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AEM 4550: Economics of Advertising Prof. Jura Liaukonyte LECTURE 10: ECONOMICS OF ONLINE ADVERTISING

Lecture Plan  HW 3  Required additional readings:  HBS case: Google Inc.  HBS industry note: Paid Search Advertising  Online Advertising Models  Long Tail  Real time bidding

Online Advertising

US Consumer Time Spent versus Media Ad Spending, 2013

PAY-PER-CLICK Advertising Model  Targeted advertisement based on two effectiveness measures: 1. Click-Through Rate (CTR): specifies on how many ads X, out of the total number of ads Y shown to the visitors, the visitors actually clicked; in other words, CTR = X/Y. CTR measures how often visitors click on the ad 2. Conversion Rate: it specifies the percentage of visitors who took the conversion action. Conversion rate gives a sense of how often visitors actually act on a given ad, which is a better measure of ad’s effectiveness than the CTR measure

Recent History of CPC Method  Cost Per Click is the predominant advertising payment method, made popular by search engines such a Google and Overture (now part of Yahoo!).  Google introduced CPC AdWords program in  Combining a particular ad payment method with a particular targeting method. For Google and Yahoo! the two main models are the keyword-based PPC and the content-based PPC models.

Cost Per Click Two problems with the Cost per click model:  Although correlated, good click-through rates are still not indicative of good conversion rates  It does not offer any “built in” fundamental protection mechanisms against the click fraud

Problems with PAY-PER-CLICK  CLICK FRAUD: People clicking on products/advertisements excessively without the real intent of actually making any purchases. Click fraud in AdWords   Make the competitor pay more   If you’re second, click on the competitor’s advertisement enough so that he will hit his budget for the day

Google’s Pay-per-Click Advertising Model  AdWords  A program allowing advertisers to purchase CPC-based advertising that targets the ads based on the keywords specified in the users’ search queries.  Ad Rank = CPC x QualityScore  QualityScore- a measure identifying the “quality” of the keyword and the ad combined  The more the advertiser is willing to pay (CPC) and the higher the click through rate on the ad (CTR), the higher the position of the ad in the listing is.

Example of Search Ads in a Search Engine

Organic Paid Paid

The “Golden Triangle” for Search Engine Results

Creating an AdWords Ad15

Temporal Pattern of Influence in Consumer Auto Searches

Google AdSense  Google AdSense is a program for website owners to display Google’s ads on their websites and earn money from Google as a result.

Uses of AdSense  AdSense for Search (AFS): publishers allow Google to place its ads on their websites when the user does keyword-based searches on their sites.  AdSense for Content (AFC): the system that automatically delivers targeted ads to the publisher’s web pages that the user is visiting. These ads are based on the content of the visited pages, geographical location and some other factors.

AdSense for Content  Contextually-targeted ads  Example: cheese.com

Opportunity Cost  Calculate the missed opportunity cost (forgone revenue) # of people searching for a specific keyword x engine share (Google ~= 80%) x expected click-through rate average conversion rate average transaction amount x x   E.g.10,000/day x 80% x 10% x 5% x $100 = $4,000/day

Challenge to advertisers  Keyword choice  This is the most critical  Market efficiencies: high CTR words have high prices  What matters is the cost effectiveness: the ROI or ROA  E.g., plurals get more clicks and more conversions than singulars: “Diamonds” more valuable than “diamond”  How much to bid  Measure cost-per-acquisition and/or ROA

Bidding Strategy  Determine value per click  Probability of purchase x profit margin  Determine relationship between cost and clicks  How much do you have to pay to get x clicks?  Equivalently use incremental cost per click

BIG DATA AND ADVERTISING

What is BIG DATA?  Collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications.  Data sets grow in size in part because they are increasingly being gathered by ubiquitous information-sensing mobile devices, aerial sensory technologies, software logs, cameras, microphones, etc.  The volume of business data worldwide, across all companies, doubles every 1.2 years, according to some estimates

What is BIG DATA?  Data companies are collecting enormous amounts of information about consumers.  They sell information about certain demographic factors:  whether you're pregnant or  divorced or  trying to lose weight,  about how rich you are and  what kinds of cars you have  Are you an allergy sufferer?

How much do these companies know about individual people? names, addresses and contact information, demographics, like age, race, occupation and education level Data on life event triggers: getting married, buying a home, sending a kid to college getting divorced.

How much do these companies know about individual people? Marketing divisions of Credit report agencies (Experian, Equifax) can and do sell: names of expectant parents and families with newborns marketing.html marketing.html Salary and pay-stub information

Where are they getting this info from?  The stores where you shop sell it to these data brokers.  Store loyalty cards is a primary source of data  Government records and other publicly available information  Department of Motor Vehicles may sell personal information such as name, address, and the type of vehicles you own — to data brokers  Public voting records, which include information about your party registration and how often you vote can be sold to data companies

What about Online?  Some data companies companies record and then resell screen names, web site addresses, interests, hometown and professional history, and how many friends or followers you have.  Some companies also collect and analyze information about users’ tweets, posts, comments, likes, shares, and recommendations.  Collects information about which social media sites individual people use, and whether they are a heavy or a light user.

Regulation  9 Data brokers are currently under review by the FTC. (1) Acxiom, 2) Corelogic, 3) Datalogix, 4) eBureau, 5) ID Analytics, 6) Intelius, 7) Peekyou, 8) Rapleaf, and 9) Recorded Future.)  The FTC is seeking details about:  the nature and sources of the consumer information the data brokers collect;  how they use, maintain, and disseminate the information; and  the extent to which the data brokers allow consumers to access and correct their information or to opt out of having their personal information sold.

Required “reading” for the exam!   Note:  What are the most current trends in advertising buying?  What are the implications of such changing conditions for consumers, advertisers and content providers

The Long Tail  The internet vs. brick-and-mortar  Nearly unlimited capacity  Distribution and shelving costs approaching zero  Global distribution channels  A changing economy  Popularity no longer has a monopoly on profitability  Can generate significant revenues by selling small number of millions of niche products vs. selling millions of a small number of “hits”

The Long Tail

Wal-Mart vs. Rhapsody  Wal-Mart  39,000 songs on CDs in average store  Must sell at least 100,000 copies of a CD to cover its retail overhead and make a sufficient profit  Less than 1 percent of CDs sell that much  Therefore, can carry only “hits”  Itunes/Rhapsody/Spotify  Millions of songs in archives  Cost of storing one more song is essentially zero  More streams each month beyond its top 10,000 than in the top 10,000  Therefore, no economic reason not to carry almost everything

Long Tail Examples: Travel

Netflix Long Tail