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Posts Tagged ‘Consideration’

Dear Readers, In coming days the blog-site Consumer Gateway well be re-dressed. The format applied in the past decade (which I still like) has become outdated, and it also no longer supports new editing and design features. During the transition period disruptions may be caused to the look of some posts and pages; your patience and forgiveness will be much appreciated until I sort these out. I hope you find the new appearance pleasant to view and to read through. 


Brands can be imagined as signposts that help consumers navigate through their purchase decision processes. On many occasions brands simplify and shorten the decision process; a strong brand may show the consumer the route to an easier and safer choice decision. Over the years, symbolic (e.g., self-expression, self-image), social (e.g., status, relationship) and emotional meanings of brands gained more attention and emphasis in research and practice. However, we should not let those latter influences of brands overshadow or mitigate our recognition of the essential and useful role reserved for brands in organising and directing consumer decision-making.

An insightful approach to the function of brands in decision-making draws from the theory of information economics. Consumers are commonly met with imperfect and asymmetric information about products they intend to buy, and under these terms they have to make decisions. ‘Imperfect’ implies that the information is usually partial, and may also be inaccurate; ‘asymmetric’ information in particular means that the producers or suppliers know more about the products they sell (e.g., physical attributes, costs) than the consumers who buy from them.  The brand of a product can function in such settings as a signal of the credibility of the product’s origin. The signal could thus serve as a decision aid that helps consumers make a better or more gratifying choice. Theory and research of the past twenty years suggest that the brand as a signal may have impact not only on the outcome of the choice decision (e.g., its quality) but furthermore on the whole course of the decision process (e.g., consideration, evaluation, choice).

The perspective of information economics is relatively less familiar than other theoretical viewpoints. Reference is made here primarily to cognitive-driven theories of attitudes and information processing that receive greater coverage than information economics in the context of brands. Yet, the information economics viewpoint of the brand as a signal, led by Erdem, Swait and Louviere, can be employed beneficially side-by-side with Aaker’s model of brand assets or Keller’s concept of (differential) brand knowledge. These views offer complementing aspects with respect to the role and effects of brands in decision processes. The efficacy of the brand as a signal for credibility applies especially to strong brands. Moreover, each approach describes how consumer-based brand equity is built-up or materialised through decision processes, and also proposes how to model and measure it.

A more formal definition of the brand as a signal specifies the ability of a brand to act as a credible signal (e.g., trustworthy, believable) reflecting on positioning overall of the branded products. It implies that consumers’ perceptions of the branded product on multiple aspects, primarily perceived quality, would be stronger, more believable, or more reliable. Subsequently, we need to understand what can make the brand a more credible signal. Main drivers that contribute to brand credibility include consistency of the brand owner in delivering on its claims or promises (e.g., in advertising), which would make those claims more trustworthy; consistency in the performance of actions on marketing mix elements (e.g., pricing, product capabilities); clarity of messages (e.g., to support its positioning); and the scale of investment in the brand (e.g., offline and online advertising, website and mobile app, sponsorships).

Greater brand investment directly enhances brand credibility. But consistency in execution of marketing actions seems even more important by contributing directly, and strongly, to brand credibility as well as by supporting clarity, which is likely to further add to credibility of the brand. Consequent benefits of higher credibility to consumers are likely to be support for higher perceived quality, reduced perceived risk, and lower information costs (e.g., less search and validation of information). Perceiving less risk in buying the branded product can in addition free the consumer from looking for more information, and therefore reduce in turn the information costs even lower. [1, 2]

In a multi-attribute choice model, each product alternative is assigned values on a set of attributes according to a consumer’s perceptions or beliefs about those attributes. These perceptions may be ‘coloured’ by associations that the consumer holds with the product’s brand name (some associations would ascribe to physical or functional attributes of the product {or service} whereas others may relate to an intangible image of the brand). Utility weights are added for attributes, as applicable by the decision rule — these weights may differ between brands, for any attribute that may be judged, for example, as more compatible with, important for, or even unique to a specific brand. The brand hence may impact the choice decision from consideration of which brands to include in the choice set, through perceptions about the branded products, to utilisation of the information in the decision rule applied (e.g., by alternative or by attribute). (Note: Details about  random error components of perceptions and utilities are omitted here.)[2]

