Developing the first market entry for a seed-stage startup

Approach:

ThermalCyclones is a seed-stage deeptech climate startup based in London. They are backed by Norrsken Launcher and the winner of the H&M Foundation Global Change Award. They create industrial heat pumps designed to decarbonize manufacturing operations.

My goal was to develop an analysis for first market entry by identifying and prioritizing European industrial sites with strong potential for adoption of high-temperature heat-pump technologies based on thermal profiles, operational characteristics, and feasibility constraints.

Step 1:

First, I reviewed the top criteria for a target customer. This included:

• Sustainability commitments

• The company's revenue is large enough to purchase our product

• History of heat pump installation/operational upgrades

• Operations at a manufacturing site

• Steam Temperature

• Steam kg/hr

• Steam kWt

• Heat Temperature

• Cooling Supply

• Cooling Return

• Cooling kWt

• Waste Heat Temperature

• Operational Hours/Day

This information is helpful to calculate the Coefficient of Performance (the efficiency of our heat pump), how many hours a company would need to use our device, whether it’s compatible with its manufacturing process, and the spark gap to determine the unit economics of how much money this could save a company.

Step 2:

Then, I researched companies in our target geography of Western Europe that operated within known steam manufacturing industries such as textiles, brewing, and food production. I created a list of over 200 companies, their sustainability initiatives, an overview, their revenues, and potential use cases.

Step 3:

From there, the list was narrowed down to companies in the most ideal sectors. I collected all the site-level data for these companies. I collected this data using public company manufacturing information from annual reports, supply chain profiles, and made estimates based on sectoral engineering averages for data that wasn’t available.

Step 4:

After that, I built a ratings system of 1-5 (5 being ideal) to determine the order to approach a potential customer. This was built through determining the most necessary and ideal thermodynamic values, as well as criteria that make a factory site ineligible. I created an Excel formula to help my spreadsheet automatically calculate the ratings system and use conditional formatting to easily visualize this.

Step 5:

I compiled the data collected about manufacturing sites into Python and used the pandas library along with other tools to visualize different sites, such as mapping sites located in the United Kingdom, mapping sites located in Western Europe, and mapping sites in a heat pump by rating level.

Outcome:

I was able to identify the top 20 highest-fit sites across food and beverage, dairy, and brewing. This surfaced several thermal sites with strong electrification feasibility and supportive corporate sustainability strategies. This helped to provide actionable market-entry recommendations, enabling rapid business development targeting.