How Analysts Evaluate Startup Business Models

The Qualitative Assessment: Evaluating the Foundation

Evaluating a startup's business model is a complex process, involving thorough financial analysis as well as an ability to see the future strategically and creatively. An analyst sees the business model of a startup as more than just a means for the startup to generate income; rather, it is a hypothesis as to how a startup will create, deliver and capture value within a competitive environment.

This article examines the overall analytical structure used by analysts when reviewing a startup's business model. It examines everything from qualitative evaluations of teams and markets to quantitative indicators of long-term sustainability.

Analysts look to "soft" factors prior to looking at spreadsheets. In the initial phase of a startup, qualitative factors tend to carry much more weight since there is limited historical financial information for the business.

Team "Jockey" vs. "Horse"
In general, investors express the opinion that they would prefer to invest in a "Grade A" team with a "Grade B" idea rather than a vice versa. Analysts assess Factors such as:

• The Team's Domain Expertise - Does the team possess deep, relevant experience within the industry they are attempting to disrupt?

• The Team's Execution History - Have any of the founders previously scaled businesses successfully?

• Complementary Skillsets - Is there a complimentary balance of skillsets between technical skill and commercial skill?

Market Dynamics "Problem-Solution" Fit

Even the best business model will fail in a vacuum. Analysts often look at various market dynamics using frameworks such as TAM, SAM, and SOM for quantification of Opportunity and to determine Actual Market Size:

• Total Addressable Market (TAM) = Demand for Product on a Global Basis

• Serviceable Available Market (SAM) = Portion of TAM to be targeted by the startup's products based upon geographical reach

• Obtainable Market (SOM) = Portion of SAM that can realistically be captured over the short term.

The Structural Analysis: The Business Model Canvas

Business Model Canvas is a tool analysts use to depict how the various portions of a startup interplay. It can assist in determining whether the business model has internal consistencies. The following blocks are essential:

• What is the specific "pain point" this product/service addresses? Is the problem so significant that it must be solved, or is it simply nice to have it addressed?
• How will the company generate revenue? Will it use a single sale, recurring subscription (software as a service); marketplace commissions; or advertisement income?
• Does the company require significant investment for hardware (capital-intensive), or can it achieve its goals without investing in extensive physical assets (asset-light)?
• Who are your ideal customers? Are you targeting a certain demographic?

The Quantitative Engine: Unit Economics

To put it simply, understanding your Unit Economics is key to assessing your business model. Unit Economics refers to the Revenue and Costs associated with a single unit of Sale (typically a customer).

The LTV/CAC Ratio

For analysts, the most important ratio is the Lifetime Value (LTV) to Customer Acquisition Cost (CAC) ratio.

• Customer Acquisition Cost (CAC): Total dollars spent on Marketing and Sales divided by the number of New Customers acquired.

• Lifetime Value (LTV): Total Net Profit a business expects to make from a Customer over the course of their relationship with that Customer.

Startups are often looking to achieve a healthy 3:1 LTV to CAC ratio, while a 1:1 ratio would indicate that a startup is spending as much on acquiring a Customer as they will ever earn back, which indicates a high likelihood of failure.

CAC Payback Period

This is the length of time (measured in months) it will take for a Customer to "pay back" the costs incurred in acquiring the Customer. For any High Growth SaaS company, analysts would typically want to see a CAC Payback Period of less than 12 months.

Scalability and the "Moat"

The model will probably work for 100 customers. But how well will it perform for 1,000,000? Operating Leverage is the degree to which revenue grows much faster than expenses.

Competitive Moat (Defensibility)

To assess the defensibilty of the business model, analysts determine what "moat" protects it from being easily replicated by current competitors and new entrants. There are four types of Competitive Moats:

1. Network Effects: A product becomes increasingly valuable as more users join, e.g. eBay and Airbnb.
2. Switching Costs: How difficult it is for a customer to leave, e.g. transfering data from one specialized CRM to another.
3. Intangible Assets: This includes patents, proprietary algorithms, and strong brands.
4. Cost Advantages: This could be access to unique resources or more efficient production capabilities.

Financial Health and Risk Mitigation

Analysts consider the "burn rate" and "runway" when determining whether the startup's business model has enough time to reach key milestones:

• Burn Rate: The speed with which a startup uses its VC capital to pay for its operating expenses prior to becoming cash flow positive.
• Runway: The number of months until the startup exhausts its current funding; determined by dividing the company's cash balance by its monthly operating expenses (burn rate).
• Gross Margin: A gross margin greater than 70% is indicative of a software business that can be scaled very quickly, while a gross margin of less than 20% indicates a high-volume, low-margin business model involving physical products.

The Churn and Retention Dynamics

While Life Time Value (LTV) and Customer Acquisition Cost (CAC) give an overview of how much it costs to acquire new customers to your business, Churn provides insight into the quality of your product. Churn is often considered by analysts as the most reliable metric for measuring product quality due to its correlation with customer satisfaction.

• Gross Churn vs. Net Churn: If an analyst can achieve a negative net churn, which means that the revenue generated from additional sales or upgrades to existing customers exceeds the revenue lost due to cancellations, then the analyst has created a sustainable model for growth.