A wider-angle view will account for additional phases or processes surrounding the framework of choice model described above, for instance: (1) The search for information upon which perceptions are formed or updated and the costs that may be incurred in gathering the information; (2) Learning about products by using a form of hypothesis testing to evaluate and screen information; (3) Mental processes engaged during learning and decision-making (e.g., encoding, search and retrieval from memory, preference formation). When a brand helps to organise the information, it is employed as a basis or reference for testing a hypothesis, or affects the meaning given to attribute information, it exercises, and possibly enhances, its brand equity in the minds of consumers.[2]


  • Swait, Erdem, Louviere, and Dubelaar proposed a measure (metric) of consumer-based brand equity, constructed from the perspective of information economics which regards the brand as a signal for higher quality and reputation. They called their measure the “Equalization Price“. Deriving the EP estimate for a brand is based on a comparison between two settings: (1) A hypothetical market where there is no differentiation between brand alternatives, and total utility for all alternatives is the same (for simplicity, it can be set to 0 for all brands); (2) A simulated market (choice set scenario) where brand alternatives exhibit different total utilities. Their approach is rather different from many others in its reference to a ‘hypothetical alternative’ and to the total utility of an alternative instead of a brand-specific component.
  • The Equalization Price denotes the level to which the price for a brand-product alternative can be raised until its total utility for a consumer in the simulated market (choice scenario) becomes equal to the ‘common’ utility {0} (i.e., the price at which the utilities are equivalent). Weaker brands could be assigned a negative EP. The researchers applied their brand equity estimates to analyse the potential of brands to extend from the ‘mother’ category into a ‘new’ category (e.g., Levi’s extending from jeans to athletic shoes). (Technical note: The EP estimates are derived from a probabilistic multinomial choice model based on a choice experiment — the ‘total utility’ refers to the deterministic portion of utility). [3]

Let us look next in greater resolution at differences in the chain of effects of brand credibility between stages of the decision process. The contribution of brand credibility in reducing perceived risk is more crucial in the early stage of considering which brands are eligible at all to be chosen from. Brands associated with too much risk will be eliminated in this stage of constructing the consideration set, and they will be excluded from any further operations. The savings that can be gained in information costs will also be important at this stage. In other words, “perceived risk and information costs saved play a screening role in the choice process”. On the other hand, enhancing perceived quality, in virtue of greater brand credibility, has greater impact when evaluating alternatives prior to making the choice decision. Therefore, brand credibility can increase the probability of the branded product of both being considered for buying and of being eventually chosen, but there is a difference in how the outcomes are achieved between those decision stages. [1]

It has also been found that this distinction in impact of perceived risk and perceived quality between stages will be more pronounced in product categories characterised by greater uncertainty and higher sensitivity to uncertainty. At the brand level, inconsistency in executing marketing mix elements (e.g., pricing, distribution) is likely to increase consumer uncertainty regarding the brand claims, and thereof hurts the credibility of the brand (see the effect via clarity noted earlier). Erdem and Swait discuss managerial implications of the role of brand credibility for customer relationship management (e.g., cognitive and affective impacts of credibility) and brand extensions. They also review other research in which they substantiated the contributions of specific aspects of brand credibility over choice stages and product categories (e.g., overall and distinct effects of trustworthiness by consistently delivering on brand claims and expertise in execution of elements of the marketing mix, such as technological competence in product development and design).

The Internet opens before the consumers an ocean rich with information at their fingertips on personal computers and mobile devices, in a plethora of commercial and non-commercial websites and mobile applications. So it would seem that a great part of the problems confronted by the field of information economics have been resolved for consumers. Yet, searching and gathering relevant information for a purchase decision in many product categories still takes time and requires cognitive effort, and sometimes also psychic effort or emotional stress.

