There are two major ways of selling impressions in display advertising. They are either sold in spot through auction mechanisms or in advance via guaranteed contracts. The former has achieved a significant automation via real-time bidding (RTB); however, the latter is still mainly done over the counter through direct sales. This paper proposes a mathematical model that allocates and prices the future impressions between real-time auctions and guaranteed contracts. Under conventional economic assumptions, our model shows that the two ways can be seamless combined programmatically and the publisher's revenue can be maximized via price discrimination and optimal allocation. We consider advertisers are risk-averse, and they would be willing to purchase guaranteed impressions if the total costs are less than their private values. We also consider that an advertiser's purchase behavior can be affected by both the guaranteed price and the time interval between the purchase time and the impression delivery date. Our solution suggests an optimal percentage of future impressions to sell in advance and provides an explicit formula to calculate at what prices to sell. We find that the optimal guaranteed prices are dynamic and are non-decreasing over time. We evaluate our method with RTB datasets and find that the model adopts different strategies in allocation and pricing according to the level of competition. From the experiments we find that, in a less competitive market, lower prices of the guaranteed contracts will encourage the purchase in advance and the revenue gain is mainly contributed by the increased competition in future RTB. In a highly competitive market, advertisers are more willing to purchase the guaranteed contracts and thus higher prices are expected. The revenue gain is largely contributed by the guaranteed selling.
Combining guaranteed and spot markets in display advertising: selling guarant...Bowei Chen
While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect how advertisers split their purchases between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers’ purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both channels can increase the publisher’s expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.
Multi-keyword multi-click advertisement option contracts for sponsored searchBowei Chen
In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine’s revenue; and the weak loyalty between advertiser and search engine. In this article, we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to purchase among the prespecified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers’ loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.
Pricing average price advertising options when underlying spot market prices ...Bowei Chen
Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.
Expense constrained bidder optimization in repeated auctions.
the nature of what this budget limit means for the bidders themselves is somewhat of a mystery. There seems to be some risk control element to it, some purely administrative element to it, some bounded-rationality element to it, and more...
A two parameter model for expense constraints in online budgeting problems.
Optimal bid can be mapped to static auction with a shaded virtual valuation.
Paper has more contents: MFE analysis and a finite horizon model.
Combining guaranteed and spot markets in display advertising: selling guarant...Bowei Chen
While page views are often sold instantly through real-time auctions when users visit websites, they can also be sold in advance via guaranteed contracts. In this paper, we present a dynamic programming model to study how an online publisher should optimally allocate and price page views between guaranteed and spot markets. The problem is challenging because the allocation and pricing of guaranteed contracts affect how advertisers split their purchases between the two markets, and the terminal value of the model is endogenously determined by the updated dual force of supply and demand in auctions. We take the advertisers’ purchasing behaviour into consideration, i.e., risk aversion and stochastic demand arrivals, and present a scalable and efficient algorithm for the optimal solution. The model is also empirically validated with a commercial dataset. The experimental results show that selling page views via both channels can increase the publisher’s expected total revenue, and the optimal pricing and allocation strategies are robust to different market and advertiser types.
Multi-keyword multi-click advertisement option contracts for sponsored searchBowei Chen
In sponsored search, advertisement (abbreviated ad) slots are usually sold by a search engine to an advertiser through an auction mechanism in which advertisers bid on keywords. In theory, auction mechanisms have many desirable economic properties. However, keyword auctions have a number of limitations including: the uncertainty in payment prices for advertisers; the volatility in the search engine’s revenue; and the weak loyalty between advertiser and search engine. In this article, we propose a special ad option that alleviates these problems. In our proposal, an advertiser can purchase an option from a search engine in advance by paying an upfront fee, known as the option price. The advertiser then has the right, but no obligation, to purchase among the prespecified set of keywords at the fixed cost-per-clicks (CPCs) for a specified number of clicks in a specified period of time. The proposed option is closely related to a special exotic option in finance that contains multiple underlying assets (multi-keyword) and is also multi-exercisable (multi-click). This novel structure has many benefits: advertisers can have reduced uncertainty in advertising; the search engine can improve the advertisers’ loyalty as well as obtain a stable and increased expected revenue over time. Since the proposed ad option can be implemented in conjunction with the existing keyword auctions, the option price and corresponding fixed CPCs must be set such that there is no arbitrage between the two markets. Option pricing methods are discussed and our experimental results validate the development. Compared to keyword auctions, a search engine can have an increased expected revenue by selling an ad option.
