Department of Policy and Planning Sciences
Discussion Paper Series

No.1347

Multichannel Shopper Segments of Purchase Channels
and Media Touchpoints using Single Source Panel Databy中野 暁、近藤 文代(Satoshi NAKANO and Fumiyo KONDO)

May 2017

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPANMultichannel Shopper Segments of Purchase Channels andMedia Touchpoints using Single Source Panel Data
Satoshi Nakano (University of Tsukuba/ INTAGE Inc.)
Fumiyo Kondo (University of Tsukuba)
Abstract
With changes in the retail environment due to the development of digital devices and social media, customers
can easily obtain appropriate information and purchase products from stores or online, which enables marketers to
improve access to their customers. The key issue for designing effective multichannel strategies can be tounderstand customers' purchase channels and media touchpoints simultaneously under such environment and toprovide more seamless connections between advertisement targeting and promotion.
This study provides customer segmentation using Latent-Class Cluster Analysis, which focuses on the purchasechannels of bricks-and-mortar stores and online, and the media touchpoints of PC, mobile and social media as well
as psychographic and demographic characteristics. This study extends the framework of prior studies from anempirical perspective through 1) providing a behavioral data based approach by using single source panel data, and2) analyzing uninvestigated products of low-involvement, more frequent purchased category.
The analyses of Japanese panel data revealed seven segments composed of four store-focused customers
including two research shoppers, one uninvolved shoppers and two multichannel enthusiasts. The results suggestthat differences in psychographics divide the research shoppers into the opinion-seeking type and thetime-constrained type. Similarly, the multichannel enthusiasts segment is divided into the price-conscious type whopurchased more at online stores using PCs, and the innovator type who shopped with enjoyment using all media
actively.

Keywords
Multichannel, Shopping behavior, Segmentation, Latent-Class Cluster Analysis, Single Source Panel Data

1. Introduction
There are many opportunities for customers to shop through a variety of channels owing to developments indigital media in the past ten years. In multichannel, multimedia retailing environments, customers can easily obtainappropriate information and purchase products from stores or online. Under these circumstances, individual
differences in purchasing behavior and information search routes are widening due to the diversification oflifestyles and IT literacy levels. On the other hand, for firms, the recent digitalization of marketing is a big change(Leeflang et al., 2014). The firms that take advantage of mobile and social media for promotions to target
individual customers are increasing. In this marketing environment, it is useless to capture customer behavior on
average. Neslin and Shanker (2009) addressed the issue of “how firms should segment customers in a multichannel
environment” and that is more important now
As the digitalization of marketing evolves, an important challenge is the integration of purchase channels andmedia touchpoints seamlessly. Purchase channel refers to the place where a product is purchased. Purchasechannels such as a bricks-and-mortar store or online are influenced by the kind of media touchpoints the everyday
customer uses. In an environment where it is easy to approach individual customers digitally, it is possible for firms
1＊＊＊

