This study is focused on multi-channel shopping, which refers to the integration of various channels in the consumer decision-making process. The term was coined in the early 2000s to signify the integration of offline and online shopping channels. It has since evolved to encompass the proliferating number of channels and media used to formulate, evaluate and execute buying decisions. With the explosion of mobile technologies and social media, multi-channel shopping has indeed become a journey in which customers choose the route they take and which, arguably, needs to be mapped to be understood. Existing consumer decision-making models were developed in pre-internet days and have remained for the most part unquestioned in the digital marketing discourse. Darley, Blankson and Luethge concluded that there is a ‘paucity of research on the impact of online environments on decision making’, which has also been observed in the multi-channel context. Our study adopts an inductive approach allowing for realistic patterns to emerge of how consumers use and react to different media and channels in their shopping journeys for cosmetics. It therefore provides a threefold contribution: (1) it systematizes what are widely used yet largely misunderstood practices (ZMOT, webrooming and showrooming); (2) it defines the key multi-channel influences across different stages of decision making; and (3) it segments actual customer journeys into three distinct patterns that brands can use to optimize their multi-channel strategies.
Background and rationale
With an explosion of mobile technologies and social media, multi-channel shopping has become a journey in which customers choose the route they take and which, arguably, needs to be mapped to be understood. Existing consumer decision-making models were developed in pre-internet days and have remained for the most part unquestioned in the digital marketing discourse.1, 2, 3, 4 Recently, these new multi-channel consumer behaviours have led to fresh developments in marketing practice. These practical decision-making strategies emerged in response to the fact that the web is a place where consumers can collect information quickly and in a number of different formats. Some of the behaviours explored in this study are as follows.
1. Zero Moment of Truth (ZMOT) — This refers to the first exposure a user has to a product or service through various social media networks. It is a term coined by Jim Lecinski at Google, defined as ‘a decision-making moment that takes place a hundred million times a day on mobile phones, laptops and wired devices of all kinds. It’s a moment where marketing happens, where information happens and where consumers make choices that affect the success and failure of nearly every brand in the world’.5
2. Showrooming — Consumer behaviour of viewing a physical product in-store but deciding to purchase it online, possibly due to the ease of price comparison. This could result in consumers leaving the store empty-handed and placing an order online.6, 7, 8
3. Webrooming — Consumer behaviour where the research is conducted online on a stationary or mobile device, but the product is purchased in-store.9
Taken collectively, these effects indicate that people are exposed to increasingly complex multi-channel shopping journeys. This complexity, however, is added only from the marketer’s perspective. From a consumer perspective, those new behaviours have emerged as a way of simplifying the decision-making processes in the ever-expanding digital universe.
Our paper seeks to enrich and extend prior research on multi-channel marketing by adopting the consumer viewpoint. It is a much-needed addition to marketing discourse, complementing the growing number of academic and practitioner articles on the retailer’s view of multi-channel strategy.2, 10 More specifically, by adopting inductive research, we allow for realistic patterns to emerge of how consumers use and react to different media and channels in their shopping journeys. The aim of this paper, therefore, is to explore and map those journeys, benefiting both practitioners and academia. Such insights are of direct relevance to brands, to help them manage the customer experience better and to analyse channel attribution. It is imperative for brands to embrace the multi-channel experience, yet seamlessly integrating the physical and digital worlds is an ongoing chellange.11
Theoretical discussion — The funnel is dead, long live the loop
Consumers navigate channels in a way that suits them on any particular shopping occasion, and they expect retailers to be accessible through every touchpoint. In order to understand how ZMOT, showrooming and webrooming shape the customer journey, the interaction of customers across multiple channels needs to be examined. An empirical study by Frambach et al. has demonstrated that ‘the buying stage has an important influence on channel usage intention’.12 Consumers seek different benefits at the pre-purchase stage than during or after purchase. This can lead to dynamic channel preference during the whole buying decision process. While Frambach et al. focused on the dichotomy of online–offline, such dualism is now largely outdated, and the utilisation of the growing number of channels by consumers is yet to be examined in the light of consumer decision making.12
To understand fully consumer shopping behaviour and engagement with different touchpoints, the terms customer journey and consumer decision-making process must be clarified. A review of consumer decision-making models led to the identification of the general stages consumers are said to go through to reach (or reject) a purchase decision.13, 14 There are various consumer decision-making models, such as the AIDA model15, hierarchy of effects,16, 17 hierarchy of sequence18 and Howard Sheth buying behaviour model.19
One of the most-cited and widely known consumer buying process models is the five-stage consumer decision-making process.20 It consists of five stages a consumer is expected to go through during the process — need recognition, information searches, alternative evaluation, purchase and post-purchase. The model has been previously discussed in the online context,3, 4, 21 as well as multi-channel shopper segmentation.22 Because it is a schematic representation of the consumer’s cognitive stages, it is particularly applicable to ‘high-involvement products’. It has been recognized that the extent of involvement within a purchase decision does have an impact on the length and stages of the decision-making process. For high-involvement products, extended problem solving has been observed in past studies, but for habitual or emotional purchases the decision-making process can be much shorter.23
Having conducted a meta-analysis of research published about online consumer behaviour in marketing and consumer behaviour journals between 2001 and 2008, Darley et al.3 found that not a single study examined the parts of the decision-making process having to do with problem recognition, internal search, consumption or dis-investment, and only one study investigated cognitive dissonance. Clearly, there are many gaps in the extant literature, and it is proposed that the lack of research in this area may stem from the lack of applicability of existing models. Our paper proposes the customer journey as an alternative conceptualisation of consumers’ multi-channel behaviours.
