LIBRARY
OF THE
MASSACHUSETTS INSTITUTE
OF TECHNOLOGY
SPRINTER mod
I
:
A Basic
New Product Analysis Model
by
Glen
397-69
L.
Urban
June 1969
Dewey
.
SPRINTER mod
I:
A Basic New Product Analysis Model**
by Glen L. Urban
INTRODUCTION
It is well known that there is a high potential for growth and
profits in new products.
For example, the sales growth of consumer
product firms is largely accounted for by new products.
in new product introduction is high.
But the risk
The most conservative failure rate
for new products introduced on the market is 30%.
2
This was reported by
Booz, Allen, and Hamilton and was an average across all types of products.
It can be expected that the failure rate is higher for frequently purchased
consumer goods and in fact failure rates as high as 80% - 90% have been
reported.
3
In this paper a method is proposed to reduce the failure rate
by better forecasting and to improve new product strategies.
The method of improving performance rests on the new technology
that is now available to help managers.
computers and management science models.
This technology is expressed in
It represents a significant
opportunity for innovative companies and decision makers.
The specific
utilization of the technology in this case is a model of the new product
Introduction process.
This computer based mathematical model is called
**
SPRINTER is an acronom for Specification of PRofits with INTERdependencies
See [4]
Mod I is the most elementary version. Mod II and mod III exist.
and the final section of this paper.
^See [1], p. 5
2
.
See [1], p. 11
^See [2] and [3]
-
6< ^
'^
O
Page 2
SPRINTER and represents the most vital elements from the real world
process.
The model views sales as being made up of triers, repeaters,
and loyals.
is,
The basic process is one of diffusion of innovation.
That
the flow of purchasers through the trial, repeat, and loyalty states
represents a description of the rate of adoption and spread of the new
product acceptance.
The model forecasts by combining the trial rate
with the number of potential triers in the target group to get the number
of people who try the product in each period.
The number of repeaters
is determined by considering the number who tried in the past, their
frequency of purchase, and their repeat rate.
The number of loyals
who buy in each period is calculated by considering the nximber who have
repeated, their frequency of purchase, and the percent of people who
will buy after having repeated.
This procedure is in contrast to the usual procedure of com-
bining test market shares or sales and with some subjective adjustments to
derive
a national forecast.
The model is based on the premise that
good forecasts must be based on a good understanding of the market's
process of acceptance.
For example, the model implies that frequency is
an important parameter of market acceptance and that the changes in trial
proneness over time must be included in a good forecasting model.
To
better understand the model's operations and potential in new product
analysis consider the following cases.
THE CASE OF "MOUNTAIN FRESH"
Mountain Fresh is a new washing cream which has the qualities
of cold cream, but washes off to leave clean and cosmetically soft skin.
-
Page 3
CO.
OENtR.I- OOOKBINDINO
,^
QUALITY CONTROU
.ol 3y
MARK
The producer of this hypothetical product is a medium sized soap manufacturer.
The scene opens with the new products manager (Al)
,
the brand
manager (Scott), and the market research manager (George) entering the
office of the vice president of marketing (Bill).
In order to
called this meeting to review Mountain Fresh.
here
so we can use
console
computer
a
I
have
thinking,
aid our
console.
the
me
activate
Let
model.
product
our new
(Bill types GO SPRINTER and the computer asks him a series
all the
Bill's typing is underlined
of questions.
computer.)
the
typed
by
remainder is
Bill:
I
—
"P GO
bfr.INTb.i-
bPKIN'lEK
I
I
A NEV. PhODUCT ANALYSIS MODEL
-
COMPUTEK AbKb QUEbTIONb IN blANUAKD FOkM
COMPU'iEK AbKS QUESTIONS IN bHOKT FOHM
1
'^
ANb=J_
ENTEK NEK DATA
USE bAVED DATA
1
'^
ANb=J_
BhAND NAME: ^^OUNTAIN FKESH
LENGTH OF PERIOD: MONTH
NAME OF FlKbT PEhIOD: AFKIL
NO. OF TIME PEKIODb (MAX=36):
36
TIME VAhYlNG DATA. TYPE F FOK FINISHED IF YOU WISH TO COPY
THE CUKhENT VALUE FOk ALL KEMAINING TIME PEHIODb.
\
t
^^^1*
Plrat,
Scott:
We estimated the target group to be women using beauty soaps,
which numbers about 25 million.
we need some information on the target group
SIZE OF TARGET GKOUP FOK OUK PKODUCl
DURING PERIOD
1
:
2:
250tJM
X
IN THOUSANDS OF BUYERS
Page 4
Bill:
Next we need to enter the trial rates.
What are they Scott?
Scott:
Based on our panel data, the trial rate has been good, but
dropping recently. We went fron 5% to 7.5Z, then 6. OX,
and now down to 5.2%.