中野暁 ：筑波大学大学院
近藤文代 ：筑波大学

システム情報工学研究科 社会工学専攻/ 株式会社インテージ

システム情報系

社会工学域

E-mail：nakano-s@intage.co.jp

to implement media advertising plans to easily target and reach customers by their purchasing habits andinformation consumption patterns. Concerning multichannel, multimedia environments, Dholakia et al. (2010)
stated that “It is important to understand how customers utilize the multiple media and channels available to them,
manage their complementarities and conflicts, and come to rely on particular media and channels.” For example, itis easy to imagine that a person who almost never uses digital devices would have a hard time purchasing products
online. If it is possible for a firm to understand customer characteristics in purchase channels and media
touchpoints, it will then grasp the fundamental parts of a customer's activity style, which is a prerequisite todeveloping policies for individual brands and stores. Hence, it is very important in terms of marketing activities.
Under these conditions, customer segmentation is an effective way for planning channel strategies (Neslin et al.,2006). Konus et al. (2008) presented a clear case study for multichannel customer segmentation and showed its
managerial value. They proposed a Latent-Class Cluster Analysis framework that classifies customers based onchannel preferences in the stages of information search and purchase, and explain the segments from both apsychographic and demographic perspective. Their analysis, which uses online survey data, resulted in threesegments of customers—multichannel enthusiasts, uninvolved shoppers, and store-focused customers. The
segmentation framework of Konus et al. (2008) was later refined in several ways by other researchers. For instance,
Wang et al. (2014) hypothesized that the effect of psychographic and demographic covariates was different whensearching for information and making purchases. Keyser et al. (2015) and Sands et al. (2016) considered theafter-purchase process. Sands et al. (2016) also captured media channels more finely and expanded the scope by
including mobile and social media. Their research made a new contribution by taking into account the research
shopper, who researched online and purchased offline (Verhoef et al., 2007), and differentiated segments by mobile
and social media usage.
However, there are certain limitations to these studies because the results are based on self-reported surveys.
Attitudes reported in self-reported surveys do not necessarily match actual behavior (cf. Presser, 1990; Prior, 2009).
The problem of common method bias pertaining to survey data has been pointed out, and additional validation
based on behavioral and longitudinal data has been recommended as a future study issue (Konus et al., 2008; Wanget al., 2014). Considering the recent data utilization environment, the processes of collecting behavior logs and thenturning these data into single source data linked to each individual, have evolved rapidly (Taneja and Mamoria,2012). Single source panel is an example, in which survey monitors collect media contact logs at the same time as
they collect purchase scan panel data. The advantages of single source panel are that they capture customers on a
behavioral basis as well as at an individual level. In addition, because it is longitudinal panel data, it can capture theentire picture of a market, including competitive products. Furthermore, the findings obtained from the behavioral
data are suitable for digital marketing. Targeting methods are incorporated directly into the behavioral history data
existing on the data management platform and the findings can also be applied to marketing actions. In this context,
this study proposes new segmentations using single source panel data.
We focus, also, on product categories that have not been studied in our analysis. Past studies on multichannel
customer segmentation analyzed high involvement products such as clothing, electronics, holiday travel, computers,
books, mortgages, and insurance. Online marketplaces for these categories matured relatively early. Therefore, they
were suitable categories for research at that time. However, in recent years, there has been remarkable growth in the2low involvement, more frequently purchased categories such as groceries, cosmetics, and sundries. In fact, in theJapanese market, according to the results released by a 2015 Ministry of Economy, Trade and Industry survey1), theonline purchase rate of growth from the previous year, in descending order, were as follows: groceries,
beverages/alcoholic beverages, clothing/accessories, office supplies/stationery, and cosmetics/drugs. Accumulationof knowledge on low involvement, more frequently purchased categories is a challenge under this changing
business environment (Sands et al., 2016), and this study focuses on these categories for analysis.
This study proposes a customer segmentation method that focuses on purchase channels and media touchpoints.
We assess types of purchasing channels—bricks-and-mortar stores and online stores—and three types of media
touchpoints used for information search—mobile, PC, and social media. This research extends frameworks
proposed by previous research from two viewpoints. The first is to propose a behavior-based segmentation method
using single source panel data. The second is to validate the method with the low involvement, more frequently
purchased category. In addition, this study deals with a Japanese market whose segmentation in this kind has neverbeen conducted. Therefore, the objectives of this study are to answer the following questions: (1) How cancustomers be classified on the basis of purchase channel usage and media touchpoints? (2) What is the relationshipbetween customer’s psychographic and demographic covariates and segment membership?
The organization of this paper is as follows: Section 2 presents a review of previous studies concerning
multichannel customer segmentation. Section 3 outlines the concepts and methods of this study. Section 4 presents
the analysis results, and Section 5 discusses and summarizes the results and raises issues for future study.

2. Multichannel Customer Segmentations
2.1 Literature Reviews
In prior empirical studies, a number of segments were identified for multichannel customers. These segments
have a basis on preferred channels of shopping and clear definitions assigned. For example, Keen et al. (2004)
derived four segments of Generalists, Formatters, Price sensitives, and Experiencers. Generalists are averagepeople for whom psychographics and demographics do not really influence channel selection. Formatters only
shop at bricks-and-mortar stores. Price sensitives tend to select low-price channels. Experiencers tend to selectchannels where they have had a satisfying experience in the past. Thomas and Sullivan (2005) used the corporate
database of a U.S. retailer that operated through three channels (stores, online and catalogs) and identified two
characteristic segments: 1) the catalog segment, and 2) the bricks-and-mortar segment. Further, Ganesh et al.
(2010) derived a number of shopper types from their differences in motivation in relation to online or offline
shopping.
However, most of the previous studies focused only on segmentation based on purchase channel preference.
Stages in the purchasing process, such as the information search stage and the purchase stage, were not taken into
account. To rectify this, Konus et al. (2008) proposed a method of customer segmentation using purchase channels(stores, online and catalogs) and purchase stages. They used Latent-Class Cluster Analysis, which can help identify
segments and encompass psychographics and demographics that influence segment membership for segmentation.
In their study, they surveyed Dutch customers regarding multichannel purchasing using stores, the Internet, and3catalogs to purchase several product categories such as mortgage, health insurance, holidays, books, computers,
electronics, and clothing. They identified three clear segments: 1) Multichannel enthusiasts who have a hightendency to use all channels and have a high level of innovativeness and shopping enjoyment; 2) Store-focusedcustomers who have a high tendency to use stores and have a high level of brand and channel loyalty; and 3)
Uninvolved shoppers who have a low tendency to use any channel and low purchasing involvement.
The concepts clarified by Konus et al. (2008) evoked a number of studies. Wang et al. (2014) extended theconcepts by exploring the possibility of perceived values being different between the information search stage andthe purchase stage. Their results indicate the existence of two segments: innovative customers and conventionalcustomers, from the channel usage standpoint of online vs. offline. The results of their analysis were based on
surveys of Chinese consumers that asked their preferences in apparel, computers, television sets, jewelry, toys,books, MP3/MP4 players, headphones, and cars. Keyser et al. (2015) surveyed information search, purchase, andafter-sales preferences in stores, the Internet, and call center channels, with customers of a Dutch telecom retailer
that sells mobile devices and their accessories. They included after-sales aspects in its scope. The results of theanalysis revealed six segments: Research shoppers after-sales-store, Web-focused shoppers, Store-focused
shoppers, Research shoppers after sales-Internet/store, Web-focused shoppers after sales-store/call center, andCall center-prone shoppers. Their major finding is the identification of research shoppers. Research shoppers arecustomers that use one channel for information search and a different channel when making purchases (Verhoef etal., 2007). A general pattern in this case would be to search for information on the web and then purchase atbricks-and-mortar stores. Verhoef et al. (2007) posited that there are three forces in the background of research
shopping behavior. These are attribute differences (e.g., the web offers the convenience of flexible searches for avariety of information; bricks-and-mortar stores allow shoppers to actually see and feel the product, and theirprivacy is protected), customer lock-in (e.g., it is much easier to leave a website than to leave a store while being
waited on by a salesperson), and cross-channel synergy (e.g., it is much easier to purchase a product in a store if
you have already compared products online and decided which one to purchase). Sands et al. (2016) took media
characteristic differences into account when classifying these research shoppers. They extended their scope ofinquiry to the Internet, mobile, and social media contact behavior. They surveyed Australian customers on clothing,consumer electronics, and holiday travel. Their results identified two groups using two purchase channels, the
ROPO (Research Online, Purchase Offline) segment and the Internet-focused segment. Furthermore, they
demonstrated that each of these could be divided into five subgroups based on the differences in their media usage:
ROPO anti-mobile/social media, ROPO multichannel enthusiasts, ROPO social media enthusiasts,
Internet-focused anti-mobile enthusiasts, and Internet-focused multichannel enthusiasts.
Konus et al. (2008), in their initial research, took purchasing stages into account. However, their output was
expressed in a way that depended on the number of channels; that is, the concern was about “Which channels are
preferred?” and whether the preference is for “Single channel or multichannel?” Recent studies by Keyser et al.
(2015) and Sands et al. (2016) identified people who use different channels for information search and for purchaseas research shoppers. Moreover, Sands et al. (2016) demonstrated that suggested approaches to customers who
obtain information from media channels that also include mobile and social media, can be segmented at the finegranular level.

