sales forecasts

7/29/2019 Sales Forecasts

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SALES FORECAST

Dr. Ashutosh Kumar


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Forecast is used to a prediction for a futureperiod, such as a weather forecast


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Market Potential

Sales Potential

Sales Forecast


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Market Potential/ Industry sales forecast

It is the estimated sales for all sellers in the entiremarket/industry for a specific period of time.

For Ex: The market potential for personal computers inIndia for the year 2005-06 is estimated to be 4 millionnumbers.


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Sales Potential/ Company Sales Potential

A sales potential is an estimate of the maximum possiblesales opportunities present in a particular market segmentopen to a specified company selling G & S during a statedfuture period.

Example:

Sales potential of ICICI-Prudential is expected to be close to 5% of thegross premium collection of life insurance industry in India in comingyears.

A sales potential represents sales opportunities available to a particular

manufacturer, such as to ICICI Company, while a market potentialindicates sales opportunities available to an entire industry.


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Sales forecast/ Company sales forecast

It is the estimated company sales of a given product orservice, under a proposed marketing plan, in a givenmarket, for a specific period of time. A company may makea sales forecast for an entire product line or product item.

The estimate for sales potential indicates how much acompany could sell if it had all the necessary resources anddesired to use them.

On other hand, sales forecast though related but havedifferent estimateit indicates how much a company with agiven amount of resources can sell if it implements aparticular marketing program.


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Thus, an operating/short-term sales forecast is a predictionof how much of a companys particular product (or Product

line) can be sold during a future period under a given

marketing program and an assumed set of outside factors.

The main goal of forecasting is to maximize certainty and precision

in business decisions.


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Forecasting Approaches

THE BREAKDOWN AND BUILDUP APPROACHES

(A)Break-down approach (top-down)

the basic steps in the Break-down approach are

1 The manager studies the firms internal & external

environments to determine which factors may influencesales.

External factors, such general economy, industryactivity, competitors activities and governmental actions,may be taken into account.


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General Environment Forecast

Industry Sales Forecast

Company Sales Potential

Company Sales Forecast

Sales/Marketing managers forecasts for regions,

branches, territories, and customers

Fig: 1.1: Basic steps in Breakdown Approach

2 The manger makes a sales forecast for the industry.3 Company sales potential.4 Company sales forecast.

5 Sales/marketing managers forecasts for regions, branches,territories & customer.


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Build-up Approach / Bottom-up

The build-up approach of sales forecasting is basically the reverse of thebreak-down approach.

It starts with the companys area or branch mangers asking its salesperson toestimate or forecast the sales in their respective territories.

Fig: 1.2: Basic steps in Build-up forecasting approach

Combined into company sales forecast

Combined into Regional/Zonal sales forecast

Combined into Area/ Branch sales forecast

Salespersons sales forecast of individual

customers


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Break-Down / Build-up

Approach

It requires less timeand cost as it uses

data on forecast fromsecondary sources likeeconomic Research,New Delhi.

Very accurate forshort term forecast

(up to 1 year) as it isbased on primary datacollection. Thus moreof cost and timerequired.


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SURVEY /QUALITATIVE

METHODS

MATHEMATICAL /

QUANTITATIVE METHODS

Executiveopinion

Delphi method

Testmarketing

Sales force

composite

Users

expectation

RegressionMoving averages

DecompositionNave/ratio

fig: 1.3: The popular forecasting method


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Executive Opinion

--- In this, sales forecasts are made either by taking the average

of all the executives individual opinion or throughdiscussions among the executives.

--- Executive opinion is based on experience, judgment andintuition.

MERITS:

1. Quick & easy way to turn out a forecast.

2. Less expensive when compared with other methods.

3. Conducive & popular among small companies.

4. Useful when adequate sales & market statistics are missing, or whenthese figures have not yet been put into the form required for more

sophisticated forecasting methods.


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DEMERITS:

1. Unscientific

2. Increases the work load of key executives, requiring them tospend time that they would otherwise devote to their areas ofmain responsibility.

3. Forecast made by this method is difficult to break down into

subunits such as regions, branches of the organization.


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Delphi Method

A new version of executive opinion method.

It consists of an attempt to arrive at a consensus in an uncertainarea by questioning a group of experts repeatedly until theresponses appear to converge along a single line (consensus).

Each participants are supplied the responses to previousquestions from others in the group by the coordinator.

The coordinator provides each expert with the responses of theothers including their reasons.

Each expert is given the opportunity to react to the information or

considerations advanced by others.

Successfully used in the area of technological forecasting i.e.predicting technical changes.


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MERITS:

1. Useful for technology, new product & industry sales forecast.

2. Both long and short-term forecasting possible.

DEMERITS:

1. Difficult to arrange panel of experts.

2. Longer time for getting consensus.

3. Break-down of forecast into products or territories is not

possible.


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Sales force composite method

1. Often refers to grass-roots approach, individual sales

personnel forecast sales for their territories, then individualforecasts are combined and modified, as managementthinks necessary to form the company sales forecast.