I don't understand that.
Al:
think we have just gotten the easy ones and it will be
increasingly more difficult to get the remainder to try.
I would not be surprised if it eventually dropped to 2% or
I
33:.
George: This is a basic process of diffusion. The innovators try
first and they are usually more trial prone than the others.
Al, I agree with you, based on past products, we can expect
this trial rate to drop to 2%.
That is, 2% of those who have
never tried before will try in a given month.
Bill:
OK, let's enter that and go on.
PEk cent of THObE WHO HAVE NOT TRIED BEFOhE WHO TKY DURING PERIOD
1
2
3
Page 5
George:
That is true, but the model will help us consider that. The
fact that the repeat rate is low does raise some questions for
Perhaps we should do more research on the reasons for the
me.
lack of product satisfaction and repeat?
Bill:
Let's continue and see what the forecast looks like first. Next
we need some data on frequency of purchase. George, do you have
that?
Yes. Our special user studies indicate an eight month maximum
interpurchase time and the following percents purchasing each
period, each two periods, etc.
George:
AVEKAGE: NUf^BEK OF
1
I
t^E
PEKlOUb bET^ EEN PUhCHAbEb
FOr.
LIGHT UbRhi.:
PEK CENI OF PEOPLE WHO PUkCHAbE EVERY
1
:
?.:
^
J_0_
3: J_0
4: \'A
5: 15
6: 15
7: fe5
Al:
It looks like
That is less frequently than I had hoped.
Mountain Fresh is being used as a special purpose soap by women
rather than a regular cleaning soap. That is an important fact
in determining the number of repeat sales we will get each month.
Scott:
That purchase frequency is a little long, but
Mountain Fresh will be a winner.
Bill:
The next question pertains to profit analysis.
P^.0^''01
1:
2:
3:
4:
5:
I'JNAL BUUGE'l
IN
I
still think
IHOUbANUb OF DOLLAKS FOh PEKIOD
750
75W
75M
50fe)
F_
FIXED INVESTMENT (NOT INCLUDING PKOMOTION)
IN THOUbANDb OF DOLLAKb:_5M0
TARGET KATE OF KETUKN IN PER CENT PER YEAR:
k!5
GRObb PROFIT MARGIN Ab PEK CENT OF bALEb UNIT:
AVEhAGE RETAIL PkICE OF OUR BRAND IN DOLLARb:iS^
50
Ji.
Page 6
PEr.
IN
CbINT
OF TAKGET GKOUF WHO AKF. NOW UbINf-. ANOTHF.K bKAND
THIS PhODUCT CLAbb:
Bill:
_0_
Finally, we are viewing this as a real new product, and no
other brands of washing cream are on the market; therefore,
the percent using existing brands is zero.
Now let's ask for the output and forecast.
1
Page
7
Page 8
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The number of triers is dropping over time and with the lower repeat
In fact,- sales
rates we just never make the sales volume we need.
are forecasted to drop to less than 800,000 units with $160,000 losses
per month.
Bill;
Although the test sales looked good, it is pretty clear we have
The product seems to be appealing to
some serious problems.
innovators, but not so much to others. More importantly, the
product use response is unsatisfactory.
Scott;
Let's work on that.
Al:
I
Bill:
OK then, let's make a decision to try to Improve, the perceived
product use experience and the promotions ability to get triers.
If we can not do this, a NO GO decision will be necessary.
I
don't want to give up yet.
agree.
Page
9
There are some sharp contrasts between this procedure and the
method many firms use.
In many situations the new product decision is
highly influenced by optimism and the enthusiasm of the brand's champion.
The champion of the product is
a.
key element in any new product launching.
But it is important to get the champion on the right horse and pointed
in the right direction.
The analytic environment fostered by the model
in this case helped limit the enthusiasm of the champion, Scott, while
the initial GO, ON, NO decision was being made.
is
This unemotional approach
important since, in some companies, whatever happens in test will be
rationalized away.
In these cases the test is not even needed since the
organizational commitment was for a GO decision before test marketing.
In this case, the model also helped the decision makers understand
the market, which is the key to all good marketing decisions.
In particular
the explicit consideration of the diffusion process made a more informative
forecast possible.
This process consideration also leads to a fuller
utilization of the test market data.
In many companies the test data
represents an overwhelming stack of reports and the manager is faced
with a "data glut."
With a model, this data can be made relevant to the
decision and money spent in data collection will be more efficiently utilized.
The model functions to foster (1) analytic thinking,
understanding of the market processes,
(3)
(2)
a better
better utilization of data,
and (4) improvement in forecasting.
To develop these contributions further, let us consider a real new
product analysis and how the SPRINTER model was used to improve the introductory strategy.
Page 10
THE CASE OF "BLOND BRIGHT"
The case relates to a product which shall be disguised by calling
it "Blond Bright",
a hair color with an optical brightner.