Different costs may be more significant these days than were in the pre-Internet age but they cannot be discarded. For example, with so many sources of information available and easily accessible, it takes more time to review just several of them, and it is increasingly necessary to cross-check information found on various websites or apps (e.g., direct competitors, online shopping platforms, trade and professional portals). In reality, consumers normally access and review only a small portion of information available in a domain (e.g., how many and how often consumers open a window to read technical specifications).

Furthermore, even if information is less imperfect, there are still issues concerning asymmetric information because a greater part of information on products and services is controlled and provided by interested commercial businesses. In addition, biases and diversions could be luring in online information sources that consumers may not suspect, because they are not directly associated with the companies and brands originally providing the product or service of interest (e.g., search engines, online shopping platforms, social media — younger consumers increasingly stay in the confinements of “closed gardens” of social network platforms and do not explore the Internet enough).

Addressing brand equity from the perspective of information economics highlights a crucial value a brand can offer, brand credibility, with a very practical function in purchase decision-making. There is somewhat an illusion in believing that consumers are far less challenged today by constraints and costs of obtaining and using information for making choice decisions. If only for that reason, brands are promised to continue to play a vital facilitating role in the decision process. Moreover, when consumers can rely on credibility of a brand as a signal, this continues to reinforce the brand equity.

Ron Ventura, Ph.D. (Marketing)

Feel Well. Keep Good Health.

 

References:

[1] The Information-Economics Perspective on Brand Equity; Tülin Erdem and Joffre Swait, 2016; Foundations and Trends in Marketing, 10 (1), pp. 1-59 (DOI: 10.1561/1700000041)

[2] Brand Equity, Consumer Learning and Choice; Tülin Erdem, Joffre Swait, Susan Broniarczyk, Dipankar Chakravarti, Jean-Noël Kapferer, Michael Keane, John Roberts, Jan-Benedict E.M. Steenkamp, & Florian Zettelmeyer, 1999; Marketing Letters, 10 (3), pp. 301-318

[3] The Equalization Price: A Measure of Consumer-Perceived Brand Equity; Joffre Swait, Tülin Erdem, Jordan Louviere, & Chris Dubelaar, 1993; International Journal of Research in Marketing, 10, pp. 23-45

 

 

 

 

 

 

 

 

 

 

 

 

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A shopper may well know what types of products he or she is planning to buy in a store, but what products the shopper will come out with is much less sure. Frequently there will be some additional unplanned products in the shopper’s basket. This observation is more often demonstrated in the case of grocery shopping in supermarkets, but it is likely to hold true also in other types of stores, especially large ones like department stores, fashion stores, and DIY or home improvement stores.

There can be a number of reasons or triggers for shoppers to consider additional products to purchase during the shopping trip itself — products forgotten and reminded of by cues that arise while shopping, attractiveness of visual appearance of product display (‘visual lift’), promotions posted on tags at the product display (‘point-of-purchase’) or in hand-out flyers, and more. The phenomenon of unplanned purchases is very familiar, and the study of it is not new. However, the behaviour of shoppers during their store visit that leads to this outcome, especially the consideration of product categories in an unplanned manner, is not understood well enough. The relatively new methodology of video tracking with a head-mounted small camera shows promise in gaining better understanding of shopper behaviour during the shopping trip; a research article by Hui, Huang, Suher and Inman (2013) is paving the way with a valuable contribution, particularly in shedding light on the relations between planned and unplanned considerations in a supermarket, and the factors that may drive conversion of the latter into purchases (1).

