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📝 Intro

Today’s study from New York’s Columbia University and Beijing’s Tsinghua University is one of those that made me repeat in my head “Oh man, I love science”.

This research would not have been technologically possible 10 years ago.

Here’s what the researchers did:

1. Took huge samples of startups listed on Crunchbase and public companies in specific categories (e.g. food, consumer electronics).

2. Found the websites of all these companies, in the year each startup was launched (using WaybackMachine, a historical archive of web pages).

3. Scraped the text on these websites and ran them through a natural language processing (NLP) machine learning algorithm (doc2vec).

4. Measured how different the startups’ value propositions were to those of competitors (e.g. focused on a niche product feature vs others that talked about price).

5. Looked at the success of the startups today in terms of funding received and outcome (e.g. IPO, acquisition).

6. Combined the results to see how much differentiation at launch is related to a startup’s success

And here are the results.

Previous insight: The effect of [first name] in email subject lines (100+ more insights here)

Highly differentiated startups raise more funding and are more likely to succeed in the long term

Channels: Marketing strategy | Brand positioning | Business strategy
For: Both B2C and B2B
Research date: June 2022

📈 Recommendation

Strongly differentiate your company positioning from the start. You will be more likely to raise early-stage funding, raise more of it, and succeed in the long-term.

For example, focus on a specific niche in your industry, a new technology, or low price (if competitors aren’t).

Make sure you communicate your distinct value proposition clearly and effectively.

🎓 Findings

• Startups can have a more or less differentiated value proposition from their competitors. For example, a company that sells iPhone cases is not very differentiated. One that sells iPhone cases with a built-in earbud holder is more differentiated.

• Startups that launched with a highly differentiated value proposition (in the top 10%) - compared to those with low differentiation (bottom 10%):

  • Are 10% more likely to raise early-stage funding (e.g. angel funding, pre-seed, seed) and raise 117% more of it

  • Are more likely to ‘exit’ (going public through an IPO, being acquired) in the long-term, but less so in the short-term

    • 19% lower chance in the year of launch

    • Slightly lower chance in the first 6 years

    • 8% higher chance by year 7

    • 28% higher chance by year 10

• Overall, the level of differentiation of startups at launch seems to statistically explain:

  • 30% of how much early-stage funding is received

  • 20% of the probability of ‘exiting’

🧠 Why it works

• Startups and new companies can either try to be similar to existing competitors - to gain legitimacy - or try to be as different as possible - to face less competition.

• When startups are very differentiated they take longer to be seen as credible by larger competitors that might acquire them. However, once they reach that point, it seems that they have better chances of success compared to those that are not very differentiated.

• Note: this study did not focus on the causes of the findings. There are likely other factors involved.

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✋ Limitations

• This study found correlations between differentiation at launch and future startup outcomes, but did not establish strong evidence that one causes the other. There could be other reasons driving this. For example, it could be that experienced entrepreneurs are more likely to launch differentiated startups - and receive better funding and outcomes because of their experience - not because they were very differentiated. That said, previous research does suggest that differentiation has this effect.

• The natural language processing model used for the analysis is still likely imprecise compared to other methods, such as a panel of human judges. What it gains in sheer volume is probably lost in the quality of analysis of how different or similar value propositions actually are.

🏢 Companies using this

• A good example of differentiation is Southwest Airlines, with the slogan “Low fares. Nothing to hide. That’s TransFarency!”, versus Delta Airlines, with the slogan “World’s Most Trusted Airline”.

• Examples of startups in the study found to be highly differentiated at launch:

  • Runteq, which develops wearables that give personalized running advice. It was differentiated compared to competitors in the wider space, such as the device retailer Brookstone, or the medical device company Biozoom

  • Coda Signature, which sells high-end cannabis chocolates and gummies. Compared with the chain Caribou Coffee, or the drinks company Pulse Beverage

• Examples of startups found to be weakly differentiated at launch:

  • CorasWorks, that develops websites using Microsoft Sharepoint technology

  • Olomomo Nut Company, which produces traditional nut-based products

• Of the startups analyzed in the study:

  • 68% received early-stage financing

  • 29% received series A financing

  • 16% were acquired

  • 1.7% had an IPO

⚡ Steps to implement

• Analyze the competitive market you are planning to enter.

• Find an angle, niche, or specific value proposition you bring to the table that is different from competitors.

• Once you’ve decided your positioning, use this method to most efficiently find product-market fit.

🔍 Study type

Market observation (analysis of the websites of 12,406 startups listed on Crunchbase, founded between 2003 and 2019, and their publicly listed competitors at the time of launch)

The authors made all the data and Python code used in this study publicly available on Github.

📖 Research

Measuring Founding Strategy. Management Science (June 2022).

🏫 Researchers

• Jorge Guzman. Columbia University

• Aishen Li. Tsinghua University

Remember: This is a new scientific discovery. In the future it will probably be better understood and could even be proven wrong (that’s how science works). It may also not be generalizable to your situation. If it’s a risky change, always test it on a small scale before rolling it out widely.

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