2. It is often used by industrial or business marketing

companies and is a common practice in the oil-field supplyindustry.

For Ex:

A company selling drilling trucks that, when fully equipped,sell for $500,000. the firm cannot afford to carry a largeinventory and requires that its salespeople contact allpotential customers. So each salesperson gives anestimate of future sales, and his or her immediate manager


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then formulates a forecast that is passed up to the regionalmanager. Corporate management thus uses the sales forcecomposite forecast to determine how many drilling trucksshould be produced for the coming year.

3. Sales representatives make the sales estimate inconsultation with customers and sales supervisor, and orbased on their experience and intuition.

MERITS:

1. Forecasting is done by salespeople who are closest to the

market and have better insight into sales trends than anyother group in the company.

2. Detailed sales estimate broken down by customer, product,sales representative and territory are possible.

3. High reliability of sales forecast.


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DEMERITS:

1. Sales forecast are often pessimistic or optimistic, assalespeople are not trained in forecasting.

2. If sales forecast are used to set sales quotas, which arelinked to incentive schemes, salespeople may deliberatelyunderestimate the demand.

3. Many salespersons are not interested in sales forecastingand prefer to spend time in the field meeting customers.


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Users expectation/ survey of buyers

intentions

This method is also known by market research or marketsurvey.

It includes asking existing & potential customers abouttheir likely purchases of the companys product and

services for the forecast period.

Some companies employ consumer panels that aregiven products and asked to supply information on theproducts quality, features, price, and whether they would

buy it. The information collected from buyers help the company

to make effective decisions not only in sales andmarketing areas, but also on production, research &development.


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MERITS:

1. Useful in forecasting sales for industrial products, consumerdurables and new products.

2. It also gives customers reasons for buying or not buying.

3. Relatively inexpensive and fast, when only a few customersare involved (industrial buyers survey).

DEMERITS:

1. Expensive and time-consuming in consumer non-durable

markets where consumers are very large in number.2. Sometime consumer find difficult to predict their future buying

habits. Often, they say Yesin a survey but are not willing topay for the product in the store. Thus, forecasts based solely

on this method tend to be overly optimistic.


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Test markets

--Test markets are a popular method ofmeasuring

consumer acceptance of new products and establishedproduct in a new territory.

--various methods for consumer-product market testing are:-

1. Full-blown-test markets:

Company select a limited number of medium-sized citiessuch as (2 to 6 cities) for promotion campaign.

Time duration varies from a few months to 1 years,depending on repurchase period of the new product.

Survey is conducted to know about consumer attitude,

usage & satisfaction towards the new product.


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Trial rate Re-purchase

Rate

Re-purchase

rate

Trial rate

High

Low

Fig:1.4

TEST MARKETS

If the test markets show high trial rate & high repurchase rates, the product should

be launched nationally;

If test markets show high trial rate & a low re-purchase rate, the new productsshould be redesigned or dropped;

If they show a low trial rate & a high repurchase rate, the product is acceptable;

If they show low trail rate & a low repurchase rates, the new product should be leftpermanently.


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2. Controlled test marketing

Company hires research firms and gets a panel of stores at agiven location.

the task performed by research firm are:

-- deliver the new product to the panel of stores.

-- arranges promotions at the stores.

--measures the sales of the new product.

-- interviews sample consumer to get their perceptions onthe new product.

Both full-blown test market and controlled testmarketing expose the new product to the competitors.


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3. Simulated test marketing

In this method, 30-40 consumers or shoppers are selected,based on their brand familiarity and preferences in a specific

product category, such as detergent, cosmetic, drink product.

Consumer or shoppers are exposed to commercial or printads of branded product and new product without any specificmention.

Consumers are given money to make purchase any of theitems in a store.

Close observation is maintain to know how many consumersbuy the product and competing products.

Consumer are interviewed to know about their buying andnot-buying intentions. Satisfaction level if buying andrepurchases intentions.

This method gives accurate results.


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Moving Average Method

The moving average (MA) is a technique that attempts tosmooth out the different rates of change for the immediatepast, usually the past 3 to 5 years.

The forecast is the mean of these past periods & is only validfor one period in the future.

The forecast is updated by eliminating the data for the earliestperiod & adding the most recent data.

When a forecast is developed for the next period, the sale inthe oldest period is dropped from the average and is replacedby sales in the newest period, hence the name Moving

Average.


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If the company operates in a stable environment, a short twoor three year average may be most useful.

If a firm in an industry with cyclical variations, the moving

average should use data equal to the length of a cycle.

Take, for example, the data in Table 1.1. A companys salesforecast was worked out by calculating moving averages for 3

years time periods.