Shopper marketing is an evolving specialisation which gains increasing attention in  marketing and retailing. It concerns activities of consumers performed in a ‘shopper mode’ and is strongly connected with or contained within consumer marketing. Innovations in this sub-field by retailers and manufacturers span digital activities, multichannel marketing, store atmospherics and design, in-store merchandising, shopper marketing metrics and organisation. However, carrying out more effective and successful shopper marketing programmes requires closer collaboration between manufacturers and retailers — more openness to each party’s perspective and priorities (e.g., in interpretation of shopper insights), sharing information and coordination (2).

In-Store Video Tracking allows researchers to observe the shopping trip as it proceeds from the viewpoint of the shopper, literally. The strength of this methodology is in capturing the dynamics of shopping (e.g., with regard to in-store drivers of unplanned purchases). Unlike other approaches (e.g., RFID, product scanners), the video tracking method enables tracking acts of consideration, whether followed or not by purchase (i.e., putting a product item in the shopping cart).

For video tracking, a shopper is asked to wear, with the help of an experimenter, a headset belt that contains the portable video equipment, including a small video camera, a view/record unit, and a battery pack. It is worn like a Bluetooth headset. In addition, the equipment used by Hui et al. included an RFID transmitter that allows to trace the location of the shopper throughout his or her shopping path in a supermarket.

Like any research methodology, video tracking has its strengths and advantages versus its weaknesses and limitations. With the camera it is possible to capture the shopper’s field of vision during a shopping trip; the resulting video is stored in the view/record unit. However, without an eye-tracking (infrared) device, the camera may not point accurately to the positions of products considered (by eye fixation) in the field of vision. Yet, the video supports at least approximate inferences when a product is touched or moved, or the head-body posture and gesture suggest from which display a shopper considers products (i.e., the ‘frame’ closes-in on a section of the display). It is further noted that difficulties in calibrating an eye-tracking device in motion may impair the accuracy of locating fixations. The video camera seems sufficient and effective for identifying product categories as targets of consideration and purchase.

Furthermore, contrary to video filmed from cameras hanging from the ceiling in a store, the head-mounted camera records the scene at eye-level and not from high above, enabling to better notice what the shopper is doing (e.g., in aisles), and it follows the shopper all the way, not just in selected sections of the store. Additionally, using a head-mounted camera is more ethical than relying on surrounding cameras (often CCTV security cameras). On the other hand, head-mounted devices (e.g., camera, eye-tracking), which are not the most natural to wear whilst shopping, raise concerns of sampling bias (self-selection) and possibly causing change in the behaviour of the shopper; proponents argue that shoppers quickly forget of the device (devices are now made lighter) as they engage in shopping, but the issue is still in debate.

Video tracking is advantageous to RFID  and product scanners for the study of unplanned purchase behaviour by capturing acts of consideration: the RFID method alone (3) enables to trace the path of the shopper but not what one does in front of the shelf or stand display, and a scanner method allows to record what products are purchased but not which are considered. The advantage of the combined video + RFID approach according to Hui and his colleagues is in providing them “not only the shopping path but also the changes in the shoppers’ visual field as he or she walks around the store” (p. 449).

The complete research design included two interviews conducted with each shopper-participant — before the shopping trip, as a shopper enters the store, and after, on the way out. In the initial interview, shoppers were asked in which product categories they were planning to buy (aided by a list to choose from), as well as other shopping aspects (e.g., total budget, whether they brought their own shopping list). At the exit the shoppers were asked about personal characteristics, and the experimenters collected a copy of the receipt from the retailer’s transaction log. The information collected was essential for two aspects in particular: (a) distinguishing between planned and unplanned considerations; and (b) estimating the amount of money remaining for the shopper to make unplanned purchases out of the total budget (‘in-store slack’ metric).

237 participants were included in analyses. Overall, shoppers-participants planned to purchase from approximately 5.5 categories; they considered on average 13 categories in total, of which fewer than 5 were planned considerations (median 5.6). 37% of the participants carried a list prepared in advance.

