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The Accuracy of the Juster Scale for Predicting Purchase Rates of Branded, Fast-Moving Consumer Goods

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This paper examines the suitability of the Juster Scale for predicting demand for different brands of fast moving consumer goods, within two product categories. The products used in the study were three brands of canned soup (Watties, Campbells and Heinz), and four brands of yoghurt (Ski, Yoplait, Fresh and Fruity, and No-Frills). The purchase probability data was obtained from the 1992 Palmerston North Household (face-to-face) Omnibus survey. Respondents were reinterviewed by telephone, four weeks after the omnibus survey to obtain recalled estimates of actual purchases. The Juster scale overestimated purchases, both for product categories and for brands. Purchase rates for soup were overestimated by 5% and for yoghurt by 6%. The overestimate for individual brands were slightly larger; the average over all 7 brands being 8%.Although the accuracy of the predictions of purchase rates was disappointing, the predictions of brand share were more accurate. This study has demonstrated that it is possible to obtain quite accurate estimates of market share for branded products using the Juster Purchase Probability Scale, as well as accurate estimates of the purchase rate for each brand.
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Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 1 of 6 http://marketing-bulletin.massey.ac.nz
The Accuracy of the Juster Scale for Predicting Purchase
Rates of Branded, Fast-Moving Consumer Goods
Mike Brennan and Don Esslemont
This paper examines the suitability of the Juster Scale for predicting demand for different brands of
fast moving consumer goods, within two product categories. The products used in the study were
three brands of canned soup (Watties, Campbells and Heinz), and four brands of yoghurt (Ski,
Yoplait, Fresh and Fruity, and No-Frills). The purchase probability data was obtained from the 1992
Palmerston North Household (face-to-face) Omnibus survey. Respondents were reinterviewed by
telephone, four weeks after the omnibus survey to obtain recalled estimates of actual purchases. The
Juster scale overestimated purchases, both for product categories and for brands. Purchase rates for
soup were overestimated by 5% and for yoghurt by 6%. The overestimate for individual brands were
slightly larger; the average over all 7 brands being 8%.Although the accuracy of the predictions of
purchase rates was disappointing, the predictions of brand share were more accurate. This study has
demonstrated that it is possible to obtain quite accurate estimates of market share for branded products
using the Juster Purchase Probability Scale, as well as accurate estimates of the purchase rate for each
brand.
Keywords: Juster Scale, purchase probabilities, intentions, estimates
Introduction
Dissatisfied with the accuracy of predictions of purchase behaviour based on socio-economic
and demographic variables, attitudes, and purchase intentions, researchers in the 1960's
shifted their attention to purchase probabilities. This led to the development of a purchase
probability scale, commonly known as the Juster Scale (Juster 1966).
Although various forms of the Juster Scale have been used (see Day, Gan, Gendall &
Esslemont 1991, for a review), the "standard" form consists of an eleven point numerical
scale, ranging from 0 to 10, each point associated with both a verbal and a numerical
probability statement (Juster 1966).
This scale (see Figure 1) has been used to predict purchase rates for a range of items, in
different product classes (durables, services and fast moving consumer goods), over various
time periods. In all cases, the Juster Scale has proved to be a better predictor than purchase
intention scales, although the accuracy of prediction has varied considerably for different
types of goods or services (Juster 1966, 1969; Gruber 1970; Heald 1970; Clawson 1971;
Gabor & Granger 1972/73; Pickering & Isherwood 1974; Isherwood & Pickering 1975; Gan,
Esslemont & Gendall 1985; Gendall, Esslemont & Day 1991).
The Juster Scale, with one exception, has been applied to product categories rather than
brands. U, Esslemont and Brennan (1991), who were primarily concerned with estimating
purchase quantities, used three branded products in three separate product categories. This
paper reports the findings of a study that investigated the suitability of the Juster Scale for
predicting demand for different brands of fast moving consumer goods within two product
categories.
Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 2 of 6 http://marketing-bulletin.massey.ac.nz
Figure 1. The Juster Purchase Probability Scale
10 Certain, practically certain (99 in 100)
9 Almost sure (9 in 10)
8 Very probable (8 in 10)
7 Probable (7 in 10)
6 Good possibility (6 in 10)
5 Fairly good possibility (5 in 10)
4 Fair possibility (4 in 10)
3 Some possibility (3 in 10)
2 Slight possibility (2 in 10)
1 Very slight possibility (1 in 10)
0 No chance, almost no chance (1 in 100)
Method
The purchase probability data for this study was obtained from the 1992 Palmerston North
Household Omnibus survey. This is an annual project conducted by the Department of
Marketing, Massey University. The survey covers households within the Palmerston North
city boundary, and is based on clusters of four interviews (two with males, two with females,
15 years of age or older) around randomly selected starting points. Substitutions were made
for households where an interview was refused or households where no contact could be
made with the respondent after three attempts. The response rate was 55%; there were 417
completed interviews.
At the end of the interview, respondents were asked for their consent to be re-interviewed; no
indication was given as to the subject of the further research. The re-interviews were
conducted by telephone, four weeks after the omnibus survey, by professional interviewers;
239 respondents were successfully re-interviewed (90% of those who agreed to be re-
interviewed).
The products used in the study were three brands of canned soup (Watties, Campbells and
Heinz), and four brands of yoghurt (Ski, Yoplait, Fresh and Fruity, and No-Frills).
Procedure
Before obtaining the purchase probability data for the soup and yoghurt, respondents were
introduced to the Juster Scale in the manner used by Juster (1966), using the prospects of
shifting house as a practice exercise. The respondents were then asked about their prospects
of buying a can of soup, and of buying specified brands of soup.
"Taking everything into account, what are the prospects that you personally
will buy at least one can of soup some time within the next four weeks; that
is, between now and the end of May?" RECORD RESPONSE. ASK THE
FOLLOWING QUESTIONS TO RESPONDENTS WHO GIVE A
PROBABILITY GREATER THAN ZERO.
Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 3 of 6 http://marketing-bulletin.massey.ac.nz
"I now want you to consider three brands of canned soup: Watties, Heinz,
and Campbells."
"What are the prospects that you personally would buy one or more cans of
<Watties> soup in the next four weeks?" RECORD RESPONSE
This process was repeated for the two other brands of soup and the four brands of yoghurt.
Results
The Juster scale overestimated purchases, both for product categories and for brands (see
Table 1). Purchase rates for soup were overestimated by 5% and for yoghurt by 6%. The
overestimate for individual brands were slightly larger; the average over all 7 brands being
8%.
Table 1. Predicted and actual purchase rates
Predicted Actual Error of
Prediction
% n % n %
Soup 27.8 66 23.0 55 +4.8
Watties 26.9 64 15.9 38 +11.0
Campbells 10.4 25 4.6 11 +5.8
Heinz 9.0 22 4.6 11 +4.4
Total 111 60
Yoghurt 49.0 117 42.7 102 +6.3
Fresh &
Fruity 36.2 87 22.6 54 +13.6
Yoplait 27.1 65 15.9 38 +11.2
Ski 20.5 49 14.6 35 +5.9
No Frills 5.6 13 0.8 2 +4.8
Total 214 129
Mean absolute error over all brands 8.1
Note. n = 239
Although the accuracy of the predictions of purchase rates was disappointing, the predictions
of brand share were more accurate (see Table 2). Since respondents at the reinterview were
asked only whether they had bought the brand, and not how much they had bought, "brand
share" here represents the number of people buying the brand divided by the sum, over all
brands, of the number of people buying each brand. Thus for canned soup, for example, 55
people bought soup but some bought more than one brand, and the number buying Watties,
Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 4 of 6 http://marketing-bulletin.massey.ac.nz
plus the number buying Campbells, plus the number buying Heinz is 60 (see Table 1). Brand
share for canned soup is calculated as the number buying the brand divided by 60.
