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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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Mapping the customer journey is often suggested as a vital step for better understanding customer experiences, before appropriate measures can be planned for improving on them. At the core of a “customer journey” is the purchase decision process, yet the evolved concept of “journey” encompasses broader aspects of customer behaviour and experience. Particularly with respect to consumers, the term “process” may have seemed to many (e.g., practitioners, managers) as too technical and logical while a “journey” is perceived as more imaginative and more likely to be imbued with emotion. There is still a significant parallel between the two concepts, yet the concept of journey has been extended in some important ways and emphasises the following aspects:

  • More frequently, the relation of a consumer with a company or a brand does not end with the act of purchase (transaction) of its product, good or service — following the purchase decision process there are likely to be additional immediate activities like further enquiries about product usage, feedback to the company or exchange of impressions with friends and family; in many cases, especially for on-going services and durables, there are continued interactions of customer service and technical support.
  • In any task concerned with purchase or usage customers more often engage multiple channels and touchpoints to complete their tasks and accomplish their goals (e.g., visiting a company’s Web site, a product & price comparison online portal, and a brick-and-mortar store before buying, interacting with a company by Facebook and e-mail to receive technical assistance).
  • Processes entailed in a “customer journey” tend to be cyclic rather than uni-directional processes with clear start and end points — there is continuity or flow from one purchase episode to the next such that if a subsequent purchase of a similar or related product is made from the same company customer loyalty can develop, but there are also possible cycles and repetition of activities performed by a consumer during a single purchase decision process.

Therefore, the customer journey may be not only longer than what a purchase decision process implies but also more multi-faceted and complex. To be honest, some of the extending aspects have been already suggested within the framework of the purchase decision process. For instance, post-purchase stages such as feedback and product divestment have been suggested in decision models in the 1990s (e.g., Engel, Blackwell and Miniard). Reliance on multiple information sources (marketer- and non-marketer controlled) has also been long considered  in the course of a purchase decision process. And if we concentrate on the path of a single decision process, decision models described and depicted by prominent scholar Jim Bettman in the late 1970s are all but simple, uni-directional and straightforward. Consumers frequently move back-and-forth, collect and use different pieces of information according to various decision rules, evaluate their options, and if necessary return to revise their consideration set, collect more information or re-examine their prior analysis. Those concepts and models have been tested and developed by Bettman together with his colleagues John Payne and Eric Johnson under the theoretical framework of adaptive decision-making (1993). Hence, the customer journey clearly builds on the foundations of earlier theories and models of consumer decision-making.

However, the concept of customer journey contributes several new perspectives. First, journey models give more weight to post-purchase activities compared with purchase decision models that traditionally address these activities only briefly, leaving them to be treated in other model types. Sharing opinion in social media networks, crowd sourcing for assistance, or asking for customer support from a company-provider, all these are important for business practice; accounting for these activities recognises that positive experiences in these activities build the link from one purchase to the next with the same company  (i.e., replacement, cross-sell, and up-sell). Journey maps vary nonetheless in their scope: taking a broad-view of a relationship journey with a company or focus on specific tasks and activities (e.g., enquiry about billing); considering all aspects of a purchase decision process, including any engagement with offers by competitors, or concentrating on interactions between the company concerned and its customers, as “customer journey” literally suggests.

Second, journey models appear to give more room to expression of emotions and affective reactions by customers, for example, in giving feedback or during service-related interactions with the company. Mapping studies that rely on interviews with customers even encourage such expressions. However, it should be noted that literature on decision-making, particularly in the past 10-15 years, already recognises the incorporation of both cognitive and affective components as co-influencers of decision processes.

Third, making probably the most important contribution, customer journey models address the employment of multiple channels by customers through various associated touchpoints with companies to perform purchase, usage or service tasks. This aspect appears to be driven primarily by business enterprises in response to the contemporary reality of their relations with customers. These channels furthermore are expected to be co-ordinated. In some cases, however, ambiguity arises whether each touchpoint defines an independent channel or multiple touchpoints are nested within a single channel:

  • In a brick-and-mortar store, shoppers may encounter touchpoints with the retailer in front of a shelf display (this is also a potential touchpoint with a manufacturer’s brand) and at the cashier;
  • On the internet, a customer may experience a touchpoint with a company on its main commercial website when learning about its products, but she may also transfer to the company’s blog linked to the website or launch a chat conversation from the website to ask for assistance from a service representative.
  • “Mobile” is commonly considered a channel by itself but nested within are a variety of resources and tools that can be used on the mobile devices, some of them have parallels in other modes of communication (websites, e-mail, social media), some are specially designed for mobile (e.g., apps).