42.2 Conceptual Framework
Figure 1 shows the conceptual framework for this study. This study uses single source panel data, wherepurchase scan panel data, media log data, and surveys are associated with the same ID to materialize thisframework. The novelty of the present study is that it adopts the approach of integrating behavioral data andsurveys.

Figure1. Conceptual Framework
The proposed conceptual framework (Figure 1) assumes that the usage rate of purchase channels and media
touchpoints depends on the channel utility that the customers obtain from purchasing and information contact. The
considered purchase channels are bricks-and-mortar stores and online stores. The media touchpoints used in thisstudy were mobile, PC, and social media similar to the study by Sands et al. (2016). The purchasing frequency atbricks-and-mortar stores and online stores corresponds to the purchasing phase in the purchasing behavior process,and the usage of mobile, PC, and social media to the information searching phase. In this study, to grasp thefundamental parts of a customer's activity style, for the purchase channel, we used overall purchase volume by eachpurchase channel in low involvement, more frequently purchased categories, and overall usage by each media formedia touchpoints.
After segmenting the customers from channels utilities, we identify the psychographics and demographics
contributing to those segment memberships. For this purpose, we use the demographics of gender, age, marital
status, number of family members, number of children, education, and household income, and the six
psychographics proposed by Konus et al. (2008). The first psychographics is Innovativeness. This variable
expresses the propensity to try new and different products and to seek new experiences (Midgley and Dowling,1978). The second is Brand/retailer loyalty. This variable expresses the tendency to continue using specific brands
and channels (Ailawadi et al., 2001; Klemperer, 1995). The third is Motivation to conform (opinion seeking). Thisvariable expresses the extent to which consent from others is required while making a purchase decision (Ailawadi
et al., 2001; and Chandon et al., 2000). The fourth is Shopping enjoyment. This variable expresses the extent towhich entertainment and emotional benefit is sought (Babin et al., 1994). It expresses the hedonic value of
shopping. The fifth is Time pressure. This variable expresses the scarcity of the customer's time resources. It is5known that people who lack the time tend to shop in a planned way (Kleijnen et al., 2007). The final variable isPrice consciousness. This variable expresses the degree to which customers focus on paying low prices(Lichtenstein et al. 1990). Customers have particular perceptions regarding the prices of products in particularchannels, and this influences channel selection (Verhoef et al., 2007).

