1.
Simplified Forecasting
Masterclass
1

2.
Warm up exercise
2

3.
Instructions
There are ten questions. You choose a range
so that you are 90% confident you have the
answer inside your range.
Write down the question number, low and high.
Some as tables, some as individuals.
3

4.
Instructions (cont)
For example, “Q1: How many plays did
Shakespeare write?”
Your answer could look like this:
Q1: 10 to 20
4

5.
5
QUIZ!!! Hidden in separate slide

6.
Now please hand your page to your neighbour
Statistically, there is a 99.9% chance that you
got at least 6 correct.
6

7.
The answers
7

8.
Conclusions
Most people doing this don’t get as many right
as expected.
Humans tend to be overconfident.
So now you’re all extra analytical …
8

9.
Download Slides
(SlideShare)
http://goo.gl/tkJWwS
PDF: https://goo.gl/IKNJ3n
9

10.
Introduction
Tim Richardson, CPA, B.A., B.Sc, M. Acct.
10

11.
My Career
Developer at small, innovative IT firm. Large clients.
Large European firm acquires small Aus IT firm (to access technology and clients)
Now even larger clients, in Asia.
→ Sydney → Jakarta.
Works with Finance Director. Joins Philips, put into Finance talent pool
→ Singapore → Eindhoven (NL).
Controller then Finance Director for retail then manufacturing and sourcing.
Back to Australia. Equity raising, CFO of a fairly large international online retailer, then
started GrowthPath.
11

12.
Your Career
Accounting has twice been found to be the
most likely profession to be replaced by expert
systems (modern learning AI)
In one survey, retail checkout was the only job
more likely to go. In another, it was call centre staff
pushing accounting to #2.
12

13.
The accountant of the future …
13

14.
Table Introductions
Please appoint a row leader
Survey your table-mates for their forecast
experience and how important it is to their role
Time budget: 6 minutes
14

15.
Glossary
“linear”: an equation of a straight line.
sales = 10000 + 56X is linear
Sales = 10000 + 56^1.6 * sqrt(sales_last_year -
fc)^2 is not linear.
15

16.
Glossary
‘Regression’ is finding a formula which explains the shape of data.
Called a trend line in spreadsheets.
More advanced methods are not limited to straight lines; they can find
non-linear lines, like exponential lines.
“Correlation” scores how close the line is to the data.
16

17.
Glossary
‘sand-bagging’: putting in artificially high costs or low sales, expected to
be later ‘thrown overboard’ when needed, as in a hot air balloon.
‘gaming’: manipulating forecasts to reach objectives.
‘sensitivity analysis’: changing an important input (like selling price) to
see how it affects the outcome of a forecast.
‘big data’: collections of massive amounts of business data which never
makes it to accounting records and used to be invisible.
17

18.
“A good player plays where the puck is.
A great player plays where the puck is going
to be”
Wayne Gretzky, ice hockey great.
18

19.
19
STRATEGY
Points of difference
Competitive Advantage
Barriers to entry
Plan,
Objectives
Operations
Business Control
Are we on track?
Corrective action.
Are there surprises and
opportunities?
Traditional
forecast
Influential
Forecasting

20.
It’s not just about
numbers.
There are people too
20

21.
True or False?
Politics is a sign of a less effective forecast:
True or False?
21

22.
Forecasting and anthropology
Forecasting is a numerical process
But it also involves opinion, power, politics and the allocation of resources.
Processes which do not affect resource allocation are not important.
Processes which do affect resource allocation will be challenged.
Changing how you do forecasting can therefore meet resistance.
22

23.
Forecasting outcomes
How important is this outcome of forecasting:
to accurately predict the future.
Rate your answer from 0 = Not important to 10 = the most important outcome
26

24.
Forecasting outcomes
How important is this outcome of forecasting …
To choose the best future from a forecasted
selection of possible futures
27

25.
Forecasting outcomes
How important is this outcome of forecasting …
To understand how to influence the future by
understanding the link between decisions made
today and future outcomes
28

26.
Now ...
Now that you’ve seen all three questions, do
you change your first two scores?
Are there other questions to add?
29

27.
Our objective today
Something simple: why?
Something relevant to management
Something useful for decisions: why?
30

28.
Why is simplicity so valuable?
Easier to understand.
When someone else can understand your forecast and
own it to convince others of the right course of action, you
have communicated effectively.
Faster to prepare
More time for other things
31

