Category Attractiveness for Organic Private Label SKUs
LayPeng Tan, Jack Cadeaux, University of New South Wales, Australia
Abstract
This study of category attractiveness for organic private label SKUs aims to test whether
findings from conventional supermarkets extend to the context of organic retailing. It also
presents a pioneering test of the effects of competition between retail formats and the
likelihood of PL presence. It analyses store level cross category data from an independent
organic retailer in Australia and field data for competitor information. The results show that
organic private label SKUs are more likely to be present in sub categories with relatively
greater sales and when supermarket competition exists.
What Sub Category Characteristics of Organic Products Attract Private Label SKUs?
Some prior studies of private label products have looked at market and category
characteristics that are conducive to private label introduction in the conventional
supermarkets (e.g., Hoch and Banerji, 1993; Raju et al., 1995; Sayman and Raju, 2004;
Sethuraman, 1992). This study aims to conduct some limited tests of extant findings beyond
the conventional supermarket context, namely into organic specialty retailing.
To introduce or not to introduce private labels (PL) is a strategic decision for a retailer. The
retailer must take on all responsibility of its own PL and play the determining role for its
success or failure (Hoch and Lodish, 1998). Development of a PL programme requires the
retailer to invest and perform functions such as branding, packaging and production
coordination which are normally played by manufacturers (Hoch and Banerji, 1993). A
retailer bears the total costs and risks for its own PL effort, and that includes the opportunity
cost of shelf space (Hoch and Banerji, 1993). To recover these costs, logically, a retailer
would be more likely to introduce PL SKUs in categories with greater sales as greater sales
plausibly imply potential for larger profits (Hoch and Banerji, 1993; Sethuraman, 1992).
Further, unlike manufacturer brands, with limited distribution a retailer would need larger
volume for each PL SKU within its store or store chain to achieve economies of scale in
production, distribution and marketing (Dhar and Hoch, 1997). This makes categories with
greater sales potential more conducive to the introduction of PL SKUs.
Arguably, retailers might also introduce PL SKUs in response to competition from other
stores (McMaster, 1987). This argument may well be applicable for an independent organic
retailer in the face of increasing between-retail-format competition from conventional
supermarkets. Organic retailers perceive supermarkets to be threatening competitors due to
the one-stop shopping convenience and lower prices they provide for their organic product
range (Chang et al., 2003; Kinnear, 2002). Lockie et al. (2002) found empirical evidence that
42% of the 200 respondents who consumed organic foods bought half or more of the organic
food they consumed at conventional supermarkets. Arguably, an organic retailer may also be
more likely to maintain the presence of PL SKUs in sub categories with supermarket
competition as a differentiation strategy. That is, an organic retailer may use its PL SKUs as
strategic tools for building store loyalty (Corstjens and Lal, 2000) and to retain store visits of
its customers (following Fox and Sethuraman, 2006).
Competition within a category is expected to increase with PL introduction. Raju, Sethuraman,
and Dhar (1995) analytically showed that PL introduction is more likely to increase category
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profits for the retailer if the category has relatively more manufacturer brands (Hoch and
Banerji, 1993; Sethuraman, 1992). Therefore, a retailer might arguably be more likely to
introduce PL SKUs in categories with a higher number of manufacturer brands (MB). Based
on the discussion above, a limited set of hypotheses for the category attractiveness for PL
SKUs is hence, H1abc: Private label SKUs are more likely to be present in sub categories with
a) larger sales, b) the presence of supermarket competition, and c) with a higher number of
manufacturer brands.
Research Design and Results
An independent organic food retailer in Australia provided the necessary data for a cross sub
category analysis. Competition information was collected from the two leading supermarkets
within closest proximity to the focal retailer. Predictions about what sub category
characteristics contribute to the likely presence of private label SKUs were tested with a
logistic regression model. The dependent variable was whether or not a PL SKU is present in
a sub category. T-tests were performed to compare the mean scores for total sub category
sales and MB brand count between sub categories with and without private label SKUs (Table
1). Cross tabulations and a Chi-Square test were used to test the association between the
presence of supermarket competition and the absence or presence of PL (Table 2). Table 3
presents logistic regression results and Table 4 presents the classification matrix.
