the relationship between customer loyalty and purchase incidence

Reproduced with permission of the copyright owner. Further reproduction prohibited without permission.

The Relationship Between Customer Loyalty and Purchase IncidenceStern, Philip;Hammond, Kathy

Marketing Letters; Feb 2004; 15, 1; ABI/INFORM Researchpg. 5

Marketing Letters 15:1, 5-19, 2004 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

The Relationship Between Customer Loyalty and Purchase Incidence PHILIP STERN Warwick Business School

KATHY HAMMOND* London Business School, Sussex Place, Regents Park, London, NWJ 4SA, UK

Abstract

[email protected]

Little is known about customer loyalty to brands over many dozens or even hundreds of purchases. In this paper we describe, and seek to explain, such patterns of loyalty in two very different markets: a consumer market (laundry detergents), and a more frequently used service (physicians' prescribing of anti-hypertension drugs). Purchase incidence heterogeneity - a problem in most loyalty studies - is addressed by measuring loyalty at different rates of category purchase (rather than over time). Using share-based measures we expect that loyalty will decline as purchase incidence increases, however we clarify the shape of that decline. We find that, as the number of purchases rises, loyalty initially falls steeply, but after around 15 purchases it starts to stabilize, and from 60 to 200 purchases there is very little change in observed measures of customer loyalty. A comparison of the findings with those expected from a stationary market model (the Dirichlet), suggests that the decline in loyalty seen as the number of purchases rises is largely a statistical artifact, dependent on the number of purchases used to calculate loyalty. However, we also find that the higher loyalty exhibited by heavier buyers at low purchase levels is not captured well by the model. The implication here is that, contrary to a central assumption of the Dirichlet model, brand choice is partially dependent on purchase weight.

Keywords: customer loyalty, brand loyalty, purchase incidence, Dirichlet model

1. Introduction

Measuring customer loyalty is important for all firms, but especially for those that have, or plan to implement, customer loyalty initiatives such as loyalty cards or frequent-user programs. As Dowling and Uncles (1997) state, operating loyalty programs requires knowledge of long-run customer loyalty patterns in order to justify and evaluate invest-ments. However, as noted by Dekimpe et al. (1997), even though managers need to have better information on how buyers might behave over multiple purchases within a category, there are very few reported studies of consumer behavior over the longer term (exceptions being East and Hammond, 1996; Johnson, 1984; Mela et al., 1997; Stern, 1997).

This research explores whether and how customer loyalty varies by the number of pur-chases. We seek to establish the general patterns of loyalty as the number of purchases

* CoITesponding author.


6 STERN AND HAMMOND

bought by a customer increases to 200. This is a full order of magnitude greater than com-monly reported (and the equivalent of around 10 years of purchasing in a typical packaged goods category). Marketing managers who use behavioral measures of customer loyalty to evaluate the strength of their relationship with customers need to understand the observed pattern of loyalty as purchases rise. For example, if the share of purchases customers give to their most preferred brand decreases with increasing category purchase and the level of this decrease is almost constant, then the strength of the relationship will weaken (in sales terms) and may require substantial continuous reinforcement. However, if customer loy-alty reaches an equilibrium point that is largely independent of further category purchase, the relationship might need less and perhaps only intermittent reinforcement.

Specific questions are:

(i) Brand loyalty, as calculated using share-based measures, is expected to decline as more purchases are made, but what pattern does this decline take?

(ii) Brand-level data are relatively easy to collect, and short-run brand data can be used to predict long-run brand loyalty. How close are such predictions to observed findings? What additional information do we gain by using different measures of loyalty? How well does a benchmark model such as the Dirichlet, which is normally used with brand-level data, perform when used to predict preferences for customers' 'favorite' brands?

(iii) What are the main factors determining the levels of customer loyalty seen in different markets?

We measure customer loyalty in three ways. Our first two measures are share-based measures, the third, non-share based, measure provides an indication of overall brand switching. For each measure we examine how its value changes as the number of pur-chases increases. Below we discuss the rationale for our choice of measures.

