Ben, the CEO of a mid-sized investment firm, was keen on expanding their portfolio into the rapidly growing climate technology sector. Aware of the immense potential of investments in firms in the space, but uncertain about where to focus initial efforts, Ben needed precise market data to make informed decisions.
He turned to Olympus Intel to simplify this complex task. He started by using the AI-powered market segment selector. He quickly described the investment sector he was interested in and was surprised to see how thoroughly the platform returned relevant market segments, including:
Though Ben would have included the more obvious markets himself, such as solar and wind generation, he might have left out potential customers like semiconductor manufacturing companies, as he did not consider their overlap with solar cell devices. Already, Ben was thankful for the platform’s generation of a more accurate market landscape.
Proceeding through the platform with these chosen segments, Ben inputs revenue estimates based on the business models of companies his firm has researched. His interest was less about validating data he had seen from any one company and more about understanding what types of opportunities could be worth exploring in the climate tech space. This would give him some grounding in what the sector looks like and be useful for validating information from other sources in the future. These hypothetical revenue models consisted of:
Olympus Intel then displayed Ben’s first round of data, showing the Total Market Size (by segment) and Total Addressable Market (by segment)
Already Ben had new insights; discovering though Power and Communication Line and Related Structures Construction was the largest portion of the total market, generating over $65 billion annually, it fell to the number two spot when it came to the TAM at a little over $1 billion in potential revenue. This is likely because there were fewer but larger companies in this segment, meaning an enterprise-wide selling model would result in less addressable units, something to be aware of when evaluating potential investments.
Looking at the details table, he learned there are over 8,500 firms in the US that deal with environmental consulting. Those firms represent a smaller portion of the total revenue generated in this segment but a large number of firms. This meant companies offering subscription services or platform based solutions would have ample selling opportunities, but would also need to be more cognizant of pricing, given the lower overall revenues for these types of companies. There was a lot more to unpack that would inform how he could evaluate future potential investments and guidance he could give those companies if his firm did invest.
Ben proceeded through the platform, to the SAM and SOM input, so he could get more granularity on how different constraints may impact different companies..
When it comes to adding market segment constraints, Ben left all the selected market segments included for consideration. Though he is interested in leaving the green power generation segments and removing “Power and communication line and related structures construction” he will wait until he builds a second model to do so. Ben wants to make his first model takes an expansive view of the market.
For now, he adds geographical constraints. He decides to focus on states like California, New York, Texas, Florida, Vermont, and Oregon, as these have well-established green energy initiatives or investment incentives. Activity in these states will be critical for any company he invests in to be successful, so he should learn more about those areas first.
Ben adjusted the company size constraints as he knew he would likely be interested in investing in companies prioritizing enterprise level sales–companies with less than 50 employees would likely be too small to purchase the more expensive solutions being sold by companies he preferred to invest in.
Ben finished by adding some additional market considerations and clarified how competitive and saturated the green investment market is, how loyal customers are, and qualified regulatory barriers, technological barriers, and risks.
With Olympus Intel’s powerful analytics, Ben quickly obtained detailed insights into the Total Addressable Market (TAM), Serviceable Available Market (SAM), and Serviceable Obtainable Market (SOM) for his chosen segments. It was enlightening to see how the figures narrowed down with each new market level:
The platform’s visualizations allowed him to easily interpret the data. He could see the number of firms, establishments, employees, and annual payroll figures at both national and state levels. It was intriguing to see in the SOM that though California has the highest number of establishments, Texas actually had the top two highest payrolls for the subsegments. These insights could help him focus on where to look for promising startups to invest in while also giving him ideas for how to differentiate his firm in the sea of those approaching promising entrepreneurs.
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