Search Profiles of Grocery Shoppers

ABSTRACT - Through a large random sample of U.S. grocery shoppers, the dimensions of consumer grocery search are identified and used to establish profiles of shopper segments. Coupons, unit prices, number of stores visited, brand comparisons, advertised specials, word of mouth, checking price tags, and published product evaluations constitute distinct dimensions of search. Five segments are observed and profiled on the basis of search behavior and demographic characteristics: low-effort price/brand comparison, high-effort value seeking, search averse, high search, and time-pressured low search.


Sanjay Putrevu, Kenneth R. Lord, and James W. Gentry (2001) ,"Search Profiles of Grocery Shoppers", in AP - Asia Pacific Advances in Consumer Research Volume 4, eds. Paula M. Tidwell and Thomas E. Muller, Provo, UT : Association for Consumer Research, Pages: 115-121.

Asia Pacific Advances in Consumer Research Volume 4, 2001      Pages 115-121


Sanjay Putrevu, ESSEC, France

Kenneth R. Lord, Mercer University, U.S.A.

James W. Gentry, University of Nebraska, U.S.A.


Through a large random sample of U.S. grocery shoppers, the dimensions of consumer grocery search are identified and used to establish profiles of shopper segments. Coupons, unit prices, number of stores visited, brand comparisons, advertised specials, word of mouth, checking price tags, and published product evaluations constitute distinct dimensions of search. Five segments are observed and profiled on the basis of search behavior and demographic characteristics: low-effort price/brand comparison, high-effort value seeking, search averse, high search, and time-pressured low search.


The objectives of this study are to explore the dimensions of consumer search and identify and profile shopper segments based on distinctive patterns of search behavior. An understanding of consumer search patterns is relevant to producers in their efforts to attract the attention and arouse the preference of time-pressured shoppers, to grocery retailers who structure the stor environment in which some search occurs, and to consumers themselves in their effort to optimize their shopping outcomes.

Most prior research on consumer search has modeled this construct as a function of variables such as benefits, costs, price dispersion, budget, time constraints, knowledge, and shopping enjoyment. While this body of research (described in the next section) has, in some instances, successfully predicted search levels, it has not fully explored the dimensions of consumer grocery search and their usefulness for segmenting shoppers. Understanding the search patterns that characterize distinct buyer segments could help marketers develop information strategies to assist consumers and aid buyers in evaluating and improving their decision processes.


Durable goods have been the focus of most prior research on consumer information search. Newman and Staelin (1972) and Punj and Staelin (1983), for example, reported the extent of consumer search in new-car purchases and how it varies relative to several independent variables. Beatty and Smith (1987) studied the level of consumer search for other durable goods and its relationship with some individual-difference variables.

Some scholars have examined consumer search for frequently purchased non-durable goods such as grocery products. Jacoby, Chestnut and Fisher (1978) and Moore and Lehmann (1980) conducted direct-observation experiments documenting the amount and type of consumer search for a single grocery product. Carlson and Gieseke (1983), using 1956 panel data, examined consumer search for groceries operationalized as the number of store visits reported by respondents. Hoyer (1984) and Dickson and Sawyer (1990) conducted direct in-store observation of search behaviors for the full basket of goods purchased. Urbany, Dickson and Kalapurakal (1996) studied search in the form of between-store price comparisons. A normative model of search developed by Putrevu and Ratchford (1997) captures the tradeoff between loss due to insufficient information and the costs of search. Murthi and Srinivasan (1999) modeled search for a single grocery product as a function of when in the week the shopping trip occurred, whether the product was on display or feature, and the consumer’s purchase frequency, store loyalty, availability of time, education and income. From these studies one can gain insight into the role played by a variety of market conditions, marketing and retail strategies, and consumer characteristics in affecting the amount of consumer grocery search.

An understanding of the extent of consumer grocery search is beneficial but does not tell the whole story. The ability to shape and benefit from the direction of consumer search, or to optimize one’s own search behavior as a consumer, requires an understanding of the dimensions of search and distinct groups of consumers who follow identifiable search patterns. This requires more complete assessment of search types than has been undertaken by prior research. A few scholars’ conceptualizations of search types, though not developed in a grocery-shopping context, offer some guidance in this effort.