29.
When is a number useful to
management?
32

30.
Forecasting through the ages ….
We need a definition of
forecasting that excludes
things like:
● tea leaves
● tarot cards
● chicken entrails
● signs from god
Why do we reject
these methods today?
Why did people do
them anyway?
33

31.
The use of prediction
Centuries of evidence that humans find
obviously inaccurate forecasts useful anyway.
It’s very interesting to think about why this is
the case.
34

32.
Problem with stone-age forecasts
lack cause and effect.
They make predictions, but don’t show how we
change affect the future.
There is no diagnostic feedback. If the forecast
was wrong, why?
35

33.
A good forecast method
Write three to five characteristics of a good
forecast process
Try to include organisational aspects about the process (who, how long, how is
it discussed) not just technical aspects.
What trade-offs must you make when choosing
a forecast process? A perfect forecast would take one second
and be 100% accurate. But in reality, what compromises do you make?
36

34.
And for bonus points….
Sensitivity analysis / multiple scenarios
based on cashflows with a clear separation of
relevant and sunk costs
37

35.
DECISION FOCUS:
Helps focus on what decisions, impacts and opportunity cost (tradeoffs)
CAUSE AND EFFECT:
Shows how decisions will affect the future
SIMPLE:
People get it, own it, use it.
FEEDBACK: if the future surprises us, our forecast should be diagnostic:
what happened, in a way we can action.
CASHFLOW:
Very useful to forecast on the cash effects of decisions.
Possible answers: A good forecast …
38

36.
Predicting the future
Apart from tea leaves, how can we make
predictions about the future?
What is the traditional method of forecasting?
39

37.
Traditional forecasting
Traditional forecasting believes the secret to the future is based in the past.
Often there is a lot of data. The temptation is to believe that the answer is
there, if only we apply more and more ‘power’.
Statistical forecasting (linear regression, non-linear,... neural networks) is
based on discovering trends and patterns in history to tell us what will happen.
This is a very common myth (e.g. Asimov’s Foundation books).
These methods can become so complex and powerful they approach a
priesthood. Most managers won’t understand them.
40

38.
Advanced tea leaves
Traditional forecasting can be “enhanced” with
advanced methods to mine historical data for
patterns which will repeat in the future.
But this is, in my opinion, largely a waste of time.
This is advanced tea leaves.
41

39.
Random quiz
If my sales are normally distributed, how much
of the time will my sales be below two standard
deviations from the mean?
Write down the answer. You have 10 seconds.
42

40.
Simplification though focus and aggregation
43

41.
Historical Data
Jan 2010 Feb 2010 Mar 2010 Apr 2010 May 2010 Jun 2010 Jul 2010 Aug 2010 Sep 2010 Oct 2010 Nov 2010 Dec 2010
1 2 3 4 5 6 7 8 9 10 11 12
Sales $127,843 $188,151 $244,650 $233,176 $281,558 $321,796 $185,085 $257,522 $317,497 $356,247 $345,265 $213,637
Cont Margin $51,419 $75,317 $97,433 $92,728 $113,265 $130,243 $73,400 $102,840 $127,327 $142,239 $139,073 $85,928
CM% 40.2% 40.0% 39.8% 39.8% 40.2% 40.5% 39.7% 39.9% 40.1% 39.9% 40.3% 40.2%
Overheads
Wagebill $22,573 $22,711 $23,198 $22,915 $23,692 $23,830 $23,371 $25,012 $22,320 $22,408 $24,792 $22,597
Marketing $10,674 $10,008 $9,905 $10,070 $10,766 $10,493 $10,135 $10,536 $10,450 $10,094 $10,839 $10,226
Occupancy $20,065 $19,280 $19,739 $20,784 $20,670 $20,480 $20,926 $20,790 $20,406 $21,495 $20,785 $20,580
Insurance $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076 $2,076
Interest $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595 $2,595
Depreciation $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974 $12,974
Overheads $70,958 $69,644 $70,486 $71,414 $72,773 $72,448 $72,076 $73,983 $70,820 $71,642 $74,060 $71,048
Profit before Tax -$19,539 $5,672 $26,946 $21,314 $40,492 $57,796 $1,324 $28,857 $56,507 $70,597 $65,014 $14,880
-15.28% 3.01% 11.01% 9.14% 14.38% 17.96% 0.72% 11.21% 17.80% 19.82% 18.83% 6.97%
44

