From: Subject: Online File W4.7 Date: Mon, 28 May 2007 08:23:59 +0430 MIME-Version: 1.0 Content-Type: multipart/related; type="text/html"; boundary="----=_NextPart_000_0014_01C7A101.8A6BD370" X-MimeOLE: Produced By Microsoft MimeOLE V6.00.2900.3028 This is a multi-part message in MIME format. ------=_NextPart_000_0014_01C7A101.8A6BD370 Content-Type: text/html; charset="iso-8859-1" Content-Transfer-Encoding: quoted-printable Content-Location: http://higheredbcs.wiley.com/legacy/college/turban/0471705225/web/online/ch04/w4_7.html Online File W4.7

Online File W4.7=20


ONLINE MARKET RESEARCH=20

To successfully conduct electronic commerce, especially B2C, it is = important=20 to find out who the actual and potential customers are and what = motivates them=20 to buy online. Several research institutions collect Internet-usage = statistics=20 (e.g., acnielsen.com, emarketer.com), and they also look at = factors that=20 inhibit shopping. Merchants can then prepare their marketing and = advertising=20 strategies based on this information.

Finding out what specific = groups=20 of consumers (such as teenagers or residents of certain geographical = zones) want=20 is done via segmentation, dividing customers into specific segments, = like age or=20 gender. However, even if we know what groups of consumers in general = want, each=20 individual consumer is very likely to want something different. Some = like=20 classical music while others like jazz. Some like brand names, while = price is=20 more important to many others. Learning about customers is extremely = important=20 for any successful business, especially in cyberspace. Such learning is=20 facilitated by market research.





A MODEL OF ONLINE CONSUMER BEHAVIOR=20

For decades, market researchers have tried to understand consumer = behavior,=20 and they have summarized their findings in various models of consumer = behavior.=20 The purpose of a consumer behavior model is to help vendors understand = how a=20 consumer makes a purchasing decision. If the process is understood, a = vendor may=20 try to influence the buyer=92s decision, for example, by advertising or = special=20 promotions.

Figure W4.7.1 shows the basics of these consumer = behavior=20 models, adjusted to fit the EC environment. The EC model is composed of = the=20 following parts:=20

Figure W4.7.1 identifies some of the variables in each category. In = this=20 online section, we deal briefly with only some of the variables. = Discussions of=20 other variables can be found in Internet-marketing books, such as = Strauss et al.=20 (2003) and Sterne (2001, 2002). =20

The Variables of Online Consumer Behavior=20

THE INDEPENDENT VARIABLES OF THE ONLINE CONSUMER BEHAVIOR = MODEL. Two=20 types of independent variables can be distinguished: =

Personal=20 Characteristics. Personal characteristics, which are = shown in the=20 top left portion of Figure W4.7.1, include age, gender, and other = demographic=20 variables. Several Web sites provide information on customer online = buying=20 habits (e.g., emarketer.com, jmm.com). The major demographics = that such=20 sites track are gender, age, marital status, educational level, = ethnicity,=20 occupation, and household income.

Psychological variables = are=20 another personal characteristic studied by marketers. Such variables = include=20 personality and lifestyle characteristics. The reader who is interested = in the=20 details of psychological variables should see Solomon (2002).=20

Environmental Variables. As shown in the box in = the=20 top-right portion of Figure W4.7.1, the environmental variables = can be=20 grouped into the following categories:=20

THE INTERVENING (MODERATING) VARIABLES OF THE MODEL. Some of = the=20 intervening (moderating) variables can be controlled by vendors. Others = are=20 determined by the market. In the offline environment, these include = pricing,=20 advertisement, promotions, and branding. Also important are the physical = environment (e.g., display in stores), the logistics support, and = customer=20 services. These are described in marketing textbooks.

THE = TECHNOLOGY=20 AND WEB SITE VARIABLES. Vendors can control the following technology = variables: logistics and payment support, which must be secured, easy to = use,=20 and inexpensive; technical support, which includes appropriate site = design and=20 availability of intelligent shopping aids (intelligent agents); and = customer=20 service which includes all tools of CRM (Chapter 7).

THE = BUYING=20 DECISIONS (DEPENDENT VARIABLES) OF THE MODEL. The customer is making = several=20 decisions like =93To buy or not to buy?=94 =93What to buy?=94 and = =93Where, when, and how=20 much to buy?=94 These decisions depend on the independent and = intervening=20 variables. The objective of learning about customers and conducting = market=20 research is to know enough so that the vendors who control the EC = systems and=20 provide some of the market stimuli can make decisions on the intervening = variables.