3. Method
3.1 Data collection
We used the single source panel data (i-SSP) obtained from Intage Inc.2), one of the largest Japanese marketing
research companies. The i-SSP consists of a purchase scan panel data set called SCI, and log data of mobile/PC
media contacts. These are linked with the same individual monitor ID as the key. The population of this panel isInternet users. Internet users cover 82% of the Japanese population in 2016, according to Intage Inc.’s survey3). The
subjects of the panel are sampled by assigning gender, age and area to comply with the composition of the Internet
population, assuring market representation. A total of 2,595 individuals (males and females) between the ages of 15
and 69 living in Japan were collected, and the data period was six months between April 1, 2016, and September 30,
2016 (183 days).
The SCI contains behavioral data that were retrieved by panel monitors who record purchased products bybarcode scanners that have been distributed to them. We use all products data of transected categories in SCI except
perishable categories. Those are groceries (staple foods, seasonings, and processed foods, but did not include fresh
fish, vegetables, or prepared box lunches), beverages (milk-based drinks, soft drinks, and alcohol), sundries(household good, paper goods, personal care items, baby-related goods, and pet-related goods), cosmetics, anddrugs. The mobile/PC data are retrieved automatically as log data that supplement monitors’ behavior constantly
via an application in the installed monitors’ terminals. Furthermore, psychological attributes are obtained by an
online survey administered collaterally to the monitors in October 2016.

3.2 Definition and measurement of covariates
This section describes the variables used in this study in detail. First, purchase frequency in bricks-and-mortar
stores and online stores is used as the indicator of purchase channels. Purchase frequency is the number of purchasedays in bricks-and-mortar stores and online stores, respectively, during 183 days. The bricks-and-mortar store
channel includes supermarkets, convenience stores and drugstores, department stores, and specialty stores. The
online channel includes Internet supermarkets, e-Commerce sites, and direct sales sites of brands. Second, media
usage duration is used as the indicator of media touchpoints. Media usage duration is the average number of daily
usage duration (minutes) of mobile and PC. For the touchpoint of social media, media usage duration (minutes) isused as the indicator of social media touchpoints of Facebook, Twitter, Instagram, and Mixi, which are most often
used in the Japanese market. Regardless of whether these sites were accessed via applications on mobile devices orPC, we categorized their usage duration as time spent on social media.
For demographics, we used gender, age, marital status, number of family members, number of children,6

education, and household income. Gender and marital status, were expressed by dummy variables where 1
represents “male” or “married,” respectively. Age and income were expressed by categorical variables. Number offamily members, number of children, and education (the term of study) were expressed by continuous variables.
To create psychographic variables, we first surveyed the panel monitors using a questionnaire same to the oneused by Konus et al. (2008) (as detailed in Table 1). Measurements were obtained by using a 5-point Likert scale (1= fully disagree; 5 = fully agree). The results of the reliability analysis showed that Cronbach’s alpha for multi-item
scales were all greater than 0.7 except for motivation to conform (see Table 1). Although the value for “motivation
to conform” was 0.63, it was adopted for this study, in the same way as the adoption by Konus et al. (2008) withthe value of 0.64 for “motivation to conform.” Next, we carried out principal component analysis (PCA) on each of
the questions making up the structural concept (see Table 1). The principal component scores extracted here wereused as psychographic variables.
Table1. Results of principal components analysis and reliability analysis (psychographic variables)
Innova-tivenessI am one of those people who try a new productfirstly just after launch.
I like to try new and different products.
I always have the newest gadgets.
I find it boring to use the same product(for brand) repetiively.
I regularly purchase different variants ofa product just for change.
I have favorite brands that I keep buying frequently.
The brand of the product is important for me
in my purchase dicisions.
I generally purchase the same brands.
The place where I do my shopping is very important
to me.
I generally do my shopping in the same way.
I find it very boring when other people
criticize my behavior.
Being accepted by other people is very important to me.
I like to have some problems that I can solve
without much thinking.
I like shopping.
I like shopping for groceries and commodity goods.
I take my time when I shop.
I am always busy.
I usually find myself pressed for time.
I compare the prices of various products
before I make choice.
It is important for me to have the best pricefor the product.

Loyalty

Motivation Shopping
to conform enjoyment

Time
Pressure

Price con Reliability-sciousness (C. Alpha)

0.81
0.80
0.75

0.77

0.67
0.58
0.77
0.73
0.74

0.70
0.69
0.61
0.79

0.63

0.73
0.70
0.87
0.86
0.83

0.81
0.92
0.92

0.81
0.86
0.76
0.84

3.3 Latent-Class Cluster Analysis
This study employed the same Latent-Class Cluster Analysis method as the one used by Konus et al. (2008) andSands et al. (2016). In this model, as shown in Equation 1, the number of latent classes corresponding to purchasechannels and media touchpoints was specified as K, and the effect covariate 𝑧𝑖 on membership to each latent classis examined. As shown in Equation 2, the multinomial logit model was used for the probability of segment

7

memberships. Therefore, it is called a latent-class multinomial logit model. This method is explained in detail inprevious studies like Vermunt (2010), Collins and Lanza (2009), and Hagenaars and McCutcheon (2002). We used
Latent GOLD 5.1 software for analysis (Vermunt, 2010; Vermunt and Magidson, 2013).
The purchase days used in this study had a discrete value, and the media usage duration had a continuous value.
Poisson distribution was employed to handle the number of times events emerged within the observed period, suchas the number of uses of purchase channels. Therefore, this study employed a mixed distribution, setting a Poisson
distribution for 𝑔1 , 𝑔2 and setting a normal distribution for 𝑔3 , 𝑔4 , 𝑔5 . Note that in the latent class model, the sumof the probability of segment memberships 𝜋𝑚 becomes 1, as shown in Equation 3.