The mean absolute error of prediction of market share, calculated in this way, was only 3.1%.
This, together with the fact that predictions of purchase of product categories were more
accurate than predictions of purchase of brands, suggests an alternative method of predicting
the number buying each brand.
Table 2. Predicted and actual brand share1 of purchasers
Predicted
brand share Actual
brand share Error of
Prediction
% % %
Soup (n=111) (n=60)
Watties 58.1 63.3 -5.2
Campbells
22.5 18.3 +4.2
Heinz 19.4 18.3 +1.1
Total 100.0 100.0
Yoghurt (n=214) (n=129)
Fresh &
Fruity 40.5 41.9 -1.4
Yoplait 30.3 29.5 +0.8
Ski 22.9 27.1 -4.2
No Frills 6.3 1.6 +4.7
Total 100.0 100.0
Mean absolute error over all brands 3.1
Note. n = 239
1. In this context, the "brand share" refers to the proportion of total purchasers who purchased a
particular brand.
2. Based on predicted number of buyers calculated to three decimal places, not rounded.
The number buying a brand can be predicted by multiplying the predicted brand share by the
predicted number of purchasers of the product category. For example, it was predicted that 66
people (actually, 66.442) would buy canned soup (see Table 1), and the predicted brand share
for Watties was 58.1%, so the predicted number of people who would buy Watties is (58.1 *
66)/100 = 38.6. The results of this procedure are shown in Table 3.
Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 5 of 6 http://marketing-bulletin.massey.ac.nz
The errors in predicted purchases for brands using this new method are clearly much smaller
than for the original method. There appears not to be a tendency towards consistent
overestimation, and the mean absolute error over all brands is only 1.7%, compared to 8.1%.
Table 3. Predicted and actual purchase rates using new method
Predicted
brand
share
Predicted
buyers Actual
buyers Error of
Prediction
% n % n % %
Soup (n=66)
Watties 58.1 38.6 16.2 38 15.9 +0.3
Campbells 22.5 14.9 6.3 11 4.6 +1.7
Heinz 19.4 12.9 5.4 11 4.6 +0.8
Yoghurt (n=117)
Fresh &
Fruity 40.5 47.4 19.9 54 22.6 -2.7
Yoplait 30.3 35.5 14.9 38 15.9 -1.0
Ski 22.9 26.8 11.2 35 14.6 -3.4
No Frills 6.3 7.4 3.1 2 0.8 +2.3
Mean absolute error over all brands 1.7
Conclusion
This paper reports on an exploratory study, involving only two sets of branded products, and
a small sample size. Even so, the results of this study are very promising, and suggest that
further investigation and validation of the technique used is in order.
This study has demonstrated that it is possible to obtain quite accurate estimates of market
share for branded products using the Juster Purchase Probability Scale, as well as accurate
estimates of the purchase rate for each brand. The recommended procedure is to use the
Juster Scale to obtain purchase probabilities for each of the major brands in a product
category, as well as for the brand category itself. From this data, one can then estimate both
brand share and brand purchase rates.
References
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Marketing Bulletin, 1994, 5, 47-52, Research Note 1
Page 6 of 6 http://marketing-bulletin.massey.ac.nz
Day D; Gan B; Gendall P & Esslemont D (1991). Predicting purchase behaviour. Marketing
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Gabor A & Granger CWJ (1972\73). Ownership and acquisition of consumer durables: report
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Mike Brennan and Don Esslemont are Senior Lecturers in the Department of Marketing, Massey
University.
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Consumer anticipations and models of durable goods demand
  • F T Juster
Juster FT (1969). Consumer anticipations and models of durable goods demand. In Mincer J (1969). Economic Forecasts and Expectations. National Bureau of Economic Research.