Pricing average price advertising options when underlying spot market prices ...Bowei Chen
Advertising options have been recently studied as a special type of guaranteed contracts in online advertising, which are an alternative sales mechanism to real-time auctions. An advertising option is a contract which gives its buyer a right but not obligation to enter into transactions to purchase page views or link clicks at one or multiple pre-specified prices in a specific future period. Different from typical guaranteed contracts, the option buyer pays a lower upfront fee but can have greater flexibility and more control of advertising. Many studies on advertising options so far have been restricted to the situations where the option payoff is determined by the underlying spot market price at a specific time point and the price evolution over time is assumed to be continuous. The former leads to a biased calculation of option payoff and the latter is invalid empirically for many online advertising slots. This paper addresses these two limitations by proposing a new advertising option pricing framework. First, the option payoff is calculated based on an average price over a specific future period. Therefore, the option becomes path-dependent. The average price is measured by the power mean, which contains several existing option payoff functions as its special cases. Second, jump-diffusion stochastic models are used to describe the movement of the underlying spot market price, which incorporate several important statistical properties including jumps and spikes, non-normality, and absence of autocorrelations. A general option pricing algorithm is obtained based on Monte Carlo simulation. In addition, an explicit pricing formula is derived for the case when the option payoff is based on the geometric mean. This pricing formula is also a generalized version of several other option pricing models discussed in related studies.
Expense constrained bidder optimization in repeated auctions.
the nature of what this budget limit means for the bidders themselves is somewhat of a mystery. There seems to be some risk control element to it, some purely administrative element to it, some bounded-rationality element to it, and more...
A two parameter model for expense constraints in online budgeting problems.
Optimal bid can be mapped to static auction with a shaded virtual valuation.
Paper has more contents: MFE analysis and a finite horizon model.
Banque de France's Workshop on Granularity: Basile Grassi's slides, June 2016 Soledad Zignago
Large Firm Dynamics and the Business Cycle, slides by Basile Grassi (University of Oxford & Nuffield College), joint work with Vasco Carvalho (University of Cambridge & CREI, University Pompeu Fabra GSE & CEPR), at the Banque de France and Sciences Po joint workshop on Granularity of Macroeconomics Fluctuations, 24 June 2016. Slides of presentations & discussions are available online: https://www.banque-france.fr/en/economics-statistics/research/seminars-and-symposiums/research-workshop-on-the-granularity-of-macroeconomic-fluctuations-where-do-we-stand.html
Winner Determination in Combinatorial Reverse AuctionsSamira Sadaoui
Combinatorial reverse auctions with two quantitative attributes and buyer's and sellers' constraints.
Winner determination based on genetics algorithms.
Evaluation and comparison
Nonlinear Price Impact and Portfolio Choiceguasoni
In a market with price-impact proportional to a power of the order flow, we derive optimal trading policies and their implied welfare for long-term investors with constant relative risk aversion, who trade one safe asset and one risky asset that follows geometric Brownian motion. These quantities admit asymptotic explicit formulas up to a structural constant that depends only on the price-impact exponent. Trading rates are finite as with linear impact, but they are lower near the target portfolio, and higher away from the target. The model nests the square-root impact law and, as extreme cases, linear impact and proportional transaction costs.
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)
Fai alshammariChapter 2Section 2.1 Q1- Consider the gr.docxmydrynan
Fai alshammari
Chapter 2
Section 2.1
Q1:- Consider the graph to the right. Explain the idea of a critical value. Then determine which x-values are critical values, and state why.
Q2:-
Find the relative extreme points of the function, if they exist. Then sketch a graph of the function.
f(x)equals=x squared plus 6 x plus 15x2+6x+15
Q3:-
Find the relative extreme points of the function, if they exist. Then sketch a graph of the function.
G(x)equals=x cubed minus 9 x squared plus 1x3−9x2+1
· Identify all the relative minimum points. Select the correct choice below and, if necessary, fill in the answer box to complete your choice
· Identify all the relative maximum points. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
·
· Graph the function. Choose the correct graph below.
SECTION 2.2
Q1:-
Find all relative extrema and classify each as a maximum or minimum. Use the second-derivative test where possible.
f(x)equals=negative 27 x cubed plus 9 x plus 2−27x3+9x+2
_Identify all the relative minima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_Identify all the relative maxima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice
Q2:-
Sketch the graph of the following function. List the coordinates of where extrema or points of inflection occur. State where the function is increasing or decreasing as well as where it is concave up or concave down.
f left parenthesis x right parenthesisf(x)equals=x Superscript 4 Baseline minus 4 x cubed plus 3x4−4x3+3
_What are the coordinates of the relative extrema? Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_Identify all the relative maxima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_On what interval(s) is f increasing or decreasing?
