Sales Forecasting for Management Consultants & Business Analysts

1
Sales Forecasting for
Management Consultants
A practical guide with case studies & real-life
examples

2
During many consulting projects you may be asked to forecast the sales
of the firm or check sales forecast models done by the customer.

3
Sales forecasting requires specific approach to data and also a lot of creative, out of
box thinking to address the issue of insufficient data and changing environment.

4
In this presentation I will share with you tips that will help
you to do fast and efficiently sales forecast models in Excel.

5
Useful Tools for Sales
Forecasting
Case Studies in Sales
Forecasting
Basics of Sales Forecasting
We will discuss 3 main things in this presentation

6
What you will see in this presentation is a part of my online course where you
can find case studies showing analyses along with detailed calculations in Excel
Sales Forecasting for Management
Consultants & Business Analysts
$190
$19
Click here to check my course

8
Basics of Sales Forecasting –
Introduction

9
In business you have to make a lot of important decisions
In this section we will discuss the foundations for sales forecasting. We
will need them later when we will be looking at tools and case studies.

10
In this section we will discuss the foundations for sales forecasting. We
will need them later when we will be looking at tools and case studies
Different approaches to
sales forecasting
Data used for Sales
forecasting
What you have to
account for in sales
forecasting

11
Different approaches to sales
forecasting

12
You can use different approach to sales forecasting in 3 main areas
Which side of the
market drives the sales?
What is the starting
point
What you forecast?
 Demand driven / Demand
based
 Supply driven / Capacity
based
 Bottom-up approach – you
first forecast elements and
then you sum it up (from
specific to general)
 Top-down approach – you
first forecast the overall
sales and only after that you
forecast the split by
categories, regions, units etc.
 Forecast directly the value
of sales
 Forecast each and every
component of sales
(quantity sold, average
prices sold, discounts given
etc.) separately. Value of
sales is calculated indirectly
using forecasts of
components

13
Data used
for Sales Forecasting

14
You can base your forecast on 3 main groups of data
Historical Data Future Data Capacity data
 Data from previous periods
related to sales
 Data from previous periods
that could have direct or
indirect impact on sales
 Forecasts of things that can
have indirect impact on sales
(i.e. demographic data, GDP
forecast)
 Data on contracts signed or
offers placed
 Data on the production /
delivery limitation and their
future changes
 Data on the supply chain
limitation and their future
changes
 Data on changes in the
infrastructure

15
What you have to account for
in sales forecasting

16
There are plenty of things you may have to consider when forecasting sales.
Below some of them
Seasonality effect
Demographics & other structural
changes
Growth or contraction of your
markets
Growth or contraction of your sales
channels
Cycle of sales
Different level of promotions /
marketing support
Sales elasticity
Change in customer behavior
Growth / contraction of related
markets
Bundling of products
One-offs Stock / Inventory level

17
Useful Methods for Sales
Forecasting

18
Let’s look at the most useful methods used in sales forecasting
Regression models
Decomposition Analysis
Moving Averages
Growth modeling
Scoring Models
Rankings
Saturation analysis
Estimation by proxy
Pipeline analysis Portfolio analysis
One-offs analysis & Seasonality
analysis
Season shift & structural changes
analysis

19
Useful Tools for Sales Forecasting

20
Useful Tools for Sales Forecasting
– Introduction

21
In business you have to make a lot of important decisions
In this section we will discuss certain tools and
concepts that are useful in forecasting sales.

22
In this section we will discuss certain tools and concepts that are useful
in forecasting sales
Scenario analysis Simulations
Decomposition analysis
/ Break down analysis
Sensitivity analysis
Simple Regression
Model
Random Variables

23
Scenario Analysis –
Introduction

24
Future is pretty difficult to figure out. You don’t know what will happen.
In those cases it is a good idea to consider a few different scenarios

25
Future is pretty difficult to figure out. You don’t know what will happen.
In this cases it is a good idea to consider a few different scenarios

26
Imagine that you are ice cream producer and you have to decide how much ice-
cream to produce for the next day without knowing what will be the weather.
Therefore, you have to consider different scenarios
Scenario 1 Scenario 2 Scenario 3
100 70 30