𝐾

4𝑓(𝑈𝑖𝑐 | 𝑧𝑖 ) = ∑ [∏ 𝑔𝑐 (𝑈𝑖𝑐 | 𝑧𝑖 , 𝑠𝑖 )] 𝑝(𝑠𝑖 = 𝑚|𝑧𝑖 )(1)

𝑚=1 𝑐=1𝑝(𝑠𝑖 = 𝑚|𝑧𝑖 ) =𝐾𝐾

exp(𝑧𝑖′𝛾𝑚 )′∑𝐾
𝑙=1 exp(𝑧𝑖 𝛾𝑙 )4∑ 𝜋𝑚 = ∑ [∏ 𝑔(𝑈𝑖𝑐 | 𝑧𝑖 , 𝑠𝑖 )] = 1
𝑚=1

𝑈𝑖𝑐

(2)

(3)

𝑚=1 𝑐=1

Customer i’s perceived utility of purchase channel and media touch points c.

c={1:stores purchase, 2:online purchase, 3:mobile usage, 4:PC usage, 5:social media usage}
𝑠𝑖

Indicator of customer i’s segment, equal to 1,2,…,K, where K is the number of segments.

𝑧𝑖

Customer i’s covariate vector of psychographic and demographic.

𝑓(𝑈𝑖𝑐 | 𝑧𝑖 )Probability distribution for customer i’s perceived utility of purchase channel and media touch points
c, given the customer’s antecedent variables.

𝑔(𝑈𝑖𝑐 | 𝑧𝑖 , 𝑠𝑖 )Probability distribution for customer i’s perceived utility of purchase channel and media touch points
c, given the customer’s antecedent variables and given that the customer is in segment 𝑠𝑖 .

𝑝(𝑠𝑖 = 𝑚|𝑧𝑖 )Probability that customer i is in segment m, given the customer’s antecedent variables.

4. Results
4.1 Multichannel customer segmentation
This study used the Bayesian information criterion (BIC) for determining the number of segments. BIC isconsidered to be more effective than other information criteria (such as AIC) for Latent-Class Cluster Analysis(Vermunt and Magidson, 2013; Collins and Lanza, 2009). The results of log likelihood and BIC are shown in Table
2. We obtained a minimum value of BIC for the 7-cluster model, which was selected as the best model in thisstudy.

8

Table2. BIC for each model

Model 1Model 2Model 3Model 4Model 5Model 6
Model 7
Model 8Model 9LL
-5588.7-5056.1-4676.6-4393.8-4185.4-4061.6-3957.7-3894.3-3858.2

1-Cluster2-Cluster
3-Cluster4-Cluster5-Cluster
6-Cluster7-Cluster
8-Cluster
9-ClusterBIC
11287.5
10395.3
9809.19416.49172.89097.99063.29109.29210.1

Table3. Cluster profiles of purchase days and media usage duration for each cluster (n=2595)
Cluster1 Cluster2 Cluster3 Cluster4 Cluster5 Cluster6 Cluster7 the Overall
Mean
21.3%
19.0%
15.7%
15.7%
15.4%
6.5%
6.4%
Purchase channels Store
101.3
58.2
71.1
143.2
27.9
58.7
123.8
82.3
Online
1.1
0.9
0.7
0.4
0.8
16.5
11.8
2.5
Media touchpoints Mobile
137.8
139.2
223.8
176.5
164.1
148.0
176.2
164.8
PC
55.5
46.7
130.5
102.0
80.3
90.2
99.5
81.8
Social Media
2.9
2.4
58.3
19.1
18.4
12.1
27.3
18.6
These are the total number of purchase days during the six months, and the average number of minutes of media usage per day. The
values in bold font are higher than the overall average.

Table 3 shows cluster profiles of purchase days and media usage duration. In terms of purchase channel
preferences, we observed similar classifications by having groups of the store-focused customer, uninvolved
shoppers, and multichannel enthusiasts proposed by Konus et al. (2008). The final clusters were obtained by further
classifications into subgroups based on the level of their media usage. Cluster 5 is only one cluster that can be
categorized as uninvolved shoppers (15.4%) with low frequency of purchase channel use. Media usage of
uninvolved shoppers was close to the overall mean. Cluster 1, cluster 2, cluster 3, and cluster 4 can be categorized
as store-focused customers who use bricks-and-mortar stores often. Cluster 1 and cluster 2 can be anti-digital
customers who have less usage of any media than the overall mean in terms of the media usage level. Therefore, we
named store-focused/anti-digital customers for cluster 1 (21.3%), and store-focused light/anti-digital customers forcluster 2 (19.0%). Cluster 3 and cluster 4 can be multimedia customers whose media usage is higher than theoverall mean. Cluster 3 has the highest usage of all of the media among all segments, and they also used social
media for almost one hour per day. We named store-focused light/multimedia social customers as cluster 3 (15.7%).
Media usage by cluster 4 was found to be lower than that by cluster 3, but was higher than the overall mean in all
media. We named store-focused/multimedia social customers as cluster 4 (15.7%). Cluster 6 and cluster 7 can beconsidered as multichannel enthusiasts who purchase from both bricks-and-mortar stores and online stores. The
online store usage frequency of cluster6 was the highest among all segments, exceeding six times the overall mean.
Further, their usage of PC was higher than the overall average, although their usage of mobile and social media waslower than the overall mean. We named cluster 6 as online-favored multichannel enthusiasts/PC customers (6.5%).
Bricks-and-mortar store usage frequency of cluster 7 was 1.5 times the overall mean, and their online store usage
frequency was also 4.7 times the overall mean. Their usage of all media was higher than the overall mean. We

