Behavioral targeting uses information collected on an individual’s web-browsing behavior, such as the pages they have visited or the searches they have made, to select which advertisements to display to that individual. Practitioners believe this helps them deliver their online advertisements to the users who are most likely to be influenced by them. Behavioral targeting or behavioral targeting is a technique used by online publishers and advertisers to increase the effectiveness of their campaigns.

Behavioral marketing can be used on its own or in conjunction with other forms of targeting based on factors like geography, demographics or the surrounding content. Behavioral Targeting allows site owners or ad networks to display content more relevant to the interests of the individual viewing the page. On the theory that properly targeted ads will fetch more consumer interest, the seller may ask for a premium for these over random advertising or ads based on the context of a site.

Onsite Behavioral targeting

Behavioral targeting techniques may also be applied to any online property on the premise that it either improves the visitor experience or it benefits the online property, typically through increased conversion rates or increased spending levels. The early adopters of this technology/philosophy are primarily within a retail or other e-commerce website as a technique for increasing the relevance of product offers and promotions on a visitor by visitor basis. More recently, companies outside this traditional e-commerce marketplace have started to experiment with these emerging technologies.

The typical approach to this starts by using web analytics to break-down the visitor mass into a number of discrete channels. Each channel is then analysed and a virtual profile is created to deal with each channel. These profiles can be based around Personas that gives the website operators a starting point in terms of deciding what content, navigation and layout to show to each of the different personas. When it comes to the practical problem of successfully delivering the profiles correctly this is usually achieved by either using a specialist content behavioral platform or by bespoke software development. Most platforms identify visitors by assigning a unique id cookie to each and every visitor to the site thereby allowing them to be tracked throughout their web journey, the platform then makes a rules-based decision about what content to serve. Again, behavioral data can be combined with demographic and past purchase history in order to produce a greater degree of granularity in the targeting.

Self-learning onsite behavioral targeting systems will monitor visitor response to site content and learn what is most likely to generate a desired conversion event. Some good content for each behavioral trait or pattern is often established using numerous simultaneous multivariate tests. Onsite behavioral targeting requires relatively high level of traffic before statistical confidence levels can be reached regarding the probability of a particular offer generating a conversion from a user with a set behavioral profile. Some providers have been able to do so by leveraging its large user base, such as Yahoo!. Some providers use a rules based approach, allowing administrators to set the content and offers shown to those with particular traits.

Network Behavioral targeting

Advertising Networks use behavioral targeting in a different way to individual sites. Since they serve many adverts across many different sites, they are able to build up a picture of the likely demographic makeup of internet users. An example would be a user seen on football sites, business sites and male fashion sites. A reasonable guess would be to assume the user is male. Demographic analyses of individual sites provided either internally (user surveys) or externally (Comscore \ netratings) allow the networks to sell audiences rather than sites. Although advertising networks used to sell this product, this was based on picking the sites where the audiences were. Behavioral targeting allows them to be slightly more specific about this.

Source: Wikipedia.com

This is the summary of an article by Glen L. Urban, et al. You can get the pdf of the behavioral targeting article here: Morphing Banner Advertising.

An online ad morphs whent it dynamically changes depending on a consumer segments’ preferences. These preferences are determined from the online behavior of these consumers. A dynamic program helps a website determine how the online ad should morph by exploring and exploiting; that is, the program explores by trying out various morphs and seeing which is most effective, and exploits by using ads that are based on recent anticipated results.

This paper is the first random, field testing for morphing, with a relative large sample size used to make more accurate observations regarding steady-state behavior.

Banner Advertising

Banner advertisements are paid advertisements located on a web page. Around twenty four percent of the revenue from online advertising is credited to banner advertisements, and during 2010, that’s around 6.2 billion US dollars. Therefore, a lot of businesses are interested in making banner advertisement even more effective.

Many firms try to improve banner ad effectiveness by using behavioral targeting, and increasing click through rates (CTR) in the process. However, most research show that behavioral targeting techniques today are quite static. There’s no automatic and dynamic learning involved among these strategies and they do not improve depending on how successful or bad the banners are. In other words, there is no morphing in behavioral targeting strategies.

