Even though the iPad is still more than a month away from shipping, iSuppli conducted a preliminary itemized parts breakdown. The results aren’t that surprising: Apple’s making a boatload on these things. Suppli concluded that the $499 16GB/no 3G model only costs $229 to manufacturer with the $829 64GB/3G model costing only $117 more to make even though it carries a $329 premium. Nice, eh?

These numbers can be broken down even further showing Apple’s insane margins. The 3G module only costs $24.50, but Apple charges $129 more for the option. The NAND memory chips are really the only difference between all three options, but their real costs of $29 for 16GB, $59 for $32GB, and $119 for 64GB are nowhere near proportionate with the iPad’s prices. All this data shows that Apple’s abandoning its long-held K.I.S.S. strategy.

So what if Apple got back on the keeping it simple bandwagon, only offered the high-end 64GB with 3G iPad and still sold it for $499? After all, the company would still be making at least $153 on each iPad sold. Would that turn around the iPad’s outlook?

Read the rest of this story at CrunchGear…

 What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499

 What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499
 What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499

 What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499  What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499

 What If…Apple Only Offered the 64GB/3G iPad and Sold It For $499

 iPhone App Sales, Exposed

This guest post was written by Alex Ahlund, the former CEO of AppVee and AndroidApps, which was recently acquired by mobile app directory Appolicious. He is currently an advisor to Appolicious.

One of the most commonly asked questions we get from both developers and industry outsiders is: how much money can I make developing apps? It’s a hard question to answer.

So we decided to conduct a survey. We asked for sale sdata from 124 developers that market applications ranging in price from 99 cents to $79.99. This survey was conducted on apps that ran the gamut of popularity, from wildly successful to barely breaking three figures. Developers were anywhere from funded companies with multiple titles under their belt, to first time, single-person authors. Both regular app developers, as well as game developers were included. This mining of data was intended to cover the entire iPhone app industry as a whole, without allowing outliers to skew the data too much in one direction.

There are many different metrics that must be taken into account – just because product X sold well does not mean product Y will. As a longtime publisher of app reviews, I’ve always been a bit apprehensive about sharing cold, hard statistics because of this issue. Taken as a precise gauge for future iPhone apps, statistics can be completely misleading. Therefore, I strongly encourage you to interpret this information only as an overview of the industry, which, like any others, has its blockbusters, stragglers and everything in between.

The following financial information is pulled from 96 developers who provided in-depth sales data and pricing metrics.

The average total number of units sold was 101,024 copies within an average period of 261 days. The average number of units sold per day was 387. The average price was $5.49, although the data skews due to the $49.99 outlier. In most cases, the price point was $0.99. The average number of updates released was 3.89, with the average total development cost amounting to $6,453. Several developers omitted development costs and most did not include their personal time in these figures. It is safe to assume the cost would be at least five or ten times more when using a contracted team. But on average here, iPhone developers are seeing a return of more than 15 times their initial, albeit small, development costs.

Market success still top-heavy

However, when the top 10% of the most successful apps are removed from the data set, the numbers skew much lower, giving a far better impression of what the iPhone industry looks like for most developers. In this scenario, the average sales were 11,625 total units, averaging 44 copies/day. Approximately 23% of apps sold less than 1000 units from launch (ranging from 12 to 370 days in the App Store). Further, 56% of apps sold less than or equal to 10,000 units, while 90% sold less than 100,000 units, with the remaining 10% achieving sales of 127,000 – 3,000,000 units.

While industry wisdom states that application updates always boost downloads and sales, Apple has changed how updated apps are given exposure and this now doesn’t quite hold true. Some developers reported that updating the app gave only a small—and brief—spike in downloads. What did seem to have a larger impact on sales was a drop in price, although this also tended to taper off quickly.

Being featured by Apple is the greatest contributor to spiking sales. The level of Apple
promotion, as expected, reflected what sort of increase the developer would see. Areas such as “New and Noteworthy” produced slightly less gains than “Staff Favorites” or “What’s Hot.” Generally speaking, it is safe to assume a 2-20X sales spike following being featured, with the effect lasting roughly a week or so before returning to average numbers. The key here is to use this dramatic spike to propel the app onto a top list—be it the universal top 100 or in a top list for a specific section or country. Once there, the app has a much better chance of moving up and reaching a higher plateau of sales.

From a marketing perspective, the same tactic could be applied. While not all apps have the likelihood of being featured, focusing promotional efforts within a tight timeframe can be the key. Instead of spreading out marketing and advertising over the life of a product, focusing efforts into a narrow window (preferably, in terms of days) can be much more effective in getting the app onto a top list.

