When developing a product, the startup faces the task of planning actions—what to do to achieve the set goals. In the article "Unit Economics in Practice: How to Improve Key Metrics for Maximum Profit", I explained how Goldratt's Theory of Constraints helps model future unit economics to achieve the planned contribution margin. 

In essence, we understand what business processes need to be changed to achieve the desired financial results. 

At the same time, we only have an understanding of the initial and final values of unit economics metrics but do not have a clear picture of what the business process indicators should be in each specific month of the plan. The complexity is further increased by the fact that we plan and live in monthly intervals, while unit economics operates within monthly cohorts. 

Rate of Metric Improvement 

What is our task? We have a set of metrics, each with a starting and final value. We may also know how much time we are willing to allocate to achieve the planned values— for example, 3 or 5 years. 

The next step is to understand what the values of the metrics should be each month during the plan, especially considering that the startup team may not have the skills to influence business processes, meaning they will have little impact on the process-related metrics. Usually, for such processes, hypothesis testing and the search for tools to improve the business process are used. And only after finding the tool to influence the process will the metric begin to improve. 

User Behavior in Cohorts 

In addition to understanding how the metrics will change, we need to calculate how many potential customers will be in the product each month, as well as the number of new customers and existing ones who continue using the product monthly. For this, cohort analysis will help us. We can build cohorts for each period, knowing how the UA, C₁, and APC metrics should change. This will allow us to calculate the number of potential customers, new customers, and transactions for each month of the plan. When building cohorts, it is important to account for the behavior of leads and customers in each cohort. This is crucial for accurately planning the number of clients for each specific month of the plan. 

For example, if your business is subscription-based (SaaS), your customer cohorts will gradually taper off as users stop using the product. This tapering may be quite prolonged. But if your business is retail, customer behavior will be random— after purchasing a product, a customer will return for another purchase at random intervals, driven by external events that you usually do not control. 

It’s also important to note that potential customers may take significant time to make a purchasing decision. This means that in the first month when the cohort of potential customers is formed, only a portion of the clients calculated in unit economics will convert, while the rest will need time. I described these mechanics in more detail in the Cohorts article.

Product Plan 

All of this may seem complex, but by using the ueCalc service, which automatically manages the dynamics of metric changes over time, you can set linear, exponential, sigmoidal, or step growth for your metrics. 

In the image provided, you see an example of sigmoidal changes in the C₁ metric, which consists of three phases: the first phase is the area for testing hypotheses and finding tools to improve the metric, the second phase is applying the discovered tool and improving the metric, and the third phase is saturation, where the tool no longer provides significant improvement. 

In another example, you see the stepwise change in average order value (AOV), as price changes are often tied to certain time intervals— for example, price adjustments made once a year or once a quarter. 

Once you’ve figured out how to manage metrics and cohorts, ueCalc can easily build a product plan, i.e., the monthly changes in metrics from the starting to the final values, taking into account the growth rates and specific user behavior in cohorts. 

Such a plan contains the values of all product metrics that form the unit economics of the product for each month of the plan. You can try creating such a product plan yourself using the free template "Financial Modeling. Transactional Business."