Brief Review of Online Banner Morphing

Banner morphing exists because customized banner ads are very effective in catering to the needs of a given consumer segment. A study involving morphing banners needs to be able to accomplish two things: first, based on clickstream data from a given consumer, that consumer must be assigned to a certain segment. Second, there must be a method of assigning the best banner advertisements to each segment.

Segments are identified using a priming study, wherein consumers explore a priming website, then a model is developed to be able to assign a set of consumers to certain segments related to their clickstream data.

CNET Field Experiment

The study is done in cooperation with CNET.com, a website containing reviews and news regarding the latest high tech products. Everyday, 8 million people visit the website, and the site is worth 1.8 billion dollars in the market. Advertising revenue from CNET.com has a high reliance on banner ads, and some of them are behaviorally targeted, demanding premium prices.

In the study, eight banners regarding smart phones from AT&T are used. Visiting consumers are randomly assigned to control and test cells. The banners differ in terms of characteristics that appeal to different consumers having varying cognitive styles. The control cell had randomly assigned banners, while that of the test cells contain banners that morph based on the cognitive style of the consumer.

Results of the Banner Morphing Test

More than 450,000 banners were seen by over 116,000 consumers in this study. There were behaviorally targeted banners, particularly those that are located in pages where there are pictured, discussed, priced, compared and rated phones. Around 27 percent of the consumers saw behaviorally targeted banner advertisements.

Results show that morphing is highly effective in combination with behavioral targeting. Here, banner morphing improves substantially and significantly, and clickthrough rates almost double. The control cell, on the other hand, shows an insignificant lift for consumers and banners even when behavioral targeting was used.

In addition, there was no significant lift for banners that morphed but were not behaviorally targeted. Indeed, there is an interaction between behavioral targeting and morphing.

Implications and Future Directions

Online morphing is currently at its developing stages, and this technology is going to continue to improve website designs and banner ads and make them more effective.

Further scientific studies about banner morphing will also lead to further knowledge regarding designing and developing banners that morph according to consumers’ needs. Indeed, implementing morphing technologies to come up with the best banner for each consumer segment will lead to developments in the creativity aspect of banner ads.

Behavioral targeting by itself is generally defined as identifying consumers’ browsing habits to help you make educated targeted decisions when it comes to future marketing and advertising campaigns.

For those with an online storefront, behavioral targeting is the practice of studying browsing and purchasing habits of previous customers in order to predict what customers will buy in the future. When properly used and interpreted, it can let you know how to properly target your advertising efforts to ensure that you only reach customers who are interested in your services and products.

Behavioral Targeting Pros

1. Allows you to know your customers very well

As the internet continues to open up a wealth of marketing opportunities the need to stay directly in tune with customers’ specific wants and needs will increase twofold. In order to market a product it will no longer be enough to discuss how a product works, but how a product can work for the consumer. Thus, an effective consumer marketing strategy is needed in order to create a solid customer base that recommends your services or products and continues to return for repeat visits.

Marketers should understand their craft very well, and have the guts to operate marketing without fear. This means knowing their customers very well, and understanding that their clients are not the sales reps as in the old model, but the customers themselves. Marketers should be customer oriented and focused. They should keep two marketing truisms. One is to retain existing customers and bring your market to your customers. Existing customers are a great source for new business, and you can bring your market by offering incentives on sales, causing spur buying. Another strategy is to implement win-back campaigns for old customers.

2. Anticipate the needs and wants of your current clients.

With behavioral targeting, you can anticipate the needs and wants of your current clients, and use this information to base your marketing decisions on the assumption that the needs of your current clients will close match the needs of your future clients.

Predictive behavioral targeting allows you to be ahead of the bell curve. For example, iPhone’s global success is due to it being the first among its competitors to give users apps and full easy touch screen internet access. Everyone else that followed was just a knock-off or attempted competitor.

Behavioral targeting can provide consumers everything they need before they even realize they need it, which almost always guarantees a sale. Online businesses can anticipate consumer browsing habits and offer suggestions that lead to your products.

