KPIs and Unit Economics: Deeply Explore Your Fundamentals
Part 3 of 5 in the “Financial Modelling for Startups” Series
When investors evaluate your business case, they try to develop an intuition if what you are doing now on a small scale would work on a big scale with a lot more firepower through additional funding.
Unit economics and KPIs are excellent indicators for that since they provide a micro-level view of the fundamental relationships that drive growth and potential for profitability.
Assuming that your business is not in the pre-seed stage, historical and projected unit economics and KPIs are a must in every fundraising model. The informational value that these metrics have for your potential investors goes beyond assessing the business case.
Measuring something means intentionally focusing on something that you determine a crucial factor for reaching the overall objective. Therefore, how you measure and analyze your progress gives investors insights into your philosophy to manage and grow the company.
For investors, a founder team with a deep understanding of running and scaling a business is often an even higher priority than a superior product. In this sense, when preparing your model, it makes sense to critically examine the choice and presentation of these metrics in your model and the underlying reasoning for their forward-looking projections.
Step 1: Identify relevant Metrics and Unit Economics
Illustrating a deep understanding of your business model’s dynamics requires choosing relevant and informative metrics to track and display in the financial model.
This choice is way easier for the category unit economics than it is for KPIs. While showing unit economics means summing up all economic activities related to one unit of output, KPIs are more loosely defined and can be any measurable piece of data that can communicate information about the business’s progress.
The general principle for choosing metrics to display in your model is that you want to show how you currently run the business. Therefore, you should only use KPIs you regularly integrate into your day-to-day management decisions and have historical records on. However, before going into discussions with investors, it can be valuable to double-check your current choices to see if you have the three most important aspects covered.
The starting point for approaching KPIs should always be visualizing the sales funnel that customers go through from becoming aware and interested in your product to ultimately becoming paying customers.
The typical sales funnel has three distinct steps: first, leads are generated, either organically or through marketing and sales activities. Second, a percentage of leads will be converted to paying customers, usually involving several sub-steps such as account registration or sales call. Third, post-purchase activities will determine the further customer relationship, either resulting in customer retention or losing the customer after the first purchase.
An intuitive financial model replicates the sequential structure in the sales funnel by starting with the funnel of leads and then using assumptions, such as conversion and retention rates, to narrow the funnel and logically arrive at the number of new customers won.
These metrics give investors an analytical snapshot of the channels and their efficiencies in customer and sales generation.
When implementing these assumptions in your model, make sure that you choose at least one metric for every step in your funnel (at least one metric for lead generation, conversion and post-purchase activities) so that the funnel has no logical gaps and can be easily understood.
After illustrating how you generate customers and sales, the logical next step is to set the generation of sales in relationship to their associated costs. Therefore, the second aspect that you must cover in your model is relevant unit economics.
Even though the exact calculation of unit economics is intrinsic to your business model type, the general recommendation for digital business models is to have the two most widely known metrics, Customer Lifetime Value (CLV) and Costs of Customer Acquisition (CAC), covered.
CLV follows the idea that convincing customers to stay is less expensive than persuading customers to buy. Besides the initial interaction purchase, calculating CLV also entails forecasting how much that user is worth in future revenue. The costs of customer acquisition then sum up the direct costs of acquiring that customer.
While pure sales metrics should make it intuitive to answer how you acquire a customer, unit economics answer the question of how profitable each customer is for your business.
To further elaborate on the profitability of single customers, related metrics such as CAC payback and LTV/CAC expressed as multiples are viable options to display in your model and, if possible, set in comparison to competitors to benchmark your current position in the industry.
Finally, you want to show metrics that show off the core value proposition of your product. These metrics do not necessarily have to also act as assumptions in forecasting the sales funnel or financials but should have a logical connection to your current target market and potential future monetization opportunities.
For example, nearly every digital B2C company should track metrics of customer engagement and customer behaviour. These can be the percentage of active users (daily or monthly), usage statistics, or time spent with the product or application.
