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Factors that Affect Repeat Buying Loyalty

Dag Bennett, London South Bank University

Abstract

This paper describes repeat brand-buying behavior across a variety of frequently purchasedconsumer goods categories. The main finding is that overall repeat buying tends to be higherin categories where the market shares of brands are either very stable or the category is verymuch dominated by a large brand. In contrast, low repeat-buying associates with unstablemarket shares and competition amongst many smaller brands. In addition, brands that aremuch bigger than their nearest rivals tend to have higher repeat buyingor loyalty premiums.

These repeat buying rates are predictable using the Duplication of Purchase law (DoP).

1. Introduction --Brand Buying Behavior for Individual Brands

For many firms, branding is critical, helping establish the firms identity in the marketplace orbuild a solid customer franchise (Kapferer 1997, Keller 1998). Brand strength also provides atool to counter growing retailer power (Barwise & Robertson 1992) even as retailers useprivate-label brands to build store loyalty (Corstjens & Lal 2000). The ongoing academic andindustry discussion surrounding branding often focuses on the benefits of brand loyalty andhas evolved from how to measure and assess loyalty (e.g., Cunningham 1956, Fader &Schmittlein 1993, Bhattacharya 1997) to building and managing loyalty (e.g. Reichheld &Sasser 1996, Baldinger & Rubinson 1996, Aaker & Joachimsthaler 2002).

And yet, there is little research into the underlying market conditions that might affect repeatbuying loyalty. Why should one category have high repeat buying and another low? Whatshould marketers reasonably expect repeat buying to be for their brand, and why? If 40% ofcustomers buy a brand twice in a row, is that high, low or just average?

While much has been written about loyalty this research draws on well-established empiricalgeneralizations (Ehrenberg 1972) that primarily focus on within category brand buyingregularities as described with the NBD-Dirichlet model (Ehrenberg, Uncles & Goodhardt2004), the Duplication of purchase law (DoP), (Colombo & Morrison 1989) and by the two-

purchase technique (Bennett, Ehrenberg & Goodhardt 2000, Bennett 2004). This newresearch was one of the first attempts to look at brand buying patternsbetweencategories.

It did so by examining factors that might be associated with repeat-buying loyalty levels. Thefirst step was to calculate weighted average repeat buying rates over two consecutivepurchases drawn from panel data (TNS superpanel with 15,000 UK households) covering 72FMCG categories. Each buyer who had made two category purchases was counted as either arepeat-buyer or a switcher. Repeat buying levels could then be compared, as could othermeasures such as, the average interval between purchases in weeks, market share of thelargest brand in the category, the number of brands with over 3% share, the Herfindahl-Hirschman Index (HHI) (www.dti.gov.uk/files/file17173.pd), and the weighted average

percentage change in market share between purchases.

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2. Repeat buying behavior for brands

On average, across all categories, 42% of all customers bought the same brand twice in a rowwhen making purchases within a category. Individual category repeat rates ranged from 58%

down to 24% as shown in Table 1, column 1 which presents selected categories at the high,middle and low end for repeat buying. These category repeat buying levels were consistentwith previous work (e.g. Baldinger & Rubinson 1996, Ehrenberg, Uncles & Goodhardt 2004).

One test for whether these data fit with existing theory is the Duplication of Purchase law(Colombo, Ehrenberg & Sabavala 2000), an empirical law-like relationship that says that theproportion of buyers of Brand A who also buy Brand B (denoted bB|A) can be expressed as:

bB|A =D x bB

In practice, when people buy a brand they are more likely to buy one with high penetration,

irrespective of which brand they bought before. The proportionality factor D orDuplication Coefficient reflects the likelihood of switching from the previous brand A to anew brand B, relative to how many people bought B at all. For example if 30% of thepopulation buy brand B, and 45% of brand As buyers buy brand B, the duplicationcoefficient D, is 45%/30% =1.5.

D is thus a measure of the extent to which individual brands share category buyers and can beused to calculate brand repeat buying with the formula:

Repeat Buying Rate =(D * brand penetration) +(1- D)

The second column in Table 1 shows predicted average repeat buying rates (labeled T fortheoretical) made using the D values calculated for each category.

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Table 1 Weighted Average Repeat Purchase Rates and Other Measures

Product Category

Wtd Ave %Repeat

O (T)

Ave %Share

change

LargestBrand %

share

PurchaseInterval

(weeks)

Brandsover 3%

sharePacket TeaMargarineButterCrisps

Thick Brown Sauce

Sugar ConfectionaryCarbonates with lemonadeInstant porridgeEveryday Biscuits

MarmaladeShampoo

Premium Ice CreamBody SpraysChild LolliesCough lozengesIce-cream filled Cones

58 (60)56 (58)56 (56)55 (57)54 (56)

43 (44)42 (41)42 (44)41 (40)

41 (35)41 (40)

29 (30)28 (30)25 (25)25 (25)24 (26)

97587

1310176

108

2526171824

2024266855

24182614

2111

2243241916

855332

21143

1516

913112612

119858

791212

913

118111010

Overall Average 42% (42%) 12% 24% 9 9

At first sight Table 1 shows that observed and theoretical repeat buying levels are close, andin fact the correlation between the two is r =.97. Thus whether a category is characterized byhigh or low repeat buying, the level is closely predicted by the duplication of purchase law,which is based on the measurement of penetration and so brand share.