1.1. Measures of Customer Loyalty

We initially use the share of category requirements measure (SCR) to capture the relative share of category purchases that individual households give to each brand they buy. SCR is a useful measure of brand loyalty as it is easily understood and widely used by brand managers, and we build on previous studies where SCR has been applied over specific time periods (e.g., Bhattacharya et al., 1996; Bhattacharya, 1997; Fader and Schmittlein, 1993; Johnson, 1984; Tellis, 1988). However, SCR has two potential weaknesses, and in order to address these we use two additional measures of loyalty.

The first weakness is that, particularly over the long run, the SCR measure does not allow us to distinguish between customers who have a most preferred brand, but only give that brand a fairly low share of their purchases, and a brand with the same share bought as a secondary or tertiary brand by customers who have another primary brand. Managers of loyalty programs or other customer relationship management initiatives often want to be able to differentiate these two types of customer, focusing retention programs on the former, and aiming development/re-acquisition or even customer 'sacking' programs


THE RELATIONSHIP BETWEEN CUSTOMER LOYALTY AND PURCHASE INCIDENCE 7

at the latter. For our second measure, therefore, we follow Deighton et al. (1994) and calculate the share of category purchases accounted for by the customer's most preferred brand (SCRpref). We can think of SCR as a brand loyalty measure and SCRpref as a customer loyalty measure.

A second weakness is that SCR (in common with SCRpref and all share-based mea-sures) is confounded by purchase incidence. We therefore report a third measure of loyalty, the polarization index, , which captures changes in the heterogeneity in con-sumer choice vectors as purchase incidence changes. 1 ranges between zero and one, where zero indicates pure homogeneity in consumer choice (i.e., all buyers have the same propensity to buy individual brands), while as approaches 1, there is maximum hetero-geneity (i.e., each consumer buys only their favorite brand) (Fader and Schmittlein, 1993; Sabavala and Morrison, 1977). Before describing the calculation of measures and the data, we first discuss the reason for focusing on purchase incidence rather than time as the mea-surement interval for studying loyalty over many purchases.

2. Heterogeneity in Purchase Incidence

Individual consumers exhibit differences in both category purchase incidence and brand preferences. Fader and Schmittlein (1993) have argued that heterogeneity in brand choice is the likely cause of the excess brand loyalty (excess compared with predictions from a baseline Dirichlet model) observed for high share brands. Such heterogeneity has been addressed partially by the development of models that incorporate latent segments in their brand choice component (Danaher et al., 2003). Here we are interested in heterogeneity in purchase incidence; below we discuss how four aspects of purchase incidence heterogene-ity can affect loyalty measures.

Infrequent category buyers Infrequent category buyers raise problems for loyalty mea-sures based on share. For example, if a customer makes only one purchase of the cate-gory in the time period being analyzed, then the share of purchases allocated to the brand bought must be 100%. If they buy the category twice and buy two different brands, the share for each brand can only fall to 50%, etc. One solution in the past has been to exclude these (often numerous) infrequent category buyers from the analysis of loyalty and include only buyers who make a minimum number of category purchases (e.g., 3 or 5 or 15 pur-chases) in the time period under study (Deighton et al., 1994; Krishnamurthi and Raj, 1991; Tellis, 1988).

Very frequent category buyers Panel data for research purposes are rarely available for more than two years (and very few analyses have been reported over periods longer than this), therefore even for relatively frequently-purchased products, analyses tend to con-tain few cases of very frequent category buyers. It may be that the behavioral loyalty of very frequent category buyers is substantially different from that of the average buyer in a six-month to two-year period, the basis for calculating most measures of share loyalty (Bhattacharya et al., 1996; Fader and Schmittlein, 1993; Tellis, 1988).


