FINANCIAL MODELLING VS SIMPLE SPREADSHEETS
FINANCIAL MODELLING VS SIMPLE SPREADSHEETS
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 profit-
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.
Like this article? Share it with your friends
“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 profit-
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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Cheryl MatthynssensCheryl is a life skills coach, licensed Chemical Dependency Counselor and a 20 year entrepreneur. Cheryl's dedication to achieving a life of balance led to her expanding her teaching from the simple managing of life's daily challenges to adding financial well being as well. A direct marketer with DrinkACT, she is gaining ground in the online community with her concepts of making sure business owners, entreprenuers and employees have well rounded life styles. She opened up a small affiliate site - The Balance Guide- to help others find resources for mental and emotional well being. Visit Cheryl's blog to see more of the diversity beyond business she has began offering online at www.thebalanceguide.blogspot.com - Visit Cheryl Matthynssens's Website |
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