The ultimate choice of what metrics to display is product-specific, but the informational value provided to the investor is always similar. First, engagement metrics can be a means to validate your value proposition and target market and thus give an indication about product-market fit.
A social media application without user interaction can have solid financials but still be an unattractive target for investors. Typically, a very active customer base indicates that less money will be necessary for testing hypotheses about customer preferences.
Also, nearly every type of engagement is monetizable, in the end, through the created data. In many recently successful startups, the main factor for exponential growth is not the product itself but network effects associated with other value drivers (the next article will discuss network effects in more detail).
Even if these effects are not achievable right now for your business, tracking and displaying metrics not associated with the sales funnel and meanwhile being aware of future monetization and expansion opportunities equally belong to the fundamental insights investors expect you to have about your business.
Other parts of this series also cover additional insights that are nice to have when building and examining a financial model for fundraising. However, the KPI section with relevant metrics in customer acquisition, unit economics, and customer engagement is the model’s core. It communicates the business’s fundamental micro-view that founders should always be ready to discuss with investors and use to guide their strategic decisions.
Step 2: Forecast your financial statements
The KPIs identified in the previous step illustrate information on your business case’s essential components and form the grounded, concrete foundation for projecting your progress and financial statements over the forecast horizon.
Since this series’s focus is on the thinking process in structuring and critically examining your business case in a financial model before going into fundraising discussions, I will only briefly discuss the necessary technicalities of bottom-up forecasting before going over a few suggestions that can help founders with examining their projections.
Using company-specific data to forecast financial statements comprises three main steps: first, the sales funnel metrics of relevant channels are used to estimate the sales generated as the top-line of your P&L statement. Of course, sales generation is limited to the current budget you have for marketing and sales activities.
Second, if applicable, you might add other direct costs for customer acquisition to the marketing budget (e.g., derived through total historical CAC) and complement these estimates with additional operating costs (i.e., personnel, admin, etc.) that are mostly fixed and let you run your company.
Third, you have to estimate if any further capital investments are needed or if other cashflow-related items (e.g., depreciation, interest, etc.) need to be taken into consideration. When finished, your financial model should display the standard P&L and Cashflow statement structure, including typical margin calculations (i.e., gross, operating, net).
Founders can complement these typical financial line-items, if applicable, with monthly and annually-recurring revenue projections (MRR/ARR). Thanks to the effect of potential long-term customer lock-in, recurring revenues are more predictable than one-time revenues and are essential information to display for investors.
The technical part is usually not the main difficulty when preparing such forecasts. Logically, current sales funnel metrics and KPIs used for calculating financial statements will bring recent results and thus must be subjectively adjusted to illustrate forward-looking projections that make up an attractive business case for investors.
Without extensive historical data and within a highly dynamic, competitive environment, adjusting the assumptions for forward-looking projections should not have high expected accuracy. Besides extrapolating trends visible in historical KPIs such as regular increases in sales conversion due to additional learnings, all you can do is develop solid reasoning behind your assumptions and supplement it, wherever possible, with extra data points.
Such reasoning might seem very high-level, and, indeed, an in-depth justification of every assumption in your forecasts is not possible, but it highlights something most founders do not give enough attention:
Although an overall plan has to be attractive, the way you have derived the underlying assumptions communicates a lot more to investors than the sheer magnitude of the forecasted, albeit not yet realized, results.
Step 3: Examine the sensitivities of core metrics and sync the results with your strategy and cost structure
Before going into more detail regarding examining and extending the model, a summary of how your model could currently look like when you have followed the suggestions so far: the financial model contains 4 to 5 different sheets.
The first sheet frames the market opportunity by showing complementary sizing estimates triangulating in the final market size or range. Internal financial projections include one sheet for assumptions (core assumptions are KPIs, divided into logical blocks) and distinctive output sheets for P&L statement, including startup-related financial line items such as MRR and runway), a Cashflow statement, and if requested, a balance sheet. Forward-looking assumptions in KPIs alongside other variables are, if possible, supplemented by a line of reasoning that could be based on historical trends or supplementary data.