3. Increase your productivity.

Properly used, behavioral targeting is a tool which allows you to increase your productivity without spending much initially. This involves studying the behavior of your average client so you know where to search for them and ensure that your ads are being viewed by this audience. This is a much better solution over putting out a general ad and hoping an interested party will happen to view it.

Behavioral targeting allows you to cut time and costs by utilizing behavioral targeting tactics and focusing your advertising efforts on your target audience. When you place the appropriate service or project dressed in a marketing effort they appreciate, and with a price tag that meets their expectations, your marketing sales profits will skyrocket.

The essential elements to targeting are looking for the right target segment and handing out the right advertisement towards that segment. These elements make advertisement much cheaper, because advertising companies don’t have to bombard individuals with undirected advertisement; they will just focus on individuals who are more than likely to respond to their advertisements. They do this by behavioral profiling, which obtains several kinds of information from users, including site registration data, keyword searches, demographic data IP address, country, zip code, history of ad exposures, among others.

In addition, small businesses that do not have lots of money can still take advantage of the current internet advertising market by employing behavioral targeting tactics.That’s because many advertising companies will use behavioral targeting tactics for you if you contract advertising with them.

4. Marketing Research and New Innovations

Marketers have discovered that there is another use for behavioral targeting aside from online ad sales. They found out that it is a good way to do some research and figure out the kind of people that buy their products; for example, what else are their clients interested in aside from their products.

Behavioral targeting also opens new avenues for the advancement of online advertising. For example, cell phone personalized data shows a lot of useful information, including street location. Combined with online advertising tools, the result is behavioral and geographical targeting, allowing advertisers to know the exact person most likely to buy their product and at the right time.

Cell phones can use zip code, area code and GPS data to let users know where the nearest business establishments are. Ads will show up in your cellphone, selling products sold by local businesses within your present location.

5. A Win-win situation for both customers are advertisers.

Cost-per-click (CPC) will rule the ad industry as advertisers will only pay when the shopper clicks the ad and comes back to the retailer’s site. With this CPC model, innovative ad technologies will automatically prioritize the shoppers with the greatest history of clicking on their ads, while minimizing or eliminating the ads served to online shoppers who have historically not interacted with the retailer’s banners – therefore saving retailers money by not serving ads to those who do not click. On the other hand, online shoppers simply do not have the time or interest in viewing or clicking generic ads that do not apply to their shopping tendencies.

Advertisers only want to pay for clicks that are driving sales, and consumers want relevant ads about products or services they actually care about. A study conducted by PreferenceCentral suggests online consumers are growing increasingly comfortable with targeting and retargeting. A majority of consumers prefer relevant, tailored ads as a trade-off for quality, free online content.

Cons

1. Expensive.

Behavioral targeting is twenty to thirty percent more expensive than other traditional online advertising strategies.

It is not an overnight process. In order to see segment marketing work oftentimes a company needs to rework itself from the management level up and invest in either consultation agencies or a consumer research team. The profits from segment marketing are dependent on how invested and thorough the business is when dividing its market.

2. Doesn’t work for all products

It doesn’t work for all products. Usually behavioral targeting is more effective for products that don’t have mass appeal. If your products are for senior citizens, for example, it may be a tad more difficult to find viable viewers.

3. Privacy Concerns

When the products and services offered by the company require privacy, a person may be annoyed and offended if he or she receives an email offering him those kinds of items and services. There are always strings of privacy attached to any information from individual. Conservatives call it spying when those supposed private information were exposed for the purpose of marketing.

As for now, targeting is modest because of several privacy restraints. FCC rules set limits to the use of location and other customer data. They also set other restraints, such as the ability of users to easily opt out of mobile updates anytime they want to.

Data-mining technology has raised eyebrows as privacy violation is inevitable for all of these used data without permission. Several scientists have attempted to create tools that would limit databases’ ability to extract personal information. Others suggest proper disclosure; one should limit sharing information to companies on doesn’t trust, and share only personal information to those one trusts.

