Chapter 6. Attracting Buyers with Search, Semantic, and Recommendation Technology

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Search, Semantic, and Recommendation

Technology

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Optimization

Pay-Per- Click and Paid Search

Strategies

A Search for Meaning—

Semantic Technology Recommendation

Engines

Success

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– Search Engine: an application for locating webpages or other content on a computer network using

spiders.

– Spiders: web bots (or bots); small computer

programs designed to perform automated, repetitive tasks over the Internet.

– Bots scan webpages and return information to be stored in a page repository.

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– Typically organized by categories.

– Webpage content is usually reviewed by directory editors prior to listing.

– Page Repository: data structure that stores and manages information from a large number of

webpages, providing a fast and efficient means for accessing and analyzing the information at a later time.

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Figure 6.5 Components of crawler search engines (Grehan, 2002).

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Figure 6.6 Search engines use invested indexes to efficiently locate Web content based on search query terms.

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– Enterprise search tools allow organizations to share information internally.

– An organizations’ ability to share knowledge among employees is vital to its ability to compete.

– Information is not always in the same format.

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– Structured data: information with a high degree of organization, such that inclusion in a relational database is seamless and readily searchable by simple, straightforward search engine algorithms or other search operations.

– Unstructured data: “messy data” not organized in a systematic or predefined way.

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– Limited access to certain data via job function or clearance.

– Request log audits should be conducted regularly for patterns or inconsistencies.

• Enterprise Vendors

– Used to treat data in large companies like Internet data but include information management tools.

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– Attempt to anticipate information users might be interested in to recommend new products, articles, videos, etc.

• Search Engine Marketing

– A collection of online marketing strategies and tactics that promote brands by increasing their visibility in search engine results pages (SERPs) through optimization and advertising.

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– Basic search types:

• Informational search

• Navigational search

• Transactional search

– Strategies and tactics produce two outcomes:

• Organic search listings

• Paid search listings

– Pay-per-click (produce click-through rates)

• Social media optimization

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– Technically configured mobile sites – Content designed for mobile devices

• Business search – Focused search – Filetype

– Advanced search – Search tools button – Search history

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– Google Trends – Google Alerts – Twitter Search

• Social Bookmarking Search

– Page links tagged with keywords

• Specialty Search: Vertical Search

– Programmed to focus on webpages related to a particular topic and to drill down by crawling pages that other search engines are likely to ignore.

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directory and a crawler based search engine?

2. What is the purpose of an index in a search engine?

3. Describe the page-ranking method most commonly associated with Google’s success.

4. What is the difference between search engine optimization and PPC advertising?

5. Describe three different real-time search tools.

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Optimization

Pay-Per- Click and Paid Search

Strategies

A Search for Meaning—

Semantic Technology Recommendation

Engines

Success

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Keyword conversion rates: the likelihood that using a particular keyword to optimize a page will result in conversions*.

Ranking factors

Reputation or popularity

PageRank: Google’s algorithm based on the assumption that people are more likely to link a high-quality website than poor-quality site.

Backlinks: external links that point back to a site.

Relevancy

User Satisfaction

Conversions: when a website visitor converts to a buyer

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– An approach to marketing that emphasizes SEO, content Marketing, and social media strategies to attract customers.

• Outbound marketing

– Traditional approach using mass media advertising.

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– Gaming the system or tricking search engines into ranking a site higher than its content deserves.

1. Link spamming: generating backlinks toward SEO, not adding user value.

2. Keyword tricks: embedded high-value keywords to drive up traffic statistics.

3. Ghost text: text hidden in the background that will affect page ranking

4. Shadow (ghost or cloaked) pages: created pages optimized to attract lots of people through redirect.

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a website’s content to determine how a page should be ranked in search results. These clues fall into three primary categories:

Reputation or Popularity, Relevancy, and User Satisfaction.

Explain the rationale for using each of these three categories as an indicator of a website’s content quality.

2. Backlinks were a key factor in Google’s original PageRank algorithm. Explain what a backlink is and why Google has reduced its emphasis on backlinks and instead uses many other additional factors in its ranking algorithm?

3. Explain why so-called black hat SEO tactics are ultimately short-sighted and can lead to significant consequences for businesses that use them.

