From market invisibility to credibility in enterprise conversations.

The product was growing. Market recognition had not caught up.

cloudControl had built a developer-focused, multi-language Platform as a Service and was expanding its ambitions beyond start-ups, SMEs and developers. Its white-label PaaS model opened conversations with telcos, hosting companies and infrastructure providers that wanted to offer a broader cloud stack.

As those conversations became larger and more corporate, analyst visibility started to matter. Prospects were asking what industry analysts thought of the company. In one case, the response was blunt: a prospect had looked in the Gartner database and could not find cloudControl.

For the leadership team, that made Analyst Relations a business issue rather than a communications exercise. The technology story existed. The market needed credible third parties to know it.

Remove a credibility roadblock before the next big conversation.

The immediate trigger was a partnership discussion with a large European telecom provider. Analyst opinion carried weight in that conversation, and cloudControl did not want the same lack of recognition to become a recurring obstacle.

The wider objective was to make cloudControl visible to the analysts advising enterprise buyers and partners, support entry into larger accounts and strengthen conversations around white-label PaaS, geographic expansion and business growth.

That required more than being mentioned. Analysts needed to understand how cloudControl positioned its public PaaS, why its white-label model mattered to service providers and what differentiated its technology and operating experience.

Shorten a learning curve cloudControl did not need to climb alone.

Founder and CEO Philipp Strube was building his first company and had no prior experience of the analyst market. His original assumption was that if a company built something interesting, analysts would discover it. He quickly learned that the process was far more deliberate.

Kea gave cloudControl a structured way into that market: identifying relevant analysts, explaining their research interests, helping prepare the story and briefing materials, arranging the conversations and reviewing what came back.

That allowed the leadership team to focus on explaining the company, product and technology while Kea handled the preparation, coordination and follow-through around each interaction.

Turn a technical platform story into analyst-ready evidence.

The programme centred on making cloudControl easier to understand: a public PaaS built for developers, a white-label model for service providers, and a technology position based on multi-language support, open buildpacks, scalability and operational experience.

Kea worked with the team to sharpen that story, connect it to the analysts covering the market and improve the material after each round of feedback.

1. Map the analyst landscape

   Identify the analysts covering cloud platforms, application development and adjacent markets across major research firms, rather than relying on a closed list or a single firm.

2. Shape the message

   Help cloudControl explain the public PaaS and white-label proposition in terms analysts could evaluate, while keeping the technical evidence behind the claims.

3. Prepare the founder

   Brief Philipp on the analyst, the relevance of cloudControl to their research and the emphasis required for each conversation, then let him focus on explaining the business.

4. Keep the story current

   Use follow-up and regular updates to bring new partnerships, business progress and company developments back into the analyst conversation.

The founder stayed focused on the part only he could do.

For cloudControl, one of the practical benefits was reducing the amount of time leadership had to spend learning the mechanics of Analyst Relations. Kea handled the research, preparation and coordination around each conversation.

Kea cut that learning curve dramatically. Philipp estimated that building the same AR capability internally would have taken at least twice as long, with far more trial and error.