Now, let’s take a look at specific applications. I encourage examining the apps themselves to understand what exactly went into them. The production values, complexity, niche, and pricing determine why they produced either excellent or paltry sales results. The following list reflects 50 applications from the data set that covers the range of sales:

App Name Total Sales Days in Market App Price
Xpong 20 210 0.99
ShingleNav 28 156 4.99
Fumbers 62 40 1.99
Greenthumb! 87 231 1.99
FastTrac 199 60 4.99
splojit 217 238 0.99
Size Convert 354 210 0.99
Handbook of High-Risk Obstetrics 436 210 49.99
Traveler’s Quest 532 97 2.99
Cougar Call 800 229 0.99
Seasonalysis 1000 200 49.99
The Power of Now, by Eckhart Tolle 1179 223 13.99
Star Ride 1200 270 2.99
Star Fusion 1323 217 0.99
Germs 1465 102 0.99
iWasted 1500 201 0.99
Silly Songz 2000 365 0.99
School timetable 3648 395 0.99
Pi Cubed 3775 316 9.99
CardSnap 4690 342 14.99
Adaptunes 4754 272 0.99
Theme Park Madness 4788 367 2.99
Birthday Reminder 10000 250 1.99
Craigly 10000 400 0.99
EleMints 10224 505 4.99
Gridlocked: Traffic Control 12500 270 0.99
MeetMe. 15000 180 0.99
MicroCars 16613 230 1.99
Green Screen Studio 17025 210 2.99
NineGaps 18120 278 0.99
Distant Suns 20000 450 6.99
Numerology 34905 518 4.99
iEscaper! -Escape From the Ninja’s Lair- 35000 215 2.99
TapFormsDatabase 35100 517 8.99
A Doodle Flight 38000 225 0.99
Mini Touch Golf 40000 596 0.99
Art Envi 40000 580 0.99
Mover+ 46000 195 2.99
Orbital 50000 180 1.99
Scanner Pro 52514 143 6.99
Movie Challenge 53402 475 1.99
Formula Racing 127483 127 0.99
Stitch’em Words 200749 353 1.99
Air Hockey 300000 578 0.99
Finger Physics 418000 155 0.99
Fling! 500000 205 0.99
Moto X Mayhem 800000 218 0.99
PocketGuitar 1300000 530 0.99
Flight Control 2000000 361 0.99
Bejeweled 2 3000000 600 2.99

Common marketing techniques include Facebook, forum posts, Twitter, own website, press releases, LinkedIn, app Review sites, blogs, friends, contests, YouTube, advertising (Print, PPC, and banners), flyers, newsletters, Flash Demos, physical networking and podcasts. While each of these methods helped developers in some ways, the real marketing power to make or break an app product rested in the hands of Apple and their selection choices. Apps with successful products in other industries (tie-ins) gained a significant boost from that relationship. The same held true for developers with a known presence already on the web.

The iPhone app market is something that is still in its infancy when one considers what it will look like only a few years from now. Although we are at more than 200,000 apps released, one million doesn’t seem so far fetched given the rate of growth thus far. These sales analytics should offer a starting point for understanding the general landscape, but are not necessarily indicative of one’s own app success. We’ve seen apps made in a weekend earn millions and apps taking months or more earning next to nothing. Developers can either find a niche and get extremely lucky, or produce a fantastic product with high production values. In the end, the latter is the safer route to success. Time to get crackin’…

 iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed

 iPhone App Sales, Exposed
 iPhone App Sales, Exposed

 iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed  iPhone App Sales, Exposed

 iPhone App Sales, Exposed

This guest post is written by Marcelo Calbucci, the founder and CTO of Sampa — a personal homepage creator that will be shutting down next month. He’s writing a series of posts about the lessons learned from the venture at http://blog.calbucci.com. He’s also the publisher of Seattle 2.0, a web resource for tech entrepreneurs and startups in Seattle.

Consumer startups are tough. You have two basic choices: A paid offering or a free offering (or freemium). If you charge people a penny, you’ll turn off the bulk of your visitors. If you offer free services, you might grow to be the next YouTube, WordPress or Facebook. Most entrepreneurs are not risk-averse and the dream of being big is just too appealing and the majority of us take the “free-route”.

Once you offer something for free, all shades of people will try to benefit from your service. You’d think a service like Sampa with a strong family and baby branding would just repel small business, teenagers, criminals, etc. but that’s not the case at all. And I suspect most blogging services; photo-sharing or web-site building solutions face the exact same issue we did.

Most entrepreneurs and investors will look at data analysis and talk about averages or totals: Averages number of blog posts per user per week, average number of sign-ins per user per month, viral coefficient, total number of active users, etc. Entrepreneurs who are more sophisticated will split their “averages” and “totals” in two or three groups. For example, fixing one of the dimensions into users that sign-in 30 or more times per month (very engaged), between 10 and 29 times per month (engaged), and between 0-9 times per month (on the brink of leaving) and then run the averages and totals for the different groups (e.g. “very engaged users upload 25 pictures/month, engaged users upload 7 pictures/month, etc.”)

Very few startups actually look at demographic and psychographic data as a way to group their users. Primarily, because it’s hard to get gender, age, income, interests and intentions without asking the user, and once you ask them you might just scare them way or get the wrong information.

