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Understanding Ramp-Up, Burn And Other Key Business Metrics

One of the common mistakes seen in business plans and projections is that entrepreneurs treat various key business metrics as big aggregate numbers. While this approach makes the plan easier to understand (example: addressable market of 3,000 units per year, convert 10% in year 1 at average revenue of 100 per unit), it also glosses over significant complexities involved in acquiring customers, factoring in churn and other factors that play a key role in determining how far the business can go.

While it is true that there is no 100% accurate plan or projection that is possible, it is foolhardy to not make projections that can at least help organizations be prepared for the various scenarios than be caught confused when faced with various eventualities. This post is based on a template that I normally use to model similar things. It is nowhere close to being detailed, nor is the scenario that it portrays a realistic one, but it is one that should give you a good idea how to go about creating your own model. Consider it more a template than a finished model.

ramp_up_table_1

 

Acme Corp Offerings

The table above describes the key offerings of our hypothetical company (Acme Corp). The company has five offerings, of which two are products and three are services. There is no particular reason why this mix is there other than that I wanted a decent spread of offerings. Of the lot, Service C is a big ticket item, which sells the least, while Service A, being the cheapest, sells the most. Again, for the sake of convenience, I’m not taking into account the addressable market for each offering, which is not a smart thing to do, but for now, we have to make do with it. We are also assuming that the company is being started with a 100,000 investment.

ramp_up_table_2

Acme Corp Ramp-Up

The table above shows the ramp-up scenario we have in mind for the company. The cheaper offerings are predicted to grow in a somewhat linear manner, while the expensive ones are erratic in how they grow. We are taking major liberties with factoring in churn here, as we are working backward from the total unit sales for the year than to consider how a customer’s actual lifecycle impacts the system. There are also no volume or pre-payment discounts taken into account, all for the sake of simplicity again.

ramp_up_table_3

Acme Corp Expenditure

The expenditure table is the one that sees the maximum liberties taken with numbers. The dead giveaway is the ‘Average S’ (average salary) figure. In a realistic scenario, it never stays constant over a 12-month period as the headcount grows. Same is the case with rent. There are also a raft of other costs like connectivity, travel, legal etc. that is not taken into account into the picture. Make sure you make those changes and represent them accurately, if this exercise has to be of any real use.

ramp_up

When you plot all those numbers in a graph, what shows up is that the most critical time period for the company is the 6-9 moth period. Even though the organization has its first positive cash flow month in month four, it is only during month six that it starts a streak of positive cash flow months and it is not until month nine that it actually turns in a profit, even though it is a tiny one. For the 12-month period the organization turns in a profit of 17,38,500. But this profit won’t be realized if the company cannot survive beyond the first six months.

This first six months is the period where angel/seed rounds are critical. The cash flow situation for the organization is negative through that time period and even for the extremely cheerful model presented in this post, the company would go under in five months (or less) if it can’t raise anything above 310,000 during that time. The capital raised at this time only allows for basic validation that a market exists for the product/service at the price levels they are being sold at.

Breaking down the ramp-up to this level allows us to estimate which product or service is the one that we should look to grow. A high ticket value service/product has a different sales cycle and support requirements compared to a low ticket value one. What complicates matters is also the fact that these days disruption happens through pricing which mandates larger scale and also considerably lengthen the road to profitability.

To conclude, what I will stress again on is that what is presented in this post is an oversimplified picture, but it does give us an idea about what is a good starting point to do projections and figure out the kind of ramp-up that is required over time to make the organization a sustainable and profitable one.


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