On why and how are innovation scouting analysts slowly killing themselves on repetitive tasks — What can we do about that? — A glimmer of hope.
For a corporate venturing professional (in M&A, corporate development or CVC, or a head of innovation), most of the effort is focused on disqualifying bad startup candidates. As with VCs, less than 1% of applicants (or potential matches) will end up with an engagement option.
We call it the SAD syndrome, as a three-step fatalism: Spot — Analyse — Drop. Thousands of hours wasted in vain. Too bad. SAD. Spot. Analyse. Drop. Repeat.
SAD is not a fatality. AI can take on most of that work. It is able to read, vectorise, understand, score — and match. Our estimation is that the smart use of the latest technologies can reduce analysis time by 80%, and reduce the closing time by half because of time compression in the exploratory phases.
How can we do that?
First. Personalisation and continuous improvement
Every company follows a different strategy and engages in very specific innovation programmes. To some extent, that strategy is the company’s DNA that builds its uniqueness, and therefore establishes its competitive position within the market. Yet startup databases would retrieve the same results from a similar input. This is why Novable believes in dedicated search models that are able to adapt themselves progressively to deliver the most relevant startups. Our technology is built upon machine-learning techniques that improve each model with time; realistically, this is actually the only way to make the most out of the potential of artificial intelligence.
Second. Network science
Any business lives within an environment, with connections being like the air that sustains them. Customers, partners, employees, investors and stakeholders combine to create a network that can be scrutinised by network science techniques. Understanding the links between companies and people helps us to find hidden gems, lookalikes, opportunities and threats that are not usually available in structured databases.
Third. Context-based matching
Startup databases focus on financial data and firmographics, but information about the startup’s actual activity is both brief and rare, and usually rather explicit (as it is often submitted by the startups themselves). However, corporate strategies need to be fed with content, updates, active information, accurate product descriptions, and contextual data around each startup. That’s why Novable starts by collating this live content using a bottom-up approach: crawling websites, social profiles and news feeds, and linking innovation-related data points like patents and scientific publications. Contextual intelligence holds the key to improving the matchmaking relevancy.
Fourth. Lean-back service
Delegating research to consultants or browsing a database are lean-forward activities. They require an active involvement and specific skills, and they are time-consuming. The Novable approach slides the majority of the effort required to the computer under a Pareto-like rule: 80% of the tasks are delegated to the machine, leaving the professionals free to tackle the most productive parts of the work.