_On what interval(s) is f concave up or concave down?
_ SKETCH GRAPH
Q3:-
Sketch the graph that possesses the characteristics listed.
f is concave
up at
(negative 1−1,66),
concave
downdown
at
(77,negative 4−4),
and has an inflection point at left parenthesis 3 comma 1 right parenthesis .(3,1).
SECTION 2.3
Q1:-
Determine the vertical asymptote(s) of the following function. If none exist, state that fact.
f(x)equals=StartFraction x plus 3 Over x squared plus 9 x plus 18 EndFractionx+3x2+9x+18
Q2:-
Determine the horizontal asymptote of the function.
f(x)equals=StartFraction 8 x cubed minus 8 x plus 3 Over 10 x cubed plus 4 x minus 7 EndFraction8x3−8x+310x3+4x−7
Q3:-
Sketch the graph of the function. Indicate where each function is increasing or decreasing, where any relative extrema occur, where asymptotes occur, where the graph is concave up or concave down, where any points of inflection occur, and where any intercepts occur.
f(x)equ ...
The smile calibration problem is a mathematical conundrum in finance that has challenged quantitative analysts for decades. Through his research, Aitor Muguruza has discovered a novel resolution to this classic problem.
The What, Why & How of 3D and AR in Digital CommercePushON Ltd
Vladimir Mulhem has over 20 years of experience in commercialising cutting edge creative technology across construction, marketing and retail.
Previously the founder and Tech and Innovation Director of Creative Content Works working with the likes of Next, John Lewis and JD Sport, he now helps retailers, brands and agencies solve challenges of applying the emerging technologies 3D, AR, VR and Gen AI to real-world problems.
In this webinar, Vladimir will be covering the following topics:
Applications of 3D and AR in Digital Commerce,
Benefits of 3D and AR,
Tools to create, manage and publish 3D and AR in Digital Commerce.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
More Related Content
Similar to A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
Banque de France's Workshop on Granularity: Basile Grassi's slides, June 2016 Soledad Zignago
Large Firm Dynamics and the Business Cycle, slides by Basile Grassi (University of Oxford & Nuffield College), joint work with Vasco Carvalho (University of Cambridge & CREI, University Pompeu Fabra GSE & CEPR), at the Banque de France and Sciences Po joint workshop on Granularity of Macroeconomics Fluctuations, 24 June 2016. Slides of presentations & discussions are available online: https://www.banque-france.fr/en/economics-statistics/research/seminars-and-symposiums/research-workshop-on-the-granularity-of-macroeconomic-fluctuations-where-do-we-stand.html
Winner Determination in Combinatorial Reverse AuctionsSamira Sadaoui
Combinatorial reverse auctions with two quantitative attributes and buyer's and sellers' constraints.
Winner determination based on genetics algorithms.
Evaluation and comparison
Nonlinear Price Impact and Portfolio Choiceguasoni
In a market with price-impact proportional to a power of the order flow, we derive optimal trading policies and their implied welfare for long-term investors with constant relative risk aversion, who trade one safe asset and one risky asset that follows geometric Brownian motion. These quantities admit asymptotic explicit formulas up to a structural constant that depends only on the price-impact exponent. Trading rates are finite as with linear impact, but they are lower near the target portfolio, and higher away from the target. The model nests the square-root impact law and, as extreme cases, linear impact and proportional transaction costs.
My talk in the Mathematical Finance Seminar at Humboldt-Universität zu Berlin, October 27, 2022, about my recent works (i) "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing" (link: https://arxiv.org/abs/2111.01874), (ii) "Multilevel Monte Carlo combined with numerical smoothing for robust and efficient option pricing and density estimation" (link: https://arxiv.org/abs/2003.05708) and (iii) "Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models" (link: https://arxiv.org/abs/2203.08196)
Fai alshammariChapter 2Section 2.1 Q1- Consider the gr.docxmydrynan
Fai alshammari
Chapter 2
Section 2.1
Q1:- Consider the graph to the right. Explain the idea of a critical value. Then determine which x-values are critical values, and state why.
Q2:-
Find the relative extreme points of the function, if they exist. Then sketch a graph of the function.
f(x)equals=x squared plus 6 x plus 15x2+6x+15
Q3:-
Find the relative extreme points of the function, if they exist. Then sketch a graph of the function.
G(x)equals=x cubed minus 9 x squared plus 1x3−9x2+1
· Identify all the relative minimum points. Select the correct choice below and, if necessary, fill in the answer box to complete your choice
· Identify all the relative maximum points. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
·
· Graph the function. Choose the correct graph below.
