Presentation - Session 09

7/27/2019 Presentation - Session 09

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Forecasting Techniques

Part 1



© 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




Learning Objectives

ForecastingProcess


Sizing the

Market


service

Overview of

Techniques



Time Series

Analysis



2



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.”

© Copyright Coleago 2010 3



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.




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.




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.




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




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





Learning Objectives

ForecastingProcess


Sizing the

Market


service

Overview of

Techniques



Time Series

Analysis



9



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.




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




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




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.





Learning Objectives

ForecastingProcess


Sizing the

Market


service

Overview of Techniques

Understanding the suitability of different forecastingtechniques in particular situations

Time Series

Analysis



14



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




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.





Learning Objectives

ForecastingProcess


Sizing the

Market


service

Overview of Techniques

Understanding the suitability of different forecastingtechniques in particular situations

Time Series

Analysis



17



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.

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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


E ample a mobile phone retailer needs place q arterl orders for phones



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.




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.


Practical exercise: Use Excel to fit a trend line Excel file



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

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2003 2004 2005 2006 2007

U n i t S a

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Actual Sales




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


Estimating the trend: Step 2 “synchronise” the moving averages; an



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




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.


2 Period Moving Average

Quarterly Time Series


4 Period Moving Average




Worked example: Estimating the trend Excel File “Forecasting



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.


Result: a sales forecast based only on trend which in fine for an annual



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.

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U n i t S a l e s

Quarters

Sales

Actual Sales Syncronis. 4 Period Moving Average Trend Forecast


Worked example: Calculating seasonal indices. Excel File “Forecasting



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%


Forecast based on trend and seasonality



Forecast based on trend and seasonality

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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




Session Summary

Presentation - Session 09

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