Table 1: Group Means and T-Test for Sub Categories With / Without PL SKU
Discriminating Variable
PL SKU N
Mean T-test for Equality of Means, Sig. (2-tailed)
Total Sub Category Sales
No
124 1,391
(Average A$ sales across all sub Yes
33
2,896 0.014
categories in a month)
MB Distinct Brand Count
No
124 8.12
(Average number of brands
Yes
33
14.76 0.001
across all sub categories)
Table 2: Cross Tabs: Absence or Presence of PL SKU * Supermarket Competition
No Competition With Competition
Sub Categories with No PL SKU Count 85
39
Sub Categories With PL SKU
Count 6
27
Total
Count 91
69
Total
124
33
157
Cross tabulations (Table 2) and Chi-Square test (Pearson χ2 : 27.14, p<0.001) show that there
is a significant association between competition and the absence or presence of PL SKU. The
chi-square goodness of fit index (Hosmer and Lemeshow) and the likelihood ratio test
indicate that the logistic regression model adequately fits the data (Table 3). The Nagelkerke
R Square (0.319) shows that this model explains a reasonable proportion of the variation. In
addition, the classification table (Table 4) shows that even though the absence or presence of
PL SKUs was not perfectly predicted, the correct prediction rate of 80.3% indicates the strong
discriminating power of the predictor variables. A closer look at the hit ratios reveals that
while the model predicts the absence of PL SKUs very well (94.4%), it does not predict the
presence of PL SKUs too well (27.3%). This could partly be attributed to the unequal cell size
that has defied the pure chance odds (Morrison, 1969). That is, each sub category has an a
priori 0.79 probability of not having PL SKUs and only a 0.21 probability of having a PL
SKU. To further evaluate the performance of this model, the proportional chance criterion
was then computed as it is deemed the most useful reference point in instances of unequal cell
sizes (Morrison, 1969; Patterson, 2004). The prediction is found to be better than chance, as
evidenced by a more favourable comparison to the proportional chance criterion (66%).
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Table 3: Logistic Regression Analysis of Category Attractiveness for PL SKU
Predictors
Parameter β
Total Sub Category Salesa
.010
Competition (1=Present, 0=Absent) 2.035
Total MB Brand Count
.050
Constant
-3.269
Overall Model Evaluation
Goodness of Fit Statistics
(Hosmer and Lemeshow)
Log-Likelihood Test
(Test of Overall Model)
Cox & Snell R Square
.205
Nagelkerke R Square (Maximum=1) .319
a
Total sub category sales in 100 dollars terms
Std Error β
.010
.506
.028
.508
Wald’s χ2
1.025
16.190
3.082
41.402
χ2
df
1
1
1
1
6.204
8
.624
35.959
3
.000
Sig. (p=?)
.311
.000
.079
.000
e β(odds ratio)
1.010
7.654
1.051
.038
df Sig. (p=?)
