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7/27/2019 Presentation - Session 09
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Forecasting Techniques
Part 1
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© Copyright Coleago 2010
Learning Objectives
ForecastingProcess
Understanding the process of forecasting demandand essential forecasting concepts
Sizing the
Market
Determining the potential market size for a product or
service
Overview of
Techniques
Understanding the suitability of different forecasting
techniques in particular situations
Time Series
Analysis
How to use time series analysis to make a forecast
based on trend and seasonality
1
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© Copyright Coleago 2010
Learning Objectives
ForecastingProcess
Understanding the process of forecasting demandand essential forecasting concepts
Sizing the
Market
Determining the potential market size for a product or
service
Overview of
Techniques
Understanding the suitability of different forecasting
techniques in particular situations
Time Series
Analysis
How to use time series analysis to make a forecast
based on trend and seasonality
2
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Characteristic of a “good” forecast
A forecast of market demand should have the following characteristics:
All assumptions and the way in which they impact on the results are fully
documented.
There is supporting market research.
The forecast is credible and stands up to reasonableness checks.
There are no obvious contradictions with generally accepted models of market
behaviour.
The forecast supports the objective to be achieved.
Don’t over complicate matters!
“It is better to be roughly right, than precisely wrong.”
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To calculate whether a particular course of action will increase
shareholder value, a forecast of revenue is required
Prices
Market Value
Addressable / Potential Market
Sales Volumes
Company’s Prices
Company’s Revenue
Company’s Volume Market Share
Company's Cost of Sales
Company’s Gross Margin
This session is primarily concerned with the revenue side of the business plan and
concentrates on the potential market, total market volumes, prices, market values andmarket share.
The result is a market forecast which flows into the marketing plan. The marketing
plan contains a marketing planning model which includes a detailed revenue as well
as a cost of sales forecast.
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Market forecasting is the key business driver
Prices
Traffic
Customers
ARPU
Network Dimensioning
Equipment Unit Prices
Capital Expenditure
The market forecast is the key input into the dimensioning of the business as a
whole and in particular telecoms network. Customers, ARPU and prices are combined to generate a traffic forecast.
The traffic forecast drives the network dimensioning and plan and hence the capital
expenditure forecast.
Often the marketing function
does not understand the capeximpact of their decisions.
It is essential for marketing and
technical functions to work
closely together. Only then can
the cash flow impact of
marketing decisions beascertained.
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Top down v bottom up forecasting
Top Down Forecasts
Forecast revenue by identifying a subset of a macro forecast, e.g. forecasting totaltelecommunications spend as a proportion of Gross Domestic Product.
– GDP * % Telecom Spend * % Market Share
No insight into the economics of the business.
Blunt forecasting tool.
Bottom-Up
Forecast
Top Down Forecastto benchmark the
bottom up forecast
Forecast
Bottom Up Forecasts
The total revenue forecast is the
sum of individual elements.
E.g. forecasting voice revenue by
separately forecasting customer numbers, minutes of use and tariff
per minute.
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Break a forecast down into different market segments and revenue
streams
Segmentation for marketing purposes may not always be the same as that used
for forecasting purposes. In theory market segments should be quantifiable, in practice detailed
quantitative data, particularly a historic time series, for different segments may
not available.
Corporate
Segment
SME
Segment
Consumer
Segment
Packet
Data
Customer
Minutes
Intercon.
Minutes
SMS Content
MMS
Revenue should be
decomposed into its constituentparts.
An ARPU forecast is the sum of
revenue streams for specific
services, each with their own
usage pattern and tariffs.
VASVideo
Telephony3rd Party
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Revenue forecast in nominal vs. real terms
A market forecast shows that the marketvalue is increasing by 20% per year, but
does not mention whether it is in real or
nominal terms.
It looks like a great market!
2007 2008 2009 2010 2011
M a r k e t V a l u e $ M i l l i o n
Subsequently we learn that annual
inflation is 20%. The forecast in real
terms is flat.
That does not look so good anymore!
2007 2008 2009 2010 2011
M a r k e t V a l u e $ M i l l i o n
R e a
l T e r m s
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© Copyright Coleago 2010
Learning Objectives
ForecastingProcess
Understanding the process of forecasting demandand essential forecasting concepts
Sizing the
Market
Determining the potential market size for a product or
service
Overview of
Techniques
Understanding the suitability of different forecasting
techniques in particular situations
Time Series
Analysis
How to use time series analysis to make a forecast
based on trend and seasonality
9
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Market demand is a crucial variable upon which an investment decision
rests
In order to gauge the market for a product or service you should start by defining the
addressable market – i.e. the consumers or businesses that have a conceivable
need for it and can afford to buy it.
sufficient
income
30% of
pops
Addres-
sable
market
18% of
pops
Total
population
100%
Aged 16+
60% of pops
The addressable market will provide
a broad picture within which potential
demand can be estimated.
It gives an initial reality check that will
help keep your feet firmly on theforecasting ground.
It provides a sampling universe for a
market survey.
Market research interviews should be
carried out only with individuals who
are within the addressable market.