42.
Seasonality.
Linear trend shown
How good is the
correlation?
At what time scale
does seasonality
become more
important than
trend?
The correlation is
poor, but it the trend
still valuable?
45

43.
Statistics (sales)
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Seasonality 2010 4.4% 5.8% 8.4% 8.7% 9.4% 10.2% 5.5% 8.3% 10.5% 11.5% 10.6% 6.9%
Seasonality 2011 4.4% 5.9% 8.7% 8.4% 9.2% 9.7% 6.0% 8.7% 10.4% 11.5% 10.1% 7.2%
Seasonality 2012 4.4% 6.1% 8.7% 8.6% 9.2% 10.0% 5.8% 8.6% 10.6% 11.0% 10.7% 6.3%
Avg 4.4% 5.9% 8.6% 8.5% 9.3% 10.0% 5.8% 8.5% 10.5% 11.3% 10.5% 6.8%
Year on
Year Sales
Growth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL
2011/2010 2.7% 3.4% 5.6% -1.1% 0.4% -2.4% 11.4% 6.8% 0.8% 3.0% -2.7% 6.4% 2.3%
2012/2011 2.6% 8.65% 4.65% 6.79% 4.41% 7.56% 0.32% 2.89% 6.98% -0.76% 11.20% -8.34% 2.2%
46

44.
Statistics: Profit
Year on
Year Profit
Growth Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL
2011/2010 4.9% 16.4% 10.6% -10.5% -5.4% -14.0% -138.4% 19.0% 1.5% 2.3% -12.4% 36.8% 0.5%
2012/2011 -2.5% 215.7% 8.3% 18.5% 7.7% 19.3% -130.4% 0.7% 13.2% -4.5% 24.6% -50.8% 8.2%
Profit as %
sales Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL
2010 -12.2% 0.9% 12.7% 13.1% 14.8% 16.6% -2.7% 10.8% 17.8% 19.6% 17.2% 6.4% 11.9%
2011 -12.48% 0.99% 13.26% 11.82% 13.90% 14.64% 0.92% 12.06% 17.88% 19.50% 15.45% 8.20% 11.7%
2012 -11.86% 2.88% 13.72% 13.11% 14.34% 16.23% -0.28% 11.80% 18.91% 18.77% 17.31% 4.40% 12.1%
CM Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec TOTAL
2010 40.0% 39.9% 40.0% 39.8% 39.9% 39.8% 39.8% 39.8% 39.7% 39.9% 39.9% 39.9% 39.9%
2011 39.9% 40.0% 40.1% 39.7% 39.9% 39.5% 39.9% 39.8% 39.8% 39.8% 39.8% 39.9% 39.8%
2012 40.3% 39.6% 40.0% 39.8% 39.8% 39.9% 39.5% 39.4% 39.9% 39.7% 39.7% 39.8% 39.8%
47

45.
Which is easier to forecast
accurately
Profit in a specific month
Profit in a specific quarter
48

46.
What we can we improve so far?
Variations month to month are hard to forecast from
historical data but the variations smooth out when you
aggregate.
We call this “swings and roundabouts”
Mathematically, it’s related to ‘regression to the mean’
49

47.
First steps: A simplified forecast
We first just take the existing historical P&L
data and see where common sense leads us
50

48.
51

49.
52

50.
53

51.
Based on the data we have so far,
What drives the result?
Use a simple annual growth for sale trend.
Use last year averages for overhead costs
Use avg for GM
54