Characteristics influencing unplanned consideration:  The researchers sought first to identify personal and product characteristics that significantly influence the probability of making an unplanned consideration in each given product category (a latent utility likelihood model was constructed). Consequently, they could infer which characteristics contribute to considering more categories in an unplanned manner. The model showed, for instance, that shoppers older in age and female shoppers are likely to engage in unplanned consideration in a greater number of product categories. Inversely, shoppers who are more familiar with a store (layout and location of products) and those carrying a shopping list tend to consider fewer product categories in an unplanned manner.

At a product level, a higher hedonic score for a product category is positively associated with greater incidence of unplanned consideration of it. Products that are promoted in the weekly flyer of the store at the time of a shopper’s visit are also more likely to receive an unplanned consideration from the shopper. Hui et al. further revealed effects of complementarity relations: products that were not planned beforehand for purchase (B) but are closer complementary of products in a ‘planned basket’ of shoppers (A) gain a greater likelihood of being considered in an unplanned manner (‘A –> B lift’).  [The researchers present a two-dimensional map detailing what products are more proximate and thus more likely to get paired together, not dependent yet on purchase of them].

Differences in behaviour between planned and unplanned considerations: Unplanned considerations tend to be made more haphazardly — while standing farther from display shelves and involving fewer product touches; conversely, planned considerations entail greater ‘depth’. Unplanned considerations tend to occur a little later in the shopping trip (the gap in timing is not very convincing). An unplanned consideration is less likely to entail reference to a shopping list — the list serves in “keeping the shopper on task”, being less prone to divert to unplanned consideration. Shoppers during an unplanned consideration are also less likely to refer to discount coupons or to in-store flyers/circulars. However, interestingly, some of the patterns found in this analysis change as an unplanned consideration turns into a purchase.

Importantly, in the outcome unplanned considerations are less likely to conclude with a purchase (63%) than planned considerations (83%). This raises the question, what can make an unplanned consideration result in purchase conversion?

Drivers of purchase conversion of unplanned considerations: Firstly, unplanned considerations that result in a purchase take longer (40 seconds on average) than those that do not (24 seconds). Secondly, shoppers get closer to the shelves and touch more product items before concluding with a purchase; the greater ‘depth’ of the process towards unplanned purchase is characterised by viewing fewer product displays (‘facings’) within the category — the shopper is concentrating on fewer alternatives yet examines those selected more carefully (e.g., by picking them up for a closer read). Another conspicuous finding is that shoppers are more likely to refer to a shopping list during an unplanned consideration that is going to result in a purchase — a plausible explanation is that the shopping list may help the shopper to seek whether an unplanned product complements a product on the list.

The researchers employed another (latent utility) model to investigate more systemically the drivers likely to lead unplanned considerations to result in a purchase. The model supported, for example, that purchase conversion is more likely in categories of  higher hedonic products. It corroborated the notions about ‘depth’ of consideration as a driver to purchase and the role of a shopping list in realising complementary unplanned products as supplements to the ‘planned basket’. It is also shown that interacting with a service staff for assistance increases the likelihood of concluding with a purchase.

  • Location in the store matters: An aisle is relatively a more likely place for an unplanned consideration to occur, and subsequently has a better chance when it happens to result in a purchase. The authors recommend assigning service staff to be present near aisles.

Complementarity relations were analysed once again, this time in the context of unplanned purchases. The analysis, as visualised in a new map, indicates that proximity between planned and unplanned categories enhances the likelihood of an unplanned purchase: if a shopper plans to purchase in category A, then the closer category B is to A, the more likely is the shopper to purchase in category B given it is considered. Hui et al. note that distances in the maps for considerations and for purchase conversion of unplanned considerations are not correlated, implying hence that the unplanned consideration and a purchase decision are two different dimensions in the decision process. This is a salient result because it distinguishes between engaging in consideration and the decision itself. The researchers caution, however, that in some cases the distinction between consideration and a choice decision may be false and inappropriate because they may happen rapidly in a single step.

  • The latent distances in the maps are also uncorrelated with physical distances between products in the supermarket (i.e., the complementarity relations are mental).