3. Factors associated with lower repeat buying rates

One question raised by Table 1 is; what accounts for the variation in loyalty? For example,Packet Tea and Margarine have high repeat buying rates, while Ice-Cream filled Cones andCough Lozenges have low rates. With the information on hand a correlation matrix wasconstructed to examine what factors might be associated with repeat purchase rates, shown in

Table 2 below. Higher positive correlations are outlined, negative ones shaded.

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Table 2. Correlations between Repeat Purchase and other Category Measures

wt averep

Wt ave %change

Wks/ints

lgstbrand HHI

no>3% Dom prem

wt ave repeat 1.00wt ave % change -.73 1.00wks/interval -.18 .06 1.00largest brand .23 -.03 .18 1.00

HHI .35 -.09 .17 .91 1.00

No. brands >3% -.23 .05 .04 -.56 -.56 1.00

Domination .16 .00 .19 .83 .78 -.38 1.00

Premium .11 .01 .16 .58 .50 -.32 .70 1.00

In the first column the only high correlation is between the weighted average repeat rate andweighted average percent change in brand share (r = -.73). This shows that categories inwhich brand shares are unstable tend to have lower repeat purchase rates. This relationship

was hinted at in Table 1, where lower repeat categories also had higher average share changesand can be seen clearly in Graph 1. This negative association between share instability andrepeat buying is consistent with other work on dynamics (e.g. Kato & Honjo 2006, Habel &Rungie 2005, Dekimpe et al. 1997) and with meta analysis of survey data (Bennett, 2007).

The second and third columns show that weighted average percent share change and weeksper purchase interval have no strong associationsit matters little to FMCG repeat buyingwhether a customer buys weekly or yearly.

Graph 1. Weighted Average Category Repeat Buying Declines with Brand Instability

Repeat Buying and Change in Brand Share

0

10

20

30

40

50

60

70

0 5 10 15 20 25 30

Weighted Ave % Change in Shares

Weighted

Ave Repeat

4. Factors Associated with Higher Repeat Rates

The largest brand column shows high associations, both positive and negative as does the HHIcolumn (the Herfindahl-Hirschman Index is the sum of the brand shares squared and is usedto assess industry concentration, usually to decide whether a merger or acquisition should be

allowed). These associations between largest brand and HHI and the number of brands of over3% share are largely autocorrelation. But there is also a high correlation (r = .83) with

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Domination (share of the largest brand divided by the next largest) and Loyalty Premium(r=.58) (repeat rate of the largest brand divided by the average repeat rate), showing thatwhen categories have very large brands, and therefore high HHI, the categorys largest brandalso tends to have a repeat rate higher than its competitors.

That larger brands have higher repeat rates was not unexpected. This is a standard DoubleJeopardy effect (Ehrenberg, Uncles & Goodhardt 2004). Nor was it surprising that the effectwas sometimes higher than predicted, indicating there might be a loyalty premium for verybig brands. Fader and Schmittlein (1993) argued that heterogeneity in brand choice is thelikely cause of the excess brand loyalty (i.e. greater than double jeopardy predicts) and thatthis effect is accentuated for larger brands.

Most categories however, did not have a huge brand (the mode for largest brand was 24%).The question then focused on whether there was an association between the relative size ofthe largest brand (domination) and repeat buying. For example if the largest brand wasmuchlarger than its rivals. That is, if a 10% brand is twice as big as its nearest rival, as opposed to a

20% share brand with an 18% competitor, what might be expected for category repeat rate.

The degree of domination when correlated against the weighted average category repeat ratefor each category gave a weak positive correlation of r =.16. So domination was associatedwith repeat buying, but weakly, and the effect was probably overshadowed by the simple sizeeffect. Highly dominated categories are not necessarily high repeat rate categories.

Graph 2, Brands that Dominate their Rivals Tend to Enjoy Loyalty Premiums

Domination and Loyalty Premium

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

1.6

1.8

2.0

0.0 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0

Share of Largest Brand divided by Share of next largest

Repeat Rate of

largest brand

divided by repeat of

next largest

However, there was a strong positive relationship (r =.70) between domination and loyaltypremium. In other words, brands that are much bigger than their nearest rivals tend to haveabove average repeat buying rates, and the higher the dominance, the higher the premium (seeGraph 2). Note that the calculations for loyalty premium and dominance are independent ofwhether the category has high or low overall repeat rate and show that for brands, while it isgood to be big, in terms of loyalty premium it is better to be bigger than ones rivals.

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Multiple regression analysis was performed to examine further the relationship betweenvariables. However, the results were difficult to interpret, except insofar as they confirmed theoverall importance of (in)stability of market share in determining overall category repeatpurchase rates. This will be explored further in future.

5. Conclusions

These brand repurchase findings are new because they compare repeat buying rates acrosscategories. The results show that repeat buying is largely a function of brand size, moderatedby the stability of the category. Moreover, they are predictable using the DoP law. Theanalysis was greatly assisted by the availability of prior knowledge of loyalty and switchingpatterns and serves to confirm those patterns through differentiated replication.