52.
Jan Feb Mar Q2 Q3 Q4 TOTAL Actual 2013
trend 2.20% Sales $147,178 $205,889 $293,025 $938,101 $841,460 $960,699 $3,386,352 $3,410,359
avg 39.80% GM% 39.8% 39.8% 39.8% 39.8% 39.8% 39.8%
Margin $58,577 $81,944 $116,624 $373,364 $334,901 $382,358 $1,347,768 $1,359,649
Note
avg Overheads
Fixed $24,771 Wagebill $24,771 $24,771 $24,771 $74,313 $74,313 $74,313 $297,253 $306,171
Fixed $11,312 Marketing $11,312 $11,312 $11,312 $33,937 $33,937 $33,937 $135,747 $139,820
Fixed $22,153 Occupancy $22,153 $22,153 $22,153 $66,458 $66,458 $66,458 $265,831 $273,806
Fixed $2,087 Insurance $2,087 $2,087 $2,087 $6,261 $6,261 $6,261 $25,046 $25,046
Fixed $2,609 Interest $2,609 $2,609 $2,609 $7,827 $7,827 $7,827 $31,307 $31,307
Fixed $13,045 Depreciation $13,045 $13,045 $13,045 $39,134 $39,134 $39,134 $156,536 $156,536
Profit -$17,400 $5,967 $40,647 $145,434 $106,971 $154,428 $436,048
Actual -$22,888 $6,378 $41,309 $144,049 $111,468 $146,648 $426,964
55

53.
Summary
Focus on what matters,
Take advantage of swings and roundabouts:
• Aggregate
• Forecast by quarters instead of months (take
a page from the Rolling Forecasting book)
56

54.
Was that over-simple?
We do some some analysis on what’s going on in
sales.
We are concerned about the role of price and mix
play. Maybe growth based on volume increase is
too crude?
What are some scenarios where this may be the
case?
57

55.
Synthesised KPIs
This business sells a range of products in four
categories with consistent margins. There is
seasonality and some underlying trend growth.
58

56.
Synthesised KPIs
A synthesised KPI is an indicator created form
data you already have. Accounting ratios like
days sales outstanding, or inventory stock
cover, are examples.
We will explore a measure of sale mix.
59

57.
Sales KPIs: An example of a synthesised KPI
Price effect
Mix effect
Voume effect
A small technique that may help give some
insight into sales trends of product categories
60

58.
When may it be useful?
If innovation or competition is affecting products
differently
The concept can be extended to anywhere
there is ‘something going on’ at a level of detail
which is partially or substantially influenced by
outside factors
61

59.
2009 2010 2011 2012 2013
Sales $21,135,799 $19,381,347 $21,006,389 $22,907,390 $19,363,575
CM $7,001,739 $6,268,051 $6,925,696 $7,554,288 $5,551,022
CM% 33% 32% 33% 33% 28%
YOY Sales -7% 9% 7% -24%
YOY Margin -10% 11% 7% -36%
Introduction to Sleepy Pty Ltd
62

60.
Minor spreadsheet point
To multiply one block of numbers by another,
can use SUMPRODUCT(block1, block2)
A block from f1 to f5 is written F1:F5
63

61.
2011 2012
PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales
Category 0 32824 $107 $3,512,230 21474 $109 $2,342,974
Category 1 21474 $161 $3,446,639 27037 $163 $4,417,849
Category 2 32256 $211 $6,814,688 30409 $218 $6,628,260
Category 3 28602 $263 $7,513,273 34483 $270 $9,302,344
TOTAL 115156 $185 $21,286,830 113403 $200 $22,691,427
Total sales
value increase
is $1,404,597
which is growth
of 6.6%
we will explore how the change in
sales is due to changes in quantity
sold, price, and mix
64

62.
C D E F G H
SIMPLE PRICE AND MIX EFFECT
2011 2012
PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales
5 Category 0 32824 $107 $3,512,230 21639 $109 $2,360,833
6 Category 1 21474 $161 $3,446,639 27247 $163 $4,452,914
7 Category 2 32256 $211 $6,814,688 30741 $218 $6,699,094
8 Category 3 28602 $263 $7,513,273 34822 $270 $9,394,549
TOTAL 115156 $185 $21,286,830 114449 $200 $22,907,390
Total sales value increase is $1,620,560
which is
growth of 7.6%
Price effect
if only the quantity changed, sales
would have been $22,346,677
(new qty * old prices) =
SUMPRODUCT(F5:F8,D5:D8
)
which is growth of ... 2.5%
(new qty * new prices) / (new
qty * old prices) -1
Quantity effect
If we sold an average product (no
mix)...
Due to quantity -0.6% Change in Qty =F10/C10-1
Mix effect
The remaining change explained 5.7% 65