According to Clark, a customer journey can be defined as ‘a description of customer experience where different touchpoints characterize customers’ interaction with a brand, product, or service of interest’.24 The classification of interactions often does not follow a linear structure, as ascribed by the decision-making literature. It also involves a number of channels and reflects the emotional, behavioural and cognitive responses present in the process. Figure 1 compares the different aspects that distinguish customer journeys from decision-making models.
Molenaar introduced an online consumer behaviour model that is a combination of buying stages and a non-linear collection of touchpoints present during decision making.25 The ORCA model demonstrates the concept of ‘shopping 3.0’ where consumers use various channels for information search and shopping. Many touchpoints are interconnected without a strict chronological order. This model presented in Figure 2, illustrates how many channels are utilized after problem recognition; information is gathered from search engines, websites and comparison sites before a purchase decision is made. This decision can then be executed through different distribution channels, including trading sites or a physical shop. While this is a very useful visualization, this model has not been validated academically and lacks reference to mobile and social media. Utility can, however, be gained from it when applied to multi-channel consumer journeys.
In the current study, first-hand consumer shopping experiences for cosmetics are analysed. Subsequently, the research maps actual shopping journeys inductively, utilizing the grounded theory approach. While the models discussed above are useful in identifying the simplified stages and sequences of consumer decision making, they are only used here to aid analysis. Explanatory utility is sought from both the linear buying stages within consumer buying models, with addition of emotional as well as impulse drivers and the fluid structure of the ORCA model. This will lead to an understanding of the role of each channel in the various stages, as well as the different journeys consumers can take to navigate the multi-channel landscape.
The research employed a multi-method approach, utilizing qualitative data collection methods. It aimed to explore first-hand reports of consumer shopping journeys for cosmetics products using two data collection methods: (1) personal diary and (2) interview. Darley, Blankson and Luethge assert that knowledge about online consumer behaviour could benefit from ‘(1) what people do or say in response to what people are presented with in an experiment; and (2) observed causality’.3 To this effect, first-hand accounts of multi-channel shopping journeys form the basis of this research. A cosmetics product context was chosen in order to provide respondents with a realistic shopping situation.
An inductive study using guided introspection was conducted first, asking 20 respondents (all women) to complete a cosmetics shopping diary. They were asked to record their thoughts, feelings and actions related to cosmetics products over 2 weeks, using everyday, personal language. A well-designed beauty diary with an example entry was given to each participant to incentivize them to participate. As it was an electronic diary, respondents were encouraged to incorporate multimedia — photos, videos, links.
The resulting sample of 16 research diaries was obtained and each one was followed with phase two: an individual interview to elucidate on diary entries and collect more targeted information. The data from both phases was analysed using thematic analysis. An encoding process for qualitative information resulted in a list of themes and was useful in discovering patterns in phenomena.26 The 16 responses were hand-drawn to represent each individual’s shopping journey. Respondents’ maps were then classified into segments, based on similarities and differences in their reported journeys.
Results and analysis
An aggregate analysis of all the reported shopping journeys helped identify the degree of influence each channel has during different stages of decision making. The process of mapping respondents’ self-reported shopping stages indicated that a single channel may reappear during the journey multiple times. Some channels may also be used simultaneously during one shopping stage. For cosmetics shopping, in particular, Table 1 presents the channels and information sources that have been identified as most influential at each stage.
Segmenting customer journeys
This aggregate analysis masks a myriad of shopping journeys, however, some extensive and high in channel-hopping and others shorter and low in information search. The subsequent analysis attempted to segment cosmetics shopping journeys based on journey type. All the journeys were mapped to detect patterns, and behavioural themes have emerged, leading to the identification of the following typology:
These journey segments coincide with some of the decision-making types proposed by Solomon23 in the offline setting, yet provide additional utility as the three journey types identified here have been inductively mapped as patterns in the multi-channel, multi-platform and multi-device environment. Each journey type is discussed in turn below and illustrated visually with a journey map.
During impulsive journeys, customers tend to spend less time searching for information. Instead, they refer to their previous experience, their friends and product trial as information sources to make swift purchasing decisions.
