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Posts Tagged ‘Consumer behaviour’

Transparency; reliability; trust: These key terms are rehearsed and highlighted many times in textbooks and business books, academic and trade articles about managing customer relationships. Holding up to them is based, for example, on being honest, truthful and fair when making product or service offers to customers and in any other dealings between a company and its customers. However, those concepts that are good in managerial and marketing theory are too often lost when it comes to practice.

In addition, experts, technology consultants and other advocates of digital marketing are praising the capacity gained by companies to know so much about the behaviour and personal characteristics of their customers. One of the great benefits of this customer knowledge is in enabling companies to construct offers that will closely fit the needs, preferences and consumption or usage habits of their customers. Again, a gap emerges between what companies are supposedly capable to do with digital technologies available to them, including information and tools, and what they actually do. More accurately,  oftentimes companies are not doing enough in utilising those technologies to the intended purpose of creating better fitting offerings and messages.

The present post is based on a true story of a troubling journey to acquire an iPhone from a mobile telecom service provider (it will be called here ‘WM’). But this post is not just about the case of a particular company. Similar forms of problematic conduct are likely to be encountered at competing mobile service providers as well as other telecom service companies such as TV (cable and satellite), telephony (voice and data) and Internet providers. Moreover, at least some of these types of flawed conduct will be familiar to the reader from interaction with service providers in other domains (e.g., banking and finance, credit cards, insurance, healthcare, travel and tourism). In essence, this conduct refers most typically to providers of contractual services, and particularly when services extend over months and years.

An upgrade of a customer’s mobile phone is often accompanied by a modification of his or her service package; it is justified especially when a large generation gap exists between the previous and the new model. Two-part and three-part tariff schemes have been common in mobile communication for many years, splitting the price of service between fixed and variable components. Usage possibilities and patterns have changed, however, with smartphones, pertaining in particular to the online flow of data and the use of mobile applications (‘apps’). Service packages more frequently combine bundles of included (‘pre-paid’) units — minutes (voice), messages (SMS), and data MBs/GBs (mobile websites and apps); the weight of variable cost (i.e., based on price per unit), drops vis-à-vis a fixed cost component.

Subscribed customers are encouraged to pre-commit to ever larger bundles or unit quotas, some of them could constantly be left unspent each month. At least in one category it is sensible for mobile service providers to ‘give away’ a large quantity of messages amid the expanded messaging by customers via free chatting apps (e.g., WhatsApp, Facebook’s Messenger). The marginal cost per unit of any kind could be much lower now for the mobile network companies to make it economic for them to offer larger bundles, and thus attract customers to their ‘great value’ plans (i.e., the customer gets lots of ‘free’ units). Albeit, if customers do not utilise large enough portions of their quotas, they could end up paying for units they never get to benefit from.

A service plan was offered with the new phone purchased, including 10GBs of data, 5000 minutes and 5000 messages per month. This volume signalled a dramatic increase from my previous consumption levels. No doubt the new smartphone could support a huge data volume not possible with the previous semi-smartphone model, but also a volume hard to imagine how it may be used. Nor was it perceivable how to use anything near 5000 SMS. That is the magic of large numbers — they can be fascinating and captivating, yet meaningless at least in a short to medium term. The sales representative at the store and service centre of WM promised that it will save up to 45% of my bill so far. With the service package I get also ‘marvellous high-fidelity’ wireless-Bluetooth earphones, supposedly as a bonus or gift. No other plan was suggested. The relation of the earphones to the discount was not explained. Protesting that I do not really need those earphones did not help. It was awkward, but then it seemed that the enlarged traffic volume, that one might learn how to take advantage of, with a reduction in monthly cost could be worth it. The value of the earphones was negligible to me (but apparently not to WM). That is probably where System 1 got the hold of me. When not feeling on solid ground, swapped with documentation, and distracted, one may fail to pose difficult, intelligent questions;  System 2 remains dormant or blocked. It was a combination of desire to believe the offer is good for me, and to trust the company that it will treat me fairly.

The secret behind the earphones was revealed in the next monthly bill. If paid in cash, their price was about $150 vis-à-vis $900 for the iPhone. I agreed to pay for the iPhone in 12 credit installments (adding  5% in cost). However, the additional and unexpected payment for the earphones was set to be spread over 36 months (+65%! added to price in cash). The discount on service was for 12 months. The payments for the earphones would “eat” much of the discount during the first year. Furthermore, they will drag for another 24 months while the cost of service package returns to its previous level, though of course with a much greater usage allowance. Lesson: Beware of ‘free gifts’ and make sure to get all the details (see more in the section below on contracts).

This has brought me promptly back to the service centre — the staff refused to take their earphones back and gave me another nice demonstration of their performance. However, with the help of a kind supervisor we agreed that payments for both iPhone and earphones will be changed to 6 instalments with no interest (see more in the section on execution).

The Bluetooth earphones may well be a good product and the representatives were right to offer it, but it is wrong to impose the earphones as a ‘bonus’ or incentive if the customer is not interested and declines the offer. Furthermore, at least one other package option should have been recommended that would be more aligned with previous usage in recent months. A smart system should know how to use past behaviour of the customer as a benchmark and propose a reasonable expansion of usage levels of minutes, messages and data. First, it would make the customer feel that the company knows him or her (e.g., needs and usage patterns) and is trying in accordance to provide the most suitable personalised solutions. Second, when the quota of units posits a sensible ‘ceiling’ to the customer it may serve as a goal or an aspiration level to gradually increase his or her usage towards it, and then upgrade the service plan. Otherwise, the customer may be just lost, having no appreciative reference for scaling one’s personal usage levels (perhaps that is the objective, to let customers with less self-control carry away, but that is beyond the scope of this story).

Signing contracts to purchase products or receive services is frequently a sensitive matter and a host of potential pain points. This happens because customers usually cannot fully or even adequately read the contract and comprehend it at the time of transaction, and they are not sufficiently encouraged to spend the time reading and asking questions. The contract for my smartphone included, for example, the terms of payment, basic support, terms of usage,  liability and warranty, etc.. On each desk at the store and service centre of WM stands a tablet in portrait position. Regularly, it displays ads for services and products. However, WM saves on paperwork and employs the screen also to display contracts that can be signed digitally (later sent by e-mail). Reading the contract from the screen is not very convenient and the customer also cannot control the display to the pace of his or her reading. One is quickly brought to the place for signing. The contract for the earphones was separate in origin from the iPhone’s (later corrected); when the representative came to it, he jumped to the signature position which incidentally fell at the top of the screen. When asked to see what comes before, he said this is simply to confirm that I accept the earphones. At that point I wanted to trust him and WM. This turned out to be a mistake. Lesson: Never agree to sign a contract on a screen without seeing the previous screen pages (as you should not do when signing a paper contract). The tablet screen may appear informal and friendly but the contract is binding.

  • In fact, by returning to the issue of service plans, the tablet already on the desk can be used cleverly for displaying service options to a customer while taking into account his or her personal usage patterns. That is, the company can show the customer what would be the cost implication of a proposed service plan given current usage levels, and how it may change if usage levels increase by X%.

On top of all, bad execution of proceedings can temper even actions taken in good faith. It may happen as a result of neglect, lacking proficiency by the staff (e.g., how to use the computer system), or flaws in computer software (e.g., poor execution of instructions). Here are two examples — no attempt is made to guess what has caused them:

As told above, the payment arrangement was changed with special managerial consent to six instalments with no interest, as an option in the contract allows, for both the iPhone and earphones. Unfortunately, a notice from the bank as well as the credit card monthly bill soon revealed that the whole amount was charged in a single payment. The trap is apparently in the phrasing of the contract (translated): “The sum of $$$ that will be charged in one payment (or up to six payments to the choice of the customer at the time of acquisition)”. The phrase ambiguously does not specify in how many (equal) payments, up to six, that (cash) price will be charged. This ambiguity has led to practically ignoring the content in parentheses and what was agreed accordingly. It is noted that a statement on an option of payment in instalments with interest explicitly indicates the number of payments and amount of each one. The phrasing of the first statement must similarly be fixed for that option to have any validity.