63.
2012 2013
PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales
Category 0 21639 $109 $2,360,833 28616 $111 $3,188,955
Category 1 27247 $163 $4,452,914 55583 $168 $9,347,540
Category 2 30741 $218 $6,699,094 23021 $225 $5,175,647
Category 3 34822 $270 $9,394,549 5912 $279 $1,651,433
TOTAL 114449 $200 $22,907,390 113132 $171 $19,363,575
Total sales value increase
is
which is change
of %
Price effect
if only the quantity changed,
sales would have been
(new qty * old prices) =
SUMPRODUCT(F5:F8,D5:D8
) ie new qty * old price, per
line
This means price explains a
change of
(new qty * new prices) / (new
qty * old prices) -1
TIP! New qty
* new prices
is 2013 Sales
Quantity effect
If we sold an average product
(no mix)...
Due to quantity -1.2% Change in Qty =F10/C10-1
Mix effect
The remaining change 66

64.
C D E F G H
2012 2013
PRODUCT GROUPS Qty Avg Price Sales Qty Avg Price Sales
Category 0 21639 $109 $2,360,833 28616 $111 $3,188,955
Category 1 27247 $163 $4,452,914 55583 $168 $9,347,540
Category 2 30741 $218 $6,699,094 23021 $225 $5,175,647
Category 3 34822 $270 $9,394,549 5912 $279 $1,651,433
TOTAL 114449 $200 $22,907,390 113132 $171 $19,363,575
Total sales value increase
is -$3,543,815
which is change
of -15.5%
Price effect
if only the quantity changed,
sales would have been $18,793,991
(new qty * old prices) =
SUMPRODUCT(F5:F8,D5:D8)
This means price explains a
change of 3.0%
(new qty * new prices) / (new
qty * old prices) -1
TIP! New qty
* new prices
is col H
Quantity effect
If we sold an average product
(no mix)...
Due to quantity -1.2% Change in Qty =F10/C10-1
Mix effect
The remaining change
explained -17.4%
67

65.
2012 2013
PRODUCT GROUPS Qty Avg Margin CM Qty Avg Margin CM
Category 0 21639 $39 $852,302 28616 $40 $1,139,776
Category 1 27247 $38 $1,030,982 55583 $39 $2,177,463
Category 2 30741 $67 $2,059,618 23021 $70 $1,601,269
Category 3 34822 $104 $3,611,386 5912 $107 $632,514
TOTAL 114449 $66 $7,554,288 113132 $49 $5,551,022
Total margin value increase
is -$2,003,266
which is change
of -26.5%
Margin per product effect
if only the quantity changed,
margin would have been $5,385,795
(new qty * old CM) =
SUMPRODUCT(F5:F8,D5:D8
)
This means margin per prod
explains a change of 3.1%
(new qty * new CM) / (new qty
* old CM) -1
Quantity effect
If we sold an average product
(no mix)...
Due to quantity -1.2% Change in Qty =F10/C10-1
Mix effect
The remaining change
explained -28.4%
68

66.
If price erosion and mix changes are important
developments:
new sales = (old_sales*price_effect% + old_sales*mix_effect% +
old_sales*qty_effect%)
Note: this is only an approximation and when the effects start getting > 10% more accurate approaches should be
considered.
http://www.growthpath.com.au/index.php/Business-Growth/separating-price-and-mix-in-changes-to-sales-and-margin.html
Applying these effects
69

67.
How to use
Price and mix effect are good KPIs for sales and marketing. Influencing price, mix and units sold take
different steps. These measurements may capture performance and trade-offs.
Easy to measure. Meaningful. Data is based on invoices so it is already available with little investment
in new systems or integration.
Can give effective early warning of trends.
For forecasting:
new sales = old sales + old_sales * price_effect + old_sales*qty_effect + old_sales*mix_effect
For business cases: using with margins not price can justify marketing spend based on mix
improvements
70

68.
Synthesised KPIs
Synthesised KPIs can’t reveal new information,
but they can reveal new perspectives in facts
you already have.
Since we have lots of accounting data points,
this is best place to look for them. Traditional
accounting ratios are synthesised KPIs.
71

69.
Next topic:
Making the forecast influential
73

70.
Making the forecast influential
Traditional forecasting aims to predict future accounting
reports mostly using old accounting reports, and the
outcome of forecasting is a P&L
But the decisions and the plans for the business are
probably focused on customers and innovations.
74