Table 4: Classification Matrix
Predicted Group
Actual Group
No PL SKU
With PL SKU
Sub Categories with No PL SKU
117 (94.4%)
7 (5.6%)
Sub Categories with PL SKU
24 (72.7%)
9 (27.3%)
Predicted Total
141 (89.8%)
16 (10.2%)
Overall percentage correctly classified: (117+9) / 157= 80.3%
Proportion chance criterion: (0.21)2 + (0.79)2 = 66%
Note: The cut-value is .500
Actual Total
124 (79%)
33 (21%)
157
For the individual predictors, the presence of supermarket competition (p<0.001) is found to
be significant in distinguishing sub categories with or without PL SKUs while total MB brand
count is found to be a weak discriminator (p<0.10). The positive coefficient parameters for
both predictors suggest that the presence of competition and higher brand count increase the
likelihood of PL SKU. The odds ratios (eβ) show that for each point increase in brand count,
the odds of PL likelihood increase from 1.0 to 1.051. On the other hand, the odds of a sub
category which faces supermarket competition to have PL SKUs is 7.654 times greater than
the sub categories which do not face competition. What is surprising however is the total sub
category sales are not significant in separating the sub categories with PL SKUs from those
without PL SKUs. This result is counter intuitive given that the average total sub category
sales is significantly higher (p<0.05, Table 1) in sub categories with PL SKUs. Further
diagnostics of the results found high correlations between total sub category sales and the
brand count (r=0.56, significant at p=0.01, 2 tailed). This flags the potential issue of
collinearity between the two variables. To determine if collinearity was indeed the underlying
concern, two separate logistic regression models were estimated with total sub category sales
and total brand count, each entered as a separate model. Interestingly, both predictors in
question were found to be significant predictors in separate models (Model with Sales: χ2
=4.625, p<0.05; Model with MB Brand Count: χ2 =7.786, p=0.005). The findings establish
that collinearity between the two discriminating variables has subsumed each other’s
prediction power when both variables were entered into a same model. If it is purely based on
statistical output, the conclusion would be to reject H1a (total sub category sales), and to
accept H1b (competition) and H1c (total brand count). Nevertheless, for theoretical justification,
a more parsimonious model (trimmed model) is re-estimated by keeping only total sub
category sales in the model. The results of the trimmed model are depicted in Table 5 and 6.
Table 5: Results of Logistic Regression Analysis of Category Attractiveness for PL SKU (Trimmed Model)
Predictors
Parameter β Std Error β Wald’s χ2 df Sig. (p=?) e β(odds ratio)
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Predictors
Parameter β Std Error β Wald’s χ2 df Sig. (p=?)
a
Total Sub Category Sales
.019 (.448)b
.009 (.208)
4.625
1 .032
Competition (1=Present, 0=Absent) 2.171
.499
18.936
1 .000
Constant
-2.976
.469
40.323
1 .000
2
Overall Model Evaluation
χ
df Sig. (p=?)
Goodness of Fit Statistics
5.387
8 .716
(Hosmer and Lemeshow)
Log-Likelihood Test
32.902
2 .000
(Test of Overall Model)
Cox & Snell R Square
.189
Nagelkerke R Square (Max=1)
.294
a
Total sub category sales in 100 dollars terms
b
Values in parentheses are logistic results with standardised (standard deviation) variables
Table 6: Classification Matrix (Trimmed Model)
Predicted Group
Actual Group
No PL SKU
With PL SKU
Sub Categories with No PL SKU
122 (98.4%)
2 (1.6%)
Sub Categories with PL SKU
26 (78.8%)
7 (21.2%)
Predicted Total
148 (94.3%)
9 (5.7%)
Overall percentage correctly classified: (122+7) / 157= 82.2%
Proportion chance criterion: (0.21)2 + (0.79)2= 66%
Note: The cut-value is .500
e β(odds ratio)
1.019 (1.566)
8.766
.051
Actual Total
124 (79%)
33 (21%)
157
As shown in Table 5, the chi-square goodness of fit index and the likelihood ratio test indicate
that the logistic regression model adequately fits the data. The hit ratio of 82.2% provides
further evidence for the prediction power of this model. The presence of competition
(p<0.001) and higher sub category sales (p<0.05) increase the probability that PL SKUs will
be present. The odds ratios (e β) show that for each point increase on sub category sales, the
odds of PL presence increase from 1.010 to 1.019. The odds of a sub category which faces
supermarket competition to have PL SKUs is 8.77 times greater than the sub categories which
do not face competition. In short, the results support H1a and H1b. Since, within this dataset,
the variable of total brand count is redundant due to its collinearity with total sub category
sales, the results are inconclusive regarding hypothesis H1c.