8 STERN AND HAMMOND

Small portfolio size Across many common grocery markets the average buyer makes around 12 category purchases per year (Ehrenberg et al., 2004). For this number of pur-chases customers' brand portfolios tend to be around four (Hauser and Wernerfelt, 1990). With portfolios of this size, brand SCR values average around 30% (Ehrenberg et al., 2004 ), and SCRpref is 65% to 75%, as originally observed by Cunningham (1956), and confirmed by Deighton et al. (1994). But such findings do not inform us about loyalty over many dozens of purchases when brand portfolios have the potential to be much larger.

Different purchase frequencies Even allowing for the previous problems, consumers still exhibit significant heterogeneity in terms of purchase incidence. This makes it difficult to compare loyalty levels in a specific time period across buyers with different category usage rates.

We address the first three heterogeneity problems by analysing data from two long-run (five year) panel datasets.2 The final measurement issue (different purchase frequencies) we address by repeating the analyses for different numbers of purchases rather than report-ing findings over time.

3. Benchmarking Against the Dirichlet Model

In seeking to explain the loyalty patterns we observe, we compare our findings with those predicted using the benchmark Dirichlet model. The Dirichlet is a stochastic model of buyer behavior developed for the study of branded packaged goods in established competi-tive markets (Bass et al., 1976; Goodhardt et al., 1984 ). The theory underlying the Dirichlet is that there is a small set of interrelated assumptions that describe and predict the patterns of purchase incidence and brand choice for any market that is approximately stationary and unsegmented. The Dirichlet is used here since a basic assumption of the model is that, for individual consumers, brand choice probabilities are independent of category purchase incidence. If the Dirichlet model is able to predict closely the loyalty patterns observed as the number of purchases increases, the implication is that managers can use short-run data to model brand loyalty over the longer term. However, previous researchers have noted that the assumption of the independence of purchase incidence and brand choice does not always hold (e.g., Shoemaker et al., 1977). There have also been studies report-ing systematic deviations from Dirichlet predictions; Bhattacharya (1997) describes the under-prediction of brand loyalty for niche brands; Fader and Schmittlein (1993) report the under-prediction of brand loyalty for high-share brands, and suggest that this market share premium is a consequence of consumer segmentation that favors large brands.

The usefulness of the Dirichlet model as a benchmark lies in the fact that it has success-fully characterized brand loyalty over the medium term across a wide range of categories and conditions (Ehrenberg et al., 2004; Uncles et al., 1995). Our aim is to provide an initial description of loyalty patterns over different purchase weights and rates, however our results also suggest that purchase incidence heterogeneity may provide an additional explanation for the previously observed under-prediction of brand loyalty by the Dirichlet.



4. Data and Methodology

4.1. Data

We calculate all loyalty measures for consumer purchasing of laundry detergent. We also replicate analysis of the SCRpref measure for a service category, physicians' prescribing of anti-hypertension drugs. Prescribing by physicians has been chosen as a deliberate contrast to the more common consumer products usually reported in brand loyalty studies. Physicians' prescribing differs in an important way from the purchasing of detergents -the detergent buyer will subsequently use the product whereas the physician is a service provider who writes the prescription but does not use or pay for the drug. The addition of this dataset provides a test of the potential generalizability of our findings.

4.1.1. Laundry detergent The data come from a five-year household panel of 1532 continuous category buyers. The top 11 brands were analyzed; these accounted for 92% of purchases. 3 The average purchase rate was 73 over the five years, with 816 households making at least 50 purchases, 122 households making 150 or more purchases, and 35 households making over 200 purchases.

4.1.2. Physicians' prescribing of anti-hypertension drugs Data are from a five-year continuous panel of 202 medical prescribers in the UK.4 The leading 17 brands of anti-hypertension drugs were analyzed, accounting for over 70% of total drugs prescribed in the hypertension treatment area. The average prescribing rate for the category was 250 over five years, with 162 physicians writing at least 50 prescriptions, 73 writing 150 or more prescriptions, and 48 writing over 200 prescriptions.