The research shows that while promotion (coupons or in-store flyers) for an unplanned product has a significant effect in increasing the probability of its consideration, it does not contribute to probability of its purchase. This evidence furthermore points to a separation between consideration and a decision. The authors suggest that a promotion may attract shoppers to consider a product, but they are mostly uninterested to buy and hence it has no further effect on their point-of-purchase behaviour. The researchers suggest that retailers can apply their model of complementarity to proactively invoke consideration by triggering a real-time promotion on a mobile shopping app for products associated with those on a digital list of the shopper “so a small coupon can nudge this consideration into a purchase”.

But there are some reservations to be made about the findings regarding promotions. An available promotion can increase the probability of a product to be considered in an unplanned manner, yet shoppers are less likely to look at their coupons or flyers at the relevant moment. Inversely, the existence of a promotion does not contribute to purchase conversion of an unplanned consideration but shoppers are more likely to refer to their coupons or flyers during unplanned considerations that result in a purchase.  A plausible explanation to resolve this apparent inconsistency is that reference to a promotional coupon or flyer is more concrete from a shopper viewpoint than the mere availability of a promotion; shoppers may not be aware of some of the promotions the researchers account for. In the article, the researchers do not address directly promotional information that appears on tags at the product display — such promotions may affect shoppers differently from flyers or distributed coupons (paper or digital via mobile app), because tags are more readily visible at the point-of-purchase.

One of the dynamic factors examined by Hui et al. is the ‘in-store slack’, the mental budget reserved for unplanned purchases. Reserving a larger slack increases the likelihood of unplanned considerations. Furthermore, at the moment of truth, the larger is the in-store slack that remains at the time of an unplanned consideration, the more likely is the shopper to take a product from the display to purchase. However, computations used in the analyses of dynamic changes in each shopper’s in-store slack appear to assume that shoppers estimate how much they already spent on planned products in various moments of the trip and are aware of their budget, an assumption not very realistic. The approach in the research is very clever, and yet consumers may not be so sophisticated: they may exceed their in-store slack, possibly because they are not very good in keeping their budget (e.g., exacerbated by use of credit cards) or in making arithmetic computations fluently.

Finally, shoppers could be subject to a dynamic trade-off between their self-control and the in-store slack. As the shopping trip progresses and the remaining in-store slack is expected to shrink, the shopper becomes less likely to allow an unplanned purchase, but he or she may become more likely to be tempted to consider and buy in an unplanned manner, because the strength of one’s self-control is depleted following active decision-making. In addition, a shopper who avoided making a purchase on the last occasion of unplanned consideration is more likely to purchase a product in the next unplanned occasion — this negative “momentum” effect means that following an initial effort at self-control, subsequent attempts are more likely to fail as a result of depletion of the strength of self-control.

The research of Hui, Huang, Suher and Inman offers multiple insights for retailers as well as manufacturers to take notice of, and much more material for thought and additional study and planning. The video tracking approach reveals patterns and drivers of shopper behaviour in unplanned considerations and how they relate to planned considerations.  The methodology is not without limitations; viewing and coding the video clips is notably time-consuming. Nevertheless, this research is bringing us a step forward towards better understanding and knowledge to act upon.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Deconstructing the “First Moment of Truth”: Understanding Unplanned Consideration and Purchase Conversion Using In-Store Video Tracking; Sam K. Hui, Yanliu Huang, Jacob Suher, & J. Jeffrey Inman, 2013; Journal of Marketing Research, 50 (August), pp. 445-462.

(2) Innovations in Shopper Marketing: Current Insights and Future Research Issues; Venkatesh Shankar, J. Jeffrey Inman, Murali Mantrala, & Eileen Kelley, 2011; Journal of Retailing, 87S (1), pp. S29-S42.

(3) See other research on path data modelling and analysis in marketing and retailing by Hui with Peter Fader and Eric Bradlow (2009).

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