In this case the managers accessed the more powerful behavioral
process option of SPRINTER.
This option suggests that trial is the
result of a process of gaining awareness, developing intent, and finding
the product.
The extension of the model is designed to facilitate a better
understanding of the process by which trial is obtained.
For example,
with the process option the advertising budget effect can be linked to
awareness, the appeal effect primarily to the intent level, and the
distribution effect to availability and therefore finding the product.
These process steps give a better diagnosis of a brand's strong points
and problems.
This understanding will be reflected in better forecasts.
The inputs requested and the managers answers are given in
Figures
1
to 7.
These behavioral process data were based on a continuing
awareness survey in the test market and a forecast of the behavioral
process if the test had continued.
The input session is given in Figure 1.
Before forecasting the national sales, the managers ran a validity
test.
This was based on comparing the model's market share prediction for
the nine months of test with the actual market shares observed in the
test markets.
This output for the first nine months is given in Figure
The market shares were very close to the actuals.
2.
The maximum deviation
was within one market share point and within the management's prespecified
criteria for validity.
Page 11
FIGURE 1
BLOND BRIGHT INPUT
bPHINTEK
I
A
-
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HicODUCT ANALYbIb MODEL
L'UEbTIONb IN S'lANDAKFJ FOKM
1
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.
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_
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2G_
TIME VAriYING DATA. TYPE F FOK FINIbHED IF YOU WlbH TO COPY
THE CUKKENT VALUE FOK ALL REMAINING TIME FEKIODb.
bl/.E OF TAkGET
DUi-ilNG PEKIOD
1
:
^:
173.;;;
GKOUl^
FOk OOK PKODUCT IN THOUbANDb' OF BUYEKb
Page 12
Figure 1
(Cont.)
Page 13
Figure
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Page 14
Figure
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Page 15
With a reasonable assurance of the model's accuracy, the
national forecasting procedure was begun.
Past experience in considering
the changes between test and national
the managers to reduce the
led
national availability 10% from the test values since the test had received
a
greater distribution push than national.
The intent rates were also
reduced ten percent since the point of purchase environment was also
better than the national display the product would achieve.
Finally, the
awareness level was reduced 10% because the test city radio stations
granted unexpected extra insertions based on the size of the media buy
and the discount schedule.
These discounts would not be received nationally.
These changes were made on-line and the procedure is given in Figure
The national forecast was now generated.
outlook is bleak.
No profit would be earned.
See Figure 4.
3.
The
The overall awareness
and availability were good but the number of people converting awareness
into intent and trial was low.
This indicates a weakness in the appeal,
and a GO decision was not possible without a better appeal.
The firms advertising agency responded to this by saying they
had an appeal in mind that would have a better action component.
They
estimated the awareness level for this appeal would be the same, but that
the intent rate for those aware
would increase 40%.
product with this appeal a new forecast was generated.
To evaluate the
See Figure 5.
This output is much better with a three year discounted profit of over
$534,000.
A question now was raised about the advertising budget.
it be wise
Would
to increase or decrease it since a better appeal was available?
Page 16
FIGURE 3
CHANGES FOR NATIONAL FORECASTING
DATA
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Page 17
FIGURE 4
NATIONAL FORECASTING FOR BLOND BRIGHT
I
Page 18
Figure 4
30
(Cont.)
Page 19
FIGURE 5
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Page 20
Figure 5
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Page 21
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Page 22
The three test market cities had experienced different advertising
levels so a graph of awareness versus advertising/capita yielded a
rough estimate for the effect of more advertising.
See Figure 6.
Based on this data and managerial judgment, the promotion was increased
20% and the awareness increased 5% (see Figure
mechanism).
for
7
for on-line change
This proved to be unprofitable (see Figure 8) so the search
abetter level continued by
a
reduction of 20% in advertising.
For
this lower level, it was estimated that awareness would drop by 10%.
This was profitable with total profit increasing over 75% and the ratio
of profit to investment reaching 3.1.
See Figure 9.
To see if an even
lower level was better, a run with another 10% less advertising was conducted.
This further reduction was unprofitable.
The conclusion of the analysis was for an ON decision.
If
the
appeal testing showed the new campaign to be as improved as predicted,
the advertising budget would be reduced and a GO decision would be
entertained with a forecasted discounted profit of $938,000.
This brief description describes the use of SPRINTER not only
in
forecasting, but in indicating opportunities for improving the
*
introductory strategy.
For a complete discussion of this case, see [4],
'
J
•
1
Page 2A
FIGURE 7
CHANGES FOR ADVERTISING BUDGET WITH A BETTER APPEAL
bAVE DATA
1
•d
l^klNT
3
CHAK'f.l-:
1)/\TA
.
DAlA
fJUTr-UT
i.Ebl At;l
an:j = 3_
/|
^7
^
1
2.