27
The scenario analysis consists of 5 steps
Define the thing
(goal function) you
want to analyze
Define which drivers
are the least certain
Define the scenarios
Define your
behavior / policy
Check the goal
function for every
policy
 You should be
analyzing the things
that are threatened
by different
scenarios and are
important for your
business
 It can be profit,
NPV from new
investment,
inventory you
should have etc.
 It is good to define
3-5 different
scenarios
 In every scenario
the main drivers
will have different
value
 You should assign
certain probability
to every scenario
 Scenarios do not
depend on you but
your behavior does.
 You can define a
policy / behavior
that helps you in a
specific situation
 Concentrate on
drivers that have
big impact and big
volatility
 The aim of this step
is to pick the right
policy, given the
scenarios and their
policy
 The best policy is
the one that gives
you highest
benefits (highest
goal function)

28
In the next lectures I will show you how to create and use scenario
analysis in practice using an example from airplane industry
Which price formula is the best
for my profits

29
Which price formula is the best for
my profits – Introduction

30
Now we will try to see which price formula is better for aircraft
maintenance service company
2 sites – in Poland and Croatia
Consider 4 different formulas
Consider 3 different scenarios

31
Materials
Scenario 1
 $ 30 K
Number of
manhours needed
 3 000 man-hours
Probability of the
scenario
 30%
Scenario 2
 $ 20 K
 3 400 man-hours
 25%
Scenario 3
 $ 15 K
 3 800 man-hours
 45%

32
Materials
Times & Materials
 Cost of Materials
increased by 15%
markup
Labor
 $ 50 per 1 man-hour
 We look at the real
man-hours needed
Fixed Fee
 $ 25 K
 $ 140 K
Mixed Option 1
 $ 25 K
 Fixed: $ 140 K
 On top of that 15% of
the labor cost
calculated using Times
& Materials formula
Mixed Option 2
 $ 25 K
 Fixed: $ 140 K
 On top of that for all
man-hours above 2
800 we use the Time
& Materials formula
but using the price of
$ 90 per 1 man-hour

33
Which price formula is the best for
my profits – Solution

34
Just as a reminder we were trying to decide which pricing formula is the
best for the MRO organization
2 sites – in Poland and Croatia
Consider 4 different formulas
Consider 3 different scenarios

35
It seems that the Mixed Option 2 price formula is the best solution
Gross Margin
In thousands of USD
90
58
84
117
Times & Materials Fixed Fee Mixed Option 1 Mixed Option 2

36
Check an example of scenario analysis in Excel done in consumer goods
Click here to go to the video

37
Simulation analysis –
Introduction

38
Future is pretty difficult to figure out. You can use scenario analysis or
you check ALL the potential options and see which is optimal

39
Imagine for a second that you have a small bakery trying to decide what is the optimal
number of cakes that you should bake. You want to use simulations to find out

40
For the producer of cakes that at the same time can bake from 1 to 10 cakes
using the simulation to find optimal production batch would entail calculating
the costs for all options

41
There are plenty of things you can do thanks to simulations
Find optimal solutions
Carry out sensitivity analysis
Plan & Forecast
Test the boundaries of the
system
Find weak spots

42
In the next lectures I will show you how to use simulation in practice. I
will be talking about 2 examples
What will be the effect of the
price increase
Simulation of the whole
Logistics System

43
What will be the effect of the price
increase – Introduction

44
The impact of the price change on your profit will depend on a few
factors
How big the increase is
What your competition does?
How aware of prices are the
customers?
Price sensitivity
The role of the product you
are increasing the price
Components of the average
basket

45
Imagine that you want to estimate the price change impact for a small
chain of local coffee shops
20 location in Poland
Sell coffee, cakes, sandwiches and
quiches
3 different motives for going there

46
What will be the effect of the
price increase – Solution

47
Sell coffee, cakes, sandwiches and
quiches
3 different motives for going
there

48
If we look just at coffee gross margin, we should increase the price of coffee by
9%. If we look at the total gross margin, 4% price increase makes more sense
0
2 000
4 000
6 000
8 000
10 000
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25% 26% 27% 28% 29% 30%
0
5 000
10 000
15 000
20 000
0% 1% 2% 3% 4% 5% 6% 7% 8% 9% 10% 11% 12% 13% 14% 15% 16% 17% 18% 19% 20% 21% 22% 23% 24% 25% 26% 27% 28% 29% 30%
Gross Margin – Only for Coffee vs price increase
In thousands of USD
Gross Margin – Coffee and Cakes vs price increase
In thousands of USD