• Cohort Analysis: Analysts do not simply look at total churn, but they track customer cohorts based on their month of acquisition. For example, if the 2023 cohort has achieved longer retention than the 2022 cohort, this indicates that the product has a greater likelihood of long-term success due to increasing retention over time.

Sales Motion and Go-To-Market (GTM) Strategy

Without an effective method for getting their excellent product into the hands of as many customers as possible, a company has merely created its new "hobby" project. Analysts study each sales strategy to determine how well aligned it will be with the product's pricing strategy.

• PLG (Product-Led Growth) - Is the product good enough that it essentially "sells itself"? For example, Slack or Zoom both qualify as products meeting this criteria and can achieve high profit margins and rapid scaling.

• Enterprise Sales - If you have a product that is $50k or more, does your startup have an experienced enterprise sales force that can effectively sell this type of solution?

• Channel Partnerships - Is your startup counting on 3rd party companies (i.e., AWS and Salesforce) to help find your customers? If so, what is the "platform risk"?

Regulatory and Macroeconomic Sensitivity

Models utilized in a "zero-interest-rate environment" could likely become unsuccessful when capital costs increase. Thus, analysts will use what is called "stress-testing" to test the model. In order to test this model, analysts look at the following:

• Regulatory Headwinds: For example, for Fintech or Healthtech companies, how much of the company's budget needs to be allocated towards staying compliant? Can a regulatory requirement or similar requirement (such as GDPR or a change in labor laws caused by the gig economy) disrupt the business?

• Inflation and Pricing Power: How much of the budget, if any, can be allocated towards price increases as a direct result of inflation? The ultimate value test.

The "Flywheel" Effect

Analysts will assess whether there is a "Flywheel" effect, or a virtuous cycle, occurring where each of the business segments enhances one another.

• Data Network Effects: Is there a correlation between the amount of traffic (usage) a website receives and the data created through that traffic to improve AI/website, and in turn drive more traffic (users) to the website?

• Economies of Scale: Are fixed costs (e.g., inventory/finance) significantly reduced as the business grows in size, thus allowing these companies to charge lower prices than competitors while remaining profitable?

Exit Strategy and "Strategic Fit"

An analyst assesses a business model's ultimate "liquidity event".

• M&A Potential: Is the business model a good fit for large companies such as Google, Microsoft, or Disney to add to their size?

• The Business's Ability to Survive an IPO: Does the business have the level of transparency and predictability necessary to withstand the scrutiny that comes with being a public company?

Gross Margin Profile and "Software-Level" Scalability

Analysts look at more than just revenue; they analyze COGS, which tells whether the company is a true tech company or simply a service company "dressing up" as a tech company.

• Technical Debt: Is the "proprietary AI" actually a team of humans working in a low-cost country that manually enters data? (Often called the "Wizard of Oz" MVP). Analysts expect a gross margin of 70-80% to demonstrate that the software does the majority of the work.

• Infrastructure Costs: When evaluating AI Startups, analysts will analyze Compute Costs. If the computing cost of running an LLM (Large Language Model) is excessive compared to the subscription price, then the Company may never be able to make a profit.

Sensitivity to "Platform Risk"

A number of Startups operate entirely upon the platform of a major Company. Analysts evaluate how susceptible the model is to a "Platform Change".

• The API Trap: If a Startup is 100% reliant on Twitter’s data/API or Google’s search algorithm, it could be destroyed overnight by a single change to the policies of the giant.

• Disintermediation: Once a relationship is established in a Marketplace, can a purchaser cut the startup out of the equation and go directly to the vendor?

Realized vs. Unrealized Pricing Power

Points to Consider Regarding Inflation and Competition for Startups, Pricing Power.

• Price Elasticity - when a startup increases their prices by 10%, analysts will consider how many customers leave (for example, if 30% leave, the startup does not have a mission critical product and is not likely to survive long-term).

• Seat versus Value - analysts prefer models to move to when figuring out their pricing structures and how to charge customers to maximize their potential value while allowing for scalability (ie Snowflake/AWS client usage vs per seat pricing models).

The "Terminal Value" Framework

10 years into the future, the analyst would like to have some kind of vision of what a successful, mature, and cash-generating startup looks like.

• Market Saturation - when is it likely that acquiring a new customer at an acceptable price will become a challenge? What is the cost of acquiring a new customer when it is determined that everyone that wants/needs the solution has acquired it?

• Dividend Potential - even if years have gone by, the total cash return to shareholders must occur or be on track to occur (whether through dividends or other forms of cash return).

Conclusion

Evaluating the way a startup's business model functions requires the application of both quantitative metrics and qualitative analysis. While the quantitative metrics (such as the LTV/CAC metric) provide a "reality check" on current performance, the qualitative analysis (such as the quality of the team and the strength of the startup's product in protecting it from competition) provides the "optionality" for future growth. An analyst's job is to determine if the startup is building a "leaky bucket" (high levels of churn, poor unit economics) or a "compounding machine" (ability to scale efficiently and already defendable).