Discussion and Conclusion
The logistic regression results indicate that within the focal store, PL SKUs are more likely to
be present in sub categories with larger sales volume and when supermarket competition is
present. The significant effects of sub category sales on the likelihood of PL presence are
consistent with the results of Raju, Sethuraman and Dhar (1995). This finding supports the
argument that, given the costs and risks of PL investment, a retailer will be more likely to
introduce PL SKUs in sub categories with relatively greater sales. (Hoch and Banerji, 1993;
Sethuraman, 1992). The second hypothesis represents a pioneering test of the effects of
competition between retail formats and the likelihood of PL presence. The results provide
empirical support that PL SKUs are also more likely to be present in sub categories when
there is supermarket competition. One possible implication is that private label SKUs are
introduced as the retailer’s effort to enhance store performance via building store loyalty
(Corstjens and Lal, 2000) and creating a point of differentiation in response to increasing
competition from supermarkets (following McMaster, 1987). On the other hand, the
significant effect of supermarket competition on the likelihood of PL presence may also be
due to a simple reason such as that these sub categories are sub categories with greater sales.
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Thus, while these sub categories appear to be attractive to the focal retailer to introduce a PL
SKU, they are also attractive to the supermarkets to offer some organic SKUs. In short, the
significant effect of supermarket competition on the likelihood of PL presence may be one of
a spurious relation. More work is undoubtedly needed to further understand the competitive
interaction between supermarkets and organic retailers and the effects of such competition on
their assortment decisions.
Nonetheless, one may also counter argue that PL SKUs are present in those sub categories as
retailer’s strategic tools in facing the growing competitive threats from supermarkets. As Fox
and Sethuraman (2006) point out, the competition between different retail formats is
competition for store visits rather than for customers in that retail customers include both
formats as part of their shopping strategies. Their classification of traditional and nontraditional formats is analogous to the organic retail outlet and supermarkets in this study.
This argument is consistent with empirical generalisations about buyers “split loyalty”
behaviour, for example, the multi-store, multi-brand or multi-retail format shopping
behaviours found by Ehrenberg and colleagues in the last several decades (e.g., Ehrenberg et
al., 2004; Uncles and Kwok, 2007; 1998). Under such competitive environments, PLs may
create a point of differentiation from competition and help build store loyalty for the focal
retailer. As such, PLs may indeed help to defend the “share of wallet” of their customer base
through sustaining the customers’ store visits (Fox and Sethuraman, 2006).
This study also sets out to test if a retailer might be more likely to introduce PL SKUs in sub
categories with a higher number of manufacturer brands (MB brand count) as found by Raju,
Sethuraman and Dhar (1995). The evidence for this hypothesis was inconclusive as MB brand
count had to be omitted from the final logistic model due to collinearity between it and total
sub category sales. A separate model tested with only MB brand count and supermarket
competition shows that MB brand count is a significant discriminant variable in predicting the
presence of private label SKUs. In the context of organic retailing, a plausible explanation for
the significant relationship between MB brand count and the presence of PL SKUs is that the
presence of a greater number of manufacturer brands may imply that the sub category is
relatively more established, for example, in terms of consumers’ acceptance, thus giving the
focal retailer more confidence to launch their PL SKUs. Of course, a greater number of
manufacturer brands may also be expected in sub categories with greater demand (e.g., staples)
explaining why these sub categories are more attractive to the retailers for PL introduction.
In conclusion, this study of category attractiveness for organic PL SKUs aims to test whether
findings from conventional supermarkets extend to the context of organic retailing. It found
that organic PL SKUs are more likely to be present in sub categories with greater sales and
when there is supermarket competition. Previous studies have also identified that it would be
conducive to introduce private label SKUs when 1) cross-price sensitivity among
manufacturer brands is low (Raju et al., 1995; Sayman and Raju, 2004), 2) cross-price
sensitivity between manufacturer brands and private label SKUs is high (Raju et al., 1995),
and 3) the manufacturer brands are similar in strength (Sayman and Raju 2004). These
category and market characteristics are however not observable in this study given the
unavailability of consumer panel data or market data at the category level for organic goods.
The generalisability of these findings should be verified when and if such data are available.
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