In the second case, the company left in place a monthly charge (~$6) for a quota of 70 SMS from my previous service package. Obviously, this number is negligible relative to the new allowance of 5000 SMS a month in the new service plan with the iPhone. They should have automatically removed this obsolete component together with other components from the older plan. The customer service representative at the call centre argued that I should have asked it to be cancelled. That is, instead of apologising for an honest mistake, and possibly reimbursing me for the past month, she made it look as if I may have wanted a non-significant addition of 70 SMS to 5000 SMS (>70:1 ratio). That was already infuriating because it made no sense at all. Lesson: Always check your bills carefully.

The customer journey to purchase an iPhone evolved into a kind of chain of pitfalls, acts of malpractice, and errors of unknown source or cause. It must be emphasised that the troubles are concerned with the envelope of services that enable using the iPhone and not the device itself. It is a story of failure of sales and service representatives to listen, a tendency to repeat answers regardless of the customer’s response (i.e., lack of sensitivity or rigidity forced from above), and possibly a skill problem in retrieving information and instructing their computer systems correctly. Where supervisors or managers do try to fix things, organisational and technological pitfalls may stand in their way. Nonetheless, the more disturbing moments of the experience surface when a customer feels an attempt to manipulate has been made (e.g., by diverting attention or hiding information). Being manipulated generally feels uneasy, because among other things it infringes on a consumer’s autonomy to make a decision in one’s own good, but it is all the more damaging when done just to serve the manipulator’s interest (e.g., make a sale)[*].

Companies and customers alike can help in minimising negative encounters that can spoil customer journeys. Consumers can be more vigilant, pay more attention to details, and ask questions when offers do not sound or look right. Yet in the real world consumers cannot avoid being off guard, erring in judgement, or being complacent — much of the time humans are driven by the intuitive and instinctive System 1 mode of thinking. Companies can make greater effort to ensure customers have the relevant information and comprehend it; be attentive to what customers ask or argue; and overall show respect to customers and refrain from egregiously exploiting their cognitive vulnerabilities — perhaps naïve, but not illegitimate to expect.

Ron Ventura, Ph.D. (Marketing)

 

[*] Further reading: “Fifty Shades of Manipulation”; Cass R. Sunstein , 2016; Journal of Marketing Behavior, 1 (3-4), pp. 213-244.

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The discipline of consumer behaviour is by now well versed in the distinction between System 1 and System 2 modes of thinking, relating in particular to consumer judgement and decision making, with implications for marketing and retail management. Much appreciative gratitude is owed to Nobel Prize Laureate in economics Daniel Kahneman for bringing forward the concept of these thinking systems to the knowledge of the wider public (i.e., beyond academics) in his book “Thinking, Fast and Slow” (2012). ‘System 1’ and ‘System 2’, though not always using these labels, have been identified and elaborated by psychologists earlier than Kahneman’s book, as the author so notes. However, Kahneman succeeds in making more crystal clear the concepts of these different modes of thinking while linking them to phenomena studied in his own previous research, most notably in collaboration with the late Amos Tversky.

In a nutshell: System 1’s type of thinking is automatic, associative and intuitive; it tends to respond quickly, but consequently it is at higher risk of jumping to wrong conclusions. It is the ‘default’ type of thinking that guides human judgement, decisions and behaviour much of the time. On the other hand, System 2’s type of thinking is deliberative, logical, critical, and effortful; it involves deeper concentration and more complex computations and rules. System 2 has to be called to duty voluntarily, activating rational thinking and careful reasoning. Whereas thinking represented by System 1 is fast and reflexive, that of System 2 is slow and reflective.

Kahneman describes and explains the role, function and effect of System 1 and System 2 in various contexts, situations or problems. In broad terms: Thinking of the System 1 type comes first; System 2 either passively adopts impressions, intuitive judgements and recommendations by System 1 or actively kicks-in for more orderly examination and correction (alas, it tends to be lazy, not in a hurry to volunteer). Just to give a taste, below is a selection of situations and problems in which Kahneman demonstrates the important differences between these two modes of thinking, how they operate and the outcomes they effect:

  • # Illusions (e.g., visual, cognitive)  # Use of memory (e.g., computations, comparisons)  # Tasks requiring self-control  # Search for causal explanations  # Attending to information (“What You See Is All There Is”)  # Sets and prototypes (e.g., ‘average’ vs. ‘total’ assessments)  # Intensity matching  # ‘Answering the easier question’ (simplifying by substitution)  # Predictions (also see correlation and regression, intensity matching, representativeness)  # Choice in opt-in and opt-out framing situations (e.g., organ donation)
  • Note: In other contexts presented by Kahneman (e.g., validity illusion [stock-picking task], choice under Prospect Theory), the author does not connect them explicitly to  System 1 or System 2 so their significance may only be indirectly implied by the reader.

In order to gain a deeper understanding of System 1 and System 2 we should inspect the detailed aspects differentiating between these thinking systems. The concept of the two systems actually emerges from binding multiple dual-process theories of cognition together, thus appearing to be a larger cohesive theory of modes of thinking. Each dual process theory is usually focused on a particular dimension that distinguishes between two types of cognitive processes the human mind may utilise. However, those dimensions ‘correlate’ or ‘co-occur’, and a given theory often adopts aspects from other similar theories or adds supplementary properties; the dual-system conception hence is built on this conversion. The aspects or properties used to describe the process in each type of system are extracted from those dual-process theories. A table presented by Stanovich (2002) helps to see how System 1 and System 2 contrast in various dual-process theories. Some of those theories are: [For brevity, S1 and S2 are applied below to refer to each system.)

  • S1: Associative system / S2: Rule-based system (Sloman)
  • S1: Heuristic processing / S2: Analytic processing (Evans)
  • S1: Tacit thought process / S2: Explicit thought process (Evans and Over)
  • S1: Experiential system / S2: Rational system (Epstein)
  • S1: Implicit inference / S2: Explicit inference (Johnson-Laird)
  • S1: Automatic processing / S2: Controlled processing (Shiffrin and Schneider)

Note: Evans and Wason related to Type 1 vs. Type 2 processes already in 1976.

  • Closer to consumer behaviour: Central processing versus peripheral processing in the Elaboration Likelihood Model (Petty, Cacioppo & Schumann) posits a dual-process theory of routes to persuasion.

Each dual process theory provides a rich and comprehensive portrayal of two different thinking modes. The theories complement each other but they do not necessarily depend on each other. The boundaries between the two types of process are not very sharp, that is, features of the systems are not all exclusive in the sense that a particular property associated with a process of System 1 may occur in a System 2 process, and vice versa. Furthermore, the processes also interact with one another, particularly in a way where System 2 relies on products of thought from System 1, either approving them or using them as a starting-point for further analysis. Nevertheless, occasionally System 2 may generate reasons for us merely to justify a choice made by System 1 (e.g., a consumer likes a product for the visual appearance of its packaging or its design).

Stanovich follows the table of theories with a comparison of properties describing System 1 versus System 2 as derived from a variety of dual process theories, but without attributing them to any specific theory (e.g., holistic/analytic, relatively fast/slow, highly contextualized/decontextualized). Comparative lists of aspects or properties have been offered by other researchers as well. Evans (2008) formed a comparative list of more than twenty attributes which he divided into four clusters (describing System 1/System 2):

  • Cluster 1: Consciousness (e.g., unconscious/conscious, automatic/controlled, rapid/slow, implicit/explicit, high capacity/low capacity)
  • Cluster 2: Evolution (e.g., evolutionary old/recent, nonverbal/linked to language)
  • Cluster 3: Functional characteristics (e.g.,  associative/rule-based, contextualized/abstract, parallel/sequential)
  • Cluster 4: individual differences (universal/heritable, independent of/linked to general intelligence, independent of/limited by working memory capacity).

Listings of properties collated from different sources (models, theories), interpreted as integrative profiles of System 1 and System 2 modes of thinking, may yield a misconception of the distinction between the two systems as representing an over-arching theory. Evans questions whether it is really possible and acceptable to tie the various theories of different origins under a common roof, suggested as an over-arching cohesive theory of two systems (he identifies problems residing mainly with ‘System 1’). It could be more appropriate to approach the dual-system presentation as a paradigm or framework to help one grasp the breadth of aspects that may distinguish between two types of cognitive processes and obtain a more comprehensive picture of cognition. The properties are not truly required to co-occur altogether as constituents of a whole profile of one system or the other. In certain domains of judgement or decision problems, a set of properties may jointly describe the process entailed. Some dual process theories may take different perspectives on a similar domain, and hence the aspects derived from them are related and appear to co-occur.