Kiel and Layton (1981) and Westbrook and Fornell (1979) demonstrated the use of retail (store visits), neutral (newspaper or magazine articles), and personal sources (word of mouth) in new-car purchases. Beatty and Smith (1987) measured media, retail, interpersonal, and neutral-source search in the appliance market. Sambandam and Lord (1995) showed that media and retail search relate to different stages of the automobile-purchase decision. Schmidt and Spreng (1996) offered a conceptualization of search comprised of marketer-controlled sources, reseller information, third-party independent organizations, interpersonal interaction and direct inspection.

The availability of multiple information sources creates the possibility of the existence of distinctive consumer segments that vary in their prefered search patterns. Claxton, Fry, and Portis (1974) found three search segments in the furniture and appliance market: store-intense consumers who depend primarily on store visits, thorough-balanced buyers who seek information from multiple sources, and non-thorough shoppers who engage in minimal search. Similarly, segments defined by the extent of search have been found in studies relating to new-car purchases (Westbrook and Fornell 1979; Kiel and Layton 1981; Furse, Punj, and Stewart 1984): low (few sources, minimal time deliberating purchases), selective (reliance on a subset of available sources) and high search (most sources, thorough alternative evaluation).

The above literature shows that distinct consumer search segments exist for durable goods, but the situation for non-durable goods is less clear. Murthi and Srinivasan (1999) report preliminary evidence of three segments in the ketchup market, characterized by varying levels of evaluation. This research seeks to increase knowledge by identifying the dimensions of search for the full basket of grocery products and developing profiles of distinct consumer segments that following different search patterns.


In a survey of grocery shoppers, respondents completed self-report measures of search. The use of self-reports is questionable for durable goods where the measurement may occur months after the original purchase (Punj and Staelin 1983, Srinivasan and Ratchford 1991). However, search and purchase activities undertaken by grocery shoppers are routinely undertaken each week and information regarding these activities should be reasonably accessible in the minds of consumers. Furthermore, self-reporting allows the measurement of certain aspects of search behavior that may be difficult to observe directly in the marketplace, such as scanning newspaper/magazine ads and articles or soliciting advice from friends.

The development of valid and reliable measures of search in a supermarket setting proceeded in several stages, as suggested by Churchill (1979). First, one- to two-hour depth interviews were conducted with fifteen shoppers and two managers. Information from these interviews led to the following definition of grocery search: "the effort expended gathering information related to the selection and purchase of items in the family grocery basket." It also led to the identification of nine recurring types of search: (1) comparing unit prices of products, (2) checking price tags on considered and selected items, (3) comparing competing brands on the various ingredients, (4) looking for in-store promotion, (5) clipping and using coupons, (6) looking for advertised specials in newspapers and store flyers, (7) making multiple store visits, (8) exchanging information with friends through word of mouth, and (9) reading published product evaluations in newspapers/magazines. Underlying motivations for these search behaviors include price savings (unit-price comparisons, price checks, in-store promotions, coupons, advertised specials), gauging product availability (store visits, advertised specials), seeking optimal features, attributes or benefits or updating information in an area of interest (brand comparisons, published product evaluations, word of mouth), and social rewards (word of mouth). They reflect behaviors that may occur during the pre-purchase stage or after/between purchase occasions.

Based on the literature search and interviews, multiple items were developed for each of the nine search behaviors. Several graduate students and faculty members assessed the items for face and content validity. A convenience sample of fifteen grocery shoppers was shown the list of items, given the definition of each construct, and asked whether each item constituted an appropriate measure of the construct and how easily they could respond to it. From the results of this exercise, ten items were changed to improve face validity and clarity.

To assess the reliability of the scales developed as measures of the nine search behaviors, questionnaires were distributed to a convenience sample of 180 grocery shoppers recruited from a church group, a women’s club, acquaintances of the researchers, and university associates. Responses were scaled from "never" (1) to "always" (7). After deleting items detracting from overall scale reliability, 33 remained (at least three items for each of the nine types of search behavior identified earlier; Cronbach a>.80 for eight and approximately .70 for the ninth).

Next, data were collected on the search measures and other variables from a larger, more representative sample. Three additional constructs (prior planning of grocery purchases, ease of information processing, and time pressure) were included because of their expected association with extreme patterns of search. Consumers who engage in advance planning and those who find relevant information processing to be easy were expected to engage in relatively extensive search of multiple types, while minimal search was anticipated among those who undertake their shopping expeditions under high time pressure (Titus and Everett 1995, Schmidt and Spreng 1996, Urbany et al. 1996). Measures of readership of the local newspaper (relevant to exposure to published store flyers and product articles) and several demographic items were also included.