49
Decomposition analysis –
Introduction

50
Revenue Growth
Decomposition analysis shows you what are the components, driving forces
behind certain phenomena. Imagine for a second that we are analyzing
revenue growth of a FMCG company

51
We would like to break it down to following components. This decomposition
helps us see how we managed to grow the revenues
New customers
New products
Increased sales in existing customers and products
Others
Increased price

52
Decomposition analysis helps you achieve a lot of goals
Pick the biggest contributor
You will know what to focus on to
get the required effect
Check to what extent you were
dealing with one-offs
Create a business model based on
drivers and KPIs
Forecast / Plan the future results
Evaluate the business / business
unit

53
In the next lectures I will show you how to create and use the
decomposition in practice
LFL analysis for a coffee shops
chain

54
LFL analysis
– Introduction

55
LFL analysis is the analysis of the change in revenues for stores that have been
at least for 12 months. In this analysis through decomposition we try to
pinpoint the most important aspects
Average
transaction
value (ATV)
Average selling
price (ASP)
Traffic % Conversion
Items per
transaction (IPT)
Total # of
transactions
x x
Revenue
x
x
Average first
price
% of first price
Driving KPI
Indicators dependent on others

56
Imagine that you identify the driving factors in LFL growth in the last
few years
Sell coffee, cakes,
sandwiches and quiches
15 stores are LFL stores

57
Since in the case of the coffee shops we do not give many discounts we will
stop at the level of Average Sales Price (ASP) and not go deeper
Average
transaction
value (ATV)
Average selling
price (ASP)
Items per
transaction (IPT)
Total # of
transactions
x x
Revenue
x
Driving KPI

58
LFL analysis – Solution in Power
Point

59
Sell coffee, cakes,
sandwiches and quiches
15 stores are LFL stores

60
LFL analysis shows that the biggest impact was from IPT increase.
Second in importance was traffic
Average
transaction
value (ATV)
Average selling
price (ASP)
Items per
transaction (IPT)
Total # of
transactions
x x
Revenue
x
Driving KPI
120%
20% 12% 50% 13%
5% Percentage growth between
2019 and 2015

61
LFL analysis shows that the biggest impact was from IPT increase.
Second in importance was traffic
12 960
3 472
2 025
8 679
2 170
29 306
Revenue 2015 Traffic Impact Conversion Impact IPT Impact ASP Impact Revenue 2018
Total LFL sales growth by KPI for stores existing since 2015
In millions of USD

62
Sensitivity analysis
– Introduction

63
Once you come up with an optimal solution you want to see how sensitive it is
to small changes in underlying assumptions. The solution can be pretty
stable….
Current Solution -
7%
Current Solution -
5%
Current Solution -
2%
Current Solution -
1%
Current Solution Current Solution
+1%
Current Solution
+2%
Current Solution
+5%
Current Solution
+7%

64
… or the contrary very volatile
Current Solution -
7%
Current Solution -
5%
Current Solution -
2%
Current Solution -
1%
Current Solution Current Solution
+1%
Current Solution
+2%
Current Solution
+5%
Current Solution
+7%

65
You want to carry out a sensitivity analysis for many reasons
Volatility means risks
You want to prepare for the risk /
hedge against the risk
In some cases you may want to
choose less sensitive option
Sensitivity analysis is basis for
managing a portfolio of projects
Sensitivity analysis helps you
manage expectations

66
Check a video showing an example of sensitivity analysis in Excel
Click here to go to the video

68
In many cases the outcome of certain phenomena is
unknown. In this situation we need Random Variables.

69
Random variables at the end will produce specific outcome but we
don’t know exactly what it will be
Fixed 1 Known Value Random Variable
≠
 Random Variables has many
potential outcomes
 None of the result is certain

70
Random Variable is what you will find behind the closed doors. Once you open the door you
will know for sure but before that there are only some potential results, nothing certain.

71
Let’s have a look at a few examples of random variables
The temperature of the air
The price of a stock
Height of the newborn kid at age
18
The result of flipping the coin
Size of the crops of grain
Price of bread
# of people you will meet on the
street
# of kids you will have
Sales of a product Age at which you will die
# of accidents that happen on the
roads
Market capitalization of a
company

72
What will be the outcome of the random variable is unknown.
However, we know the potential outcomes and their probabilities.

