Use Cases
Anonymized Example - G100 Customer
Discovering an unknown target.
Without Centrly
You’re an automotive company looking for a new battery provider. Pitchbook returns 10k battery companies. Which ones do you prioritize?
Centrly Data & Insights
Partners of known targets have a high chance of also being good targets, e.g. InoBat has joint ventures & other relations with 2 other battery companies - Wildcat & Group14. Centrly easily shows this via mapping 2nd degree public relationships.
Conclusion
We surfaced 3 unknown companies in a highly commoditized space where the customer challenged us to find a company they didn’t know. Acquisitions take time & are private, so to be seen regarding the final result.
Value
Missing on high-value but “hidden” companies may mean you’re missing out on a great opportunity (that’s also invisible to your competitors).
Anonymized Example – F100 Customer
Choosing a different acquisition target.
Without Centrly
Traditionally rely on financial (amount raised, valuation) metrics, where what matters is the specific technology, traction & strategic complementarity. You rank sort incorrectly, wasting time on the “brand names.”
Centrly Data & Insights
Alternative relationship data - pilots, customers, partners - and deeper taxonomy labeling for differentiation. In this case, weaker traction was a signal of likely lower buying price & desire to sell. They went for it.
Conclusion
Centrly data & ranking supported the team’s outreach & negotiation. They ended up acquiring the company.
Value
May have missed the target altogether or not known how best to pursue, but had clearer leverage given how they stack-ranked vs. others in this space.
Anonymized Example - F100 Customer
Finding an entry point into a new market.
Without Centrly
Many energy players are waiting to see when there will be sufficient demand for hydrogen fuel. The customer struggled to understand what was “real” traction given news reports, as there was a lot of “fluff” and hard to coalesce.
Centrly Data & Insights
Centrly mapped the relationships for the region of interest, showing customer & partner relationships of end users, which turned out to be many trucking companies, showing momentum building up in the last 3 years. Image shown is a report generated with Centrly data on the platform.
Conclusion
Relationship mapping revealed end-use, confirming opportunity in this region for hydrogen, especially with specific trucking companies as customers.
Value
The customer was able to see their competitors’ activity in this space, which was minimal, and generate a list of companies to reach out to as various value chain partners.