Before we discuss some of the model=92s variables, = let=92s=20 examine who the EC consumers are. Online consumers can be divided into = two=20 types: individual consumers, who get much of the media attention, = and=20 organizational buyers, who do most of the actual shopping in = cyberspace.=20 Organizational buyers include governments, private corporations, = resellers, and=20 public organizations. Purchases by organizational buyers are generally = used to=20 create products (services) by adding value to raw materials or = components. Also,=20 organizational buyers such as retailers and resellers may purchase = products for=20 resale without any further modifications.

The above model is = simplified.=20 In reality it can be more complicated, especially when new products or=20 procedures need to be purchased. For example, for online buying, a = customer may=20 go through the following five adoption stages: awareness, interest, = evaluation,=20 trial, and adoption. (For details, see McDaniel and Gates, 2001 and = Solomon,=20 2002.) Understanding the structure of the model in Figure W4.7.1, or any = more=20 complicated one, is necessary, but in order to really make use of such = models,=20 we need to learn about the decision-making process itself, as discussed = next.=20



THE CONSUMER DECISION-MAKING PROCESS=20

Let=92s return to the central part of Figure W4.7.1, where consumers = are shown=20 making purchasing decisions. Several models have been developed in an = effort to=20 describe the details of the decision-making process that leads up to and = culminates in a purchase. These models provide a framework for learning = about=20 the process in order to predict, improve, or influence consumer = decisions. Here=20 we introduce two relevant purchasing-decision models.

A Generic Purchasing- Decision Model=20

A generic purchasing-decision model consists of five major phases. = In each=20 phase we can distinguish several activities and, in some of them, one or = more=20 decisions. The five phases are: (1) need identification, (2) information = search,=20 (3) evaluation of alternatives, (4) purchase and delivery, and (5)=20 after-purchase evaluation. Although these phases offer a general guide = to the=20 consumer decision-making process, do not assume that all consumers=92 = decision=20 making will necessarily proceed in this order. In fact, some consumers = may=20 proceed to a point and then revert back to a previous phase, or skip a = phase.=20 For details, see Strauss et al. (2003).=20

A Customer Decision Model in Web Purchasing=20

The above purchasing-decision model was used by O=92Keefe and = McEachern (1998)=20 to build a framework for a Web-purchasing model, called the consumer = decision=20 support system (CDSS). According to their framework (shown in Table = W4.7.1),=20 each of the phases of the purchasing model can be supported by both CDSS = facilities and Internet/Web facilities. The CDSS facilities support the = specific=20 decisions in the process. Generic EC technologies provide the necessary=20 mechanisms, and they enhance communication and collaboration. For = details, see=20 Turban et al. (2006).
=20

HOW MARKET RESEARCH FINDS WHAT CUSTOMERS WANT=20

There are basically two ways to find out what customers want. The = first is=20 to ask them, and the second is to infer what they want by observing what = they do=20 in cyberspace.=20

Asking Customers What They Want=20

The Internet provides easy, fast, and relatively inexpensive ways = for=20 vendors to find out what customers want by interacting directly with = them. The=20 simplest way is to ask potential customers to fill in electronic = questionnaires.=20 To do so, vendors may need to provide some inducements. For example, in = order to=20 play a free electronic game or participate in a sweepstakes, you are = asked to=20 fill in an online form and answer some questions about yourself (e.g., = see=20 bizrate.com). Marketers not only learn what you want from the direct = answers,=20 but also try to infer from your preferences of music, for example, what = type of=20 books, clothes, or movies you may be likely to prefer.

In some = cases,=20 asking customers what they want may not be feasible. Also, customers may = refuse=20 to answer questionnaires, or they may provide false information (as is = done in=20 about 40 percent of the cases, according to studies done at Georgia Tech = University). Also, questionnaires can be lengthy and costly to = administer.=20 Therefore, a different approach may be needed=97observing what customers = do in=20 cyberspace.



USING SOFTWARE AGENTS TO ENHANCE B2C AND MARKET RESEARCH=20

As discussed in Chapter 9, software agents are computer programs = that=20 conduct routine tasks, search and retrieve information, support decision = making,=20 and act as domain experts. These agents sense the environment and act=20 autonomously without human intervention. This results in a significant = savings=20 of users=92 time. There are various types of agents that can be used in = EC,=20 ranging from software agents, which are those with no = intelligence, to=20 learning agents that exhibit some intelligent behavior. =

Agents=20 are used to support many tasks in EC. But first, it will be beneficial = to=20 distinguish between search engines and the more intelligent type of = agents. As=20 discussed in Chapter 9, a search engine is a computer program = that can=20 automatically contact other network resources on the Internet, search = for=20 specific information or key words, and report the results. Unlike search = engines, an intelligent agent uses expert, or knowledge-based,=20 capabilities to do more than just =93search and match.=94 For example, = it can=20 monitor movements on a Web site to check whether a customer seems lost = or=20 ventures into areas that may not fit his profile, and the agent can then = notify=20 the customer and even provide corrective assistance. Depending on their = level of=20 intelligence, agents can do many other things. In this section we will=20 concentrate on intelligent agents for assisting shoppers (see Yuan, = 2003).=20

