All Industries are Alike; All Industries are Different

To some extent, the key drivers of data center, access provider, internet app or cloud computing equity valuation are the same: revenue magnitude and revenue growth rates, profit margins, operating costs and debt loads.

But every firm in every industry can vary from others in its own industry, even when general expectations about growth and profits margins exist. The key drivers of value also vary between industries, and especially with regard to younger firms using a platform business mode.

Though all new and young firms face similar issues in terms of scaling their revenues, many of the key assets built by any firm using a platform business model are not captured by traditional financial metrics.

Platform businesses, on the other hand, necessarily must account for their success at creating ecosystems of value creation, which are not measurable using standard accounting conventions. 

That is why we often see metrics that are proxies for user engagement, such as daily active users or monthly active users. We might see citations of time spent on the platform. Perhaps we see data on conversions of visits to sales of merchandise. 

Uber might cite gross bookings paid by rider, or the number of trips riders take, the number of drivers or the number of riders per day or month. 

Airbnb might cite nights booked, gross booking value, host earnings or average daily rates as evidence of success. 

Amazon Marketplace and eBay will cite gross merchandise volume. Amazon might point to units sold, customer satisfaction ratings. 

Ebay might track active buyers or seller ratings. 

Since network effects are critical, we might see numbers about growth in the number of producers, merchants, properties, drivers, listings. We might see evidence of success in terms of growing gross merchandise sales, rides, rentals or other metrics about buying volume. 

User abandonment of the platform also could matter, so we might see evidence provided about churn rates declining. 

That noted, most firms use a traditional pipeline model, creating and then selling a discrete product line owned by the producer. And, ultimately, all firms at scale are judged by the same generally-accepted accounting rules.

Still, each industry is different, so it might be hard to disagree with the notion that data center valuation hinges on a few key matters such as location, capacity and contract base. 

Drivers of Data Center Equity Valuation	Percentage of Valuation
Location	25%
Capacity	20%
Tenant Quality	15%
Lease Terms	10%
Revenue Stability	10%
Operational Efficiency	8%
Power and Cooling Infrastructure	7%
Connectivity and Network Infrastructure	5%
Scalability	5%
Environmental Sustainability	3%

Likewise, the firm valuation of a connectivity provider will tend to vary based on a few key inputs including subscriber volume and revenue growth. 

Drivers of Telco/Connectivity Provider Equity Valuation	Percentage of Valuation
Subscriber Base	25%
Revenue Growth	20%
Network Infrastructure	15%
Competitive Positioning	12%
Customer Retention and Churn	10%
Broadband Penetration	8%
Spectrum Holdings	7%
Innovation and Technology Investments	5%
Regulatory Environment	5%
Financial Performance	3%

Firms using a platform business model have additional issues, one might argue. Successful platforms do not so much own assets as facilitate transactions. Though eventually traditional valuation metrics (revenue, profit margin) will develop, in the early days traditional metrics such as revenue and profit can be misleading. 

To be sure, that can be an issue for any new firm. But firms with a true platform business model are more complicated. They might earn revenue indirectly. They might not actually own inventory (hotels and rooms; raw materials; retail inventory or rental vehicles. 

That complicates the task of identifying assets. In many cases, the value lies in network effects, data, brand recognition or intellectual property. None of those can be quantified using traditional financial metrics. 

So we might see metrics such as the size and engagement of the user base, the strength of network effects or the ability to retain users and minimize churn.

Levels of platform usage and activity or perhaps the quality of user experience can be important.

The trust and reputation of the platform or the strength of ecosystem and partner relationships likewise can be distinguishing matters. 

It can be even harder when only a portion of an entity’s revenue is actually earned using a platform model, and perhaps 80 percent to 90 percent is earned using the more-common pipeline model, where firms create, own and sell their own products.

"The Dog ate my Homework"

On the face of it, it is not drop-dead simple why Dropbox, the storage specialist for smaller and mid-size firms, necessarily benefits if we are entering a new era of computing some would logically call the “AI era,” and superseding the “mobile computing” or “cloud computing” era.  Dropbox is most often used by companies with ten to 50 employees and $1 million to $10 in revenue, according to Enlyft.  Nor is it clear why reducing Dropbox headcount by 16 percent, though perhaps an understandable response to slowing revenue growth rates, necessarily is connected to core tasks the company has to undertake to thrive in the next era of computing.  To be sure, use of large language models and using generative AI presupposes processing of huge amounts of data and huge data sets, which of course requires lots of storage.  Some note that a single LLM training operation can cost millions of dollars. But training of LLMs is not something most smaller firms are going to be able to afford, so it is unlikely that Dropbox would see much revenue upside from serving that function.  Likewise, training and inference operations will require lots of new processing cycles. Perhaps Dropbox believes it has an opportunity to provide such intensive compute support for its current customer base.  Perhaps something else is partly at work. Retailers, for example, often explain poorer financial results by pointing to bad weather that kept shoppers away, or unusual weather that depressed demand for products with a seasonal purchase pattern.  As true as that might be, it also is a convenient excuse.  Some argue a wave of layoffs in the technology business is as much about doing what competitors are doing as much as anything else, aside from the argument that firms over-hired in the wake of the Covid pandemic.  Business customers often do the same thing, arguing they must spend to “digitally transform,” become more agile, innovate faster or apply technology to reduce costs or remake product values.  There still is a rational argument to be made that if firms expect lower revenues in the near future, then cost cutting makes sense. Cost cutting to preserve profit margins also makes sense.  On the other hand, the stated rationale for any set of actions, strategies and tactics also includes a healthy dose of “because that is what people expect us to say or do” behavior.  The “we need to get ready to invest in AI” argument is fairly new, though.
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