Trust is a main issue, and behavioral targeting companies claim that they are highly responsible for these data, imposing bans and regulations. However, some consumers aren’t confident. They are wary about what these large companies can manipulate users to doing in the future, as they manipulate us into buying certain products now.

Targeted ads are informative, but privacy entails prevention focus among consumers, such that they become more sensitive to the presence or absence of negative outcomes. Furthermore, prevention focus makes consumers more wary of manipulative intent. Ultimately, it is privacy concerns that primarily cause negative impressions on consumers.

4. Problems with cookies and usernames.

The most common way to track user online behavior is with cookies. They are used to track behavior by observing a user’s mouse clicks. The aim is to obtain enough data to record a user profile and determine his/her preferences, so that ads can be personally served, increasing a user’s purchasing desire. The downside of this technique is that there may be multiple users in one computer, such as in public venues. Furthermore, browsers now have the option of deleting cookies and choosing to prevent some or all cookies from gathering data.

Another common technique is the use of username and passwords. THis method is more effective than cookies because tracking a user’s preferences become more personalized. However, some users share their usernames and passwords with others, while it is highly likely for several users to have the same usernames and passwords. It may be frustrating for users as well to keep track of these information in various websites.

5. Problems with location-based marketing.

Location-based marketing is not that popular among the majority. Most of us would prefer privacy with our cell phones. Seeing an ad in our cell phone is not a marvellous event. The ad will need to add relevant information to the situation, otherwise users will get the feeling of being spammed.

6. Health Risks Caused by Highly Influential Ads.

Digital marketing will continue to be a strong force in the society, but it raises concerns about the possible health risks these highly influential ads can do to the society, and especially to the youth and cultural minorities that have a unique susceptibility to obesity and health related problems. Specific digital marketing practices that potentially raise these health issues should be regulated.

For example, some practices make it hard for consumers to distinguish between content and marketing, and blurs the lines between point of purchase and influence.

Generally, these practices should be developed under the considerations of adolescent and child development, government advertising rules, industry guidelines, etc. If not addressed properly, these emerging digital marketing trends could have a long-lasting unwanted effect on the public health.

7. Uneducated clients.

One of the challenges of behavioral targeting is educating clients about it, and helping them understand what it is all about. Advertisers should also be told that while behavioral targeting is effective, it works best when complemented with other advertising techniques.

8. Limited Reach.

Behavioral targeting is very effective for highly motivated consumers, but consumer preferences change rapidly; a slight targeting delay backfires for the consumer. Behavioral targeting may ignore potential customers, especially those who need motivation and influence.

http://www.youtube.com/watch?v=jXoDSu12ces

In this Youtube video Jack Myers and Mark Ghuneim explain what the implications of behavioral targeting are and what you should do as a business to use behavioral targeting AND have a good and open relationship with your customers.

This is the summary of an article by Miguel Helft and Tanzina Vega. Here’s the link to the original behavioral targeting article: Retargeting Ads Follow Surfers to Other Sites.

If you remotely show interest in buying a certain product that appears in a website, but decide not to for now, that product can follow you around as ads that appear in other websites. If you don’t know anything about behavioral targeting, it will probably creep you out. While more people are now more familiar with targeted ads, the thing is, these ads are becoming even more targeted and suited to meet individualized needs and highly specified products they have seen online. This technique is known as personalized remarketing or retargeting.

Personalized Retargeting

Companies that are now using or are planning to use this advertising technique include Microsoft, Google, Nordstrom, eBags.com, Diapers.com, and Discovery Channel store. According to Aaron Magness of Zappos, there is an overwhelmingly positive response to this technique. But not everyone is happy. For some online users, they feel that they are being stalked; that their civil liberties and privacies are being invaded.

Retargeting utilizes cookies. A person that visits a website and looks at a certain product, a cookie is placed in the browser of that person, which generates and advertisement for that particular product to be displayed in other websites visited. The person can click “Why am I seeing this?” a message inside the banner ads he or she is seeing, which explains how these ads are generated. Users can also opt out.

General Impression of Remarketing

Surveys show that generally, people don’t like retargeting or remarketing, despite the fact that some of these haters understand the technology perfectly. Still, remarketing is tried out by such huge companies as Google, forming AdWords.

