Estimating Sales of New Products Group 2B Final

8/22/2019 Estimating Sales of New Products Group 2B Final

1/13

Group 2:

Aditya Singh (B12068)

Asif Iqbal (B12077)

Debarghya Dasgupta (B12079)


2/13

Rule #1 of Forecasting

Forecasts are almostalwaysWrong


3/13

Methods of forcasting

Quantitative:

Using extrinsic data (Leading Indicators):

Eg. CRISIL industry reports, Buying power, etc.

Using intrinsic data : Past sales volumes

Qualitative:

Delphi Method Management estimate

Market Research- Intention to buy basis Applicable fornew products


4/13

Estimating Market Potential

Delphi Method: Anonymous responses taken from group of expert All responses made available to experts and revision

requested. Iterative rounds of responses and revisions to reach a

consensus.1. Avoids face to face conflict and consecutivedefensiveness in changing stance.

2. Measure against groupthink

3. Prevents a charismatic/dominant leaderfrom leading the estimate in the wrong

direction

1. Prone to usual human errors of judgement.

2. Long iterative process may seem as asuperfluous repetition

3. No way to come out of a vicious cycle


5/13

Market Research: Detailed brochures of product given to target sample

Survey of stated intentions taken from sample

Average stated probability to acquire adjusted using a

discount factor.

1. Actual consumer responses taken intoaccount.

2. Arbitrary judgement by management andclosed box thinking avoided.

1. Intention to buy usually overstated

2. Discount factor is at best a calculated guess.

Estimating Market Potential


6/13

Bass Model- Adding the time component

Developed by Frank Bass in 1969 Earlier work by Rogers on Diffusion of

Innovation; Bass refined it by adding amathematical component to it

Adopters of 2 types Innovators andImitators

L(t)= p + (q/m)*N(t)

S(t) = p*m+(q-p)N(t)-(q/m)[N(t)]2

L(t) Likelihood of adoption p Coefficient of innovation External influence q Coefficient of imitation Internal influence S(t) Sale at time t m Market potentialN(t)- Cumulative sales until t


7/13

Estimation of parameters

p, q Non linear regression orextrapolation from similarproduct

m Managerial decision,

Market research, Delphi Method

N(t)- Derived from cumulativecalculations

S(t) = p*m+(q-p)N(t)-(q/m)[N(t)]2


8/13

Case example of Bass Model Application

DIRECTV planning launch of Satellite television in 1994 Market survey conducted by asking for intention to buy

after giving out detailed brochures.

Average probability to buy found to be 0.32

Discount factor of 0.5 estimated to adjust againstoverstatements.

Market potential = 16% of television homes

p and q estimated using guessing by analogy with cable

television. Sales in 1999 estimated to be 9.4 million

Actual sales in 1999 turned out to be 9.9 million !


9/13

Bass Model and sales- A Critique

Price variations across PLC not captured however thesame have been allowed for in modifications

p, q and discount factor are guesstimates at best.

Sales and not actual demand are used as measures.

Does not account for repeat purchases.

A forward Causal Relationship?

Other possible reasons for close adherence of sales to estimates:

Production and Distribution planned as per estimates

Sales targets intentionally set close to estimates


10/13

LEK Model

Sales revenue = Customer base * Total penetration *Products share of penetration * Price per unit * Unitsper year

Customer base Appropriate segmentation Factors affecting change in size of customer base

Total penetration Percentage of customer base being served by samecategory of product

Products share of

penetration

Primary Features and benefits of the product Secondary Marketing effort, Distribution

Price per unit Perceived value to customer Price demand curves

Units per year Frequency of use

Compliance


11/13

Other Approaches

Historical review Similar product in similar market by company or

competitorTest market Product launched into test market and sales are

tracked by Nielsen or via retailers Survey for tracking awareness, trial, usage

Before after retail

simulation

Sample of target audience in simulated retail

environment purchase behaviour is tracked Before scenario product under study not there Subjects explained about product After scenario product under study is there

Normative approach Database of historical norms for product category Adjusts for marketing plan variables

Awareness trial repeatpurchase model

Advertising effort converted into television GRPequivalents and fed into a mathematical model topredict awarenessand cumulative trial rate Samples are given out for product test and results

are used to compute purchase curve


12/13

Failure of Forecasting

Tata Nano Consumer research conducted to find target group

Plant in Gujarat capable of 250,000 vehicles a year

Initially overbooked, within a year sales dropped

Around 1500 cars sold in each month of Jan, Feb,March 2013 and less than 1000 in April 2013

Sales revenue = Customer base * Total penetration *Products share of penetration * Price per unit * Unitsper year (LEK Model)

Consequence : Plant working only 4 days a week at 10% operational efficiency,


13/13

THANK YOU

Documents

Estimating Sales of New Products Group 2B Final