Some categories had average inter-purchase times of two or three days, while others hadmany weeks between purchases, but this had little affect on repurchasing. Instead, dynamic

categories where shares were unstable had lower average repurchase rates, while categorieswith very large or dominant brands had higher repurchase rates. In addition, dominance wasstrongly associated with the largest brand obtaining a loyalty premium.

The findings have both conceptual and practical significance. Conceptually, the analysis laysout simple descriptive regularities associated with repeat buying. Despite wide academic andpractitioner focus on customer retention and loyalty, little had previously been done toanalyze factors associated with high or low levels of repeat buying. It also sets the stage forfurther analysisfor example, it might be possible to explore whether repeat buying rates forbrands indicate any market polarization, or the presence of niche brands. In practical terms,the comparative study of widely differing markets or categories may lead to a greaterunderstanding of brand buying behaviour and to broader generalizations about consumerbehavior.

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References

Aaker, D., Joachimsthaler, E., 1999. The Lure of Global Branding, Harvard BusinessReview, 77 (November/December): 137-144.

Baldinger, AL., Rubinson, J ., 1996. Brand Loyalty: the Link Between Attitude andBehaviour, Journal of Advertising Research, 36(6) pp22-34

Barwise, P., Robertson, T., 1992. Brand Portfolios, European Management Journal, 10(3):277-285.

Bennett, DR., 2004. The Taiwanese are Just Like Australians in their Loyalty to Fast FoodOutlets, Australasian Marketing Journal, Vol 12, No. 3 pp97-103

Bennett, DR., 2007. Meta Analysis of Repeat Buying Loyalty, Academy of Marketing, UK,Conference 2007, Kingston University, 3l-6 July

Bennett, DR, Ehrenberg, ASC., Goodhardt, G., 2000. Two Purchase Analysis of BrandLoyalty Among Petrol Buyers, ANZMAC, Gold Coast, Australia, 29 Nov- 2 Dec.

Bhattacharya, CB., 1997. Is your brands loyalty too much, too little, or just right?Explaining deviations in loyalty from the Dirichlet norm, International Journal of Researchin Marketing, 14, 421-435

Colombo R, Ehrenberg, ASC., Sabavala., D., 2000. Diversity in Analyzing Brand SwitchingTables: The Car Challenge, Canadian Journal of Marketing Research, 19 pp23-26

Colombo R., Morrison, D., 1989. A Brand Switching Model with Implications for MarketingStrategies, Marketing Science,8 (1) pp89-99

Corstjens, M., Lal, R., 2000. Building store loyalty through store brands, Journal ofMarketing Research,37, 281-291

Cunningham, RM., 1956. Brand Loyalty what, where, how much? Harvard BusinessReview, 34, January-February, pp. 116-128.

Dekimpe, MG, Steenkamp, J-B, Mellens, M., Abeele, P., 1997. Decline and Variability in

Brand Loyalty, International Journal of Research in Marketing, Vol 14, Issue 5, pp405-420

Ehrenberg, ASC., 1972. Repeat Buying, North Holland Publishing Company, London

Ehrenberg, ASC, Uncles, MD., Goodhardt, G., 2004. Understanding brand performancemeasures: using Dirichlet benchmarks, Journal of Business Research, 57 (12), pp1307-1325

Fader, PS., Schmittlein, DC., 1993. Excess Behavioral Loyalty for High-Share Brands:Deviations from the Dirichlet Model for Repeat Purchasing, Journal of Marketing Research,30 (November), pp478-493.

Habel, C., Rungie C., 2005. Investigating market Dynamics using the double jeopardy line,ANZMAC 2005 conference proceedings, December

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http://marketing.wharton.upenn.edu/ideas/pdf/Fader/PapersNew/fader_schmittlein_jmr_93.pdfhttp://marketing.wharton.upenn.edu/ideas/pdf/Fader/PapersNew/fader_schmittlein_jmr_93.pdfhttp://marketing.wharton.upenn.edu/ideas/pdf/Fader/PapersNew/fader_schmittlein_jmr_93.pdfhttp://marketing.wharton.upenn.edu/ideas/pdf/Fader/PapersNew/fader_schmittlein_jmr_93.pdf


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Kato, M., Honjo, Y ., 2006. Market Share Instability and the Dynamics of Competition: aPanel Data Analysis of Japanese Manufacturing Industries, Review of IndustrialOrganization, March, vol. 28, Issue 2, p165-182

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Keller, K., 1998. Strategic Brand Management,Prentice Hall, Upper Saddle River, NewJersey, USA

Reichheld, FF., Sasser, WE., 1996. The Loyalty Effect: The Hidden Force Behind Growth,Profits and Lasting Value, Harvard Business School Press, Boston MA.

www.dti.gov.uk/files/file17173.pd, UK Department of Trade and Industry, reference site forthe DTIs use of the Herfindahl-Hirschman Index. For a useful explanation and calculator, seehttp://www.unclaw.com/chin/teaching/antitrust/herfindahl.htm

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