Compared to behavioral targeting, remarketing is quite similar, but more specific. Behavioral targeting has been subject too debate. In fact, some advertising executives think that highly personalized remarketing may be too much. To them, retargeting is acceptable as long as it is done in a subtle fashion.

This is the summary of an article by Steve McClellan and Brian Morrissey. Here’s the link to the original behavioral targeting article: The Future of TV.

Cable television is becoming less popular, and instead of watching tv programs on, well, tv, viewers opt to just watch them online, in YouTube or Hulu for example. In addition, Microsoft, Google and other Internet players are planning on entering the television business. Advertisers are now given a new advertising platform in the internet.

TV to Introduce Targeted Adveritising

In response, huge television companies plan to integrate the measurability and targeting capabilities of ads that are displayed online with the branding power of television. One particular company, Canoe Ventures, is working on creating a national platform for interactive and addressable targeted advertising.

To increase inventory effectiveness for advertises, and deal with the problem of wasteful mass advertising, the future TV’s will be interactive, flexible and measurable. It will work like the world wide web.

Video is a very powerful advertising tool, and it will be more powerful if the viewers who see the ads will not dismiss them as being annoying, but will actually respond to them by making impressions. That’s the motivation of a certain Project Canoe: six of the major cable companies found in the United States felt the need to collaborate with a joint venture to provide targeted television ads to their viewers.

Too Little Too Late?

According to Tim Hanlon, from Publicis Groupe’s VivaKi, it may be a little too late for cable companies to deliver targeted ads, because online videos are getting more popular and they are practically more in line with targeted video ads.

On the other hand, Canoe Ventures CEO David Verklin, stated that there’s a lot of work to do indeed, but the timing for them is perfect; that the platform will be scalable at this point in time.

Using the DVR

To pursue this platform for targeted ads on Television, the DVR will be used. It is already in 28 percent of American homes, and will be used to target individualized ads. It also allows users to pause a program and use interactive ads. Ironically, so far, 60-70 percent of DVR users skip out the ads.

Web Meets TV

The huge internet companies find their future in television. They are driven by the money obtained from advertisement, by which the largest channels for video advertisements are found in television. For now, Microsoft and Google have only taken short steps toward Television. Google has been acquiring television viewer data, while Microsoft has bought a technology company which makes it easier for operators to sell targeted advertisements on television. Google has signed deals with NBC Universal and Dish Network, while Microsoft’s Navic software is already being used by Dish Network, Charter, Time Warner cable, and Cox communications

The Next Top Model

Jeff Zucker, CEO of Universal, once said that conservative TV has no desire to “trade analog dollars for digital pennies.” But in the future, there will lots of other advertising formats in play aside from the usual commercial breaks that we are accustomed to.

There are still several unanswered questions for the future TV, including the currency model for the sellers and buyers of these ads, and who will be the dominating players for this future television.

This is the summary of an article by Raymond R. Burke. You can get the pdf of the behavioral targeting article here: Behavioral Effects of Digital Signage

According to a 2008 research, 30 to 40 percent of consumer brand decisions are made in stores. In-store marketing is really powerful. It’s even found to be more effective that outdoor, print, radio or television advertising, but less effective than online marketing. The article focuses on digital signage, flat panel monitors found in stores that continuously replay ads, and their effectiveness as point-of-purchase communication tools.

Supporting Research

The main company participating in the research is Tesco plc, an international grocery based in Britain. For each of 100 Tesco stores, 40 plus digital signages are placed in several areas of the store, such as in beauty and health care, beer, wine, etc. An advertising campaign was tagged with several characteristics, including campaign length, category and department, mesage type, frequency, price, product characteristics, etc. Sales are compared for test and control stores during and after campaigns.

Results

Analysis from the Tesco experiment showed significantly higher product lifts for entertainment, snacks and beverage items as compared to non-food items. It seems that these ads are most effective when consumers are only interested in instant, and less effective for planned purchases. Further results showed that products that highly penetrate certain categories had higher increases in sales. Effectiveness of digital signage may be due to day and time of day of the week. Responsivenss of shoppers may be due to visibility, orientation and location of signs. In addition, shorter messages may provide higher increase of sales per screen time. The supporting research is composed of several studies, the first of which is done by Video-Mining Corporation, showing video displays of paint products.