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Market research using secondary sources and benchmarks should
be carried out prior to primary market research
Published sources quickly produce a
broad understanding of the market inquestion, identifies existing forecasts or
market size estimates, and generate
many useful benchmarks.
Benchmarks such as per capita GDP,
the number of people with tertiary
education, the number of people withcredit cards and similar indicators can
be used help to make an initial estimate
of a relative market potential between
countries.
Secondary Data Sources
Internet searches
Free published statistics
Local statistical agencies
Local central bank
Buy telecoms industry reports
World Development Indicators
(World Bank)
ITU statistics
CIA Factbook
Eurostat
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Primary market research
A large number of interviews is necessary to
make the survey statistically valid for
segmentation
Requires a questionnaire to be designed and
tested
Costly and takes two months
Cheap option
Can be used to test prior to custom made
survey
Used to gain qualitative insight
Use to ascertain key issues prior to prior writing questionnaire to custom made market
survey
Custom made market survey
with 500-2000 telephone or
face to face interviews with
potential buyers
Buy questions in omnibus
surveys
Focus group research
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A market research survey provides data that helps you to arrive at a
forecast; it is not a forecast in itself
Giving an answer to a question on what an individual might buy is a long wayremoved from making the real decision to purchase something.
Stated purchase intentions such as "I would buy in the first 6 months of launch"
tend to be unreliable.
Judgment has to be exercised in interpreting survey results.
Market research surveys in combination with other qualitative methods and
market behaviour models can produce a more reliable forecast.
© Copyright Coleago 2010 13
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© Copyright Coleago 2010
Learning Objectives
ForecastingProcess
Understanding the process of forecasting demandand essential forecasting concepts
Sizing the
Market
Determining the potential market size for a product or
service
Overview of Techniques
Understanding the suitability of different forecastingtechniques in particular situations
Time Series
Analysis
How to use time series analysis to make a forecast
based on trend and seasonality
14
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Forecasting techniques are grouped into two categories:
Quantitative and qualitative forecasting techniques
Quantitative methods such as time series and regression analysis
Qualitative or technological methods such as diffusion of innovation models,
judgmental techniques, market behaviour models
Requirements
Observable, measurable, and relevanthistoric data
Trends or relationships are identifiable
and expected to continue
Relevance
Existing products
Stable, mature products
Markets not subject to dramatic change
Slow technology development
Short time horizon
Quantitative
Requirements
Depends on technique, e.g. some historicdata is required for curve fitting
Market research to estimate addressable
market
Relevance
New products
Markets that are subject to dramatic
change
Rapid technology development
Long time horizon
Qualitative
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Time series forecasting and explanatory forecasting methods are
commonly used quantitative forecasting techniques
The methods rely on the availability of sufficient quantitative information in the form
of data sets.Mathematical analysis is applied to these data sets to generate formulae that can
be used in forecasts.
Time series forecasting
Does not involve finding out why things change over time, it simply relateschange to time.
The methods can be used even if systems that affect demand are not
understood.
Explanatory (or causal) methods using regression analysis
Involve an understanding of the way in which demand reacts to variables.
They recognise that many variables that affect demand have nothing to do with
time but are a result of deliberate actions, such as the decision to reduce prices
in order to increase sale volumes.
© Copyright Coleago 2010 16
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© Copyright Coleago 2010
Learning Objectives
ForecastingProcess
Understanding the process of forecasting demandand essential forecasting concepts
Sizing the
Market
Determining the potential market size for a product or
service
Overview of Techniques
Understanding the suitability of different forecastingtechniques in particular situations
Time Series
Analysis
How to use time series analysis to make a forecast
based on trend and seasonality
17
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A time series is a sequence of values relating to a repeating sequence of points in time.
Time series methods rely on ample historical time series data being available in order that you can detect and extrapolate an existing trend or pattern in the data. The
assumption is that these patterns can be applied to the future, i.e. there is an assumption
of continuity.
Time series data
Two types of patterns are
relevant for telecoms
markets:
– Trend is the direction of
the series.
– Seasonality are usually
monthly or quarterly
fluctuations around the
trend.
50
100
150
200
250
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
2003 2004 2005 2006 2007
U n i t S a l e s
Actual Sales
© Copyright Coleago 2010 18
E ample a mobile phone retailer needs place q arterl orders for phones
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Example, a mobile phone retailer needs place quarterly orders for phones:
how many phones should be ordered each quarter?
In the past sales increase 20% each year. Sales in 2009 were 1 million phones.
How many phones are likely to be sold in 2010?
The answer 1.2 million
Therefore the shop should order 1.2 million phones for the full year, but it want to place
order quarterly so as to always get the latest phones.
In the past sales where not evenly distributed throughout the year. 4 th quarter sales were
usually 40% of total annual sales, in Q1 15%, in Q2, 20%, in Q3 25% of the annual total.
For the first quarter of 2010, should the shop order the quarterly average sales i.e. 1.2
million / 4 = 300,000 phones?
Or for the 1st quarter should the shop order 1.2 million x15% = 180,000 phones?
The increase of 20% from 2009 to 2010 is the trend.