9named store-favored multichannel enthusiasts/multimedia social customers as cluster 7 (6.4%). The cluster names
are summarized in Table 4.
Next, we discuss cluster composition ratios. Results of the research by Konus et al. (2008) showed 23%
store-focused customers, 40% uninvolved shoppers, and 37% multichannel enthusiasts; however, our results
indicated there were 72% store-focused customers, 15% uninvolved shoppers, and 13% multichannel enthusiasts. It
can be surmised that the difference in cluster composition ratios is related to product categories. Konus et al. (2008)
studied high-priced specialty products, while we studied groceries and sundries. According to the results of thee-Commerce Market Survey 2015 conducted by the Japanese Ministry of Economy, Trade and Industry1), the EC
ratio, which indicates the percentage share of e-Commerce in the market scale, was 2% for groceries and 4% for
sundries, which is lower than household appliances (28%), clothing (9%) and books (22%). Because groceries andsundries are goods that are consumed daily and have low e-Commerce ratio categories, our results can be valid, sothat it is natural to have more store-focused customers and less multichannel enthusiasts than those in the results forhigh-priced specialty products in the prior study.
Further, cluster 3 and cluster 4 are close to the research shopper identified by Sands et al. (2016) and Keyser et al.
(2015). These research shoppers actively gather information online, but make purchases at bricks-and-mortar stores.
In addition, Sands et al. (2016) identified Internet-focused/anti-mobile customers, who actively purchased online,
but did not use mobile devices for information search. Cluster 6 is close to this segment. Cluster 6 customers
mainly use PCs when making online purchases.
Table4. Cluster Names
Cluster1
Cluster2

Purchase channels
Store-focused customers
Store-focused Light customers

Media touchpoints
anti-digitalanti-digital

Cluster3
Cluster4
Cluster5
Cluster6
Cluster7

Store-focused Light customers
Store-focused customers
Uninvolved shoppers
Online-favored multichannel enthusiasts
Store-favored multichannel enthusiasts

multimedia/social
multimedia
averagePC
multimedia/social

4.2 Interpretation of the covariates
Table 5 shows the results of the psychographic and demographic covariates. The results show the impact of eachof the covariates on segment membership. A strong positive coefficient means that customers with a high score inthat covariate would be more likely to appear in that segment. A large (magnitude) negative coefficient means that
customers would not be likely to be in that segment.
According to the entire model, significant psychographic covariates are innovativeness (p < 0.01), time pressure(p < 0.05), and loyalty (p < 0.10). Significant demographic covariates are gender, age, household income (p < 0.01),
and number of children (p < 0.05). On the other hand, the psychographic covariates—motivation to conform,
shopping enjoyment, and price consciousness—, and the demographic covariates—marital status andeducation—are not significant.

10Table5. Estimates of parameters

Intercept
InovativenessLoyalty
Motivation to conform
Shopping enjoyment
Time pressure
Price consciousness
Gender(male)Age
15-24 years
25-34 years
35-44 years
45-54 years
55-69 years
Martial status(married)
Number of family members
Number of children
Education
Household income (million yen)less than 3.99
4.00-5.49
5.50-6.99
7.00-8.99
more than 9.00

Cluster1

Cluster2

Cluster3

Cluster4

Cluster5

Cluster6

Cluster7

Wald

p-value

0.427
0.008
0.020
0.014
0.059
0.034
0.018
-0.1710.837-0.082-0.067-0.005-0.061
0.041-0.0160.164-0.075-0.076-0.1200.101-0.024-0.1170.019-0.105-0.8180.106-0.025
0.027
0.031
0.137
-0.1410.826-1.442-0.189-0.0980.035-0.0950.085-0.022
0.754

0.358
0.013
0.126
-0.009-0.077-0.0780.101-0.998

0.714
0.220
0.164
-0.1630.167-0.1030.041-0.469

15.36
22.44
11.37
5.17
6.68
13.52
6.15
110.94

0.02
0.00
0.08
0.52
0.35
0.04
0.41
0.00

-0.131-0.158-0.163
0.067
0.386
0.079
0.025
0.198
0.003

-0.0030.094
0.068-0.142-0.0180.295-0.0080.011-0.0421.163
0.192-0.130-0.400-0.8250.017-0.0010.103
0.036-0.648
0.138
0.094
0.482
-0.066-0.2770.175-0.103
0.010