Need State of the Shopper

The experiment results showed that engagement is highest during the weekends. During week days, consumer traffic is highest during the afternoon and lowest in the morning. Engagement during the weekdays is highest during the evening, and lowest during the morning. These differences are due to the fact that shoppers have varying reasons during shopping for day of the week and time of day. A study conducted by DS-IQ, which run digital signage for eight weeks treated primarily according to variations in ad length. There was an 8 to 9 percent increase in sales for 30 second digital ads versus when the ads were not played. However, when 15 sec ads were shown in the same loop, the increase was greater than 50 percent.

Managerial Implications

Results show that digital signage is different from advertising done on television, and more like point of purchase displays. The best kind of digital signage messages are those that are direct to the point, relevant and simple. Managers should take note that in in-store advertising, it’s best to continue to make some adjustments with the programming schedule of digital signages based on constant analysis on the effectiveness of these ads.

This is the summary of an article by Jason Carmel and Juha Mertanen. You can get the pdf of the behavioral targeting article here: Better Together: Dynamic Content Creation and Behavioral Targeting.

Companies use online optimization to have a serious presence in the world wide web. In particular, behavioral targeting is used to give visitors of their site the right content at the right time, using browsing behavior, ethnographic or demographic information of users. The main challenging, however, is efficiently creating personalize content and keeping the costs low for better Return on Investment (ROI).

Companies can traditionally choose to just publish ads on fewer websites or make targeting a little bit more generic, but both cases limits personalization. One possible solution is Dynamic Content Management.

Dynamic Content Management

Dynamic Content Management technology will not limit targeting depth for multiple segments and breadth across the site. In theory, this technology will allow marketers to create thousands of content pieces which are highly personalized for miminum expenses.

For example, Dynamic content creation can add a targeting element to each of 40 devices in hours instead of manually creating them which would take weeks. That way, the return will be a much stronger justification for the expenses. In addition, Dynamic content management could help stores have a more targeted local presence. Apparently, that is the direction that many huge companies, such as Walmart, are heading from a global presence.

A Grain of Salt

Dynamic Content Creation is a very promising tool, but needs a lot of work ahead of it in the form of segment analysis and determining the right content pieces for the right audience. In addition, the complexity of targeting and the rules of business increases as personalization increases. Targeting is only effective when coupled with proper investment and effort.

In addition, most of the tools used for dynamic content management poses a limit to how creativity can change. As a consequence, targeting methods that require large amounts of changes or brand deviations can’t be done using this method.

This is the summary of an article by Marco Janssen. You can get the pdf of the behavioral targeting article here: Targeting Individuals to Catalyze Collective Action in Social Network.

A lot of the challenges that our society faces today are problems that require collective action, such as climate change. It is very important to study collective action within large heterogeneous groups. Governments can help provide collective action, but there’s more to the rules and laws implemented to cause a behavioral change. Some studies have shown that an individual has a greater chance in participating in a collective action through social influence, such as reputation and social pressure.

Is it possible to target the right, influential individuals so they can have a social influence on others leading to cooperative behavior? There have been studies related to spreading influence in the context of social networks and viral marketing and the primary role of peer pressure. This is the summary of a paper presenting a model of agents to solving a collective action problem, and describe which individuals to nudge.

Model Description

The model is composed of agents that make decisions on which behavior to adopt, and each behavior corresponds to a certain personal reward (individual part). An agent’s action is also influenced by neighbors in the network (social influence part). The initial behavior is A, and the main question is what conditions are necessary for an agent to adopt behavior B. Individual utility is defined as how far away or close to the preference of an agent is to the behavior. Social influence, on the other hand, increases when there are more similar behavior around the neighborhood.

Model analysis is in the investigation of the effects of homophily, or the similarity of agent attributes in a certain network. Plus, interventions are also include, such a incentives which make a certain behavior more attractive than the already is, increasing the probability that an agent will make that decision.