4.2. Calculation of Loyalty Measures

SCR is calculated as the ratio of total purchases of the brand to total category purchases among those who buy the brand. SCRpref is the share of category requirements given by a household to its most preferred brand.5 The Dirichlet model requires three parameters, M, S, and K. Mis simply the mean purchase rate and K measures buyer heterogeneity reflecting the extent to which overall purchasing differs from the mean. The S parameter measures heterogeneity in brand choice. The marginal distributions of the Dirichlet are beta for each brand. S for the category is calculated by estimating brand specific S val-ues for each marginal distribution, and taking the market share weighted average of these marginal S values (Uncles, 1989). M, S, and K are calculated from the average one-year penetration and purchase frequency data for each brand. Using these parameters, simu-lated data representing 'pure Dirichlet' purchasing patterns for thousands of 'buyers' were created for both of the markets studied. From these simulated datasets, the average brand choice probabilities together with product and brand rates of buying were calculated for different purchase rates, leading to the estimation of values for SCR and SCRpref for pur-chase rates from 5 to 200. The S parameter is reported as our third measure of loyalty, the polarization index,.


10

70

60

50

~ 40 0:: (.) (/) 30

20

STERN AND HAMMOND

--Brand 1 (act) --- Brand 2 (act) ---+--Brand 3 (act) --------- Minor Brand (act) .Brand 1 (est) X Brand 2 (est) ........ Brand 3 (est) .. .. Minor Brand (est)

.... -.. . . . . . . . ....... -....... .

. -. .. . . . . . . ....... -....... .

10 . . . . . ... . -......................... -....... .

0 0 10 20 30 40 50 60 70 80 90 100 110 120

Number of purchases

Figure 1. SCR: Actual and Estimated Values for 3 Leading Brands and 1 Minor Brand of Laundry Detergent.

5. Results

We first report the actual and expected patterns for SCR for laundry detergent, then the ac-tual and expected values for SCRpref for both laundry detergent and physicians' prescrib-ing of anti-hypertension drugs. Finally we report for laundry detergent, and describe the relationship between and SCR.

5.1. Brand Loyalty Measured Using SCR

Figure 1 shows actual and expected values of SCR, for the leading three brands and one minor brand of laundry detergent, as category purchases increase from five to 125. Brand 2 illustrates the general pattern; SCR averages 53.7% at five category purchases (48.9% expected), falling sharply to 35% at 15 purchases (33.4% expected), continuing to fall steadily to 25% (24.4% expected) at 60 purchases and then stabilizing at around this level. As expected, larger brands exhibit consistently higher levels of loyalty than smaller brands. The expected values for individual brands are generally close to actual values, except for the brand leader, where the Dirichlet consistently under-predicts SCR (by 3 to 10 percent-age points or 9% to 18%).

5.2. Customer Loyalty Measured Using SCRpref

Turning to SCRpref, Figure 2 shows the findings for laundry detergent and also, as a com-parison, for physician's prescribing. Customers exhibit greater brand loyalty to detergents than physicians do to anti-hypertension drug prescribing, but the pattern of loyalty is sim-


THE RELATIONSHIP BETWEEN CUSTOMER LOYALTY AND PURCHASE INCIDENCE

--Detergent: SCRpref (act) - - - - - Detergent: SCRpref (est) --Physicians: SCRpref (act) 701---;:---~"'=--------------i__ _ _:_:~~-~-~P~hy~sic~ia~ns~:~SC~R~pr~ef~(e~st~)----I

...................... ____________ -- -- -- -- -- --

?!'.

" a5o+---'------------------------------___, ~ () rn

0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 Number of purchases

Figure 2. SCRpref: Actual and Estimated Values - Laundry Detergent and Physician's Prescribing.