  • Evans confronts a more widely accepted ‘sequential-interventionist’ view (as described above) with a ‘parallel-competitive’ view.

People use a variety of procedures and techniques to form judgements, make decisions or perform any other kind of cognitive task. Stanovich relates the structure, shape and level of sophistication of the mental procedures or algorithms of thought humans can apply, to their intelligence or cognitive capacity, positioned at the algorithmic level of analysis. Investing more effort in more complicated techniques or algorithms entailed in rational thinking is a matter of volition, positioned at the intentional level (borrowed from Dennett’s theorizing on consciousness).

However, humans do not engage a great part of the time in thought close to the full of their cognitive capacity (e.g., in terms of depth and efficiency). According to Stanovich, we should distinguish between cognitive ability and thinking dispositions (or styles). The styles of thinking a person applies do not necessarily reflect everything one is cognitively capable of. Put succinctly, the fact that a person is intelligent does not mean that he or she has to think and act rationally; one has to choose to do so and invest the required effort into it. When one does not, it opens the door for smart people to act stupidly. Furthermore, the way a person is disposed to think is most often selected and executed unconsciously, especially when the thinking disposition or style is relatively fast and simple. Cognitive styles that are entailed in System 1, characterised as intuitive, automatic, associative and fast, are made to ease the cognitive strain on the brain, and they are most likely to occur unconsciously or preconsciously. Still, being intuitive and using heuristics should not imply a person will end up acting stupidly — some would argue his or her intuitive decision could be more sensible than one made when trying to think rationally; it may depend on how thinking in the realm of System 1 happens — if one rushes while applying an inappropriate heuristic or relying on an unfitting association, he or she could become more likely to act stupidly (or plainly, ‘being stupid’).

Emotion and affect are more closely linked to System 1. Yet, emotion should not be viewed ultimately as a disruptor of rationality. As proposed by Stanovich, emotions may fulfill an important adaptive regulatory role — serving as interrupt signals necessary to achieve goals, avoiding entanglement in complex rational thinking that only keeps one away from a solution, and reducing a problem to manageable dimensions. In some cases emotion does not disrupt rationality but rather help to choose when it is appropriate and productive to apply a rational thinking style (e.g., use an optimization algorithm, initiate counterfactual thinking). By switching between two modes of thinking, described as System 1 and System 2, one has the flexibility to choose when and how to act in reason or be rational, and emotion may play the positive role of a guide.

The dual-system concept provides a way of looking broadly at cognitive processes that underlie human judgement and decision making. System 1’s mode of thinking is particularly adaptive by which it allows a consumer to quickly sort out large amounts of information and navigate through complex and changing environments. System 2’s mode of thinking is the ‘wise counselor’ that can be called to analyse the situation more deeply and critically, and provide a ‘second opinion’ like an expert. However, it intervenes ‘on request’ when it receives persuasive signals that its help is required. Consideration of aspects distinguishing between these two modes of thinking by marketing and retail managers can help them to better understand how consumers conduct themselves and cater to their needs, concerns, wishes and expectations. Undertaking this viewpoint can especially help, for instance, in the area of ‘customer journeys’ — studying how thinking styles direct or lead the customer or shopper through a journey (including emotional signals), anticipating reactions, and devising methods that can alleviate conflicts and reduce friction in interaction with customers.

Ron Ventura, Ph.D. (Marketing)

References:

(1)  Thinking, Fast and Slow; Daniel Kahneman, 2012; Penguin Books.

(2) Rationality, Intelligence, and Levels of Analysis in Cognitive Science (Is Dysrationalia Possible); Keith E. Stanovich, 2002; in Why Smart People Can Be So Stupid (Robert J. Sternberg editor)(pp. 124-158), New Haven & London: Yale University Press.

(3) Dual-Processing Accounts of Reasoning, Judgment and Social Cognition; Jonathan St. B. T. Evans, 2008; Annual Review of Psychology, 59, pp. 255-278. (Available online at psych.annualreviews.org, doi: 10.1146/annurev.psych.59.103006.093629).

 

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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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One of the more difficult and troublesome decisions in brand management arises when entering a product category that is new to the company: Whether to up-start a new brand for the product or to endow it with the identity of an existing brand — that is, extending a company’s established brand from an original product category to a product category of a different type. The first question that would probably pop-up is “how different is the new product?”, acting as a prime criterion to judge whether the parent-brand fits the new product.

Notwithstanding, the choice is not completely ‘black or white’ since intermediate solutions are possible through the intricate hierarchy of brand (naming) architecture. But focusing on the two more distinct strategic branding options above helps to see more clearly the different risk and cost implications of launching a new product brand versus using the name of an existing brand from an original product category. Notably, the manufacturers, retailers and consumers, all perceive risks, albeit from the different perspective of each party given its role.

  • Note: Brand extensions represent the transfer of a brand from one type of product to a different type, to be distinguished from line extensions that pertain to the introduction of variants within the same product category (e.g., flavours, colours).

This is a puzzling marketing and branding problem also from an academic perspective. Multiple studies have attempted in different ways to identify the factors that best explain or account for successful brand extensions. While the stream of research on this topic helpfully points out to major factors, some more commonly agreed upon, a gap remains between the sorts of extensions predicted to succeed according to the studies and the extensions performed by companies that happen to succeed or fail in the markets in reality. A plausible reason for missing the outcomes of actual extensions, as argued by the researchers Milberg, Sinn, and Goodstein (2010), is neglecting the competitive settings in categories that are the target of brand extension (1).

Perhaps one of the most famous examples of a presumptuous brand extension has been the case of Virgin (UK), from music to cola (drink), airline, train transport, and mobile communication (ironically, the origin of the brand as Virgin Music has since been abolished). The success of Virgin’s distant extensions is commonly attributed to the personal character of Richard Branson, the entrepreneur behind the brand: his boldness, initiative, willingness to take risks, and adventurism. These traits seem to have transferred to his business activities and helped to make the extensions more credible and acceptable to consumers.

Another good example relates to Philips (originated in The Netherlands). Starting from lighting (bulbs, now more in LED), the brand extended over the years to personal care (e.g., face shavers for men, hair removal for women), sound and vision (e.g., televisions, DVD and Blue-Ray players, originally in radio sets), PC products, tablets and phones, and more. Still, when looking overall at the different products, systems and devices sharing the Philips brand, they can mostly be linked as members in a broad category of ‘electrics and electronics’, a primary competence of the company. As the company grew with time, launched more types of products whilst advancing with technology, and its Philips brand was perceived as having greater experience and good record in brand extensions, this could facilitate the market acceptance of further extensions to additional products.

  • In the early days of the 1930s to 1950s radio and TV sets relied for operation on vacuum tubes, later moving to electronic circuits with transistors or digital components. Hence, historically there was an apparent physical-technological connection between those products and the brand’s origin in light bulbs, a connection much harder to find now between category extensions, except for the broad category linkage suggested above.

Academic research has examined a range of ‘success factors’ of brand extensions, such as: perceived quality of the parent-brand; fit between the parent-brand and the extension category; degree of difficulty in making an extension (challenge undertaken); parent-brand conviction; parent-brand experience; marketing support; retailer acceptance; perceived risk (for consumers) in adopting the brand extension; consumer innovativeness; consumer knowledge of the parent-brand and category extension; the stage of entry into another category (i.e., as an early or a late entrant). The degree of fit of the parent-brand (and original product) with the extension category is revealed as the most prominent factor contributing to better acceptance and evaluation (e.g., favourability) of the extension in consumer studies.

Aaker and Keller specified in a pioneer article (1990) two requirements for fit: (a) the extension product category is a direct complement or a substitute of the original category; (b) the company, with its people and facilities, is perceived as having the knowledge and capability of manufacturing the product in the extension category. These requirements reflect a similarity between the original and extension product categories that is necessary for successful transfer of a favourable attitude towards the brand to the extension product type (2). A successful transfer of attitude may occur, however, also if the parent-brand has values, purpose or image that seem relevant to the extension product category, even when the technological linkage is less tight or apparent (as the case of Virgin suggests).

  • Aaker and Keller found that fit, based especially on competence, stands out as a contributing factor to higher consumer evaluation (level of difficulty is a secondary factor while perceived quality plays more of a ‘mediating’ role).