The intention to purchase can easily be affected by the customer’s mood or exposure to a new, attractive product display. Impulsive customers can feel overwhelmed when exposed to large amounts of data, which can push them to make an impulsive or emotionally driven decision. As the quote below illustrates, for some respondents cosmetics purchase is an on-the-spot decision taken in a physical store at a cosmetics counter (Figure 3).
… I love products with cute packaging. When I want to buy, I don’t really search for information online. I will just ask my friends and buy it at the cosmetics counter … I don’t have second thoughts on the purchase. I rarely shop online ….
An aspirational or reference group, such as friends, bloggers or celebrities, as well as traditional and digital media can trigger balanced journeys. Crucially, however, such journeys then exhibit an extended search for information and evaluation, which makes them distinct from impulsive journeys. Here customers initiate their intention to purchase through emotions and support their decision through cognitive evaluation. They often check information they find against a number of different sources across channels and platforms to arrive at a purchase decision. There is evidence of webrooming and showrooming during that process, as the quote below illustrates (Figure 4).
… I like watching bloggers and YouTubers. The products that they use look interesting but the information is just a brief product review. I Google for more in-depth reviews from blogs online. I also sometimes use the online store for references of colour swatches or product ratings. After I see the swatch and there is a store nearby, I would want to go in the store to try it out for myself. If not, I feel a bit more risk and take more time considering if I should buy the product. I will often ask my friends for advice ….
Considered journeys have an extended pre-shopping stage, where respondents do not think of themselves as shopping, but gather information from a number of sources, including news, product reviews, blogs and friends, which is then stored in their personal database. This information is then used to evaluate choices when a need or want arises. The Zero Moment of Truth (ZMOT) is most influential during these types of journey as it has an extended effect on the ultimate purchase decision by influencing the permission set customers have in their minds. The following quote illustrates the process of storing and retrieving information prior to purchase (Figure 5).
… Normally when I have free time, I will read forums on web boards and watch some YouTube videos, but I might not want to buy at that time. Whenever I want to buy products, I remember what I have read or watched and search for just specific information to make a decision ….
Role of ZMOT, webrooming and showrooming
One of the major findings of the study is the extensive evidence of what Lecinski5 termed the ZMOT and what Molenaar25 termed the Orientation Stage. This is a stage of shopping not explicitly identified in extant academic consumer decision-making models, yet one that in practice gives users first exposure to products and reviews and influences their opinions through media. Respondents reported that this happens before they think of themselves as shopping and is often seen as inspiration or ongoing horizon scanning for new trends and products.
In addition, two multichannel behaviours have been evidenced during customer journeys.
Showrooming has been reported to take place during product evaluation, where physical product attributes are important. In cosmetics, in particular, attributes such as colour and consistency of a lipstick are often evaluated in-store, as there is a limited return policy on health and beauty products once opened. The physical examination therefore reduces perceived risk of purchase, even if the product is eventually bought online.
Webrooming has been reported to take place once the initial product selection is identified. The web is then used as an online showroom where ease of price and product feature comparison can help to narrow down the consideration set further. The final purchase in this case is then completed in-store, where the final decision takes place.
All these behaviours have been reported by cosmetics purchasers in our study. There is evidently an urgent need to re-examine decision-making models in the light of new multichannel and multi-platform, and multi-device realities. More inductive studies are called for to help build theory through examining practice.
Conclusions and implications
Within the practical consumer decision-making strategies, the ever-present mobile technology and socially mediated nature of multi-channel shopping are becoming fully apparent. Understanding customer journeys has become a necessity to optimize resource allocation, help measure channel attribution and manage the multi-channel customer experience. Increasingly, customer journey maps are also used for marketing automation purposes to guide customers through the purchase funnel by matching marketing activities against stages in the buyer journey.
This study provides a threefold contribution to practitioners. First, it systematizes what are widely used yet largely misunderstood practices (ZMOT, webrooming and showrooming); second, it defines the key multi-channel influences across different stages of decision making; and third, it segments actual customer journeys into three distinct patterns that brands can use to optimize their customer-facing strategies. Customer segmentation such as that presented here can yield multiple buyer journeys and possibly different customer experiences — this will serve to match marketing strategy with the right content or message to the right customer at the right phase of the buying journey.
While this study specifically focuses on cosmetics as a context, a similar study is planned within other product categories, where involvement and risk perceptions differ, in order to understand the impact of these factors on customer journeys in a multi-channel environment. Further research is also planned quantifying some of the inductive conclusions developed in this qualitative study. During the next stage of this research, we will continue working with practitioners, and welcome further collaborations with business-to-consumer and business-to-business companies, agencies and not-for profits. If you would like to get involved, please get in touch with the corresponding author via @co_create.
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Cite this article
Wolny, J., Charoensuksai, N. Mapping customer journeys in multichannel decision-making. J Direct Data Digit Mark Pract 15, 317–326 (2014). https://doi.org/10.1057/dddmp.2014.24
- multi-channel shopping
- customer journey
- consumer decision making
- zero moment of truth ZMOT