71.
75
STRATEGY
Points of difference
Competitive Advantage
Barriers to entry
Plan,
Objectives
Operations
Business Control
Are we on track?
Corrective action.
Are there surprises and
opportunities?
Traditional
forecast
Influential
forecasting

72.
Influence and Diagnose
The important decisions for a business are
mostly based on
Customers, Competitors, Innovation.
So the closer finance gets to that, the more useful we become.
Too often finance is merely helping to manage the consequences of
those decisions.
76

73.
But we also need ...
Simplicity
Numbers
Focus
77

74.
A solution
1. Use models but use them properly
2. Base the forecast on “business drivers”
78

75.
What is the default model
of traditional forecasting?
79

76.
Models are great. Are they perfect?
Models work because they let us freeze most
real world complexity and focus on what
matters.
If the stuff we freeze is just noise, this is very
helpful.
80

77.
When models go bad
1. Black Swans
2. When the “noise” actually becomes
important.
E.g. you may ignore a stable cost but if it
turns out to be currency driven (supplier
offshored) and the AUD crashes...
81

78.
When models go bad
3. When business drivers change (competitor,
M&A, innovation, market shift, new
management)
82

79.
Black Swans
So named after the Australian Black Swan.
Other examples:
GFC
Rise of iPhone
Sept 11
83

80.
Black Swans
• "Black swans" are highly consequential but unlikely
events that are easily explainable – but only in retrospect.
• Black swans have shaped the history of technology,
science, business and culture.
• As the world gets more connected, black swans are
becoming more consequential.
84

81.
Black Swans
• The human mind is subject to numerous
blind spots, illusions and biases.
• One of the most pernicious biases is
misusing standard statistical tools
• Expert advice is often useless and
overvalued.
85

82.
Black Swans
• Most forecasting is pseudoscience.
• You can retrain yourself to overcome your
cognitive biases and to appreciate randomness.
But it's not easy.
86

83.
My Black Swans
Indonesia 96/97
Sept 11, 2001
Rise of China 2000s
EU Anti-dumping
Shift to consumer for CFLi
CO2 abatement programs
GFC 2009
87

84.
One of the biggest black swans
North American shale oil (fracking)
Remember peak oil?
Now, oil is $50 a barrel and the US no longer
needs to import. It has so much gas that cheap
energy is now a competitive advantage.
88

85.
How to deal with black swans?
Don’t be over-confident in your forecasts.
Be robust and highly responsive to opportunities.
We should value forecasting approaches which can quickly adapt to
new circumstance.
An agile, responsive business will use simple forecasting methods, but
simple forecasting methods won’t make a business agile.
Extremely expensive and sophisticated models are not better. They can
be worse because they encourage over-confidence.
Google Long-Term Capital Management.
89

86.
When models get over-extended
A model which loses its simplicity is a half-way
place, the worst of both worlds.
90

87.
One of the worst examples…
Until 15th century,
Europeans had the
earth at the centre of
the universe.
91

88.
Epicycles to explain phases of Venus
The model kept
getting more and
more complex.
92

89.
What was the “Black Swan” for
models of the solar system?
93

90.
Coping with the weakness of models
Be prepared to change models when business
focus changes.
As a habit, diagnose both favourable and
unfavourable deviations from your forecast.
94

91.
What to model?
95

92.
The P&L as a forecast model: Pros and Cons
PROS
• You are going to do it anyway
• Historical data in this format is
free
CONS
• Bad at focus
• Says little or nothing about how
the business wins customers
• Poor for diagnostics
• Extremely limited at decision
support
• Very complex (mix of cash and
imaginary numbers, and technical
considerations)
• Serves too many masters
96

93.
Business Drivers
A business driver is
●Influenceable by the business
●Measurable
●Linked to sales (or costs) via a simple
formula
97

94.
Model built on business drivers
Connect drivers to a cashflow event with linear
formulas:
cash_event = driver_score * something1 + something2
You can initially guess something1 and something2.
Measuring it means you will get more accurate.
98

95.
The Perfect Customer
A business should have a “perfect customer” in
mind.
This customer should recognise herself in the
selected handful of business drivers.
99