Brand- and Vendor- Finding Agents and Price Comparisons=20

Once the consumer has decided what to buy, a type of intelligent = agent=20 called a comparison agent will help in doing comparisons, usually = of=20 prices, from different vendors. A pioneering price-comparison agent was=20 Bargainfinder from Andersen Consulting. This agent was used only in = online=20 shopping for CDs. It queried the price of a specific CD from a number of = online=20 vendors and returned the list of vendors and prices. Today much more=20 sophisticated agents, such as Mysimon.com, Pricescan.com, = and=20 Dealtime.com, make comparisons. Some of these look at multiple = criteria,=20 not just price, and even let you prioritize the criteria. Then, the = agent makes=20 a recommendation based on your stated preferences.=20

Search Agents=20

Search agents, another type of intelligent agents, can help = customers=20 determine what to buy to satisfy a specific need (e.g., Likemind.com, = Gifts.com). This is achieved by looking for specific product = information and=20 critically evaluating it. The search agent helps consumers decide what = product=20 best fits their profile and requirements (e.g., see = salesmountain.com).=20

Collaborative Filtering Agents=20

Once a company knows a consumer=92s preferences (e.g., what music = they like),=20 it would be useful if the company could predict, without asking, what = other=20 products or services this consumer might enjoy. One way to do this is = through=20 use of collaborative filtering agents, which use customer data to = infer customer interest in other products or services. There are = several=20 methods and formulas, all using software agents, to execute = collaborative=20 filtering. Some collaborative filtering agents base predictions on = statistical=20 formulas derived from behavioral sciences (see=20 sins.berkeley.edu/resources.collab/ for details). Some base their = predictions on what is known about other customers with similar = profiles. (For=20 details of the different methods and formulas, see Ridell et al., 2002.) = One of=20 the pioneering filtering agents was Firefly (now embedded in = Microsoft=92s=20 Passport System).

FUJITSU=92S AGENTS PROFILE CONSUMERS. = Fujitsu, a=20 major Japanese vendor of consumer products, is using an agent-based = technology=20 called Interactive Marketing Interface (iMi) that allows advertisers to = interact=20 directly with targeted customers (magazine.fujitsu.com). = Consumers submit=20 a personal profile to iMi, indicating such characteristics as product = categories=20 of interest, hobbies, travel habits, and the maximum number of e-mail = messages=20 per week they are willing to receive. In turn, via e-mail, customers = receive=20 product announcements, advertisements, and marketing surveys based on = their=20 personal profile. By answering the marketing surveys or acknowledging = receipt of=20 advertisements, consumers earn iMi points, redeemable for gift = certificates and=20 phone cards. Consumers remain anonymous to the advertisers.=20

Other Agents=20

Many other software agents can aid buyers and sellers in e-commerce. = Examples are: UPS.com for optimizing deliveries, = e-Falcon.com for=20 fraud detection, and webassured.com for increasing trust levels. Other = agents=20 are described throughout the book.

The information collected by = market=20 research is used for customer relationship management (CRM), described = in=20 Chapter 8, and for advertising (Section 4.4).



KEY TERMS FOR ONLINE FILE W4.7=20

Collaborative filtering agent
Segmentation



REFERENCES FOR ONLINE FILE W4.7=20

McDaniel, C., and R. H. Gates, Marketing Research: The Impact of = the=20 Internet. Cincinnati: South-Western Publishing, 2001.
O=92Keefe, = R. M.,=20 and T. McEachern, =93Web-Based Customer Decision Support System,=94=20 Communications of the ACM, March 1998. Ridell, J., et al., = Word of=20 Mouse: The Marketing Power of Collaborative Filtering. New York: = Warner=20 Books, 2002.
Solomon, M. R., Consumer Behavior. Upper Saddle = River,=20 NJ: Prentice Hall, 2002.
Sterne, J., World Wide Web = Marketing, 3rd=20 ed. New York: Wiley, 2001.
Sterne, J., Web Metrics. New York: = Wiley,=20 2002.
Strauss, J., et al., E-Marketing, 3rd ed. Upper Saddle = River,=20 NJ: Prentice Hall, 2003.
Sweiger, M., et al., Clickstream Data=20 Warehousing. New York: Wiley, 2002.
Turban, E., et al., = Electronic=20 Commerce 2006. Upper Saddle River, N.J.: Prentice Hall, 2006. =
Yuan, S.=20 T., =93A Personalized and Integrative Comparison-Shopping Engine and Its = Applications,=94 Decision Support Systems, January 2003.=20

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