73
Let’s have a look at flipping the coin / coin tossing
The result of flipping the coin /
Coin tossing
Head
Tails
The probability of a specific
result
50%
50%

74
Let’s have a look at throwing of a dice
The result of throwing of a dice
result
𝟏
𝟔
𝟏
𝟔
𝟏
𝟔
𝟏
𝟔
𝟏
𝟔
𝟏
𝟔

75
Let’s have a look at throwing of a dice
result
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%

76
Random Variables in Excel –
Introduction

77
Just as a reminder below the results of throwing a dice with their
probabilities.
result
16.7%
16.7%
16.7%
16.7%
16.7%
16.7%

78
In order to be able to recreate the game in the Excel we will have to
reframe it.
100
16.7 16.7 16.7 16.7 16.7 16.7
From 0 to 16.7
From 16.7 to
33.3
From 33.3 to
50.0
From 50.0 to
66.7
From 66.7 to
83.3
From 83.3 to
100

79
We need 1 last piece to be able to recreate the game of
throwing a dice. We need the hand that will throw the dices.

80
We can do the throwing of the dice via Excel function called RAND
RAND
 Generates random numbers from 0 to 1
i.e. 0.74
 You can also express it in percentage. In
this case we can say it generates
random numbers from 0% to 100% i.e.
74%

81
Let’s see how it will work. Let’s assume that we have generated 45% (in
other words 0.45)
100%
16.7% 16.7% 16.7% 16.7% 16.7% 16.7%
From 0% to
16.7%
From 16.7% to
33.3%
From 33.3% to
50.0%
From 50.0% to
66.7%
From 66.7% to
83.3%
From 83.3% to
100%
45% =

82
Let’s assume that we have generated 72% (in other words 0.72)
100%
16.7% 16.7% 16.7% 16.7% 16.7% 16.7%
From 0% to
16.7%
From 16.7% to
33.3%
From 33.3% to
50.0%
From 50.0% to
66.7%
From 66.7% to
83.3%
From 83.3% to
100%
72% =

83
For more details and content check my online course where you can find case
studies showing analyses along with detailed calculations in Excel
$190
$19

84
Case Studies in Sales Forecasting

85
Case Studies in Sales Forecasting
– Introduction

86
In this section we will go through different case studies and different industries
that will help you understand how to do sales forecasting in practice
Plywood industry Book publisher Cosmetics producer
Retailer Consulting firm Drones

87
Plywood sales forecasting –
Case Introduction

88
Let’s imagine that you have to forecast the sales of 1 factory of plywood. We will see how it
can be done given that the main driving side will be the supply side – production capacity.

89
A few information about the plywood producer
5 plants
Estimate the quantity of plywood sold
in the Lithuanian plant
Carry out simulation given probability
of accidents

90
Plywood sales forecasting
– Drivers

91
Let’s see what will impact the monthly production of plywood
# of working days in
the month
=
Monthly Production
Average Daily
Production
x
# of working days in
the month
=
# of all days in the
month
# of days lost due to
holidays
-
Average Daily
Production
=
Max. production per
1 day
Production lost due
to weather
-
Production lost due
to maintenance
-
Production lost due
to accidents
-

92
Book publisher sales forecasting –
Case Introduction

93
Let’s imagine that you have to forecast the sales of a book publisher. His sales
are driven by novelties and the sales of already existing content.

94
A few information about the publisher
New and old books will behave
differently
A book fall into 1 out of 5 categories
Bestseller, High-seller, Medium, Low,
Niche
Old books will be subject also to
seasonality

95
Book publisher sales forecasting
– Drivers

96
Let’s see what impact the sales of new books
Average quantity
sold in launch month
=
Quantity sold in the
first 12 months
# of months in the
launch period
x
Average quantity
sold afterwards
12 - # of months in
the launch period
x
+
30 000
=
99 0000 3
x 1 000 9
x
+
Bestseller
5 000
=
15 750 3
x 83 9
x
+
Medium

97
Let’s see what impact the sales of old books
Basic Monthly sales
of an old book
=
Quantity sold in a
specific month
100% - % lost due to
age of the book
x
Seasonality Impact
in a specific month
x
1 000
=
372 100% - 7%
x 40%
x
Bestseller; 3.5-year-old – in February
83
=
106 100% - 15%
x 150%
x
Medium; 11-year-old – in October

98
Cosmetics Producer sales
forecasting – Case Introduction

99
Now let’s try to build a sales forecast for a cosmetics producer selling via a retail chain. Here we
will have to take into account the chain size and growth of the market for every category.