STANbAKD FOHM
bHOitT FOKM
.
AMi>=_l_
bPRCiFY
LINE;
1
LINf-I
NO. OF ITEM TO BE CHANGED OH TYPE F FOh FIK'I.'^HED
3
13 PlvONOi lONAL RUDG.CTHOUb. OF S):
CHANGE ALL PERIODS THE bAt-^E WAY ? YES
MULTIPLY
1
3 f.K}^LA(;E
J'iY
.
HY
•
ANS=J[
VALUE;
LINE:
<=.
1
.;.!
6
.
A'/JAI.KNESS
(.O:
CHANGE ALL PEkIODS THE SAME KaY
1
'd
3
MULTIPLY BY
ADD
kEPLACE by
1
.:^S
LINE:F
•
.
1
SAVE DATA
ri
Pi<INl
DA'JA
i;ATA
CHANGE
OUTPUr
hESTAfH
ANS = 4_
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A
b
1
'•
'
•
FULL OUTPUT
£ EXCLUDE SPECIFIED
3 INCLUDE S)-£CIFIED
ANSrJ.
?
YES
•
ANS=_1_
VALUE;
.
LINES
LINES ONLY
Page 25
FIGURE 8
FORECAST WITH HIGHER
BUDGET A>ID BETTER APPEAL
1
OUTPUT FOk
Page 26
FIGURE 9
FORECAST WITH A LOWER BUDGET
AND BETTER APPEAL
1
3
A
)
01 If p:
'"I
FOk
LENCIH:
HATa Fl^riK' FILE:
BLON'D hr.IGHT
MC'iVTH
Page 27
CONCLUSION
The two case descriptions in this paper indicate that a computer
based on-line model can
(1)
foster analytic thinking about a new product,
(2)
improve the forecasting of new product sales by considering
the diffusion process of the new product and basic behavioral
buying process,
(3)
lead to a better utilization of test market data, and
(4)
indicate and evaluate alternate new product strategies in
terms of their profitability.
In the SPRINTER mod
I
model these potentials can be accessed
inexpensively through a time sharing computer system.
4
Many users can
use the model with their own data so the development costs are shared.
The cost for each user is low (less than. $5,000 per product) when compared
to the cost of test marketing and the importance of the new product to the
success of the company.
Although SPRINTER: mod
I
rt^presents a description of what the
management science and computer technology may offer, it is not a full
representation of the potential of the model building technology.
To
fully utilize the potential, SPRINTER mod II and mod III have been
developed.
mod
I
These are on-line new product models that are similar to
in structure, but contain more detail and additional decision capabilities
4
This model is available through Management Decision Systems, Inc.
See [4] for a full description.
Page 28
SPRINTER mod II contains explicit consideration of the effects
of advertising, price, and distribution.
This allows the model to
recommend a best marketing mix ior the new product.
Mod II also considers
It models the
the basic response process in more detail than mod I.
in-store brand choice process and considers specific awareness to product
features
rather than merely overall awareness.
phenomena of forgetting awareness.
The model also adds the
Finally, mod II explicitly
considers competitive advertising effects so that it can better comprehend
the interdependencies in the market.
SPRINTER mod III continues this evolutionary modeling approach
to include the behavioral process of word of mouth, the subprocess of
building distribution by sales calls and deals in various types of stores,
the interdependency between new and existing products, and a detailed
consideration of specific appeal recall, preference, and intent to try
or repeat.
Finally, SPRINTER mod III considers more controllable variables;
the mix is made up of advertising, price, sales calls, middleman margin,
samples, and coupons.
This gives SPRINTER mod III the capability to
recommend highly sophisticated strategies.
product "Blond
For example, when the case
Bright" was evaluated by mod III, the model recommended a
15% lower price and 750,000 samples for the first three months of
introduction in addition to the advertising changes.
This new strategy
change improved predicted profits by an additional 60%.
The model building and computer technology is available to aid
marketing managers in forecasting sales and improving new product profit.
See [4] for full discussion of mod III and case
Page 29
The innovative firms that begin to apply it now will be on top of the
technology and gain the rewards in profit.
until the technology is established
Those who wait five years
will probably find that they suffer
from an almost insurmountable lag in experience in applying models and
their analytic approach.
Page 30
REFERENCES
(Booz Allen and Hamilton, 1968)
1.
Management of New Products
2.
O'Mera, John T., Jr., "Selecting Profitable Products,"
Harvard Business Review (Jan. -Feb. 1961), p. 83
3.
Schorr, Burt, "Many New Products Fizzle, Despite Careful
Planning, Publicity," Mall Street Journal April 5, 1961
,
.
4.
A Tool for the Analysis of
Urban, Glen, "SPRINTER mod III:
New Frequently Purchased Consumer Goods," Sloan School of
Management, MIT, working paper 364-69
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