{"id":990,"date":"2011-09-06T13:59:22","date_gmt":"2011-09-06T19:59:22","guid":{"rendered":"http:\/\/maxmarmer.com\/?p=990"},"modified":"2016-01-29T20:29:13","modified_gmt":"2016-01-30T02:29:13","slug":"a-deep-dive-into-the-anatomy-of-premature-scaling-new-infographic","status":"publish","type":"post","link":"https:\/\/www.maxmarmer.com\/blog\/a-deep-dive-into-the-anatomy-of-premature-scaling-new-infographic\/","title":{"rendered":"A Deep Dive Into The Anatomy Of Premature Scaling [New Infographic]"},"content":{"rendered":"

(Reposted from the\u00a0Startup Genome Blog<\/a>)<\/em><\/p>\n

Three days ago we launched the Startup Genome Compass<\/a>, a benchmarking tool for startups and our new research on the primary cause of failure for startups: premature scaling<\/a>.<\/p>\n

There’s been some confusion about exactly what we mean by premature scaling<\/strong> and we wanted to respond to the feedback we’ve received and elaborate on the findings from our research. To make it clearer, we need to go a little bit deeper into the theory and methodology.<\/p>\n

Since February we’ve amassed a dataset of over 3200 high growth technology startups. Our latest research found that the primary cause of failure is premature scaling, an affliction that 70% of startups in our dataset possess.<\/div>\n
The difference in performance between startups that scale prematurely and startups that\u00a0 scale properly is pretty striking. We found that:<\/div>\n

 <\/p>\n

\u00a0– No startup that scaled prematurely passed the 100,000 user mark.<\/div>\n
\u00a0– 93% of startups that scale prematurely never break the $100k revenue per month threshold.<\/div>\n
\u00a0– Startups that scale properly grow about 20 times faster than startups that scale prematurely.<\/div>\n

 <\/p>\n

What Is A Startup?<\/strong><\/div>\n

 <\/p>\n

Definition:<\/em><\/div>\n

 <\/p>\n

Startups are temporary organizations that are designed to evolve into large companies. They move through 6 stages of development throughout their lifecycle: Discovery, Validation, Efficiency, Scale, Sustain & Conservation<\/strong>. Early stage startups are designed to search for product\/market fit under conditions of extreme uncertainty. Late stage startups are designed to search for a repeatable and scalable business model and then scale into large companies designed to execute under conditions of high certainty.\u00a0<\/em><\/div>\n

 <\/p>\n

Every startup has an actual stage<\/strong> and a behavioral stage<\/strong>. Actual stage is measured by customer response to a product. We measure it by looking at metrics like numbers of users, user growth, activation rate, retention rate and revenue. The behavioral stage is made up 5 top level dimensions that the startup can control. The 5 dimensions are Customer, Product, Team, Financials and Business Model<\/strong>. Each dimension, both the actual and the 5 behavioral dimensions are always classified into one of the 6 developmental stages.<\/div>\n

 <\/p>\n

A startup is classified as inconsistent<\/strong> when any behavioral dimension is at a stage that is different than the actual stage. When a behavioral dimension is at a stage larger than the actual stage we call this premature scaling<\/strong>. Its lesser known sibling, dysfunctional scaling<\/strong>, occurs when the stage of a behavioral dimenion is smaller than the actual stage.<\/div>\n

 <\/p>\n

A clear example of premature scaling would be a web startup that rapidly scales up its team to 30-40 people before it has any customers. In this example, the actual stage of the startup would be in Validation<\/strong> (Stage 2) but the behavioral stage of the team would be in Scale<\/strong> (Stage 4).<\/div>\n

 <\/p>\n

Let’s go through some more examples and stats for how each dimension can be scaled prematurely.<\/div>\n

 <\/p>\n

Customer:<\/strong><\/div>\n

 <\/p>\n

How to scale customer dimension prematurely:<\/em> Spending too much on customer acquisition before product\/ market fit<\/div>\n
Overcompensating missing product\/market fit with marketing and press<\/div>\n
Spending money in poor performing acquisition channels.<\/div>\n
Stats:<\/em> Inconsistent startups are 2.3 times more likely to spend more than one standard deviation above the average on customer acquisition.<\/div>\n
Examples of startups that prematurely scaled on the customer dimension:<\/em> Color, Webvan, Pets.com<\/div>\n