96.
Which of these could be business drivers for a mid-sized
residential construction business operating in Melbourne’s north?
• National home lending approvals
• Number of visitors to our display homes
• Number of VCAT modifications and rejections of our plans
• Percentage of modifications per job to our standard home designs
• Avg percentage of total home project spend we capture
• Percentage of clients who take our landscape gardening bundled
service
• RBA interest rate settings
• Number of days lost to injury on site
100

97.
Strategic plan says
“We target customers looking for fast, simple
and cheaper builds”.
Choose two drivers.
101

98.
Or Strategic plan says
“We will capture high value clients by providing
a complete, custom approach to the entire
family home project including integrating green
space with the home design”.
Choose best two.
102

99.
Account for most, not all
A small range of drivers will not capture every
variation in the business, but they should
capture most.
103

100.
Recap Business Drivers
A P&L is a generic report.
But what is the fingerprint, the DNA of a
business?
104

101.
Business Drivers and business DNA
The DNA of a business is its competitive advantage. How it
is different from competitors in a way that adds value to
customers.
The selection of the top five or ten business drivers reflects
how the business plans to build on its competitive
advantages.
105

102.
A test
Business Drivers should tell an observer what
the business is doing, where it wants to go and
how it wants to get there.
A P&L + balance sheet is much less clear
106

103.
Business drivers vs leading
indicators
Leading indicators may include GDP growth or
birth rates. But we can’t affect them.
Your forecast model should lead to choices and
decisions. So the role of external leading
indicators should be limited.
107

104.
Business drivers and the sales funnel
Business drivers often make a sales
funnel
Sale funnel is often the most
important thing to forecast.
108

105.
A partial real world example
In this business, overheads are quite stable over a
three year period, except for marketing and to
some extent wage-bill. Project spending (e.g.
expansion) is excluded.
Contribution margin and spend per visit are also
stable.
109

106.
Note
These numbers are disguised and distorted to
protect client confidentiality, but not in a way
that diminished the conclusions
110

107.
Business drivers
This is a high-end consumer business.
The management team has a few common
KPIs on their bonus, including
• New customer acquisition
• Repeat rate
111

108.
Growth has rapidly escalated
112

109.
Recent marketing spend
113

110.
# customers is a
leading indicator
114
Active Customers vs MAT SalesCust
Cust

111.
Building a simple forecast model
Use management KPIs
Focus on the issue at hand: marketing spend
This is a ”disposable model”: how much has
sustained marketing spend helped? Should it
continue?
115

112.
In this business:
A customer repeats X times per year (X =2)
The avg spend is $Y per visit (say Y = $500)
There is a relationship between marketing and
new customers
116

113.
117

114.
Do a simple time shift (cut and paste in Google
Sheets)
118
→ 2 month
lag

115.
Linear correlation mkt vs customer (after time shift)
119
Active patients 4500 + (spend-150000)/300000*400

116.
Simple revenue model
120
Example, obscured data
Business Driver:
Marketing spend (monthly avg) [2 month
lag] $400,000
Active patients 4833
Business Driver: Annual visits (Repeat rate
=2) 9667
Monthly visits 806
Business Driver: Spend per visit $500.00
Monthly revenue $403,000
Done!
Tools: Google
Sheets
Time: 1.5 hours

117.
Technical forecasting
121

118.
Example: Excel 2016
Excel 2016 has a new forecasting model which
defaults to “AAA version of the Exponential
Smoothing (ETS) algorithm”
I googled it and came to a PDF with pages that
look like this:…
122

119.
123
This is 90 slides of explanation for this feature (in R, which I assume Microsoft has ported
to Excel 2016 (Windows, not mac))

120.
Technical forecasting
Technical forecasting which uses advanced
statistics on historical accounting is looking for
hard to see connections in historical data you
already have.
But the most important relationships are easy to
see
124

121.
Simple Forecasting and Big Data
I will extend meaning of ‘big data’ to include
“integrated data”.
This could mean: web stats, social media
engagement, detailed job progress data and
real time (sales, competitor, location…)
125

122.
Predictive analytics means finding hidden connections between data
and cash-flow events with neural networks and other advanced
methods.
This could be very awesome even for SMEs.
Unlike traditional technical forecasting, big data introduces new
information to the business.
.
126
Predictive analytics (Big data)

123.
Definition (Forrester)
“Using patterns discovered in heterogeneous
[mixed] sources of data, business analysts and
data scientists can create predictive models to
improve business outcomes.”
Obviously, this is very interesting to my vision of
business forecasting
127

124.
Keep it Simple
• By discovering and ranking business drivers,
predictive analytics can help simple models
work better.
• But the value of simplicity is not washed
away by neural nets and super-computers.
128

125.
Examples of simplicity visualised
129
A sales model based on non-
accounting leading-indicator drivers can
provide continually refreshed forecasts.
Simple forecasts and open-source
technology make this cheap and easy.