11

ilar for both categories: average loyalty declines sharply as the number of category pur-chases increases from five to 15, then the decline is less steep as purchases increase to around 60, and from 60 purchases onward there is very little change in loyalty.6 Our observed findings for SCRpref are consistent with previous research by Deighton et al. (1994), who reported mean SCRpref levels of 73% for powder detergent (where the av-erage number of purchases was 11.3). The pattern of decline for SCRpref is similar to that seen for SCR (the steep decline from 5 to 15 purchases, a less steep curve from 15 to around 60 purchases), but there are some key differences. If we compare values for SCR and SCRpref for laundry detergent we see that, not only is the starting point for SCRpref at a much higher level, but the slope of the SCRpref line is also shallower.

If we compare the actual and expected values for SCRpref, the correlation is high (0.976 for laundry detergent and 0.944 for physicians' prescribing), however, in both categories, particularly at low purchase rates, the model under-predicts SCRpref At low purchase rates (which, to be as consistent as possible with previous research, we take as being up to 15 purchases), the under-prediction averages 7.2 points or 10.0% for detergent and 6.0 points or 13.1% for physicians' prescribing. At medium purchase rates (16-60 purchases), the under-prediction is 3.8 points (5.5%) for detergent and 4.4 points (11.2%) for physicians' prescribing. For high purchase rates (61-200 purchases), values for SCRpref are closer to those predicted: for detergents the model under-predicts by an average of 0.8 points or 1.2%, while for physicians' prescribing the mean absolute deviation from the model is 0.7 points (2.2%).7

The fit between the actual findings and expected results for SCRpref (and for the brand leader for SCR) would suggest that the assumption that purchase incidence is independent of brand choice, although a good approximation, is not entirely valid. In order to explore this issue in more depth, we compare our findings for groups of buyers segmented by pur-chase weight. We divide laundry detergent buyers into three discrete segments according to their rate of category purchase and plot SCRpref levels over the first 20 purchases for


12 STERN AND HAMMOND

--heavy buyers medium buyers ---light buyers

50+---~--~--~--~--~--~--~--~--~--__, 5 11 13 15 17 19 21 23 25

Number of purchases

Figure 3. SCRpref over 20 Purchases for Buyers with Different Purchase Rates.

;!. 't

90

80

a. 70 c:: (.) en

60

50

~

~

5 7

--...... -

9 11

-

-

-

-

-SCRpref: purchases 5-24

-+-SCRpref: purchases 65-84

--.!r- SCRpref: purchases 1 05-124

13 15 17 19 21 23 25 Number of purchases

Figure 4. Patterns of Declining Loyalty for Different Purchase Sequences (for the Same Sample of Buyers, Laundry Detergent).

the three segments (Figure 3). The similarities between medium and heavy buyers can be clearly seen, as can the consistently lower loyalty exhibited by lighter buyers. We dis-cuss a possible explanation for this pattern and how it affects deviations from Dirichlet predictions in Section 6.

An important point to note is that this pattern of an initial steep decline in brand loy-alty followed by a much more gradual decline is not determined by where in the database the purchase records are sampled. If we look at the patterns that occur for SCRpref in three series of 20 purchases from the same buyers, each with a different starting point, we find that they are very similar (see Figure 4).8 This suggests that the pattern of ap-



0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 Number of purchases

Figure 5. Laundry Detergent: Brand Switching Parameter, rp.

parent declining loyalty revealed by share-based measures such as SCR and SCRpref is largely a statistical artifact, dependent on the number of purchases used to calculate the measure. This motivates our use of a third loyalty measure, , which is not similarly constrained.