1.390
0.369
-0.221-0.809-0.7300.111
0.269-0.1540.046-0.110-0.538-0.0180.152
0.514-0.155-0.2710.130
0.016-1.662-0.098
0.370
0.650
0.739
-0.070-0.188-0.185-0.069163.00

0.00

9.59
33.81
13.46
6.98

0.14
0.00
0.04
0.32

-0.013-0.0160.115
0.026-0.112

0.039
0.118
0.060
-0.097-0.1190.221-0.2490.007
0.105-0.0840.161
0.337-0.050-0.054-0.3940.014
0.155-0.018-0.112-0.039-0.4900.205
0.081-0.1210.324

0.069-0.550-0.195
0.253
0.423

51.52

0.00

Coefficient values exceeding +/-0.1 are shown in bold font.

Next, let us look at the results by cluster. Shoppers in cluster 5 (uninvolved shoppers) are young married maleswith many family members and a low income. They have low tendencies towards innovativeness, shopping
enjoyment, and loyalty. Konus et al. (2008) suggested that uninvolved shoppers have low loyalty and low shopping
enjoyment, tendencies that are also observed in this study. Shoppers in cluster 1 (store-focused/anti-digital
customers) are elderly females with many children and medium incomes. Shoppers in cluster 2 (store-focusedlight/anti-digital customers) are married males with medium income. However, psychographic covariates do nothave a strong influence. Shoppers in cluster3 (store-focused light/multimedia social customers) are young femaleswith a relatively large number of children, and there is bipolarization of income from low to relatively high (7.00–
8.99 million yen). They have high motivation to conform and a low tendency to loyalty and time pressure. They use
social media often, and it is assumed that they are opinion-seekers who tend to follow the opinions of others.
Shoppers in cluster 4 (store-focused/multimedia customers) are middle-aged males in an inverse U shape, with arelatively large family size and low income. They are innovative, but do not have time to spare, and have low priceconsciousness. This resembles the research shopper who often uses mobile devices and social media identified by
Sands et al. (2016) in terms of gender and age. They have high innovativeness and low time pressure as well as low
price consciousness. Shoppers in cluster 6 (online-favored multichannel enthusiasts/PC) are female, elderly, large
number of children, with a bipolarization of income from low to high, with high loyalty and consciousness. Theyuse PCs to search for good-quality products at low prices online. This cluster resembles the tendencies of“Internet-focused/anti-mobile customers” identified by Sands et al. (2016) in terms of gender, age, and high price11

consciousness. Shoppers in cluster 7 (store-favored multichannel enthusiasts/multimedia social) are females,
middle-aged to elderly, with high incomes, and tend to have a small family size and few children. They have highinnovativeness, loyalty, and shopping enjoyment. They have a relative surplus of time, with low opinion-seeking
tendencies. It is conceivable that they have their own sense of value and that they are multichannel customers with
abundant information contacts. Konus et al. (2008) demonstrated that multichannel enthusiasts have innovativenessand shopping enjoyment. This study also obtained similar findings in cluster7. On the other hand, the results
indicated that the multichannel enthusiasts, in cluster6, who prefer online purchasing, have low innovativenesstendencies.

4.3 Practical implications
The segmentation in this study has reproducibility of prior studies, and at the same time, obtains deeper insights.
This study discovered two subgroups in both the research shoppers and multichannel enthusiasts. Our findings havepractical applications for these segments as marketing targets.
Research shoppers were divided into “opinion-seeking type” (cluster 3) and “time-constrained type” (cluster 4)
according to those characteristics. The opinion-seeking types make active use of social media, tend to follow theopinions of others, and have low-loyalty tendencies. They are influenced by others. On the other hand, thetime-constrained types have innovativeness, do not have time to spare, and have low price consciousness. It isinferred that in addition to feeling that collecting information at bricks-and-mortar stores is a chore, these people
are taking advantage of the Internet, where it is easier to obtain information (Verhoef et al., 2007).
Among multichannel enthusiasts, we discovered the “price-conscious type” (cluster 6) and the “innovator type”(cluster 7). The price-conscious types use PCs to carefully select good-quality products and inexpensive products
and employ multiple channels to make purchases. In addition, because they have loyalty, it is surmised that it ispossible for firms to lock them in by providing products that they like. Because it is possible to provide anabundance of product information on PC websites, it is possible to attract these people by creating websites that
focus on high quality. Meanwhile, the innovator type actively uses mobile/social media and seeks shopping
enjoyment. Further, they have low opinion-seeking tendencies. They can judge information by their own sense ofvalue, making it possible to tell that they are people who influence others. We believe that there are advantages toproviding information to them that is communicated by mobile screens and by short messages on social networks,
which motivates them intuitively.
The key points for the practical application of these results by retailers are 1) to understand who are “the people
who judge information by their own standards” and who are “the people influenced by others” and, 2) to consider
how to output information on media and devices. The innovator type multichannel enthusiasts correspond to thepeople who judge information, and it is critical to acquire this type of customer. These customers become a hub,and if they also post good evaluations such as reviews on the Internet, it becomes possible to attract research
shoppers (in particular, the opinion-seeking type). Moreover, for media and devices, it is necessary to understandthe characteristics of PC and mobile channels. An important finding of our study is that even though mobile usage
has been growing in the recent media environment, a constant number of price-conscious type multimedia

12enthusiasts exist, who carefully research a wealth of information on PCs. However, one cannot overlook innovator
type enthusiasts who are suited for mobile promotions. It is necessary to first get an understanding of the target
customers, and then tailor the quality of information on the media and devices to be used.