Model Analysis

The following are some of the model’s initial results. The default case used in model analysis is one wherein the agents can only obtain global feedback, which means that each agent has an equal share of the initial behavior. Investigation on the effects of local information is the next step, and it has been shown that adoption rate increases where homophily is at a high level.

Another batch of experiments investigates 4 types of interventions. It has been shown that those agents who are not socially influenced by the behavior of others are the ones more likely to be affected by these interventions. Those who have lots of connections are least affected because of the larger influence of peer pressure.

Conclusion

Improvement of this model can be done by including other human behavior assumptions including the process of deriving information and product preference changes, for example, due to changes in price. Testing the model on empirical data is also challenging.

Some of the interesting findings in this simplistic model include the following: the desired behavior can be increased to 400 percent if feedback on the adoption is given only to the agents in their social network, instead of on a global level. Second, the most effective strategy for increasing adoption rates is by targeting agents that are least likely to be affected by social influence in decision-making.

This is the summary of an article by Bernard J. Jansen and Simone Schuster. You can get the pdf of the behavioral targeting article here: Bidding on the Buying Funnel for Sponsored Search and Keyword Advertising.

Search engine marketing comes in various terms, such as search engine advertising, pay-per-click and keyword advertising. It is used by companies for product and service promotion on SERPs, or search engine results pages and its effective use.

Among the major search engines, this is the central business model used. The paradigm used by SEM is primarily called the buying funnel, a staged process that describes how consumers decide how they buy. The first stage is Awareness, the second stage is Research, third stage is Decision, and final stage is Purchase.

The buying funnel is widely accepted, so it is very important to investigate if this is the accurate way of explaining how consumers behave in response to keyword advertising campaigns. Is the buying funnel an advertising paradigm or is it really how consumers behave? Should a SEM campaign target the Purchase stage right away, or should each stage be targeted? These are some of the open questions.

Buying Funnel

The foundations of the buying funnel comes from information processing theory which is the main theory for all consumer behavior models. It is used to model how consumers can be reached by advertisers, and it is a nice fit to decision making among consumers.

Awareness is a consumer’s conscious need and desire to address that need with a service or product. Research is an information seeking process which includes looking for the right product governed by several factors, including affordability, necessity, etc. Decision is when a consumer comes up with a purchase set or a list of possible products. Purchase is when the consumer has actually made a decision to purchase or not purchase a certain good.

According to this paper, there is a few published empirical research regarding the buying funnel among literature related to SEM. This research has both practical and academic implications and so is a very worthwhile pursuit.

Research questions

The research question is: For online consumers, do the purchase and search interactions with keyword advertisements follow the buying funnel stages? Specifically, the study aims to determine if it is possible to tell in what stage of the buying funnel an online consumer is based on his or her search query. The following are the hypotheses to the research quesion.

First Hypothesis: There is a significant difference among the queries of each buying funnel stage based on the average number of impressions. Whenever an advertisement appears on a search engine results page after a user submits a search query, that’s called an ad impression.

The second to sixth Hypotheses are all similar to the above hypothesis, where the significant differences on each stage of the buying funnel are based on average number of clicks, average cost per click, average sales revenue per query, average number of orders, and average number of items ordered, respectively.

Methods and Results

Data is obtained from daily keyword advertising information from a nationwide retail chain that has a sales presence in both brick and mortar and online stores, spanning four years amounting to a little less than 7 million records and 40,000 keyword phrases. These key phrases are then classified according to one of the four stages found in the buying funnel.

Based on the occurences of classified queries, 51 percent are found to belong to the Research stage, followed by 28 percent for Awareness, 17 percent for Decision and finally 4 percent for Purchase. One research has previously concluded that most online consumers use search engines to research about products or services.

The hypothesis testing shows that indeed the buying funnel represents the behavior of actual consumers online. All of the hypotheses have significant differences, and so the buying model is really a workable model. On the other hand, the results also show that the buying funnel only works well for the classification of the focus of online ecommerce queries and not a good model to describe the movement of a consumer from one stage to the following stage.