A random sample of 2000 households in a two-county metropolitan area in northeastern United States (stratified on the basis of income) received the instrument via mail and produced a response rate of 30.6 percent (612 returned questionnaires, of which 588 were complete in most categories and used in the final analysis). The sample was demographically representative of the broader population (76 percent married, all age and income groups represented with 35-44 as the median age category and a median income in the range of U.S. $30,000B$39,999, average of three persons per household). There were no significant differences between questionnaires received in the first two weeks after mailing and those received in the last two weeks of the acceptance period, suggesting that there was no systematic non-response bias.


A maximum-likelihood factor analysis of the 54 non-demographic items produced thirteen factors with eigenvalues greater than one and multiple large cross-loadings between factors. Ten items with low loadings across all factors were deleted for subssequent analysis. The remaining items included four indicators of the use of unit prices (Cronbach a=.88), three of price tags (a=.80), four of comparing brands (a=.84), one of in-store promotions, seven of coupons (a=.84), four of advertised specials (a=.89), three of shopping at multiple stores (a=.89), three of word-of-mouth advice (a=.82), five of published product evaluations (a=.87), four of planning purchases in advance (a=.66), one of ease of processing, and five of time pressure (a=.90).

Repeating the factor analysis without the deleted items, ten common factors emerged with eigenvalues greater than one, accounting for 61.3 percent of the variance among the 44 items. An oblique rotation was used to allow for correlation among the various dimensions of search (promax with Kaiser normalization). Results are shown in Table 1.

As seen in the factor loadings, eight of the nine behaviors identified in the earlier stages of the research represent distinct but correlated dimensions of consumer grocery search. Factor 1 captures shoppers’ use of coupons. Six of the seven indicators of coupon usage account for the highest loadings on that factor (.73B.91), while the remaining indicator has the eighth highest loading (.64). Other high loadings (greater than .50) reflect searching for advertised specials. Checking unit prices is the dominant contributor to Factor 2, with the four loadings for that construct (.76B.90) eceeding those of all other variables. Factor 3 captures time pressure (the five measures of that variable have loadings with absolute values between .72 and .86, while those of all other variables are below .30). The three indicators of the number of store visits load on Factor 4 (.81B.99); the only other measure with a loading greater than .50 (checking newspaper ads) loads more strongly on the factor it shares with other indicators of checking advertised specials. Brand comparisons unambiguously explain Factor 5, with the only high loadings on that dimension coming that construct’s three measures (.73B.91). Shoppers’ tendency to check for advertised specials define Factor 6; the four measures of that construct have the highest loadings (.72B.91), with coupon usage and planning exerting subordinate influence. The only items with high loadings on Factor 7 are the three indicators of word of mouth (.71B.80). Factor 8’s highest loadings (.79B.82) fall on the price-checking items, with smaller contributions from advertised specials. Factor 9 stems primarily from the prior planning of grocery purchases (the highest two loadingsB.70 and .63); weaker loadings are associated with advertised-special and coupon items. Three published-product-evaluation indicators produced the only high loadings for Factor 10 (.73B.84).

Significant correlations exist among the factors representing the various dimensions of search, most notably between the coupon-usage, advertised-specials, and planned-purchase factors (all coefficients greater than .60). The two price-checking factors (unit and tag prices) are also correlated (r=.52), and the tag-price factor is correlated with use of store flyers (r=.62). All other correlation values are less than .50.





K-means cluster analysis (derived from the above factor analysis) was used to define shopper segments on the basis of patterns of search. Five clusters emerged, whose sample sizes and centers (mean factor scores) are depicted in Table 2.

Consumers in Cluster A (30.6 percent of the sample) check unit prices more than average and engage in a modest amount of in-store brand comparisons. Feeling some degree of time pressure, however, they perform none of the other search behaviors at an above-average level. Some of the more time-consuming behaviors (e.g., multiple store visits, word of mouth, published product evaluations) occur in this cluster at levels substantially below the sample average. This is the low-effort price/brand-comparison segment.

Consumers in Cluster B (17.8 percent of the sample) are highest in comparative shopping across multiple stores and commonly check advertised specials. They are also above average in checking price tags, using coupons and word of mouth, and planning their grocery purchases in advance. But they are unlikely to engage in non-price brand comparisons or to read published brand evaluations. They constitute a high-effort value-seeking segment.