SECTION 2.2
Q1:-
Find all relative extrema and classify each as a maximum or minimum. Use the second-derivative test where possible.
f(x)equals=negative 27 x cubed plus 9 x plus 2−27x3+9x+2
_Identify all the relative minima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_Identify all the relative maxima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice
Q2:-
Sketch the graph of the following function. List the coordinates of where extrema or points of inflection occur. State where the function is increasing or decreasing as well as where it is concave up or concave down.
f left parenthesis x right parenthesisf(x)equals=x Superscript 4 Baseline minus 4 x cubed plus 3x4−4x3+3
_What are the coordinates of the relative extrema? Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_Identify all the relative maxima. Select the correct choice below and, if necessary, fill in the answer box to complete your choice.
_On what interval(s) is f increasing or decreasing?
_On what interval(s) is f concave up or concave down?
_ SKETCH GRAPH
Q3:-
Sketch the graph that possesses the characteristics listed.
f is concave
up at
(negative 1−1,66),
concave
downdown
at
(77,negative 4−4),
and has an inflection point at left parenthesis 3 comma 1 right parenthesis .(3,1).
SECTION 2.3
Q1:-
Determine the vertical asymptote(s) of the following function. If none exist, state that fact.
f(x)equals=StartFraction x plus 3 Over x squared plus 9 x plus 18 EndFractionx+3x2+9x+18
Q2:-
Determine the horizontal asymptote of the function.
f(x)equals=StartFraction 8 x cubed minus 8 x plus 3 Over 10 x cubed plus 4 x minus 7 EndFraction8x3−8x+310x3+4x−7
Q3:-
Sketch the graph of the function. Indicate where each function is increasing or decreasing, where any relative extrema occur, where asymptotes occur, where the graph is concave up or concave down, where any points of inflection occur, and where any intercepts occur.
f(x)equ ...
The smile calibration problem is a mathematical conundrum in finance that has challenged quantitative analysts for decades. Through his research, Aitor Muguruza has discovered a novel resolution to this classic problem.
The What, Why & How of 3D and AR in Digital CommercePushON Ltd
Vladimir Mulhem has over 20 years of experience in commercialising cutting edge creative technology across construction, marketing and retail.
Previously the founder and Tech and Innovation Director of Creative Content Works working with the likes of Next, John Lewis and JD Sport, he now helps retailers, brands and agencies solve challenges of applying the emerging technologies 3D, AR, VR and Gen AI to real-world problems.
In this webinar, Vladimir will be covering the following topics:
Applications of 3D and AR in Digital Commerce,
Benefits of 3D and AR,
Tools to create, manage and publish 3D and AR in Digital Commerce.
The digital marketing industry is changing faster than ever and those who don’t adapt with the times are losing market share. Where should marketers be focusing their efforts? What strategies are the experts seeing get the best results? Get up-to-speed with the latest industry insights, trends and predictions for the future in this panel discussion with some leading digital marketing experts.
Short video marketing has sweeped the nation and is the fastest way to build an online brand on social media in 2024. In this session you will learn:- What is short video marketing- Which platforms work best for your business- Content strategies that are on brand for your business- How to sell organically without paying for ads.
Everyone knows the power of stories, but when asked to come up with them, we struggle. Either we second guess ourselves as to the story's relevance, or we just come up blank and can't think of any. Unlocking Everyday Narratives: The Power of Storytelling in Marketing will teach you how to recognize stories in the moment and to recall forgotten moments that your audience needs to hear.
Key Takeaways:
Understand Why Personal Stories Connect Better
How To Remember Forgotten Stories
How To Use Customer Experiences As Stories For Your Brand
When most people in the industry talk about online or digital reputation management, what they're really saying is Google search and PPC. And it's usually reactive, left dealing with the aftermath of negative information published somewhere online. That's outdated. It leaves executives, organizations and other high-profile individuals at a high risk of a digital reputation attack that spans channels and tactics. But the tools needed to safeguard against an attack are more cybersecurity-oriented than most marketing and communications professionals can manage. Business leaders Leaders grasp the importance; 83% of executives place reputation in their top five areas of risk, yet only 23% are confident in their ability to address it. To succeed in 2024 and beyond, you need to turn online reputation on its axis and think like an attacker.\
Key Takeaways:
- New framework for examining and safeguarding an online reputation
- Tools and techniques to keep you a step ahead
- Practical examples that demonstrate when to act, how to act and how to recover
Mastering Local SEO for Service Businesses in the AI Era is tailored specifically for local service providers like plumbers, dentists, and others seeking to dominate their local search landscape. This session delves into leveraging AI advancements to enhance your online visibility and search rankings through the Content Factory model, designed for creating high-impact, SEO-driven content. Discover the Dollar-a-Day advertising strategy, a cost-effective approach to boost your local SEO efforts and attract more customers with minimal investment. Gain practical insights on optimizing your online presence to meet the specific needs of local service seekers, ensuring your business not only appears but stands out in local searches. This concise, action-oriented workshop is your roadmap to navigating the complexities of digital marketing in the AI age, driving more leads, conversions, and ultimately, success for your local service business.