Volckner and Sattler (2006) worked to sort out the contributions of ten factors, as retrieved from academic literature, to the success of brand extensions; relations were refined with the aid of expert advice from brand managers and researchers (3). Contribution was assessed in their model in terms of (statistical) significance and relative importance. The researchers found  fit to be the most important factor driving (perceived) brand extension success in their study, followed by marketing support, parent-brand conviction, retail acceptance, and parent-brand experience. The complete model tested for more complex structural relationships represented through mediating and moderating (interacting) factors (e.g., the effect of marketing support on extension success ‘passes’ through fit and retailer acceptance).

For brand extensions to be accepted by consumers and garner a positive attitude, consumers should recognise a connectedness or linkage between the parent-brand and the category extension. The fit between them can be based on attributes of the original and extension types of product or a symbolic association. Keller and Lehmann (2006) conclude in this respect that “consumers need to see the proposed extension as making sense” (emphasis added). They identify product development, applied via brand (and line) extensions, as a primary driver of brand growth, and thereby adding to parent-brand equity. Parent-brands do not tend to be damaged by unsuccessful brand extensions, yet the authors point to circumstances where greater fit may result in a negative effect on the parent-brand, and inversely where joining a new brand name with the parent-brand (as its endorser) may protect the parent-brand from adverse outcomes of extension failure (4).

When assessing the chances of success of a brand extension, it is nevertheless important to consider what brands are already present in the extension category that a company is about to enter. Milberg, Sinn, and Goodstein claim that this factor has not received enough attention in research on brand extensions. In particular, one has to take into account the strength of the parent-brand relative to competing brands incumbent in the target category. As a starting point for entering the extension category, they chose to focus on how well consumers are familiar with the competitor brands vis-à-vis the extending brand.  Milberg and her colleagues proposed that a brand extension can succeed despite a worse fit with the category extension due to an advantage in brand familiarity, and vice versa. Consumer response to brand extensions was tested on two aspects: evaluation (attitude) and perceived risk (5).

First, it should be noted, the researchers confirm the positive effect of better fit on consumer evaluation of the brand extension when no competitors are considered. The better fitting extension is also perceived as significantly less risky than a worse fitting extension. However, Milberg et al. obtain supportive evidence that in a competitive setting, facing less familiar brands can improve the fortune of a worse fitting extension, compared with being introduced in a noncompetitive setting: When the incumbent brands are less familiar relative to the parent-brand, the evaluation of the brand extension is significantly higher (more favourable) and purchasing its product is perceived less risky than if no competition is referred to.

  • A reverse outcome is found in the case of better fit where the competitor brands are more highly familiar: A disadvantage in brand familiarity can dampen the brand extension evaluation and increase the sense of risk in purchasing from the extended brand, compared with a noncompetitive setting.

Two studies performed show how considering differences in brand familiarity can change the picture about the effect of brand extension fit from that often found without accounting for competing brands in the extension category.

When comparing different competitive settings, the research findings provide a more constrained support, but in the direction expected by Milberg and colleagues. The conditions tested entailed a trade-off between (a) a worse fitting brand extension competing with less familiar brands; and (b) a better fitting brand extension competing with more familiar brands. In regard to competitive settings:

The first study showed that the evaluation of a worse fitting extension competing with relatively unfamiliar brands is significantly more favourable than a better fitting extension facing more familiar brands. Furthermore, the product of a worse fitting brand extension is preferred more frequently over its competition than the better fitting extension product is (chosen by 72% vs. 6%, respectively). Also, purchasing a product from the worse fitting brand extension is perceived significantly less risky compared with the better fitting brand. These results indicate that the relative familiarity of the incumbent brands that an extension faces would be more detrimental to its odds of success than how well its fit is.

The second study aimed to generalise the findings to different parent-brands and product extensions. It challenged the brand extensions with somewhat more difficult conditions: it included categories that are all relevant to respondents (students), and so competitor brands in extension categories are also relatively more familiar to them than in the first study. The researchers acknowledge that the findings are less robust with respect to comparisons of the contrasting competitive settings. Evaluation and perceived risk related to the worse fitting brand competing with less familiar brands are equivalent to the better fitting brand extension facing more familiar brands. The gap in choice shares is reduced though in this case it is still statistically significant (45% vs. 15%, respectively). Facing less familiar brands may not improve the response of consumers to the worse fitting brand extension (i.e., not overcoming the effect of fit) but at least it is in a position as good as of the better fitting brand extension competing in a more demanding setting.

  • Perceived risk intervenes in a more complicated relationship as a mediator of the effect of fit on brand extension evaluation, and also in mediating the effect of relative familiarity in competitive settings. Mediation implies, for example, that a worse fitting extension evokes greater risk which is responsible for lowering the brand extension evaluation; consumers may seek more familiar brands to alleviate that risk.

A parent-brand can assume an advantage in an extension category even though it encounters brands that are familiar within that category, and may even be considered experts in the field: if the extending brand is leading within its original category and is better known beyond it, this can give it a leverage on the incumbents if those brands are more ‘local’ or specific to the extension category. For example, it would be easier for Nikon leading brand of cameras to extend to binoculars (better fit) where it meets brands like Bushnell and Tasco than extending to scanners (also better fit) where it has to face brands like HP and Epson. In the case of worse fitting extensions, it could be significant for Nikon whether it extends to CD players and competes with Sony and Pioneer or extends to laser pointers and faces Acme and Apollo — in the latter case it may enjoy the kind of leverage that can overcome a worse fit. (Product and brand examples are borrowed from Study 1). Further research may enquire if this would work better for novice consumers than experts. Milberg, Sinn and Goodstein recommend to consider additional characteristics that brands may differ on (e.g., attitude, image, country of origin), suggesting more potential bases of strength.

Entering a new product category for a company is often a difficult challenge, and choosing the more appropriate branding strategy for launching the product can be furthermore delicate and consequential. If the management chooses to make a brand extension, it should consider aspects of relative strength of its parent-brand, such as familiarity, against the incumbent brands of the category it plans to enter in addition to a variety of other characteristics of product types and its brand identity. However, the managers can take advantage as well of intermediate solutions in brand architecture to combine a new brand name with an endorsement of an established brand (e.g., higher-level brand for a product range). Choosing the better branding strategy may be helped by better understanding of the differences and relations (e.g., hierarchy) between product categories as perceived by consumers.

Ron Ventura, Ph.D. (Marketing)

Notes:

1. Consumer Reactions to Brand Extensions in a Competitive Context: Does Fit Still Matter?; Sandra J. Milberg, Francisca Sinn, & Ronald C. Goodstein, 2010; Journal of Consumer Research, 37 (October), pp. 543-553.

2.  Consumer Evaluations of Brand Extensions; David A. Aaker and Kevin L. Keller, 1990; Journal of Marketing, 54 (January), pp. 27-41.

3.  Drivers of Brand Extension Success; Franziska Volckner and Henrik Sattler, 2006; Journal of Marketing, 70 (April), pp. 18-34.

4. Brands and Branding: Research Finding and Future Priorities; Kevin L. Keller and Donald R. Lehmann, 2006; Marketing Science, 25 (6), pp. 740-759.

5. Ibid. 1.

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It is usually not a pleasant feeling to be alone in a scary place or event — think of being stuck in a dark elevator or being involved in a car accident. People commonly seek to be with someone for comfort and company. But the companion does not always have to be another person. A research by Dunn and Hoegg (2014) provides corroboration that the need to share fear matters to humans while the identity of the companion, whether a person or an object, is less critical.  More specifically, sharing fear with a product from an unfamiliar brand may facilitate a quick emotional attachment with that brand without requiring to build a relationship over a lengthy period of time (1).

Fear is evoked by the presence or anticipation of a danger or threat. Feeling fear may be triggered by an unfamiliar event to which one is unsure how to respond (uncertainty) or an unexpected event at a specific moment (surprise); experiencing fear is furthermore likely when the event encountered is both unfamiliar and unexpected. It is important to note, nonetheless, that not every encounter with an unfamiliar or unexpected event necessarily leads to  fear. The Amygdala in the temporal lobe of the brain is the “centre” where fear arises. However, the amygdala like other brain structures is responsible for multiple functions. The amygdala is activated in response to unfamiliarity, unpredictability or ambiguity, but not every instance necessarily means the evocation of fear. For example, tension from facing an unfamiliar problem that one is at loss how to solve may not result in fear. Additionally, fear as well as other states of emotion are the outcome of appraisal of physical feelings (e.g., faster heartbeats, startle, warmth), considering the conditions in which they were triggered; it is a cognitive interpretation of their meaning (“why do I feel that way?”). Activation of other brain structures together with the amygdala may influence whether similar feelings triggered by an unexpected event are interpreted, for instance, as fear, anger, or surprise. The context in which an event occurs can matter a lot for the appraisal of emotions (2).

