Why Novable?
Novable’s strategic assumption is that companies today don’t take the most out of technology to turn their startup scouting activities faster, easier and much more efficient.
Novable combines two key words for us: “enable” and “innovation” that’s what we do.
novable, adj. /ˈnəʊ.veɪ.bəl/ ● deserving attention because of being very good, interesting or likely to foster or to be turned into innovation. “This startup is definitely novable: it fits into our innovation strategy.”
Innovation is a matter of survival
In today’s fast-paced business world, innovation management has become a matter of survival. The future of corporates lies in their capacity to innovate fast and efficiently. Yet, most of them are static and slowed down by bureaucracy, and can’t afford an internal R&D department. A couple of figures* illustrate this threat:
- 98% of the economy is influenced by modernization
- 50% of current Fortune 500 companies will not exist anymore in 10 years
- 80% of business executives believe that their current business models are at risk
- 84% think that innovation is a crucial factor of their growth strategy
- 82% of corporations believe that it is important to work with startups to innovate
(* Source: McKinsey)
“At least 40% of all businesses will die in the next 10 years… if they don’t figure out how to change their entire company to accommodate new technologies.”
— John Chambers, Cisco Chairman
Startups are a glimpse of the future
Startups are the best future prediction engine ever. Innovation comes from small, agile organizations, from people taking risks and pushing boundaries forward. Startups are disrupting whole industries and changing the business landscape forever, as this article explains. Since 2010, over 21.800 startups have exited worldwide for a total deal value of about 1.2 trillion dollars.
Corporate venturing is on the rise
Corporations have long understood that turning to startups was a mandatory exercise and a major component of their innovation arsenal. We can expect to see more partnerships and collaborations taking place between corporations and startups as 80% of corporates are interested in knowing more about startups and pursuing a professional relationship. Born in the eighties, corporate venturing is now on an unprecedented rise.
Separating the wheat from the chaff
The world counts 472 million entrepreneurs. A new business opens every three seconds. For companies, it is very difficult, tedious and time-consuming to spot the right startups to work with. In big corporations, extended teams can be dedicated to spotting and analyzing startup files. In midsized companies, this work is often done by a single individual who has this task on top of his other duties. It’s either very expensive, or inefficient. In both cases, it’s not optimal.
When it’s not done on a self-service or opportunistic basis, companies use the following three means for their startup scouting activities:
Startup databases -Tools like Crunchbase or Pitchbook are a great data source for accessing a very large number of startups. Downsides: they focus on financials and firmographics (less on actual activity), they retrieve one-size-fits-all results (search is made through keywords and taxonomy), they have lots of errors and outdated data (startups are moving and dying fast), and they don’t prevent you from running your own research behind each result line. They are forward-based tools.
Startup events and ecosystems — Companies host or attend startup events to attract or browse startups in their interest areas. They can also be fed with startups through ecosystems (incubators, accelerators, angel networks, schools). Downsides: reach is limited by design, and it requires loads of energy, money and time.
Consultants — Companies also delegate to specialised consulting firms or scouting consultants, like headhunters for startups, to increase the quality level of startups to analyze. Downsides: manual work is very expensive with a limited reach.
Whatever the way, opportunities are missed. New technologies like machine learning, NLP and the most-recent data science techniques are almost non-existent in that business.
In the startup area (increasingly chaotic, worldwide, too fertile, noisy), a new way of scouting startups is through personalization, context and networks, that can be tapped with artificial intelligence.
That’s what we’re up to.