 <\/p>\n

Product:<\/strong><\/div>\n
How to scale product dimension prematurely:<\/em> Building a product without having validated problem\/solution fit, Investing into scalability of the product before product\/<\/div>\n
market fit,\u00a0 Adding lots of \u201cnice to have\u201d features<\/div>\n
Stats:<\/em> Inconsistent startups write 3.4 times more lines of code in the discovery phase and 2.25 times more code in efficiency stage. Inconsistent startup outsource 4-5 times as much of their product development than consistent startups.<\/div>\n
In discovery phase 60% of inconsistent startups focus on validating a product and 80% of consistent startups focus on discovering a problem space. In the validation phase, where startups should be testing demand for a functional product, inconsistent startups are 2.2 times more likely to be focused on streamlining the product and making their customer acquisition process more efficient than consistent startups. It’s widely believed amongst startup thought leaders, that successful startups succeed because they are good searchers and failed startups achieve failure by efficiently executing the irrelevant.<\/div>\n
Examples of startups that prematurely scaled the product dimension:<\/em> Cuil, Webvan, Joost, Google Wave, Slide, 6Apart,\u00a0most startups that don’t find product market fit or “build something nobody wants”.<\/div>\n

 <\/p>\n

Team:<\/strong><\/div>\n
How to scale team dimension prematurely:<\/em> Hiring too many people too early, Hiring specialists before they are critical: CFO\u2019s, Customer Service Reps, Specialized Network\/System Adminstrators or Database specialists, etc., Adopting multilevel management hierarchy, hiring managers (VPs, product managers, etc.) instead of doers, Having more than 1 level of hierarchy,<\/div>\n
Stats:<\/em> The team size of startups that scale prematurely is 3 times bigger than the consistent startups at the same stage. However startups that scale properly end up having a team size that is 38% bigger at the\u00a0initial\u00a0scale stage than prematurely scaled startups, and almost surely continue to grow. Startups that scale properly take 76% longer to scale to their team size than startups that scale prematurely.<\/div>\n
Examples of startups that prematurely scaled the the fundraising dimension:<\/em> Webvan, Pets.com, VOX.com.<\/div>\n

 <\/p>\n

Financials:<\/strong><\/div>\n
How to scale fundraising dimension prematurely:<\/em> Raising too much money, thereby making the startup undisciplined, giving lots of breathing room for other dimensions to scale prematurely, and eliminating exit optionality.<\/div>\n
Stats:<\/em> Before scaling, funded inconsistent startups are on average valued twice as much as consistent startup and raise about three times as much money.<\/div>\n
Examples of startups that prematurely scaled the the fundraising dimension:<\/em> Cuil, Webvan, Color.<\/div>\n

 <\/p>\n

Business Model:<\/strong><\/div>\n
How to scale business model prematurely:<\/em> Focusing too much on profit maximization too early, Over-planning, executing without a regular feedback loop, Not adapting business model to a changing market, Failing to focus on the business model and finding out that\u00a0you can\u2019t get costs lower than revenue at scale.<\/div>\n
Stats:<\/em> Inconsistent startups monetize 0.5 to 3 times as many of their customers early on.<\/div>\n
Examples of startups that prematurely scaled the business model dimension:<\/em> Myspace,\u00a0 Groupon (time shall tell), 6Apart, Lala.<\/div>\n

 <\/p>\n

The focus of this post is on premature scaling, but for context, here are a few example of dysfunctional scaling: Tokbox, Friendster, Orkut, Wesabe, Digg, SixApart, Myspace (on product), and ChatRoulette.<\/div>\n

 <\/p>\n

In our research we also found that the following attributes have no influence on whether a company is more likely to scale prematurely: market size, product release cycles, education levels, gender, time that cofounders knew each other, entrepreneurial experience, age, number of products, type of tools to track metrics and location.<\/div>\n

 <\/p>\n

Now to further illustrate how we describe startups let’s look at an example mapped onto the Startup Lifecycle Canvas<\/strong>.<\/div>\n

 <\/p>\n

Below we have an infographic where we plot Color, today’s most talked about inconsistent startup, against Rally, a startup we worked closely with while building out the model, that was consistently in the Efficiency stage 2 months ago when they made this\u00a0announcement<\/a>. Although now I’m happy to say they’re starting to scale.<\/div>\n

 <\/p>\n

To view the infographic in full, scroll to the bottom of the image and select “download full size”. If you’re having trouble reading the infographic you can download it here<\/a>.<\/em><\/div>\n
\"\"<\/div>\n

 <\/p>\n

You can read more about premature scaling in our full report here<\/a>.\u00a0And you can also assess your own startup for premature scaling with our tool the Startup Genome Compass<\/a>, which we released on Monday.<\/div>\n

 <\/p>\n

This post doesn’t discuss how different types of startups<\/a> vary thru the developmental stages. That’s for another time.<\/em><\/div>\n","protected":false},"excerpt":{"rendered":"

(Reposted from the\u00a0Startup Genome Blog) Three days ago we launched the Startup Genome Compass, a benchmarking tool for startups and our new research on the primary cause of failure for startups: premature scaling. 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