Dunn and Hoegg emphasise the emotional charge of consumer attachment with a brand versus cognitive underpinnings. Brand attachment has often been conceptualised as the product of a relationship between consumers and the target brand built over time. It should take a longer time to achieve a more solid brand attachment because of cognitive processes for establishing brand connections in memory and stronger favourable brand attitudes. However, this explanation is subject to criticism of missing the important role of emotions in bonding between consumers and a brand which does not necessarily require a long time. By focusing their studies on unfamiliar brands, Dunn and Hoegg intended to show that emotional attachment can emerge much more quickly when the consumers are distressed and are looking for a partner to share their fear with, and that partner or companion can be a brand of a given product.

On the same grounds, the researchers chose a scale of emotional attachment (Thomson, MacInnis and Park, 2005 [3]) as more appropriate over a scale that combines emotional and cognitive aspects of attachment and gives greater weight to cognitive constructs (Park, MacInnis et al., 2010 [4]). The emotional scale comprises three dimensions: (a) Affection (affectionate, friendly, loved, peaceful); (b) Passion (passionate, delighted, captivated); (c) Connection (connected, bonded, attached). Nevertheless, in the later research Park and MacInnis with colleagues offer a broader perspective that accounts for two bases of brand attachment: (i) a connection between self-concept and a brand; and (ii) brand prominence in memory.

While ‘brand prominence’ can be regarded as more cognitive-oriented (accessibility of thoughts and feelings in memory), a ‘brand-self connection’ entails the expansion of one’s concept of self to incorporate others, such as brands, within it — and that involves an emotional element. Park and MacInnis et al. emphasise the brand-self connection as the emotional core of their definition of brand attachment, while brand prominence is a facilitator in actualizing the attachment (analyses substantiate that brand attachment is a better predictor than attitudes of intentions to perform more difficult types of behavior reflecting commitment, and the brand-self connection is more essential for driving this behaviour). The three-dimension scale of emotional brand attachment seems very relevant for the research goals of Dunn and Hoegg, even though it is more restricted from a stand-point of the theoretical roots of brand attachment.

The desire to affiliate with others in scaring and upsetting situations is recognised as a mechanism for coping with negative emotions in those situations. Episodes of armed conflict, terrorist attacks, and natural disasters make people get closer to each other, unite and show solidarity. However, the researchers note that the act of affiliation is essential for coping rather than the affiliation target. That is, the literature on affiliation or attachment relates to interpersonal connections as well as attachment to objects (although objects are viewed as substitutes in absence of other persons [pet animals should also be considered]). We can find support for possible attachment to products and their brands in the human tendency to animate or anthropomorphise objects by assigning them traits of living beings, whether animals or humans. Brands may be animated in order to help consumers relate with them more comfortably, making them appear more vivid to them. It is one of the processes that facilitates the development of consumers’ relationships with their brands in use; consumers connect with brands also through the role brands fulfilled in their personal history, heritage and family traditions, and how brands integrate in their preferred lifestyles (5).

Dunn and Hoegg investigate how consumers connect with a brand on occasions of incidental fear. They make a clear distinction between events that may trigger fear (or other emotions) and fear appeals strategically planned in advertising (e.g., in order to induce a particular desired behaviour). Events that incidentally cause fear would be independent and uncontrolled. Additionally, the intensity and range of emotions felt is expected to differ when consumers actively participate in an event and hence experience it directly in contrast to watching TV ads — in direct consumer experiences, emotional feelings are likely to be more intensive and specific.  In a model for measuring consumption emotions developed and tested by Richins, fear is characterised as a negative and more active (as opposed to receptive) emotion, next to other emotions such as anger, worry, discontent, sadness and shame (6).

  • In their experiments, the researchers try to emulate incidental fear by displaying to participants clips from cinema films or TV series’ episodes, and present evidence that manipulations successfully elicited the intended emotions as dominant in response to each video clip. Yet, it remains somewhat ambiguous how real and direct the experience of watching scenes in a film or a TV programme is perceived and felt with regard to the emotions evoked.

The following are more concrete findings from the studies and their insights:

Emotional brand attachment is generated through perception that the brand shares the fear with the consumer — Study 1 confirms that emotional attachment with an unfamiliar brand is generated when a product (juice) by that brand is present and can be consumed during the fear-inducing experience (more than for emotions of sadness, excitement and happiness). But moreover, it is shown that the emotional attachment is mediated (conditioned) by perception of the consumer that the brand shared the fear with him or her.

Humans precede product brands —  Sharing fear with a brand contributes to stronger emotional brand attachment, but only if they still have a desire generated by fear to affiliate with others. If conversely that desire is satiated by a perception of the consumers that they are already socially affiliated with other people, the effect on brand attachment is muted.

  • Note: Participants in Study 2 were asked to perform a search with words related to feelings of affiliation and social connectedness (e.g., included, accepted, involved) to prime affiliation. Given the statements used to measure (non-)affiliation (e.g., “I feel disconnected from the world around me”), it is a little questionable how effective such a priming condition could be (though the authors show it was sufficient). It might have been more tangible to ask participants to think of people dear to them, family and close friends, and write about them.

Balancing negative and positive emotional effects on attitudes — Based on analyses in Study 2 the researchers also suggest that increased positive effect of emotional brand attachment may counterbalance and override a negative influence of ‘affect transfer’ on attitudes due to fear.

Presence of the brand and attention to it are required yet sufficient — Study 3 demonstrates that neither consumption of the product (juice) nor even touching it (the bottle), both forms of physical interaction, are really needed for feeling affiliated and forming emotional attachment — forced consumption in particular does not contribute to stronger perceived sharing or emotional attachment than merely seeing the product when feeling fear, that is making an eye contact and visually attending to the product in search for a companion. (Unexpectedly, in the case of action and excitement, consuming the drink increases emotional attachment.) Study 4 stresses, nevertheless, that the brand must be present during the emotional event for generating increased emotional attachment — having the brand nearby while experiencing the fear is essential for consumers to feel connected with the brand as their sharing partner (tested with a different product, potato chips).

The research paper suffers from a deficit in practice. That is, marketing managers and professionals might be disappointed to discover that it could be most difficult to have any control of those situations of incidental fear and to act on them to their advantage. In order to have any influence on the consumer a company would be required to anticipate an individual event in advance and to find a way to intervene (i.e., make their product present) without being perceived too intrusive or self-interested — two non-negligible challenges. An additional restriction is posed by the relation of the ‘fear effect’ to brands not previously familiar to the consumers.

Let us consider some potential scenarios where brands might benefit and the difficulties that are likely to arise in implementing it:

Undertaking medical treatments or tests — Some treatments can be alarming and frightening on occasion to different patients. A sense of fear is likely to enter already, and perhaps especially, while waiting. It is a opportunity for introducing the brand-companion in the waiting hall; even more so given that patients are usually not allowed to or prevented from using artifacts during the treatment (mostly no food and drinks). First, a company may have a difficulty to obtain access to places where patients wait for treatment. Second, consumers-patients are likely to bring products with them from home to entertain them (of brands they know). Third, patients often arrive with a family or friend companion, thus satisfying their need for affiliation with another person which dominates affiliation with an object. Still, there is room for ingenuity how to locate the brand close enough to the treatment episode (e.g., shops offering books or toys, especially for children, in the premises of a clinic or hospital).

Trekking or hiking in nature — Some routes, particularly in mountainous areas, can be quite adventurous, not to say dangerous. If a brand could find a way to introduce its product just before the consumer starts the hiking trip, it may benefit from being with him or her if fear arises. One problem is that hikers are advised and even required not to embark alone on more dangerous routes. Another problem is that those trekking or hiking sites often offer local brands, that while not being familiar to the consumers they also are not likely to be available to them at home, and thus the opportunity to develop a relationship based on the early emotional attachment is lost.