The degree in which quarterly sales differ from the average quarterly sales is called
seasonality.
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Separating trend from seasonality
If we know what the trend is and how seasonality impacts, could use this information to
make a forecast.
In order to discover what the trend is and what the effect of seasonality is we have to
separate the two:
– Step 1: Remove the effect of seasonality, so we are left with trend only.
– Step 2: Use the TREND function is Excel to make a forecast based on trend only.
– Step 3: Calculate “seasonality indices, which indicate the effect of seasonality.
– Step 4: Apply these seasonality indices to the forecast base trend and this will
produce a forecast based on trend and seasonality.
© Copyright Coleago 2010 20
Practical exercise: Use Excel to fit a trend line Excel file
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Practical exercise: Use Excel to fit a trend line. Excel file
“Forecasting Techniques”, tab Example 1
Highlight the column “Actual Sales” and create a line graph
Click in the graph
Single click on the series
Right click and select ADD
TRENDLINE
Select MOVING
AVERAGE
Under “Period” select 4, to
represent quarters
Click OK
View the solution on the
tab Example 1S
50
100
150
200
250
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
2003 2004 2005 2006 2007
U n i t S a
l e s
Actual Sales
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Estimating the trend: Step 1 calculate the moving average
The trend is a line of good fit with the moving average of a quarterly time series. Trend
does not take account seasonality, it only answers the question by how much sales
increase year on year.
Therefore in order to calculate the trend we first have to remove the effect of
seasonality.
This is done by calculating the 4 period (quarters) moving average. Each average
always contains one 1st quarter, one 2nd quarter, one 3rd quarter and one 4th quarter.
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
4 Period Moving Averages
Example: A Quarterly Time Series
Year 1 Year 2 Year 3 Year 4
© Copyright Coleago 2010 22
Estimating the trend: Step 2 “synchronise” the moving averages; an
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Estimating the trend: Step 2 synchronise the moving averages; an
illustration
14Feb
1 Jan – 31 Mar 1 Apr – 30 Jun 1 July – 30 Sep
Sales 1,000 Units Sales 2,000 Units Sales 3,000 Units
Aver. Sales 1,500 Units Aver. Sales 2,500 Units
Aver. Sales 1,750 Units
15 May 15 Aug30 Jun31 Mar
The averages of thequarters refer to mid
points between the
quarter dates
Taking the averages of the
average brings us back to
quarter end dates
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Estimating the trend: Step 2 “synchronise” the moving averages
The 4 period moving averages refer to mid points between the quarter dates
Taking the averages of the mid point brings the data back to the end of quarter dates.
To do this, the 2 period moving average of the 4 period moving average is taken.
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
2 Period Moving Average
Quarterly Time Series
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4
4 Period Moving Average
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Worked example: Estimating the trend Excel File “Forecasting
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Worked example: Estimating the trend. Excel File Forecasting
Techniques”, tab Example 2
Step 2: Synchronise the averages
with the timing of the observed data.
To do this, the 2 period moving
average of the 4 period moving
average is taken. Since the 4
period moving averages refer to mid
points between the quarter dates,taking the averages of the mid point
brings the data back to the end of
quarter dates.
Step 3: In order to make a forecast
based on the trend, the Excel Trend
Function is applied to the 4 Period
Synchronised Moving Average.
Step 1: Calculate the 4 quarter moving averages by averaging the quarters 1-4, then 2-5,etc. Each calculation is placed in the mid point of the dates used to calculate the average.
© Copyright Coleago 2010 26
Result: a sales forecast based only on trend which in fine for an annual
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y
forecast but not for a quarterly forecast
The quarterly forecast
based on trend is not
seasonally adjusted, i.e. the
effects of seasonality are
ignored.
This is fine if only the
annual total is required.
Often quarterly salesforecasts are required, e.g.
to order SIM cards.
The next step: Calculating and applying seasonal indices to the quarterly forecast data
based on trend produces a quarterly forecast that takes account of both of trend and of
seasonality.
50
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150
200
250
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
U n i t S a l e s
Quarters
Sales
Actual Sales Syncronis. 4 Period Moving Average Trend Forecast
© Copyright Coleago 2010 27
Worked example: Calculating seasonal indices. Excel File “Forecasting
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p g g
Techniques”, tab Example 3
Step 1: The ratio of actual data to the
corresponding moving average data is
calculated, producing seasonal indicesfor each period.
Step 2: Group the quarterly indices by
year and arrange them in a table.
Step 3: For each quarter, calculate the
average seasonal index.
Step 4: The average of the 4 seasonal
indices may not be 1. Adjust the factors
proportionally so that the average is 1.
Step 5: Multiply the seasonal indices
with data generated the trend function.
Result: quarterly forecast taking account
of trend and seasonality.
61 / 55 = 111%
© Copyright Coleago 2010 28
Forecast based on trend and seasonality
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Forecast based on trend and seasonality
50
100
150
200
250
300
1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
U n i t S a l e s
Quarters
Sales
Actual Sales Syncron. 4 Period Moving Average
Trend Forecast Sales Forecast
© Copyright Coleago 2010 29
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Session Summary