FINANCIAL MODELLING VS SIMPLE SPREADSHEETS

“God does not play dice with the Universe”

Albert Einstein He may not, but in the world of small business uncertainty is the rule of the day. In early stage markets, good solid market research is often difficult to get. It is no wonder that early stage companies have a high failure rate.

In the face of this uncertainty we need to find the few elements that matter and concentrate, even over resource on those areas. We need to identify those pivot points in the business enterprise and dig down to understand them better.

Our Methodology Anyone can create a spreadsheet showing a halcyon 5% growth in profits and revenue each quarter and the usual best worst and base case all showing profitability after 6 months of ever lessening losses.

Spreadsheet analysis is much better at predicting future behavior that it is at predicting future revenues or profits. It should lead to insights into the dynamics of the business rather than lulling one into a false sense of complacency The Venture Catalysts perform the following:

· Analyze the main business drivers and put wide maximum and minimum values on such things as price, margins, sales cycle collection cycles, and the major cost items.

· Run Monte Carlo simulations on all business drivers from both the random choice basis and a full permutations basis.

· We can run 1000’s of permutations to create scatter maps, surface maps or histograms which then often indicate that there are a few drivers that matter a lot more than others that may not have been immediately obvious to the business owners.

· We will run the above analysis against the bank line while zeroing the equity line to determine a more realistic idea of the capital requirements of the business, against the cumulative P&L line to get a better idea of the time frame to break even or against the company’s parameter of choice.

· Determine the few drivers that matter in a more granular fashion and then delve more deeply into those areas that are shown to be the major pivot points in the business. Often other drivers are uncovered during this phase.

· Analyze the historical volatility of those drivers if they are available or extrapolate from the nearest equivalents if they are not.

· We then analyze these critical pivot points from a business perspective to determine how to best reduce the risk in these few areas.

The Best Strategy We recommend our clients over resource in highrisk areas in order to reduce the uncertainty and get the most “bang for your buck.” We can then make some realistic assumptions around greater cost in these areas against potentially reducing the time frame to profitability and/or the capital required to profitability.

We also seek to uncover:

· Which expenses and/or drivers are step functions and which ones are more linear in behavior.

This has serious implications as to how much cash has to be put up and in what size increments in order to create a better opportunity for success.

· The under-rated risks that don’t seem obvious on the surface such as currency risk that show up only when the Canadian currency moves 20% or more and driving costs beyond projections.

Example: Making Sense of an Advertising Business We reviewed a company that was in the advertising business and was expanding its advertising locations.

We only used a few runs here to illustrate.

By modeling the increase in locations across a range of values we generated the following simple graph.

This indicated that as we increased locations that revenue was going to get higher but more volatile over time. While the above looks at the percentage of ads sold rather than revenue specifically it was clear to us that a significant number of the above scenarios would not be profitable. As a result of this we recommended that they look at consider increasing the length of their contracts even if it was at a lower per ad price and over resourcing on the sales sideby increasing the number of sales people. It also gave us a better idea of how many sales people would be needed per unit of capacity.

We made some assumptions about the cost of a sales person and the range of the amount of sales that a salesperson would make. We made this later range quite wide because some salespeople would not work out. By examining a variety of changes in the capacity of the ad network and the sales expected per sales person we reached two conclusions.

1. That we should add more sales people than orig inally thought and 2. We should add them at steadily throughout the growth period.

We found, naturally enough,as you can see that adding sales people at the rate of one every three months resulted in greater volatility of sales (the lines at the lower end of the graph below).

We reduced volatility of revenues as we increased sales people, which is what one would expect.

This obviously allows the firm to manage to profit1 ability much more easily but it has implications on the scale required. However, common sense dictates that this graph has nothing to do with market acceptance and as we have learned over the past several years scaling up quickly can be a recipe for financial disaster without other controls in place.

This had a much larger impact on cumulative profits that we expected and has led to further investigation of other variables. (ramp-up rate, sales cycles etc).

The main point of this simple example is that by building realistic uncertainty into the financial models we can gain greater insight into the pivot points in the business and uncover dynamics that may not have been obvious looking at a simple spreadsheet that marches off to financial heaven.

Other General Considerations Small companies have a time decay curve that is seriously under estimated due to their usually limited funds.

Time risks, especially for fragile enterprises and projects are woefully underestimated especially around sales cycles, negotiations, strategic alliances, ramp up rates with larger customers etc. There is no magic bullet here. We can only get a better appreciation for the possible “cash’ decay curve that is going to occur with the above type of analysis.

By building uncertainty into the financial model and by testing 100’s or even 1000,s of scenarios across a selection of values we can gain insight into some of the dynamics of your business in order to make better business decisions and avoid being seduced by our own linear projections.

FINANCIAL MODELLING VS SIMPLE SPREADSHEETS - To learn more about this author, visit Jim Adams's Website.

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