126.
Forward drivers can be shown easily
130

127.
Diagnosis
131

128.
Communication and analysis
132
Measures which everyone understands. They get how to influence them. They are bonused on them.
Result: very strong alignment and engagement and everyone is on the same page.

129.
It’s simple
It’s customer facing
It’s aligned to relevant decisions and focus
Is it diagnostic?
So far, so good.
133

130.
Growth-driven forecasting
Simple forecasts can change fast. We are not
committed to long complex processes and
heavy IT.
So, let’s take advantage ...
134

131.
We sit in a discussion about growth
We have done some “predictive analytics”. It
shows that customers who buy a more diverse
range of products are more likely to be repeat
customers.
What business drivers does this suggest?
135

132.
Our aim is to reforecast the business in a few minutes
Come up with a sketch of a new driver and try
to include it in a forecast model.
136

133.
What about cost drivers?
Forecasting significant costs is the same.
Influenceable, measurable drivers.
Is currency influenceable?
137

134.
What makes this more than just a
basic spreadsheet?
The selection and use of business drivers is the
magic sauce. In terms of actually doing it,
though, a spreadsheet is completely adequate.
<Insert funny SAP R/3 story here>
138

135.
Finding cost and/or sales drivers...
Follow the points of difference (the competitive
advantage) OR management plans.
This will reveal where to focus.
A business selling a commodity (iron ore):
where are its points of difference?
139

136.
Using simple forecasting for
accountability
The forecast should reflect the key decisions
through the business and cost drivers in use.
These should then be measured for diagnosis
and accountability.
140

137.
External factors beyond influence?
Such as: Currency, economic growth, droughts, Chinese economic collapse …
Two options
1: Be nimble, assume competitors are equally affected. React & forecast when it happens
and otherwise ignore.
2. We can’t influence them, but we can include them in the simple forecast for sensitivity
analysis
Discussion … Which approach fits best with the “simple forecast” philosophy?
141

138.
Simple forecast maturity model
Level 1:
Streamline the traditional forecast method. Use
drivers or KPIs synthesised from accounting
data such as price & mix effect, ratios like
debtors days outstanding.
142

139.
Simple forecast maturity model
Level 2
Use non-accounting business drivers for sales funnel
and/or key costs, feed this into traditional forecasting.
Measure actual drivers and diagnose, several times per
year but outside of the normal reporting and analysis
143

140.
Simple forecast maturity model
Level 3:
Business drivers are aligned to strategic plan and incentive
schemes. All business drivers meet the definition.
They are the central discussion in forecasting and
reviewing results. They are used for sensitivity analysis.
Dashboard tools provide daily or real-time insight into
business drivers and automatically updated forecasts.
144

141.
Simple forecast maturity model
Level 4.
Business drivers are partly based on predictive
analytics. Forecast models are revised to follow
changes in management focus or market
developments.
145

142.
Conclusions
Simplified forecasting is part of the new finance
function. It brings finance closer to influencing
profitable decision-making by aligning with the
points of difference which lead to growth.
146

143.
Conclusions
Simplified forecasting swaps analysis of historical
accounting data for a small set outward-looking, predictive
measurements
It is diagnostic.
It uses simple models, and new models when needed
147

144.
Conclusions
Skilling up
• Understand or suggest a small set of business drivers
• The future added value of finance will be around
numerical strengths. Consider CPD on statistics,
predictive analytics and decision theory.
148

145.
Books to read/sites to visit
• Black Swan: Impact of the highly improbable (N. N.
Taleb)
• Visual Explanations (E. Tufte)
• The Emperor’s New Clothes (H. C. Andersen)
• Khan Academy: Probability and Statistics (website)
• Superforecasting (Tetlock and Gardner)
149