Offering legal, financial, insurance, and technical services in events of crisis — In various occasions of accidents, malfunctions, and disasters, people need help to cope with the crisis and the negative emotions it may evoke, particularly fear. A service provider would be expected to counsel the customer in his or her distress, and of course propose a solution (e.g. how to fix one’s home after a fire or an earthquake). Unfortunately,  one cannot make an eye contact with an intangible service. The company has to find creative and practical ways to make itself readily visible and accessible to the consumer when needed by offering instruments and cues for making contact (e.g., an alarm and communication device for the elderly and people with more risky medical conditions).

  • Dunn and Hoegg are aware of the limitation of the findings to unfamiliar brands. They reasonably propose that “because fear leads to a general motivation to affiliate, emotional brand attachment would be enhanced regardless of the familiarity with the brand” (p. 165). It should take further research, however, to substantiate this proposition.

Despite the possible difficulties companies will likely need to deal with, the doors are not completely shut to them to benefit from this phenomenon. But they must come up with creative and non-intursive solutions to make their brands and products present in the right place at the right time. At the very least, marketers should be aware of the potential effect of sharing fear with the consumer and understand how it can work in the brand’s benefit. It is worth remembering, after all, the saying “a friend in need is a friend indeed” whereby in some incidents the friend can be a brand.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “The Effect of Fear on Emotional Brand Attachment”; Lea Dunn and JoAndrea Hoegg, 2014; Journal of Consumer Research, 41 (June), pp. 152-168.

(2) “What Is Emotion?: History, Measures and Meanings”; Jerome Kagan, 2007; New Haven and London: Yale University Press. Also see: “The Experience of Emotion”; Lisa Feldman Barrett, Bejta Mesquita, Kevin N. Ochsner, & James J. Gross, 2007; Annual Review of Psychology, 58, pp. 373-403.

(3) “The Ties That Bind: Measuring the Strength of Consumers’ Emotional Attachments to Brands”; Mathew Thomson, Deborah J. MacInnis, & C. Whan Park, 2005; Journal of Consumer Psychology, 15 (1), pp. 77-91.

(4) “Brand Attachment and Brand Attitude Strength: Conceptual and Empirical Differentiation of Two Critical Brand Equity Drivers”; C. Whan Park, Deborah J. MacInnis, Joseph Priester, Andreas B. Eisengerich, & Dawn Iacobucci, 2010; Journal of Marketing, 74 (November), 1-17.

(5) “Consumers and Their Brands: Developing Relationship Theory in Consumer Research”; Susan Fournier, 1998; Journal of Consumer Research, 24 (March), pp. 343-373.

(6) “Measuring Emotions in the Consumption Experience”; Marsha L. Richins, 1997; Journal of Consumer Research, 24 (September), pp. 127-146.

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The location-based technology of beacons is a relatively recent newcomer in the retail scene (since 2013). Beacons provide an additional route for interacting with shoppers in real-time via their smartphones as they move around in stores and malls. Foremost, this technology is about marrying between the physical and the digital (virtual) spaces to create better integrated and encompassing shopping experiences.

It is already widely acknowledged that in-store and online shopping are not independent and do not happen completely separate from each other; instead, experience and information from one scene can feed and drive a shopping experience, and purchase, in the other scene. In particular, mobile devices enable shoppers to apply digital resources while shopping in a physical shop or store.  Beacons may advance retailers and shoppers another step forward in that direction, with the expectation to generate more purchases in-store. The beacon technology was received at first with enthusiasm and promising willingness-to-accept by retailers, but these subdued in the past year and adoption has stalled. A salient obstacle appears as consumers remain hesitant and cautious about letting retailers communicate through beacons with their smartphones and the implications it may have on their privacy.

In essence, beacons are small, battery-powered, low-energy Bluetooth devices that function as transmitters of information — primarily unique location signals — to nearby smartphones with an app authorised to receive the information. The availability of an authorised app (e.g., retailer’s, mall operator’s) installed on the consumer’s smartphone (or tablet) is critical for the communication technology to function properly. Upon receiving a location signal, the app is thereby triggered to display location-relevant content for the shopper in-store (e.g., product information, digital coupons, as well as store activities and services).

Additional requirements may be in force such as the retailer’s app being open during the shopping trip or that the shopper consents (opts-in) to allow the app receive information from beacons, but these do not seem to be necessary or mandatory conditions for the technology to work (e.g., an app may be set with ‘approval’ as default). Ambiguity that seemingly prevails about the extra requirements could be one of the sour points in the technology’s implementation. On one hand, the application of beacons is more ethical when setting up at least one of these requirements, and should endow it with greater credibility among consumers. On the other hand, any additional criterion for access of beacons to smartphones — assuming the app is already installed — could limit further the number of participating shoppers and reduce its marketing impact.

  • Only smartphones (and tablets) support apps, not any mobile phone. It should not be taken for granted that everyone has supporting smartphones, hence raising another possible limiting requirement on access for beacons (though in decline in developed countries). Another problem, yet, concerns the distinction between Apple iPhones operated with iOS and smartphones of other brands operated with Google’s Android — beacons have to work with either type of operating system and compatible apps but they do not necessarily do so (e.g., iBeacons are exclusive for Apple’s own mobile devices).

There are some more variations in the application of beacon technology in retail. Beacon devices may be attached to shelves next to specific product displays or to fixtures and building columns in positions aimed at capturing smartphones of shoppers moving in a close area (e.g., an aisle). If the beacon is associated with a particular product, the shopper may engage using the app by actively approaching the phone to the beacon. Otherwise, the app communicates with the beacons without  shoppers taking any voluntary action. Furthermore, some applications of beacon technology suggest sending information other than location signals from the beacon, such as product-related information, and receiving customer-related information by the beacon from the smartphone.

Reasonably, retailers would be interested first in applications of the technology for practical marketing purposes in their stores. However, beacon technology may also be utilised in research on shopper behaviour, a purpose now appreciated by many large retailers.

Marketing Practice in Retail

The instant sales-driven idea of application of beacon technology evoked by retailers is to introduce special offers, discount deals and digital coupons for selected products as shoppers get near to their displays. Notwithstanding this type of application, location-based features and services enabled via beacons can be even more creative and useful for shoppers, and beneficial for the retailers.

Relevance is key in achieving an effective application of the technology. Any message or content must be relevant in time and place to the shopper. That is, the content must be related to available products when the shopper is getting close enough to them. The content should not be too general in reference to any product in the store but to products in a section of the store where the shopper passes-by. Triggering an offer for a product just after the shopper entered a store is less likely to be effective, unless, for example, there is a special promotional activity for it in a main area of the floor. The retailer should not err in introducing an offer for a product item that is not available in the specific store at that time. Furthermore, if the app can link product information with customer information, it may be able to generate better content that is both location-relevant and personalised. The app could make use of accessible information on personal purchase history, interests and demographic characteristics. This higher-level application surely requires greater resources and effort of the retailer to implement.

The beacons’ greatest enemy could be their use for bombardment of shoppers with push or pop-up messages of offers, deals, discounts etc. This practice is suspected as a major fault in the early days of the technology that may be responsible for the slowdown in adoption lately. There could be nothing more irritating for a shopper if every few meters walked in the store he or she is interrupted by a buzz and message of “just today offer on X” that appears on the smartphone’s screen. Retailers have to be selective lest customers will avoid using their apps. It is much more important to produce adaptive, relevant and customer-specific messages and content overall (Adobe, Digital Marketing Blog, 4 February 2016).

  • The grocery retail chain Target, that launched a trial with beacons in 50 US stores in the second half of 2015, committed, for instance, to show no more than two promotional (push) messages during a store visit (TechCrunch.com, 5 Aug. ’15).

More intelligent and helpful ways exist to apply the beacon technology in interaction with the app than promotional push messages. First, content of the “front page” of the app can change as the shopper progresses in the store to reflect information that would be of interest to the shopper in that area of the store (e.g., show hyper-linked ’tiles’ for nearby product types). Second, beyond ‘technical’ information on product characteristics and price, a retailer can facilitate shopper-user access to reviews and recommendations for location-relevant products via the app. Third, if the shopper fills-in a shopping list on a retailer’s app (e.g., a supermarket), and the app has a built-in plan of the store, it can help the shopper navigate through the store to find the requested products, and it may even re-order the list and propose to the shopper a more ‘efficient’ path.

Beacons are associated mostly with stores (e.g., department stores, chain stores, supermarkets). However, beacons may also be utilised by mall operators where the ‘targets’ are stores rather than specific products. An application programme in a mall may command collaboration with the retailers (e.g., store profile and notifications, special promotional messages [for extra pay], content contributions).