As mentioned earlier, the high probability of inaccurate forecasts in venture planning makes a founder with in-depth knowledge about the fundamental dynamics that drive a business model extraordinarily valuable.
One way to develop a more profound understanding is to apply sensitivity analysis - also called what-if analysis- to understand the model’s outcomes for various variables.
Sensitivity analysis is an analytical tool that, in general, helps to determine how target variables are affected by changes in related variables. Thus, in comparison to scenario planning, which will be the topic of the next part of the series, sensitivity analysis means marginally changing only one or a few variables to see the potential impact on bottom-line outcomes.
In this step, interpreting and understanding the results to prepare for discussions with investors is more important than displaying the results. Thus, in an excel spreadsheet, founders not familiar enough with excel can easily do the analysis manually by marginally changing core metrics and examining the effects described below.
Applying sensitivity analysis on venture projections typically serves two primary functions.
First, sensitivity analysis enables founders and investors to check the robustness of the projections regarding mis-estimations in core variables. When talking about validating robustness, the critical outcome variable that you want to test sensitivity for is cash flow, or more specifically, runway, as these sensitivities direct you to the risk factors in your current plan.
When looking at a B2B business, for instance, one approach could be to test the runway’s sensitivity to changes in expected pipeline conversions or relevant cost factors. Even if it sounds very trivial to say that failing to convert your biggest PoC means you will earn less money, the point of testing for sensitivity is to set these marginal changes in variables in relation to concrete resulting changes for the whole plan. Being aware that not getting that PoC will mean your runway decreases from 8 to 6 months will set you up to efficiently formulate strategic options to react to if the time comes.
Limited funding and the focus on quick growth makes it very difficult to have a high degree of robustness. However, being aware of the most critical and most sensitive variables can enhance a founder’s understanding and strategic focus, which will not go unnoticed by potential investors in discussions.
For example, being able to respond to investors remarks on your top-heavy pipeline with the strategy to win a potentially significant customer, the amount of revenue on the line, the corresponding length of the runway on the line, and a back-up plan for maintaining the initially planned runway will make an excellent impression.
Second, similar to pointing to potential risk factors, sensitivity analysis can be equally helpful for validating your current strategic focus and cost structure. In this case, the logical variables to test sensitivities for are customers, revenues, or in some cases, gross profit.
The primary purpose here is to identify the main bottlenecks in your business and ensure that your strategy is tailored to improving them.
The term bottleneck originally comes from logistics but is also highly relevant in systems theory, a field which, in general, has many fascinating parallels to managing and scaling venture companies.
“Bottleneck” refers to the single constraint that limits the throughput of a system and, therefore, has the highest sensitivity for its outcome when improved or impaired.
Seeing a business model as a system and the throughput as the rate with which this system can produce monetizable results, there are many possibilities to test for bottlenecks, but I will briefly discuss the most common example.
The most straightforward application is testing for the bottleneck in your sales funnel by calculating the sensitivities of revenues generated to marginal changes in each metric corresponding to one step in the funnel. The metric with the highest sensitivity is your current bottleneck; in other words, a marginal improvement in this metric (compared to similar, proportional improvements in other metrics) will bring the most impact for your measured result. In this case, that is revenues.
The last conversion step that results in a newly signed customer will often be the mathematical and logical bottleneck. However, irrelevant where the actual bottleneck lies, further considerations should be twofold.
On the one side, bottlenecks should be further assessed and dissected. In the sales funnel example, this could be done by gathering data or asking questions about the exact reasons that the conversion was unsuccessful (e.g., lack of information or support, pricing, etc.) or at which exact point the potential customer backed out (e.g., received information material, complex signing process, etc.).
