estimating sales of new products group 2b final
TRANSCRIPT
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Group 2:
Aditya Singh (B12068)
Asif Iqbal (B12077)
Debarghya Dasgupta (B12079)
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Rule #1 of Forecasting
Forecasts are almostalwaysWrong
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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
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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
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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
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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
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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
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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 !
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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
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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
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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
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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,
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THANK YOU