In another interesting form of collaboration, the fashion magazine Elle initiated a programme with ShopAdvisor, a mobile app and facilitator that assists retailers in connecting with their shoppers through beacons. As an enhancement to its special 30th anniversary issue, Elle launched a trial project in partnership with some of its advertisers (e.g., Guess, Levi’s, Vince Camuto) to introduce their customers to location-based content with the help of ShopAdvisor (focused on promotional alerts)(1).

Consumers are concerned about tactics of location-based technologies like beacons that get intrusive and even creepy; they become adverse towards the way such apps sometimes surprise them (e.g., in dressing rooms). Indeed, only shoppers who installed an authorised app can be affected, but for customers who installed such a retailer’s app, with other benefits in mind, it can be disturbing at times. The hard issue at stake is how the app alerts or approaches its shoppers-users with location-based messages. Shoppers do not like to feel that someone is watching where they go.

The shopper may believe that if the app remains closed on the smartphone he or she cannot be approached. But if, as reported in CNBC News, a dormant app can be awaken by a beacon signal, this measure is not enough. This may happen because the shopper previously allowed the app to receive the Bluetooth signal or the app “assumed” so as default.  The shopper must take an extra step to disable the function at the app-level or device-level (Bluetooth connectivity). Retailers should let their customers opt-out and be careful in any attempt to remotely open their apps on smartphones (so-called “welcome reminders”), because imposing and interfering with customer choices may get the opposite outcome of removing the app.

The app may display ‘digital’ coupons for the shopper to “pick-up” and show later at the cashier (or self-service check-out). It is reasoned that if coupons are shown at the right time shoppers will welcome the offer, no resentment. The manner shoppers are alerted can also matter, by not being too obtrusive (e.g., “Click here for coupons for products in this aisle”). Shoppers told CNBC News that if digital coupons were offered to them by the app just when relevant, they would be glad to use this option, being more convenient than going around with paper coupons, but they would want the ability to opt-out.

Shopper Behaviour Research

The beacon technology may further contribute to research on shopper behaviour in stores or malls. Specifically, it may be suitable for collecting data of shopper traffic to be used in path analysis of the shopping journeys. The information may cover what areas of the store shoppers visit more frequently, how long one stays in a given area, and sequences of passes between areas.

Nonetheless, there are methodological, technological and ethical factors retailers and researchers have to consider. At this time, there are distinct limitations to be recognized that may inflict on the validity and reliability of the research application of beacons. Ethical issues discussed above regarding the provision of access of beacons to mobile apps furthermore apply in the research context.

This methodology involves tracking the movements of shoppers. Beacon technology may record frequency of visits in each area of the store separately or it may track the presence of a particular shopper by different beacons across the store. A beacon may also be able to send repeated signals at fixed intervals to a smartphone to measure how long a shopper remains in a given area. However, this type of research is not informative about what a shopper does in a specific location as in front of product shelves, and thus it cannot provide valuable details on her decision processes. Hence, retailers cannot rely on this methodology as a substitute for other methods capable of studying shopper behaviour more deeply, especially with respect to decision-making. A range of methods may be used to supplement path analysis such as interviewer’s walk-along with a shopper, passive observations, video filming, and possibly also in-store eye-tracking.

An implementation of the technology for research would require a comprehensive coverage of the premises with beacons, perhaps greater than needed for marketing practice. It should be compared with alternative location-based technologies (e.g., Radio Frequency Identification [RFID], Wi-Fi)  on criteria of access, range and accuracy, and of course cost-effectiveness. For example, the RFID technology employs tags ( transmitters) regularly attached to shopping carts — if a shopper leaves the cart at the end-of-aisle and goes in to pick-up a couple of products, the system will miss that; smartphones, however, are carried on shoppers all the time. Beacon technology may have an important advantage over RFID if location data is linked with customer characteristics, but this is a sensitive ethical issue and at least it is imperative to ensure no personal IDs are included in the dataset. All alternative technologies may also have to deal with different types of environmental interferences with their signals. Access would have both technical and ethical aspects.

A mixture of problems emerges as responsible for impairing the utilisation of beacon technology, according to RetailDive (online news and trends magazine), mainly consumers who do not perceive beacon-triggered features as useful enough to them and retailers troubled by technical or operational difficulties. Among the suggestions made: encourage pull of helpful information from beacons by shoppers rather than push messages, and speed-up calling staff for assistance via beacons (RetailDive, 17 December 2015). A recent research report by Adobe and Econsultancy on Digital Trends for 2016 indicates that retailers are becoming more reluctant to implement a geo-targeting technology like beacons this year compared with 2015 (a decrease in proportion of retailers who have this technology in plan or exploring it, against an increase in proportion of those who are not exploring or do not know). Conspicuously, there seems to be much more optimism about high effectiveness of geo-targeting technology at technology and consultancy agencies than among retailers, who seem to be much more in the opinion that it is too early (2). Agencies could have better understanding of the field, yet it signals an alarm of disconnect between agencies and their clients.

There is potential to beacon technology with clearly identifiable benefits it can deliver to retailers and consumers. It is still a young technology and requires more development and progress on various technical, applied and ethical aspects.  Promotional messages are  important tools but must be used in a good and sensible measure. A retailer cannot settle for a small set of fixed messages. It has to develop a dynamic ‘bank’ of messages, large enough to be versatile over products, (chain) stores, and consumer groups, and maintain regular updates. However, retailers have to develop and provide a more rich suite of clever content and practical tools based on location. Consumers will have to be convinced of the benefits enabled by beacons, yet feel free to decide when and how to enjoy them.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) “App Helps Target Shoppers’ Location and Spontaneity”, Glenn Rifkin, International New-York Times, 31 December 2015 – 1 January 2016.

(2) “Quarterly Digital Intelligence Briefing: 2016 Digital Trends”, Adobe and Econsultancy, January 2016 (pp. 24-25). The findings are considered with caution because of relatively small sub-samples of respondents on this topic (N < 200).

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Psycographic-oriented research of consumer lifestyles based on surveys for collecting the data is losing favour among marketers and researchers. Descriptors of consumer lifestyles are applied especially for segmentation by means of statistical clustering methods and other approaches (e.g., latent class modelling). Identifying lifestyle segments has been recognized as a strategic instrument for marketing planning because this kind of segmentation has been helpful and insightful in explaining variation in consumer behaviour where “dry” demographic descriptors cannot reach the deeper intricacies. But with the drop in response rates to surveys over the years, even on the Internet, and further problematic issues in consumer responses to survey questionnaires (by interview or self-administered), lifestyle research using psychographic measures is becoming less amenable, and that is regrettable.

The questionnaires required for building lifestyle segmentation models are typically long, using multi-item “batteries” of statements (e.g., response on a disagree-agree scale) and other types of questions. Initially (1970s) psychographics were represented mainly by Activities, Interests and Opinions (AIO). The measures cover a wide span of topics or aspects from home and work, shopping and leisure, to politics, religion and social affairs. But this approach was criticised for lacking a sound theoretical ground to direct the selection of aspects characterising lifestyles that are more important, relevant to and explanatory of consumer behaviour. Researchers have been seeking ever since the 1980s better-founded psychology-driven bases for lifestyle segmentation, particularly social relations among people and sets of values people hold to. The Values and Lifestyles (VALS) model released by the Stanford Research Institute (SRI) in 1992 incorporated motivation and additional areas of psychological traits (VALS is now licensed to Strategic Business Insights). The current version of the American model is based on that same eight-segment typography with some updated modifications necessary to keep with the times (e.g., the rise of advanced-digital technology) — the conceptual model is structured around two prime axes, (a) resources (economic, mental) and (b) motivation or orientation. Scale items corresponding to the AIOs continue to be used but they would be chosen to represent constructs in broader or better-specified contexts.

Yet the challenge holds even for the stronger-established models, how to choose the most essential aspects and obtain a sufficient set of question items consumers are likely to complete answering. Techniques are available for constructing a reduced set of items (e.g., a couple of dozens) for subsequent segmentation studies relying on a common base model, but a relatively large set (e.g., several dozens to a few hundreds of items) would still be needed for building the original model of lifestyle segments. It is a hard challenge considering in particular the functions and limitations of more popular modes of surveys nowadays, online and mobile.