One time we went to pitch Sampa to a VC in Seattle, and out of the blue he mentions this other startup growing amazingly fast – had nothing to do with our business. After the meeting I went to check the startup website. Their Compete and Alexa growth was just amazing. Their website contained profiles of all users since it was a public social network. So I clicked on the profile of the 20 people featured on their homepage (“most recent users to join”). Of those, about 75% were girls between the age of 9 and 13 – likely the worst demographic to make any revenue from.

Did the startup know about this? Oh, yeah. Did that VC that was looking at investing on them? Likely not.

In the middle of 2008 we decide to do a qualitative analysis of our user base. People of all kinds were creating sites on Sampa. There wasn’t an automated way to know if it was a baby site, a family site, a small business, a technology blog, etc. We looked at more than 300 sites, randomly selected and created a spreadsheet with the category, the demographic of the author (if we could figure out) and we plugged that into our own analytic system to split our averages and totals for each site category. The results sucked!

Just 20% of our users were on the target audience. That meant 80% were not building any kind of family or baby site. Ok, maybe we can live with that. But it turned out that more than 25% were by pre-teens. There are two problems with that: First, It’s actually illegal in the US and most countries to allow a younger than 13-year-old to sign up to your service without parental consent. Second, pre-teens are not a great audience to build an advertising-based business model.

However the data showed an even worse picture. Pre-teens were a quick burning flame. They would come, upload lots of pictures, write lots of blog posts, “bling” their site, invite 20+ friends and they would be completely gone in a month. That behavior skewed our data enough that once we looked at our growth, viral rates, and everything else, our business didn’t look so great.

Being Proactive Can Backfire

Can you force uses to comply with your Terms-Of-Service and still be successful on a UGC service? Yes, you can. Facebook manage to be very aggressive on the enforcement of their TOS, and so did Flickr. However, if you look at most Web 2.0 startups, they are not doing that at all. The most prominent case is YouTube, which allowed copyright infringement on their website and can plot a $1.6B exit based on their “turn a blind eye” strategy.

We didn’t do that at Sampa, and I’m sure we could have seen 2 or 3 times more growth if we had used the same strategy. We proactively removed pre-teens websites. They weren’t easy to find, but every time we found one, we would remove the website and notify the owner she was 12-years-old. They would be mad at us and tell that “Jamie, Emily and Sally also have a website on Sampa”, and we would say thank you and delete all their friends websites too.

We would also proactively delete porn websites. There is nothing wrong with porn. It’s not illegal or immoral in my view, but it didn’t go well with our family-oriented business proposition. Also, most UGC porn sites are infringing in someone else copyright and we just didn’t want to deal with DMCA or lawyers.

We also found criminal websites, from people trying to steal credit-card and passwords to the ugly side of online pedophilia. We had the FBI come over twice to collect evidence.

And let’s not forget link-farms. Although we had CAPTCHA and email confirmation for new websites, every once in a while someone managed to create dozens of websites in a single day all full of links to some bank, real estate agent, mortgage broker, auto dealer, etc. I’m sure the business that were benefiting from it didn’t know they hired a “black-hat” SEO.

Pretty much every Social Network-builder, website builder or content sharing site deals with the same issues we dealt with. A good number of entrepreneurs (and most investors) will be oblivious to those facts and just think that everything is going great and the growth is sustainable and proof they are creating great value and soon will be able to turn a huge profit or to sell for hundreds of millions of dollars, until someone takes the time to figure out what people are using their service for and finds out it’s really not what they thought it was.

Crunch Network: CrunchBoard because it’s time for you to find a new Job2.0

 The Little Secret of Web Startups
 The Little Secret of Web Startups

 The Little Secret of Web Startups  The Little Secret of Web Startups  The Little Secret of Web Startups  The Little Secret of Web Startups  The Little Secret of Web Startups

 The Little Secret of Web Startups

With more than four million users, Mint’s personal finance platform no doubt has a massive amount of data that it can mine regarding consumer’s spending habits. In fact, the site already uses some of this data to show seasonal trends. Today, the Intuit owned company is launching its realtime customer data insights to the public, after soft launching the product almost two months ago.

Mint Data aggregates anonymous spending data from Mint’s users to give you realtime insight on what people are spending on across the country. For example, the platform lists the most popular restaurants in San Francisco (by visits), the top shopping spots in New York City (by highest average spend), and the highest spending cities in the U.S.

Mint Data will also show spending data both by average purchase price and by popularity, which is defined by number of transactions per month. The rankings can be viewed by category, such as “food and dining,” by specific business, and broken down to the city level. For example, Mint Data shows that the average spend at a Starbucks in New York City is $5.38. The site also compares this to the average spend at coffee shops overall, which is slightly higher.

In terms of actual regional data, you can choose from 300 cities in the U.S. to compare spending. And Mint.com users can compare their own personal finance and spending habits by category or merchant against averages in their area, or against the national average.

As a consumer product, this data is pretty fascinating, and a great way to get a little more insight as to how your spending stacks up against the rest of the consumers in your city or at a particular store. I can imagine that that some of this data could be mined even deeper to compare demographics and spending.

Information provided by CrunchBase

 Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...

 Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...
 Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...

 Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...  Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...

 Mint Data Delivers A View Into The Spending Habits Of Its 4 Million...

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