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Where the Rubber Meets the Road: A Model of In-store Consumer Decision Making

J. Jeffrey Inman and Russell S. Winer, 1998, 98-122

Behind the organized layout of every store, behind the frequent shopper cards, coupons, and circulars, is a battle worth billions of dollars for prime shelf and display space. Is the blitz to inundate and influence consumers justified? Are retailers and manufacturers alike putting their money where it will have the most impact?

Increasing category or product sales can only be achieved by understanding how consumers make in-store purchase decisions. This process is not well understood; in fact, the last benchmark on in-store decision making took place over 30 years ago. Authors Inman and Winer remedy this research gap. By developing and testing one of the most comprehensive models of in-store consumer decision making to date, they provide a basis for targetting consumers at the point of purchase.

What Drives Consumer Choice?

Inman and Winer’s model of consumers’ decision making identifies the characteristics of the shopper, or consumer (e.g., average shopping trips per week, demographics, and psychographics) and characteristics of the specific shopping trip (e.g., major vs. fill-in trip, aisles shopped, location of in-store displays, and shopping party size) that affect in-store purchases.

Their framework of consumer decision making includes these stages:

  • Exposure to product categories and in-store displays
  • Motivation to process in-store stimuli
  • Recognition of potential needs (generated by exposure to product categories and in-store displays, and subject to advance planning to purchase particular categories)
  • Shopping decision (whether purchase was specifically planned, generally planned, a brand switch, or unplanned)

They tested their model empirically using data on over 30,000 choices provided by a large-scale field intercept study of 4,200 consumers in 14 cities conducted by the Point of Purchase Advertising Institute. Consumers’ purchase intentions were measured prior to entering the store and their register tapes were collected upon checkout. Characteristics of the shopping party and of the store environment were collected as well. In addition to this data, a survey of over 600 respondents gathered psychographic measures.

Findings

Although both shopping-trip-specific and consumer-specific factors are significant drivers of in-store decision making, shopping-trip-specific factors play a greater role. By far the biggest effects on in-store decision making were exhibited by the exposure-related factors of number of aisles shopped and trip type (major or fill-in shopping trip). Unplanned, in-store purchase decisions were much more likely for major trips and when all aisles were visited.

Other findings:

  • Overall, the study found that 59 percent of purchases were unplanned, 30 percent were specifically planned, and the rest were generally planned or brand switches.
  • Consumers were more likely to make an unplanned decision when the product was displayed at the end of the aisle or at the checkout register than if it was displayed in-aisle.
  • Counter to prediction, consumers with a higher level of purchase involvement were less prone to making in-store decisions.
  • Motivation-related factors played a significant role, with deal proneness having the third largest effect across all factors.
  • In-store decision making is greater for larger households, larger shopping parties, households with greater incomes, and women.

Several surprising findings emerged from this study. First, shoppers with a list made more specifically planned purchases but fewer generally planned purchases than shoppers without a list. Both groups, however, were equally likely to make unplanned purchases. The implication? Shoppers with a list tend to plan their purchases down to the brand level, but those without plan only to the category level.

Second, the authors’ results are strikingly similar to Kollat and Willett’s 1967 benchmark study. The level of unplanned and specifically planned purchases has remained relatively constant over the past three decades. Finally, in both studies, specifically planned purchases are more prevalent than unplanned purchases when the number of purchases is small, but the proportion of unplanned purchases increases with the size of the basket.

Managerial Implications

Several factors that drive in-store decision making are under managerial control, particularly those regarding exposure. Thus, managers should encourage consumers to shop as many aisles as possible and provide exposure to as many product categories and in-store displays as possible. This can be achieved through innovative aisle layout and shelf design. For instance, frequently purchased products or “destination” items (e.g., milk) can be displayed in locations that will lead consumers past as many other categories as possible, or displayed next to less frequently purchased products. Another way to expose consumers to more categories is through the creative use of in-store coupons and nonprice promotions.

In addition, targeting individuals/households with larger baskets will tend to result in more unplanned purchases. This profile includes younger consumers, larger households, higher income households, and women. Importantly, becoming recognized as the store of choice for major shopping trips is essential, as shoppers making a major trip make proportionally more unplanned purchases and have larger shopping baskets on average.

Finally, certain segments tend to make more in-store decisions than others. These consumers (e.g., deal-prone and less frequent shoppers) should be more profitable to target in a direct marketing effort.

J. Jeffrey Inman is Associate Professor of Marketing, University of Wisconsin-Madison. Russell S. Winer is the J. Gary Shansby Professor of Marketing Strategy, Haas School of Business, University of California at Berkeley.

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