5.3. Brand Loyalty Measured Using the Polarization Index,

Figure 5 plots the observed values of, the polarization index or switching parameter, as the number of purchases increases. The decline in suggests that buyers become more similar in their brand choices as purchase incidence rises. influences share-based mea-sures of loyalty, but as falls, its impact on SCR and SCRpref declines. This is demon-strated in Figure 6(a) which plots the relationship between SCR and at one purchase rate (75) for a market with three simulated brands (with market shares of 40%, 20% and 1 % ). For the 40% and 20% brands SCR stabilizes when is about 0.25 and shows almost no further decline. For the 1 % brand, SCR continues to decline for values of less than 0.25. In practice this means that once falls below 0.25 (which equates to an S value of 3) there is little further incremental change in SCR or other share-based measures, unless the brand has a very small market share. In order to illustrate in more detail the relationship between and SCR, Figure 6(b) shows expected values of SCR for a 20%-share brand at different values of and at three different purchase rates (5, 15, and 75). We find that Dirichlet predictions of SCR decline in line with. Superimposed on the theoretically gen-erated curves are actual values of SCR for a 20%-share brand at different purchase rates and different values of. In each case the SCR values are higher than expected given the values.


14

70

60

50



lent to 4-12 months' purchasing for laundry detergent, and a very common base period for loyalty analyses), is very different from that observed at high levels of purchase incidence.

However, when we model customer loyalty using the Dirichlet, which assumes that brand choice probabilities are independent of purchase incidence, we find similar patterns of declining loyalty. This suggests that:

The pattern of apparent declining customer loyalty is largely a statistical artifact, dependent on the number of purchases used to calculate the measure.

6.2. But, Heavier Buyers Are More Loyal

However, we do find that purchase weight (i.e., whether a buyer is a light, medium or heavy purchaser of the category), affects share-based measures of loyalty. This seems a real effect rather than one determined by statistical artifact.

The commonly reported finding that high loyalty tends to be driven by light buyers (who are perhaps 100% loyal because they make few purchases, as suggested by Ehrenberg et al., 2004), holds true only if the comparison is between light and heavier buyers in the same time period.

If we compare brand loyalty across an equal number of purchases for light and heav-ier buyers (and therefore control for the small-number effect) we find that heavier buyers are more loyal.

This may be an important consideration when designing customer relationship manage-ment programs (where customers are often segmented by weight of purchase).

6.3. The Modeling of Customer Loyalty

What do our findings imply for the modeling of loyalty? Managers who wish to predict long-run brand and customer loyalty can gain considerable insight by using a simple sta-tionary market model such as the Dirichlet, and operationalizing the model with parameters derived from short-term aggregate purchase data. The Dirichlet closely predicts the general pattern of loyalty, but consistently under-predicts SCR for the brand leader. In addition, for the share of category requirements satisfied by the most preferred brand, the model predic-tions, while describing a pattern similar to that observed, have a lower starting point and a shallower slope, so that loyalty is under-predicted at low purchase rates.

Do buyers of different purchase weights contribute equally to this excess loyalty? In Figure 7, we compare observed SCRpref for light, medium, and heavy buyers, with the overall Dirichlet model predictions (which are the same for buyers of all purchase weights). We find that:

Dirichlet predictions are closer to the actual values for light buyers than they are for heavier buyers. The contribution of heavier buyers to brand sales could partially explain the under-prediction of brand loyalty previously reported for some high share brands.


16

80

~ 0 ~ 70 c. 0:: \ (.) en

--....

60

0 10 20 30 40

STERN AND HAMMOND

~heavy buyers -----.>-----medium buyers ---e--- light buyers predictions

------ ----------- _,.. ____ ... __ ---- ----.... -....

50 60 70 80 90 100 110 120 Number of purchases

Figure 7. SCRpref: Heavy, Medium, and Light Buyers - Actual vs. Dirichlet Model Predictions, Laundry Detergent.

Why are heavy buyers more loyal than lighter ones at low levels of purchasing? We observe from the laundry detergent data that, where a buyer has a two-brand portfolio consisting of brands A and B, the sequence of brand purchases, 'A, A, A, A, A, B, B, B, B, B' is far more common than 'A, B, A, B, A, B, A, B, A, B.' A tentative explanation for this observation is that lighter buyers may have less opportunity to incorporate feedback or learning effects into their purchase decisions, in practical terms they 'forget' more easily than more frequent buyers. This could explain why a zero-order model such as the Dirichlet is better at predicting loyalty for light buyers than for heavier buyers.

