Sweet Seasonal Ham 16.96 36.94
Lavish Lamb Curry 16.06 34.98
Vampire Staked Steak 15.25 33.21
Stardust Stew 13.81 30.08
Tostada de Carne Asada 13.36 29.10
Shu Mai Dumplings 12.19 26.55
Triple Berry Cheesecake 11.11 24.20
Kung Pao Stir Fry 8.86 19.30
Spaghetti and Meatballs 8.19 17.83
Tony’s Classic Pizza 7.96 17.34
Pumpkin Pie 7.60 16.56
Atomic Buffalo Wings 5.35 11.66
Fiery Fish Tacos 4.41 9.60
Lemon Butter Lobster 4.36 9.50
French Onion Soup 3.82 8.33
Smoked Salmon Latkes 3.46 7.54
Macaroni and Cheese 2.20 4.80
Caramel Apples 1.75 3.82
Buttermilk Pancakes 1.21 2.65
Tikka Masala Kabobs 1.17 2.55
Jumbo Shrimp Cocktail 0.61 1.33
Powdered French Toast 0.60 1.31
Jammin’ Jelly Donuts 0.58 1.27
Crème Fraiche Caviar 0.51 1.12
Super Chunk Fruit Salad 0.45 0.98
Chicken Gyro and Fries 0.25 0.55
Bacon Cheeseburger 0.20 0.43
Chips and Guacamole 0.10 0.22

Special Bonus: I’ve now moved my spreadsheet over to this Google Spreadsheet.  Now you can see all the rows of calculation for some insight into Café World Economics.  As usual, let me know if you find mistakes or have questions…


I’ve added the following posts on Café World Economics since this one.