Lifestyles reflect in general the patterns or ways in which people run their ordinary lives while uncovering something of the underlying motives or goals. However,  ‘lifestyles’ have been given various meanings, and researchers follow different interpretations in constructing their questionnaires. The problem may lie in the difficulty to construct a coherent and consensual theory of ‘lifestyles’ that would conform to almost any area (i.e., product and service domain) where consumer behaviour is studied.  This may well explain why lifestyle segmentation research is concentrated more frequently on answering marketing questions with respect to a particular type of product or service (e.g., banking, mobile telecom, fashion, food). It can help to select more effectively the aspects the model should focus on and thereby also reduce the length of the questionnaire. The following are some of the concepts lifestyle models may incorporate and emphasise:

  • Values that are guiding and driving consumers (e.g., collectivism vs. individualism, modernism vs. traditionalism, liberalism vs. conservatism);
  • In the age of Internet and social media consumers develop new customs of handling social relations in the virtual world versus the physical world;
  • In view of the proliferation of digital, Internet and mobile communication technologies and products it is necessary to address differences in consumer orientation and propensity to adopt and use those products (e.g, ‘smart’ products of various sorts);
  • How consumers balance differently between work and home or family and career is a prevailing issue at all times;
  • Lifestyles may be approached through the allocation of time between duties and other activities — for example, how consumers allocate their leisure time between spending it with family, friends or alone (e.g., hobbies, sports, in front of a screen);
  • Explore possible avenues for developing consumer relationships with brands as they integrate them into their everyday way of living (e.g., in reference to a seminal paper by Susan Fournier, 1998)(1);
  • Taking account of aspects of decision-making processes as they may reflect overall on the styles of shopping and purchasing behaviour of consumers (e.g., need for cognition, tendency to process information analytically or holistically, the extent to which consumers search for information before their decision).

Two more issues deserve special attention: 

  1. Lifestyle is often discussed adjacent with personality. On one hand, a personality trait induces a consistent form of response to some shared stimulating conditions in a variety of situations or occasions (e.g., responding logically or angrily in any situation that creates stress or conflict, offering help whenever seeing someone else in distress). Therefore, personality traits can contribute to the model by adding generalisation and stability to segment profiles. On the other hand, since lifestyle aspects describe particular situations and contexts whereas personality traits generalize across them, it is argued that these should not be mixed as clustering variables but may be applied in complementary modules of a segmentation model.
  2. Products that consumers own and use or services they utilize can illustrate  figuratively their type of lifestyle. But including a specific product in the model framework may hamper the researcher’s ability to make later inferences and predictions on consumer behaviour for the same product or a similar one. Therefore, it is advisable to refer carefully to more general types of products distinctively for the purpose of implying or reflecting on a pattern of lifestyle (e.g., smartphones and technology-literacy). Likewise, particular brand names should be mentioned only for an important symbolic meaning (e.g., luxury fashion brands, luxury cars).

Alternative approaches pertain to portray lifestyles yet do not rely on information elicited from consumers wherein they describe themselves; information is collated mostly from secondary databases. Geodemographic models segment and profile neighbourhoods and their households (e.g., PRIZM by Claritas-Nielsen and MOSAIC by Experian). In addition to demographics they also include information on housing, products owned (e.g., home appliances), media used, as well as activities in which consumers may participate. However, marketers are expected, by insinuation, to infer the lifestyle of a household, based, for instance, on appliances or digital products in the house, on newspaper or magazine subscriptions, on clubs (e.g., sports), and on associations that members of the household belong to. Or consider another behavioural approach that is based on clustering and “basket” (associative) analyses of the sets of products purchased by consumers. These models were not originally developed to measure lifestyles. Their descriptors may vicariously indicate a lifestyle of a household (usually not of an individual). They lack any depth in describing and classifying how consumers are managing their lives nor enquiring why they live them that way.

The evolving difficulties in carrying-out surveys are undeniable. Recruiting consumers as respondents and keeping them interested throughout the questionnaire is becoming more effortful, demanding more financial and operational resources and greater ingenuity. Data from surveys may be complemented by data originated from internal and external databases available to marketing researchers to resolve at least part of the problem. A lifestyle questionnaire is usually extended beyond the items related to segmentation variables by further questions for model validation, and for studying how consumers’ attitudes and behaviour in a product domain of interest are linked with their lifestyles. Some of the information collected thus far through the survey from respondents may be obtained from databases, sometimes even more reliably than that based on respondents’ self-reports. One of the applications of geodemographic segmentation models more welcome in this regard is using information on segment membership as a sampling variable for a survey, whereof characteristics from the former model can also be combined with psychographic characteristics from the survey questionnaire in subsequent analyses. There are furthermore better opportunities now to integrate survey-based data with behavioural data from internal customer databases of companies (e.g., CRM) for constructing lifestyle segments of their customers.

Long lifestyle questionnaires are particularly subject to concerns about the risk of respondent drop-out and decreased quality of response data as respondents progress in the questionnaire. The research firm SSI (Survey Sampling International) presented recently in a webinar (February 2015 via Quirk’s) their findings and insights from a continued study on the effects of questionnaire length and fatigue on response quality (see a POV brief here). A main concern, according to the researchers, is that respondents, rather than dropping-out in the middle of an online questionnaire, actually continue but pay less attention to questions and devote less effort answering them, hence decreasing the quality of response data.

Interestingly, SSI finds that respondents who lose interest drop-out mostly by half-way of a questionnaire irrespective of its length, whether it should take ten minutes or thirty minutes to complete. For those who stay, problems may yet arise if fatigue kicks-in and the respondent goes on to answer questions anyway. As explained by SSI, many respondents like to answer online questionnaires; they get into the realm but they may not notice when they become tired or do not feel comfortable to leave before completing the mission, so they simply go on. They may become less accurate, succumb to automatic routines, and give shorter answers to open-end questions. A questionnaire may take forty minutes to answer but in the estimation of SSI’s researchers respondents are likely to become less attentive after twenty minutes. The researchers refer to both online and mobile modes of survey. They also show, for example, the effect of presenting a particular group of questions in different stages of the questionnaire.

SSI suggests in its presentation some techniques for mitigating those data-quality problems. Two of the suggestions are highlighted here: (1) Dividing the full questionnaire into a few modules (e.g., 2-3) so that respondents will be invited to answer each module in a separate session (e.g., a weekly module-session); (2) Insert break-ups in the questionnaire that let respondents loosen attention from the task and rest their minds for a few moments — an intermezzo may serve for a message of appreciation and encouragement to respondents or a short gaming activity.

A different approach, mentioned earlier, aims to facilitate the conduct of many more lifestyle-application studies by (a) building once a core segmentation model in a comprehensive study; (b) performing future application studies for particular products or services using a reduced set of question items for segmentation according to the original core model. This approach is indeed not new. It allows to lower the burden on the core modelling study from questions on product categories and release space for such questions in future studies dedicated to specific products and brands. One type of technique is to derive a fixed subset of questions from the original study that are statistically the best predictors of segment membership. However, a more sophisticated technique that implements tailored (adaptive) interviewing was developed back in the 1990s by the researchers Kamakura and Wedel (2).

  • The original model was built as a latent class model; the tailored “real-time” process selected items for each respondent given his or her previous responses. In a simulated test, the majority of respondents were “presented” with less than ten items; the average was 16 items (22% of the original calibration set).

Lifestyle segmentation studies are likely to require paying greater rewards to participants. But that may not be enough to keep them in the survey. Computer-based “gamification” tools and techniques (e.g., conditioning rewards on progress in the questionnaire, embedding animation on response scales) may help to some extent but they may also raise greater concerns for quality of responses (e.g., answering less seriously, rushing through to collect “prizes”).

The contemporary challenges of conducting lifestyle segmentation research are clear. Nonetheless so should be the advantages and benefits of applying information on consumer lifestyle patterns in marketing and retailing. Lifestyle segmentation is a strategic tool and effort should persist to resolve the methodological problems that surface, combining where necessary and possible psychographic measures with information from other sources.

Ron Ventura, Ph.D. (Marketing)

Notes:

(1) Consumers and Their Brands: Developing Relationship Theory in Consumer Research; Susan Fournier, 1998; Journal of Consumer Research, 34 (March), pp. 343-373

(2) Lifestyle Segmentation With Tailored Interviewing; Wagner A. Kamakura and Michel Wedel, 1995; Journal of Marketing Research, 32 (Aug.), pp. 308-317.

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