5. Conclusion
This study provided customer segmentation using Latent-Class Cluster Analysis that focuses on purchasechannels and media touchpoints. Further, we identified psychographics and demographics that impact segment
membership. This study extended the framework of prior studies from an empirical perspective through thefollowing two points: 1) eliminating the common bias that exists in a survey method due to the difference between
intention and actual behavior by using single source panel data, and (2) providing an empirical analysis ofuninvestigated products of low-involvement, more frequent purchased category. Here is a summation of the resultsfrom two possible outcomes.
The first question is how customers can be classified on the basis of purchase channels and media touchpoints.
This study identified seven segments. From the standpoint of purchase channel use behavior in bricks-and-mortar
stores and online stores, the seven segments were consistent with the segments proposed by Konus et al. (2008).
The seven segments in our study were composed of four store-focused customer segments, one uninvolved shopper
segment, and two multichannel enthusiast segments. Furthermore, from the consideration of media touchpoints,
two store-focused consumer segments were identified as research shoppers, who used many online information
contacts but made actual purchases at bricks-and-mortar stores. This is consistent with the frameworks of Sands etal. (2016) and Keyser et al. (2015). Further, we found that among multichannel enthusiasts, the people who used
the online channel for making purchases more often used PCs and that people who purchased more atbricks-and-mortar stores more often made active use of mobile, PC, and social media. Furthermore, by the analysis
of low-involvement, more frequently purchased category in the Japanese market, this study found that percentagesof store-focused consumers (72%) were higher compared to those in Konus et al. (2008) and those of multichannel
enthusiasts (13%) were lower compared to the same. These results have suitability in light of the purchasing statusof these categories in the current Japanese market. However, it can be anticipated that e-Commerce sites will
mature, efficiency of logistics systems will improve, and the number of online purchasers for these categories will
increase in the future. Therefore, a future increase in multichannel enthusiasts is a point that deserves serious
attention.
The second question is what the relationship is between customer’s psychographic and demographic covariates
and their segment membership based on actual behavior. We observed that demographics differed for all segments.
Further, the results suggest that differences in psychographics divide the research shopper into two types: theopinion-seeking type and the time-constrained type. Similarly, the multichannel enthusiasts segment is divided into
the price-conscious type and the innovator type. These suggestions would be effective in practical application. First,
it is important to discover who “the people who judge information by their own standards” are and who “the people
who are influenced by others” are. If a firm could capture as customers the innovator-type multichannel enthusiasts
who can judge information, they could earn a positive evaluation from them over the Internet. That will likely exert

13

a positive effect on the opinion-seeking type research shoppers who are influenced by others. In addition, firms
need to develop the information that they publish on media and devices. To acquire price-conscious multichannel
enthusiasts, it is necessary to publish a wealth of information to prompt judgment on their PC websites. To acquireinnovator type multichannel enthusiasts, it is necessary to take advantage of mobile and social media andpost-intuitive information that gives the feeling of shopping enjoyment.
There are some limitations of this study and future research challenges. This study performed segmentation thatconsidered the entire picture of purchase channel usage and media touchpoints; however, it is necessary to refine
the media touchpoint side in the future. The media touchpoints handled in this study were PC, mobile, and social
media usage amounts. The limitation of the study is that the media touchpoints were not associated with productcategories. When a model is built based on behavior logs, it is extremely difficult to associate all the countless
websites and apps that exist globally with product categories. This is an issue that does not occur with models
based on self-reported surveys. These circumstances call for a method for aggregating websites and app groups thatpurchasers of a certain product category are likely to visit. Once such a method exists, it will be necessary to revisit
the framework of this study by category. However, the overall picture that has emerged in this study will serve to
be useful as a context for consumer behavior when analyzed in more detail.
Furthermore, it is necessary to perform customer segmentation in the low-involvement, more frequently
purchased category in other countries as well. This study performed its analysis for the Japanese market, but if avalidating analysis were to be done in other countries with different purchasing, media, and socio-economic
environments, it would be necessary to confirm whether different influencing covariates would surface.

Endnotes
1) Ministry of Economy, Trade and Industry, “e-Commerce Market Survey (2015)” (in Japanese),
http://www.meti.go.jp/press/2016/06/20160614001/20160614001.html (accessed March 10, 2017)
2) INTAGE Inc. official web site (in Japanese), https://www.intage.co.jp/english/(accessed March 10, 2017)

3) INTAGE’s multi-device usage surveys (in Japanese), http://www.intage.co.jp/library/20160601/(accessed

March 10, 2017)

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