The SAD syndrome

Why and how are innovation scouting analysts slowly killing themselves on repetitive tasks and what can be done about it?

For a corporate venturing professional (in M&A, corporate development/CVC, or a head of innovation), most of the effort goes into 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, standing for: Spot — Analyse — Drop. Thousands of hours wasted in vain, with minimal progress made. Spot, analyse, drop, repeat. SAD

Although SAD poses quite an obstacle, it can be overcome. Thanks to the newly developed AI tools, they are the ones who can take on the tedious work. AI is able to read, 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.

Why and how are innovation scouting analysts slowly killing themselves on repetitive tasks and what can be done about it? For a corporate venturing professional (in M&A, corporate development/CVC, or a head of innovation), most of the effort goes into 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, standing for: Spot — Analyse — Drop. Thousands of hours wasted in vain, with minimal progress made. Spot, analyse, drop, repeat. Although SAD poses quite an obstacle, it can be overcome. Thanks to the newly developed AI tools, they are the ones who can take on the tedious work. AI can read, understand, score — and match. We estimate that the smart use of the latest technologies can reduce analysis time by 80%, and reduce the closing time by half.

1. 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.
2. Network science. Any business lives within an environment, connections being the air sustaining 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 find hidden gems, lookalikes, opportunities, and threats not available in structured databases.
3. Context-based matching. Startup databases focus on financial data and firmographics. 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 matchmaking relevancy.
4. Lean-back service. Delegating research to consultants or browsing a database are lean-forward activities. They require 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.