Actual sales for past 3 or 5 years

Sales forecast for next year = ---------------------------------------------

Number of years (3 or 5 years)


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Year Salesvolume(Rsmillion)

Sales for(3) yrperiod

3-yrmovingaverage

2000 200

2001 250

2002 300 750

2003 350 900 300

2004 450 1100/3= 366.6

2005 ? (366.6)

Table 1.1

Example of Moving Average Method


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MERITS:

1. Simple and easy to calculate.

2. Useful for short-term and medium-term sales forecasts.

DEMERITS:

1. Unable to predict a downturn or upturn in the market.2. Historical data is needed.


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Decomposition method

In this method the companys previous periods salesdata is broken down into four major components, suchas trend, cycle, seasonal and erratic events.

Each components are then recombined to produce thesales forecast.

For Example: Assume that various analysis havebroken down the previous sales data into the followingcomponents:

(TREND COMPONENT)

A growth of 3 percent in sales due to the developmentin technology, capital formation and population.

(ERRATIC EVENTS)


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(ERRATIC EVENTS)Increased terrorist activities are expected to reduce sales by 5percent.

(CYCLIC COMPONENT)A 10 percent reduction in sales is expected due to a recessionin demand.

(SEASONAL COMPONENT)The sales in the third quarter of the year are expected to go upby 15 percent due to festive season, as compared to otherthree quarters.

The forecaster would combine the different components, asunder, in order to forecast sales for 2005.

A k l i 2004 R 450 illi


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As we know sales in 2004 was Rs. 450 million. (Assume Table1.1)

1. The Trend Component for 2005 sales will be Rs. 463.5 million (450*3%).

2. The sales reduced due to erratic event component to Rs. 440 million

(463.5 * 5%).3. The sales forecast changes due to cyclic component of recession to Rs.396.3 million.

therefore, the annual sales forecast for 2005 is Rs. 396 million.

The quarterly sales forecast would be Rs. 99 million (396 / 4) if seasonalcomponent is not considered. On the other hand

4. Seasonal component for the specific period is Rs. 113.9 (99 * 15%) for thethird quarter.

As it is observed from the above that, trend, cyclic and erratic events areincluded in the calculation of annual sales forecast. However, theseasonal component is used for forecasting sales for less than a year,like quarterly or monthly sales forecast.


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The biggest drawback of decomposition method is that, difficultand complex statistical methods are needed to break downsales data into various components and needed historical data.


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Nave/Ration method

It is a time series method of forecasting, which is based on theassumption that what happened in the immediate past willcontinue to occur in the immediate future. The formula is statedthis way:

Next years sales = This years sales X This years sales

Last years sales

let us assume the same table 1.1, and forecast the sales for the year 2005.this year sales (2004) is Rs. 450 million and that sales of last year (2003) wasRs. 350 million.

The next year (2005) sales forecast would be [450 X 450= 350

Rs. 578.5 million.


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MERITS:

1. Simple to calculate.

2. Require little data and statistical manipulation.3. Accuracy is good for short-term forecast, especially if trends

are stable or are changing in a relatively consistent manner.

DEMERITS:

1. Not useful for long-term periods and new products.

2. Accuracy of sales forecast would be less, if past sales

fluctuate considerably.


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Regression Analysis

It is a statistical method used to incorporateindependent factors that are thought toinfluence sales into the forecasting procedure.

It deals with two sets of variable:Dependent variable Y--- i.e., past sales.Independent variable X--- i.e., population,income, salesforce size, expenditure, etc.

Then company identifies causal (cause & effect)relationship between the company sales andthe independent variables, which affect thesales.

If th i l i d d t i bl (X) l ti


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If there is only one independent variable (X), say population,it is plotted on a graph of paired data of past sales andpopulation. It is called by linear or simple regression. In otherword, simple regression procedures use only oneindependent variable, such as population.

The relationship between the dependent and independentvariables can be one of two basic types. A linear regressionassumes the relationship is a straight line, as shown inFig.1.5 this simple regression example shows a directrelationship between sales and population. As populationincreases, so do sales. If population decreased, sales alsowould decrease.

A curvilinear relationship is a nonlinear regressionproducing a line that is not straight Fig1.6 (B). This lineshows that sales increase as population increases until apoint is reached at which sales begin to decrease. However,

the line can take numerous shapes.


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Population

Sales

Sales

Population

Linear Relationship (A) Curvilinear Relationship (B)

Fig. 1.5: Regression Analysis

X X

YY

OO

M l i l i h h h d


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Multiple regression on the other hand uses two or moreindependent variables, such as population and sales forcesize or population, income & sales force size.

The availability of computer software forecasting packagessuch as Statistical Analysis System (SAS), and Statistical

Package for the Social Sciences (SPSS), has increased the

usage of regression analysis in many companies.

MERITS:

1. High forecasting accuracy, if relationship between variablesare stable.

DEMERITS:1. Technically complex and required use of computer and

software packages.

2. Expensive and time consuming.


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Steps to Improve Forecasting Accuracy

1. Use multiple forecasting methods

2. Identify suitable methods

3. Develop a few factors4. Obtain a range of forecasts

5. Use computer hardware and software tools


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