Key Takeaways:
Embrace AI for Local SEO: Learn to harness the power of AI technologies to optimize your website and content for local search. Understand the pivotal role AI plays in analyzing search trends and consumer behavior, enabling you to tailor your SEO strategies to meet the specific demands of your target local audience. Leverage the Content Factory Model: Discover the step-by-step process of creating SEO-optimized content at scale. This approach ensures a steady stream of high-quality content that engages local customers and boosts your search rankings. Get an action guide on implementing this model, complete with templates and scheduling strategies to maintain a consistent online presence. Maximize ROI with Dollar-a-Day Advertising: Dive into the cost-effective Dollar-a-Day advertising strategy that amplifies your visibility in local searches without breaking the bank. Learn how to strategically allocate your budget across platforms to target potential local customers effectively. The session includes an action guide on setting up, monitoring, and optimizing your ad campaigns to ensure maximum impact with minimal investment.
How to Run Landing Page Tests On and Off Paid Social PlatformsVWO
Join us for an exclusive webinar featuring Mariate, Alexandra and Nima where we will unveil a comprehensive blueprint for crafting a successful paid media strategy focused on landing page testing.With escalating costs in paid advertising, understanding how to maximize each visitor’s experience is crucial for retention and conversion.
This session will dive into the methodologies for executing and analyzing landing page tests within paid social channels, offering a blend of theoretical knowledge and practical insights.
The Pearmill team will guide you through the nuances of setting up and managing landing page experiments on paid social platforms. You will learn about the critical rules to follow, the structure of effective tests, optimal conversion duration and budget allocation.
The session will also cover data analysis techniques and criteria for graduating landing pages.
In the second part of the webinar, Pearmill will explore the use of A/B testing platforms. Discover common pitfalls to avoid in A/B testing and gain insights into analyzing A/B tests results effectively.
Is AI-Generated Content the Future of Content Creation?Cut-the-SaaS
Discover the transformative power of AI in content creation with our presentation, "Is AI-Generated Content the Future of Content Creation?" by Puran Parsani, CEO & Editor of Cut-The-SaaS. Learn how AI-generated content is revolutionizing marketing, publishing, education, healthcare, and finance by offering unprecedented efficiency, creativity, and scalability.
Understanding
AI-Generated Content:
AI-generated content includes text, images, videos, and audio produced by AI without direct human involvement. This technology leverages large datasets to create contextually relevant and coherent material, streamlining content production.
Key Benefits:
Content Creation: Rapidly generate high-quality content for blogs, articles, and social media.
Brainstorming: AI simulates conversations to inspire creative ideas.
Research Assistance: Efficiently summarize and research information.
Market Insights:
The content marketing industry is projected to grow to $17.6 billion by 2032, with AI-generated content expected to dominate over 55% of the market.
Case Study: CNET’s AI Content Controversy:
CNET’s use of AI for news articles led to public scrutiny due to factual inaccuracies, highlighting the need for transparency and human oversight.
Benefits Across Industries:
Marketing: Personalize content at scale and optimize engagement with predictive analytics.
Publishing: Automate content creation for faster publication cycles.
Education: Efficiently generate educational materials.
Healthcare: Create accurate content for patients and professionals.
Finance: Produce timely financial content for decision-making.
Challenges and Ethical Considerations:
Transparency: Disclose AI use to maintain trust.
Bias: Address potential AI biases with diverse datasets.
SEO: Ensure AI content meets SEO standards.
Quality: Maintain high standards to prevent misinformation.
Conclusion:
AI-generated content offers significant benefits in efficiency, personalization, and scalability. However, ethical considerations and quality assurance are crucial for responsible use. Explore the future of content creation with us and see how AI is transforming various industries.
Connect with Us:
Follow Cut-The-SaaS on LinkedIn, Instagram, YouTube, Twitter, and Medium. Visit cut-the-saas.com for more insights and resources.
Monthly Social Media News Update May 2024Andy Lambert
TL;DR. These are the three themes that stood out to us over the course of last month.
1️⃣ Social media is becoming increasingly significant for brand discovery. Marketers are now understanding the impact of social and budgets are shifting accordingly.
2️⃣ Instagram’s new algorithm and latest guidance will help us maintain organic growth. Instagram continues to evolve, but Reels remains the most crucial tool for growth.