Constructing a mapping diagram of customer journeys is a specialisation with its own techniques; it falls in the domain of information visualisation or graphic design and is beyond the scope of this post-article. Such maps can quickly become complicated, rich in detail, because there are many pathways that customers may follow in their journey. A common way to deal with the complexity, and in order to make journeys more accessible and vivid to managers is to identify “typical” customers with characteristic personal attributes and pathways they go through, and build accordingly exemplary profiles, also known as ‘personas’ (e.g., common in the area of user experience [UX]).

But it could matter on what type of input the profiles of these personas are based. Are methods of quantitative research for collecting relevant data from customers sufficient? Bruce Temkin, expert on customer experience and head of the consulting group by his name, recommends in his blog, Customer Experience Matters, that companies combine between input from discussions (‘think tank’) of their managers responsible for customer relationships, and data from customer research (e.g., in-depth interviews, ethnographic techniques). These steps would preferably be conducted in this order. It should be helpful, however, to use quantitative data to construct plausible journeys and identify most relevant and interesting customer personas. Surveys may not be economic and efficient as a method to collect detailed-enough data. Yet, surveys can be useful for at least characterising main stages in a journey as well as the channels and touchpoints engaged, that could still enable better generalisation or validity of the information. Even quantification of input collected during in-depth interviews can help to pinpoint more frequent activities or stages, and paths or links between them so as (1) to depict significant or salient journey scenarios; (2) to identify key segments; and (3) to construct more meaningful and realistic personas that managers can effectively rely upon in their planning. Relevant approaches and techniques may be learned from the areas of means-end chain models and path analysis, for instance of shoppers’ journeys in physical stores (i.e., a true physical journey that is nonetheless relevant in this context).

Better established maps of customer journey layout a chain of main stages as the foundation or “spine” of the journey, and then add more detail on specific activities, customer impressions and reactions, costs and benefits, etc. A map would be devised for each key segment or prototypical persona. Maps can get more complex as one tries to account for cycles in the flow of events and activities during the journey (e.g., initial exploration on a website, visit to a store, return to the website for more information, and so on). A model proposed by Forrester Research, for example, defines four primary stages in a customer journey: discovery, explore, buy, and engage. The general model distinguishes between reach channels used for discovery, depth channels appropriate for exploration, and relationship channels through which customers engage with the respective company over time (i.e., strengthening relationships). McKinsey & Co. define more explicitly their orientation: they offer a model named the “consumer decision journey”. It is a cyclic journey model which includes four main phases: (a) initial consideration set for research and learning; (b) active evaluation of alternatives; (c) the moment of purchase; and (d) post-purchase experience, which can cycle back through a “loop” of loyalty to purchase. Noteworthy about this model, it recognizes that consumers may check again new alternatives and update their consideration set during active evaluation.

The Big Data sphere is also recruited to the mission of mapping customer journeys. However, the approach taken in such applications tends to be more strictly focused on performance of particular tasks by customers with the client company (e.g., product enquiry and service). Furthermore, th0se maps seem to over-emphasise the role of touchpoints as used by customers, particularly digital ones, as the nature of data sources used dictates. Temkin (see above) criticises the interpretation of a customer journey map as a touchpoint map, as typically adopted in systems based on big data. He argues that concentrating on individual interactions is prone to lose sight of the “broader context of how that touchpoint fits within the overall goal and objectives of the customer.” Systems in the field do show links or transitions between touchpoints, but the maps provide a rather narrow viewpoint of the journey and its context.

A map may zoom for instance on a particular touchpoint such as a call centre (by phone) and show how many customers visited previously a webpage of the company and how many ended the journey at the call centre or proceeded to another touchpoint for completing their task. Conspicuous figures or pathways may start a discussion of what that means and what should be done to improve the experience. However, such applications “see” only computer-based channels or touchpoints associated with the company, that is, mapping strictly customer journeys of technological interactions with the company. What if the customer consulted a friend on the phone, responded to a TV ad, or visited a store? The effectivity of the maps relies also on strong connectivity between the different channels of communication and interaction operated by the company (e.g., PC website, mobile, phone call centre). Silos in the organisation can hamper the construction of journey maps. Finally, it is important to study not only what customers do but also how they perceive their own actions and their attitude towards them. It would help companies to tap into subjective sensitivities of customers about their behaviour and avoid infringing into areas of customer desired privacy.

Mapping the customer journey can be used to improve many aspects of decision processes and post-purchase experiences (e.g., foster linkage between physical stores and information through mobile devices). Focusing on the journey of customers for narrowly defined tasks that involve interaction with a company can help indeed in resolving concrete problems or issues in customer experience. Nevertheless, companies should also take a broader perspective to map the journeys of more elaborate processes and experiences that extend in time through a relationship with the company. Models should also avoid being too restricted to customer interactions with the company and explore interactions with other potential influencers.

Ron Ventura, Ph.D. (Marketing)

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