4. How do organizations evaluate the effectiveness of their search engine optimization (SEO) strategies and tactics?

5. Explain why providing high quality, regularly updated content is the most important aspect of any SEO strategy.

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Optimization

Pay-Per- Click and Paid Search

Strategies

A Search for Meaning—

Semantic Technology Recommendation

Engines

Success

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– PPC advertising campaigns:

1. Set an overall budget 2. Create ads

3. Select associated keywords

4. Set up billing account information

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– Click through rates (CTR): used to evaluate keyword selection and ad copy campaign decisions.

Keyword conversion: should lead to sales, not just visits.

– Cost of customer acquisition (CoCA): amount of money spent to attract a paying customer.

– Return on advertising spend (ROAS): overall financial effectiveness.

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– Determined by factors related to the user’s experience.

• Expected keyword click-through-rate (CTR)

• The past CTR of your URL (web address)

• Past effectiveness

• Landing page quality

• Relevance of keywords to ads

• Relevance of keywords to customer search

• Ad performance on difference devices

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between organic listings and PPC listings on a search engine?

2. What are the four primary steps to creating a PPC advertising campaign on search engines?

3. In addition to the “bid price” for a particular keyword, what other factor(s) influence the likelihood that an

advertisement will appear on a search results page? Why don’t search engines just rely on the advertisers bid when deciding what ads will appear on the search results page?

4. How do webpage factors influence the effectiveness of PPC advertisements?

5. Describe four metrics that can be used to evaluate the effectiveness of a PPC advertising campaign.

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Optimization

Pay-Per- Click and Paid Search

Strategies

A Search for Meaning—

Semantic Technology Recommendation

Engines

Success

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– Meaningful computing using metadata: application of natural language processing (NLP) to support information retrieval, analytics, and data-integration that compass both numerical and “unstructured”

information.

• Semantic Search

– Process of typing something into a search engine and getting more results than just those that feature the exact keyword typed into the search box.

• Metadata

– Data that describes and provides information about other data.

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– Developed by W3C.

– Resource description framework (RDF)

• Used to represent information about resources – Web ontology language (OWL)

• Language used to categorize and accurately identify the nature of Internet things

– SPARCQL protocol

• Used to write programs that can retrieve and manipulate data scored in RDF

– RDF query language (SPARCQL)

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– Related searches/queries – Reference results

– Semantically annotated results – Full-text similarity search

– Search on semantic/syntactic annotations

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– Concept search

– Ontology-based search – Semantic Web search – Faceted search

– Clustered search

– Natural language search

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technology is enhancing the search experience of users.

2. How do metadata tags facilitate more accurate search results?

3. Briefly describe the three evolutionary stages of the Internet?

4. Define the words “context,” “personalization,” and

“vertical search” and explain how they make for more powerful and accurate search results.

5. What are the three languages developed by the W3C and associated with the semantic Web?

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Optimization

Pay-Per- Click and Paid Search

Strategies

A Search for Meaning—

Semantic Technology Recommendation

Engines

Success

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– Content-based filtering: products based on product features in past interactions.

– Collaborative filtering: based on user’s similarity to other people.

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– Cold start or new user: challenging since no starting point or preexisting information exists.

– Sparsity: unable to create critical mass due to few ratings or similar groups are unidentifiable.

– Limited feature content: manual information entry is prohibitive where there are many products.

– Overspecialization: narrowly configured results may only recommend the same item, but in different sizes or colors.

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– Weighted hybrid: results from different

recommenders are assigned weight and combined numerically to determined final recommendations.

– Mixed hybrid: results from different recommenders presented along-side of each other.

– Cascade hybrid: results from different

recommenders assigned a rank or priority.

– Mixed hybrid: results from different recommenders combines results from two recommender systems from the same technique category.

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engine?

2. Besides e-commerce websites that sell products, what are some other ways that recommendation engines are being used on the Web today?

3. What are some examples of user information required by recommendation engines that use collaborative filtering?

4. Before implementing a content-based recommendation engine, what kind of information would website operators need to collect about their products?

5. What are the four distinct methodologies used by recommender systems to create recommendations?

6. What is a recommendation engine called that combines different methodologies to create recommendations? What are three ways these systems combine methodologies?

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