3️⃣ Collaboration will help us unlock growth. Who we work with will define how fast we grow. Meta continues to evolve their Creator Marketplace and now TikTok are beginning to push ‘collabs’ more too.
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
2. Find ways to show EEAT
3. Repurpose across all platforms
Mastering Multi-Touchpoint Content Strategy: Navigate Fragmented User JourneysSearch Engine Journal
Digital platforms are constantly multiplying, and with that, user engagement is becoming more intricate and fragmented.
So how do you effectively navigate distributing and tailoring your content across these various touchpoints?
Watch this webinar as we dive into the evolving landscape of content strategy tailored for today's fragmented user journeys. Understanding how to deliver your content to your users is more crucial than ever, and we’ll provide actionable tips for navigating these intricate challenges.
You’ll learn:
- How today’s users engage with content across various channels and devices.
- The latest methodologies for identifying and addressing content gaps to keep your content strategy proactive and relevant.
- What digital shelf space is and how your content strategy needs to pivot.
With Wayne Cichanski, we’ll explore innovative strategies to map out and meet the diverse needs of your audience, ensuring every piece of content resonates and connects, regardless of where or how it is consumed.
In this presentation, Danny Leibrandt explains the impact of AI on SEO and what Google has been doing about it. Learn how to take your SEO game to the next level and win over Google with his new strategy anyone can use. Get actionable steps to rank your name, your business, and your clients on Google - the right way.
Key Takeaways:
1. Real content is king
2. Find ways to show EEAT
3. Repurpose across all platforms
Come learn how YOU can Animate and Illuminate the World with Generative AI's Explosive Power. Come sit in the driver's seat and learn to harness this great technology.
How to Use AI to Write a High-Quality Article that Ranksminatamang0021
In the world of content creation, many AI bloggers have drifted away from their original vision, resulting in low-quality articles that search engines overlook. Don't let that happen to you! Join us to discover how to leverage AI tools effectively to craft high-quality content that not only captures your audience's attention but also ranks well on search engines.
Disclaimer: Some of the prompts mentioned here are the examples of Matt Diggity. Please use it as reference and make your own custom prompts.
5 big bets to drive growth in 2024 without one additional marketing dollar AND how to adapt to the biggest shifting eCommerce trend- AI.
1) Romance Your Customers - Retention
2) ‘Alternative’ Lead Gen - Advocacy
3) The Beautiful Basics - Conversion Rate Optimization
4) Land that Bottom Line - Profitability
5) Roll the Dice - New Business Models
Top 3 Ways to Align Sales and Marketing Teams for Rapid GrowthDemandbase
In this session, Demandbase’s Stephanie Quinn, Sr. Director of Integrated and Digital Marketing, Devin Rosenberg, Director of Sales, and Kevin Rooney, Senior Director of Sales Development will share how sales and marketing shapes their day-to-day and what key areas are needed for true alignment.
Top 3 Ways to Align Sales and Marketing Teams for Rapid Growth
A dynamic pricing model for unifying programmatic guarantee and real-time bidding in display advertising
1. A dynamic pricing model for unifying programmatic
guarantee and real-time bidding in display advertising1
Bowei Chen, Shuai Yuan, Jun Wang
Department of Computer Science
University College London
2014
1
The Best Paper Award in ADKDD’14, New York City, NY, USA
(in ACM proceedings database: dl.acm.org/citation.cfm?id=2648585)
2. PG and RTB – currently two independently processes
Advertiser or DSP
Publisher or SSP
RTB
in . 1
[ , ]
n n
t t
Guaranteed
contracts in .
0
[ , ]
n
t t
Estimated impressions in .
1
[ , ]
n n
t t
Allocation
Pricing
PG
[t0, tn] is the time period to sell the
guaranteed impressions that will be
created in future period [tn, tn+1]
3. Motivation
There is need of a price and allocation engine that brings automation into PG and
connects RTB
• Both PG & RTB are growing rapidly:
$3.9bn for RTB, $3.5bn for programmatic guaranteed (PG), US, 2014
$10.5bn for RTB, $6.5bn for PG, US, 2017 projected2
• They both have great potential:
$42.78bn for online advertising, US, 2013 FY3
2
MAGNA GLOBAL Ad Forecasts: Programmatic Buying Reaching a Tipping Point, 2014
3
IAB Internet Advertising Revenue Report, 2014
4. Objective function
The optimization problem can be expressed as
max
( Z T
0
(1 − ωκ)p(τ)θ(τ, p(τ))f(τ)dτ
| {z }
G = Expected total revenue from guaranted selling
minus expected penalty of failling to delivery
+
S −
Z T
0
θ(τ, p(τ))f(τ)dτ
φ(ξ)
| {z }
H = Expected total revenue from RTB
)
,
s.t. p(0) =
(
φ(ξ) + λψ(ξ), if π(ξ) ≥ φ(ξ) + λψ(ξ),
π(ξ), if π(ξ) φ(ξ) + λψ(ξ),
where
ξ =
Remaining demand in [tn, tn+1]
Remaining supply in [tn, tn+1]
=
Q −
R T
0 θ(τ, p(τ))f(τ)dτ
S −
R T
0 θ(τ, p(τ))f(τ)dτ
.