On the other side, the learnings about the current bottleneck should directly influence your short-term strategy and cost structure. Viewing the KPIs as the pieces of data that best describe the business’s progress, your cost structure, apart from the standard and fixed operational expenses, basically serves as a layout of how management decisions and concrete activities will benefit chosen KPIs.
In this sense, costs can be misspent when they aim to improve a part of the sales funnel that is not the bottleneck and only has a slight sensitivity, hence, impact on the outcome you ultimately want to improve.
Staying with the example above, if the final conversion step to signing is a severe bottleneck in your sales process, substantial costs for more qualified sales personnel or a better understanding of customer behaviour within this last step (e.g., A/B testing) might be more valuable than using the same budget for qualifying more leads at the beginning of the funnel.
Sensitivity analysis and the procedures just described embody the essence of data-driven decision-making and management of startups. Understanding which KPIs or variables have the most significant impact on the result you are focusing on, what underlying activities and processes are connected with these KPIs, and then systematically testing (yet again another reference to hypothesis testing) how you can improve them.
Therefore, analyzing your projections this way not only gives you guidance and validation of strategic focus and cost structure, but it also enables you to elaborate in fundraising discussions on your forecasts in a way that signals to investors that you have a thorough understanding and vision on how you want to capture the previously framed market opportunity.
Unit Economics and Key Performance Metrics: Key Takeaways
A deep understanding of a business’s underlying fundamentals is critical to making the right management decisions.
As in every other section of the series, integrating KPIs and Unit Economics in your model to support your business case is really about building a foundation that supports a clear hypothesis:
The founder has an in-depth knowledge of his business model and a clear plan to capture and monetize the market opportunity previously identified.
Therefore, you want to show investors that you know how progress can be tracked and analyzed (identify KPIs and Unit Economics as a basis for financial projections). You have put some thought into the assumptions made in your projections (forecasting) and have identified the essential variables that can accelerate or limit your growth (sensitivity analysis to identify critical variables and bottlenecks). Optimally, you have developed a reasonable game plan to move those variables in the right direction (improvement plan reflected in cost structure).
Step 1: Identify relevant Metrics and Unit Economics
- Visualize your sales funnel and show at least one metric for every step in your model to forecast sales
- Calculate unit economics and be ready to interpret and discuss them
- Refer to known growth levers when thinking about further complementary KPIs to track and display
Step 2: Forecast your financial statements and check the sensitivities of core metrics and business economics for your results
- Forecast your statements according to standard financial modelling practices and by taking historical developments into account
- If possible, supplement forward-looking assumptions with supplementary data and develop a line of reasoning
Step 3: Examine the sensitivities of core metrics and sync the results with your strategy and cost structure
- Check the sensitivities of your metrics and the implications marginal changes have for important line-items such as revenue or cashflow
- Identify critical variables and bottlenecks
- Critically examine your strategy and cost structure and check if they reflect the findings of the previous steps
Even if you have a great game plan derived with a flawless line of reasoning from your business’s fundamental dynamics, the theoretical model you have set up will never precisely predict the future.
Investors know this and therefore pay as much attention to the founders’ thinking process and personal impressions as to the business opportunity itself.
If things do not go as planned, and they will not always, investors want to know that the founders they invest in can quickly adapt to changing circumstances while also maintaining a clear vision of how to achieve the big goal.
Using your financial model projections to convey that image is the topic of the next part of the series.
If you are a founder looking for an investment, I hope you find this article series helpful for your preparation.
Find the additional parts here:
- Part 1: Purpose and Organization
- Part 2: Market Sizing
- Part 3: Unit Economics and KPIs
- Part 4: Scenario Planning
- Part 5: Storytelling
Especially if you are working in the fields of FinTech, Blockchain or AI, I would love to hear about your company so feel free to connect with me on LinkedIn.
Similarly, if you are a VC Investor and have some feedback or additional notes on this topic, I would love to learn more about your views.