6.4. Future Research

In terms of future research, the relationship between the measures of loyalty used here and other loyalty measures, such as portfolio size (the number of different brands utilized by a customer), requires investigation. Preliminary research indicates that similarly predictable patterns will be found. We expect that just as SCR and SCRpref decline rapidly and then stabilize, portfolio size will increase and then stabilize. We also expect that light buyers will have larger brand portfolios than heavier buyers at low purchase rates but that there will be little difference by purchase weight as the number of purchases increases. There is also a need to simulate loyalty measures for a range of different markets characterized by varying structural parameters. We would expect that the shape of the customer loyalty curve would be determined largely by the number of purchases, with the level of loyalty depending on .



In this research, the aim was to explore the general pattern of customer loyalty and we did not have access to additional explanatory variables such as price, promotion, availabil-ity, or who buys versus who consumes. We believe that the relationship between customer loyalty and purchase incidence revealed by this research is likely to generalize. However, the inclusion of additional explanatory variables may improve the model fit where the fore-casting of long-run loyalty for individual brands is the focus of attention.

If the deviations from Dirichlet model expectations reported here prove to be systematic, the model could be adapted to take account of the short-run under-prediction of loyalty or a model could be developed for each purchase weight segment. Finally, in the simulation procedure used to generate expected values for SCR and SCRpref we held S constant, however as our findings suggest that S increases (i.e. falls) as the number of purchases rises, this may be an additional factor contributing to deviations in model fit. A future development could be to introduce dynamic parameters into the model to reflect these empirical findings.

Acknowledgements

The authors are grateful to TN AGB for providing access to the detergent data and to Colin Maitland at ISIS research for the Jigsaw data on anti-depressant prescribing. We are also indebted to the editor and two anonymous reviewers for their thoughtful and very helpful comments on earlier versions of this paper.

Notes

1.


18 STERN AND HAMMOND

hype1tensives - used to reduce blood pressure and prevent and treat heart disease. The market shares of the top five brands were: 14.2%, 12.1 %, 8.8%, 6.9%, 6.6%.

5. We also studied 2nd and 3rd prefeITed brands in order to obtain an indication of the impo1tance of the most prefeITed brand. For laundry detergent, on average, the brand most prefeITed by individual buyers accounts for between 60% and 90% of purchases and there is relative! y little difference across brands. The brand prefeITed second accounts for between 17% and 27% of purchases. Third prefeITed brands always made up less than 10% of purchases.

6. When repo1ting the findings for drug prescribing, for simplicity we refer to purchases rather than prescriptions written.

7. t-tests gave p = 0.86 for physicians' prescribing at high purchase rates, for all other rates for physicians' prescribing and for all rates for detergent purchasing, t-test p values were



Sabavala, Darius J. and Donald G. Monison. (1977). "A Model of TV Show Loyalty," Journal of Advertising Research, 17(6), 35-43.

Shoemaker, Robert W., Richard Staelin, Joseph B. Kadane, and F. Robe1t Shoaf. (1977). "Relation of Brand Choice to Purchase Frequency," Journal of Marketing Research, 14( 4 ), 458-468.

Stem, Philip. (1997). "Long Run Loyalty -An Exploratory Study," Marketing and Research Today, 25(2)(May), 70-78.

Tellis, Gerard J. (1988). "Advertising Exposure, Loyalty and Brand Purchase: A Two-Stage Model of Choice," Journal of Marketing Research, 25(2), 134-144.

Uncles, Mark D. (1989). BUYER. Buyer Behavior Software. Uncles, Mark D., A. S. C. Ehrenberg, and Kathy Hammond. (1995). "Patterns of Buyer Behavior: Regularities,

Models and Extensions," Marketing Science, 14(3), Part 2 of2, G71-G78.