5. Distribution of bids in RTB
¶ Log-normal distribution: X ∼ LN(µ, σ2)
The expected per impression payment price from a second-price auction is
φ(ξ) =
Z ∞
0
xξ(ξ − 1)g(x)
1 − F(x)
F(x)
ξ−2
dx,
where
g(x) =
1
xσ
√
2π
e
− (ln(x)−µ)2
2σ2 , F(x) =
1
2
+
1
√
π
Z ln(x)−µ
√
2σ
0
e−z2
dz.
· Empirical method
Robust Locally Weighted Regression (see Algorithm 1)
6. Purchase behaviour
¶ One buys less if an inventory is expensive
Given τ and 0 ≤ p1 ≤ p2, then θ(τ, p1) ≥ θ(τ, p2), s.t. θ(τ, 0) = 1.
· One buys less if it is early
Given p and 0 ≤ τ2 ≤ τ1, then θ(τ2, p) ≥ θ(τ1, p).
We adopt the functional forms of demand:
θ(τ, p(τ)) = e−αp(τ)(1+βτ)
,
f(τ) = ζe−ητ
,
where α is the level of price effect, β and η are the levels of time effect, and the demand
density rises to a peak ζ on the delivery date, so that θ(τ, p(τ))f(τ)dτ is the number of
advertisers who will buy guaranteed impressions at p(τ).
7. Demand surface θ(τ, p(τ))f(τ)dτ
α = 1.85,
β = 0.01,
ζ = 2000,
η = 0.01,
T = 30.
0
10
20
30
2
4
6
0
500
1000
1500
τ = T−t
p(τ)
θ(τ,
p(τ))f(τ)dτ
Daily demand on
specific series of
guaranteed prices
8. Solution
The objective function is solved by Algorithm 2, in which the optimal guaranteed price
can be described as follows:
p(τ) =
e
λ
1 − ωκ
+
1
α(1 + βτ)
.
The notation e
λ(α, β, ζ, η, ω, κ, γiS) represents the dependency relationship among e
λ and
other parameters.
9. Solution
Algorithm 2:
function PGSolve(α, β, ζ, η, ω, κ, λ, S, Q, T)
t ← [t0, · · · , tn], 0 = t0 t1 · · · tn = T.
τ ← T − t, m ← # of simulations.
loop i ← 1 to m
γi ← RandomUniformGenerate([0, 1])
R T
0 θ(τ, p(τ))f(τ)dτ ← γiS
ξi ← (Q − γiS)/(S − γS)
Hi ← (1 − γi)Sφ(ξi)
Gi ←
R T
0 (1 − ωκ)p(τ)θ(τ, p(τ))f(τ)dτ
pi ← arg max Gi,
s.t.
Z T
0
θ(τ, p(τ))f(τ)dτ = γiS,
p(0) =
(
φ(ξi) + λψ(ξi), if π(ξi) ≥ φ(ξi) + λψ(ξi),
π(ξi), if π(ξi) φ(ξi) + λψ(ξi).
Ri ← max Gi + Hi
end loop
γ∗ ← arg maxγi∈Ω(γ){R1, . . . , Rm}
p∗ ← arg maxpi∈Ω(p){R1, . . . , Rm}
return γ∗, p∗
end function
11. Datasets
Table: Summary of RTB datasets.
Dataset SSP DSP
From 08/01/2013 19/10/2013
To 14/02/2013 27/10/2013
# of ad slots 31 53571
# of user tags NA 69
# of advertisers 374 4
# of impressions 6646643 3158171
# of bids 33043127 11457419
Bid quote USD/CPM CNY/CPM
Table: Experimental design of the SSP dataset.
From To
Training set 08/01/2013 13/02/2013
Development set 08/01/2013 14/02/2013
Test set 14/02/2013
13. Bidding behaviours
Table: Summary of the winning advertisers’ statistics from the SSP dataset in the training
period: the numbers in the brackets represent how many advertisers who use the combined
bidding strategies.