100
A few information about what you have to do
You have to predict the sales in value
for 5 products
You have sales data from previous
year (quantity and price)
For every product we have estimated
growth of the market
We also know in how many stores
our product will be available

101
Cosmetics Producer sales
forecasting – Drivers

102
Let’s see how we can estimate the sales of a specific product in each
month
Quantity Sold of
Product A next year
=
Revenues for Specific
Month for Product A
Average Price for
Product A next year
x
Quantity Sold of
Product A next year =
Quantity Sold of A per
store PY
1+ Change of the
market in %
x
Average Price for
Average Price for
Product A PY
1+ Change in the price
in %
x
# of stores next year
x

103
Let look at a short example
Quantity Sold of
Product A next year
=
Month for Product A
Average Price for
Product A next year
x
Quantity Sold of
Quantity Sold of A per
store PY
1+ Change of quantity
sold per store
x
Average Price for
Average Price for
Product A PY
1+ Change in the price
of A next year
x
# of stores next year
x
105 000 = 10 0000 1+ 5%
x 10
x
6 = 5 1+ 20%
x

104
Let look at a short example
Quantity Sold of
Product A next year
=
Month for Product A
Average Price for
Product A next year
x
105 000
=
630 000 6
x

105
Retailer sales forecasting –
Case Introduction

106
Let’s imagine that you have to forecast the sales of a retail chain. They have
10 old stores and plan to open another 5. Use the data from the previous year.

107
A few information about the firm
Retailer has 10 stores and will open
5 new
Assume the same seasonality as in
the previous year
Retailer operates 2 formats: Small
& Big
Stores have different age and
different expected LFL growth

108
Retailer sales forecasting
– Drivers

109
For simplicity, let’s assume that we will be estimating the sales of a
chain consisting of 2 stores
An old / existing store A new store
 Store that has been
previously open
 Future sales will be
estimated using sales from
previous period and
assumed LFL / same store
growth
 The older the store the
lower usually LFL growth
 New store sales will depend
on the type of the store,
format, location etc.
 In the first-year total sales
will be heavily impacted by
the # of months during
which the store is opened

110
Let’s see how we can estimate the sales of this 2-store retail chain
Sales of the old store
next year
=
Total Sales
Sales of the new
Store
+
Sales of the old
Store next year
=
Sales of the old store
previous year
1 + Change in % of
sales (LFL change)
x
Sales of the new
Store
=
Average monthly
sales assumed
# of months during
which store is open
x

111
Consulting Firm Sales Forecast –
Case Introduction

112
Let’s try to predict the sales level for a consulting firm. This is difficult due to the
high unpredictability of the projects. We will try to account for it in our analysis.

113
A few information about the firm
They have 30 consultants
They charge USD 25 K per month per
consultant
For next year they have in the pipeline
10 projects
Every project has different probability
of happening

114
Consulting Firm Sales Forecast –
Drivers

115
On the face of it is easy to predict sales in consulting as there are only 3
drivers
Project 1 Sales
=
Total Sales ….
+ Project n Sales
x
# of consultants
=
Project Sales
Duration of the
projects in months
x
Price per consultant
per month
x

116
Drones Sales Forecast –
Case Introduction

117
Now let’s try to estimate the sales of a drone producer. He is
selling via 2 channels: Amazon and 3rd party retail chain.

118
They sell via Amazon and Retail Chains
Sales are subject to seasonality and
random effect
Prices are subject to seasonality
A few information about drone firm
Estimate sales using average of 100
simulations

119
Drones Sales Forecast
– Drivers

120
Let’s see how we can estimate the sales of the drone producer in one
channel (Amazon or Retail Chains)
# of drones sold
=
Sales in a channel
during the month
Average Price
x
# of drones sold
Basic monthly sales
in quantity
=
1 + Seasonality
change in %
x
1 + Random change
in %
x
Average Price Basic Price
=
1 + Seasonality
change in %
x

121
For more details and content check my online course where you can find case
studies showing analyses along with detailed calculations in Excel
$190
$19

122
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