Cluster C, 12.0 percent of the sample, participates in minimal search despite experiencing no more time pressure than average. Below average on all search dimensions and the lowest of the five clusters in checking unit prices and reading published product evaluations, members of this cluster can be considered a search-averse segment.

The largest cluster (D, with 31.9 percent of the sample) is the highest in its propensity for prior planning of grocery purchases and in the use of coupons, unit prices, brand comparisons, store flyers, word of mouth, price tags, and published brand evaluations. Low in time pressure, these consumers appear to enjoy all forms of search and are labeled the high-search segment.

At 7.8 percent of the sample, Cluster E is the smallest. Consumers in this segment are highest in time pressure and low in all search dimensions; they are the lowest in planning purchases, visiting multiple stores, checking price tags, and using coupons, store flyers and word of mouth. These consumers constitute a time-pressured low-search segment.

Demographic analysis reveals no significant gender or occupational-sttus (full-time, part-time, unemployed, retired, etc.) differences between segments. With respect to marital status (c2=21.24, p<.05), a plurality of singles (42.5 percent) fall within the low-effort, price/brand-comparison segment, while most married respondents are divided quite evenly between that segment and the high-search cluster (both at 30.6 percent). A plurality of divorced/separated (35.7 percent) and widowed (46.9 percent) respondents are in the high-search cluster.

From a significant age relationship (c2=32.56, p<.05) it is seen that the low-effort price/brand-comparison and high-search segments claim a higher proportion of younger than of older respondents. In the low-effort price/brand-comparison segment, membership declines systematically with age (37.5 percent of respondents in the under-25 age group, to 15.0 percent of those in 65-or-older category). About 13 percent of respondents in the high-search segment fall in each of the two youngest categories, with only 5 to 9 percent in the four older groups. No systematic age differences are observed in the other search segments.

Educational level differs between the segments (F=5.84, p<.001). A significant Duncan test (p<.05) shows that this is due to a higher mean educational level in the time-pressured low-search segment (15.38 years) than in the other clusters (13.39 to 13.93 years).

Household size also shows significant differences between clusters (F=2.80, p<.05). As seen from Duncan test results, there are significantly fewer household members in the time-pressured low-search segment (mean 2.65) than in any of the other clusters (3.08 to 3.38).

Finally, income varies significantly between segments (F=5.79, p<.001), with higher income in the time-pressured low-search segment (about $50,000) than in the other segments. Clusters A (low-effort/price-brand comparison) and C (search-averse) had a higher mean income (about $40,000) Cluster D (high-searchBabout $30,000).


This study used a large, representative sample from a major metropolitan area to establish the dimensionality of the consumer-grocery-search construct and define customer segments characterized by varying patterns across search dimensions. Coupons, unit prices, number of stores visited, brand comparisons, advertised specials, word of mouth, checking price tags, and published product evaluations were shown to exist as distinct search dimensions. This finding adds to earlier research for other product categories (e.g., Beatty and Smith 1987, Schmidt and Spring 1996, Murthi and Srinivasan 1999) by showing how search types apply to consumer grocery-shopping behavior. Search dimensions were shown to coalesce around five patterns that represent viable market segments: low-effort price/brand comparison, high-effort value seeking, search averse, high search, and time-pressured low search. The finding of systematic demographic differences between segments adds to the value of search-behavior differences as a basis for segmentation.

An examination of demographic and search characteristics in the different segments affords a better understanding of which of a firm’s targeted consumers are likely to search and how. This in turn allows for more efficient educational and promotional efforts. Those seeking to help consumers optimize their expenditures can use the results to obtain a clearer understanding of the search dimensions that benefit those in greatest need (e.g., elderly, lower income, less educated, divorced or separatedB groups that fortunately do not fall disproportionately in the search-averse segment); that insight could then be used to formulate more effective educational campaigns. And overt awareness of their own segment membership allows consumers to evaluate whether their search practices (or lack thereof) are producing the desired consumption results.

Confirming or repudiating the validity of these results requires additional reearch in other markets with different household profiles, cultural values, marketing practices and economic conditions. Future research could also yield more insights by expanding the array of potential dimensions of consumer grocery search (e.g., the Internet), constructing a psychographic profile of search segments, and investigating the motivations underlying search patterns and the extent to which search outcomes are consistent with those motivations.


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Sanjay Putrevu, ESSEC, France
Kenneth R. Lord, Mercer University, U.S.A.
James W. Gentry, University of Nebraska, U.S.A.


AP - Asia Pacific Advances in Consumer Research Volume 4 | 2001

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