Bidding # of # of change Average change rate Ratio of payment
strategy advertisers imps won of payment prices price to winning bid
Fixed price 188 (51) 454681 188.85% 43.93%
Non-fixed price 200 (51) 6068908 517.54% 58.94%
Table: Summary of advertisers’ winning campaigns from the DSP dataset. All the advertisers use
the fixed price bidding strategy. Each user tag contains many ad slots and an ad slot is sampled
from the dataset only if the advertiser wins more than 1000 impressions from it.
Advertiser # of # of # of Average change rate Ratio of payment
ID user tags ad slots imps won of payment prices price to winning bid
1 69 635 196831 58.57% 36.07%
2 69 428 144272 58.94% 34.68%
3 69 1267 123361 79.24% 30.89%
4 65 15 3139 104.19% 22.32%
15. Demand per impression reflects the market competition
1 2 3 4 5 6 7 8 9 10111213141516171819202122232425262728293031
0
50
Ad slot ID
26282430 1 7 9141612211820 3 5 210 61911 417 81315222325272931
0
0.5
1
1.5
2
2.5
3
Score
of
average
distance
in
Ad slot ID
Group of ad slots
with a higher level
of competition
Group of ad slots
with a lower level
of competition
16. Empirical example 1: (AdSlot14) demand per impression 3.39
In a less competitive market:
• Fewer buyers are willing to buy in advance
• Less impressions to PG (here 42.40%)
• PG prices are not expensive
• Revenue mainly contributed by RTB
0 3 6 9 12 15 18 21 24 27 30 33 36 38
0.5
1
1.5
t (where tn
= T = 37, tn+1
= 38)
Price
(a)
p(τ), τ = T−t for t∈[0,T]
Expected risk−aversion cost in RTB
Expected payment (2nd) price in RTB
New expected risk−aversion cost in RTB
New expected payment (2nd) price in RTB
0 5 10 15 20 25 30 3538
0
1000
2000
t (where tn
= T = 37, tn+1
= 38)
#
of
impressions
(b)
0 5 10 15 20 25 30 3538
0
1000
2000
t (where tn
= T = 37, tn+1
= 38)
#
of
impressions
(c)
500 1000 1500 2000 2500
0
1
2
3
Sequential auctions in [tn
, tn+1
]
Price
(d)
Winning bid
Payment price
B−I B−II B−III R−I R−II
0
1000
2000
3000
Revenue
(e)
17. Empirical example 2: (AdSlot27) demand per impression 9.63
In a competitive market:
• More buyers are willing to buy in advance
• More impressions to PG (here 66%)
• PG prices are higher
• Revenue mainly contributed by PG
0 3 6 9 12 15 18 21 24 27 30 33 36 38
0.6
0.8
1
1.2
1.4
1.6
1.8
2
t (where tn
= T = 37, tn+1
= 38)
Price
(a)
0 5 10 15 20 25 30 3538
0
2000
4000
6000
t (where tn
= T = 37, tn+1
= 38)
#
of
impressions
(b)
0 5 10 15 20 25 30 3538
0
2000
4000
6000
t (where tn
= T = 37, tn+1
= 38)
#
of
impressions
(c)
2000 4000 6000
0
2
4
Sequential auctions in [tn
, tn+1
]
Price
(d)
B−I B−II B−III R−I R−II
0
5000
10000
15000
Revenue
(e)
18. Revenue analysis
Table: Summary of revenue evaluation of all 31 ad slots in the SSP dataset.
Group of ad slots
Revenue maximization Price discrimination
Estimated Actual Difference Ratio of Ratio of
revenue revenue of RTB actual 2nd actual optimal
increase increase revenue price reve reve to actual
between to actual 1st price
estimation 1st price reve
actual reve
Low competition 31.06% 8.69% 13.87% 67.05% 81.78%
High competition 31.73% 21.51% 6.23% 78.04% 94.70%
20. Parameter estimation 2
0.5 1 1.5 2 2.5 3
0
0.2
0.4
0.6
0.8
1
x or p (x=p)
Demand
(probability)
z(x) = 1−F(x)
Fittest c(p) to z(x): α = 1.72
c(p) = e
−α p
, α ∈ [0,5]
α is calculated based on the smallest RMSE between the inverse function of empirical
CDF of bids z(x) = 1 − F(x) and the function c(p) = e−αp
21. Concluding remarks
This paper proposes a mathematical model that allocates and prices the future
impressions between real-time auctions and guaranteed contracts. Under conventional
economic assumptions, our model shows that the two ways can be seamless combined
programmatically and the publisher’s revenue can be maximized via price discrimination
and optimal allocation.