Tuesday, March 26, 2024

Can AI Improve Ad Effectiveness by 50% or So?

One of the verities of advertising has been that "half my investment is wasted, but I don't know which half." Artficial intelligence should help, in that regard, allowing use of greater analytics to improve precision. Cookiers were supposed to help. But other alternatives are coming.


And it does matter, as so much content access is subsidized by advertising.


Market research firm PQ Media estimated 54 percent of total media consumption time was spent with ad-supported media in 2022. 


Of course, much user engagement with content happens in social media or within the context of search. Google in 2023 earned about $238 billion from search, according to Statista. Social media booked about $199 billion globally in 2023, according to The Business Research Company. 


Social Media

Revenue $ Billions

Source

Meta (Facebook, Instagram, etc.)

132.1

Insider Intelligence

Alphabet (Google)

85.7 (Social Media segment)

Alphabet Investor Relations 

TikTok

6.1

Insider Intelligence 

Twitter

5.8

Insider Intelligence 

Snap

5.1

Insider Intelligence 

Search

Revenue $ Billions

Source

Google

237.9

Statista 

Microsoft (Bing)

12.0

Insider Intelligence 

Baidu (China)

31.0

Insider Intelligence 


So to the extent that advertising enables citizen use of such information and content sources, advertising actually does support media and content access. 


Cookies have been an essential building block for online advertising, but are going to  be replaced. Replaced “by what” remains an issue, though. 


There isn't one single expected replacement for "cookies" as a tracking mechanism for advertisers. Instead, the industry is moving towards a multifaceted approach. 


First-party data is likely to become more important, where data is directly gotten from users with consent.


More efficient is contextual targeting, where advertisers target ads based on the context of the web page or app they are using. This can involve factors like the content itself, user demographics, and browsing history within the specific platform. Artificial intelligence is likely to help with that. 


Efforts also will be made to target based on cohort data rather than individual information. 

FLOC (Federated Learning of Cohorts) is a Google-developed proposal where users are grouped into cohorts based on similar browsing behavior without revealing individual data points.


Unified ID 2.0 is an industry-backed initiative that aims to provide a privacy-preserving alternative to third-party cookies by using a single, anonymous identifier across different platforms. 




Monday, March 25, 2024

CxO AI Concerns Vary by Job Title

As always is the case with any information technology deployment in an enterprise, CxOs have distinctly different concerns about using generative artificial intelligence, C-suite surveys generally suggest. 


CEOs might generally face issues understanding the potential applications of generative AI across the organization and making informed decisions about where to invest. CEOs also will be concerned with the strategic alignment of generative AI initiatives with overall business goals and objectives; impact on company culture and employee morale due to potential job displacement or skill gaps or the long-term sustainability and scalability of generative AI solutions. 


For CEOs, return on investment often is an issue as well. 


Chief marketing officers especially might have issues with evaluating potential applications and making informed decisions about how generative AI can be used to improve marketing campaigns, create personalized content, and generate creative assets.


CMOs also must evaluate potential for inauthentic or misleading content generation that could damage brand reputation. Measurement and attribution of marketing campaigns that involve AI-generated content can also be concerns. 


As you would guess, CIOs and CTOs have other top concerns about security, privacy, documenting the logic, integration with existing systems, total cost of ownership, vendor management and employee training. Regulatory compliance and ethical concerns also exist. 


Issue

CIO Concerns

CTO Concerns

Security and Data Privacy

Maintaining data security and user privacy with large data sets.

Ensuring robust security infrastructure can handle the demands of generative AI models.

Transparency and Explainability

Difficulty in understanding and explaining AI outputs for decision-making and compliance.

Mitigating the "black box" nature of complex models and building interpretability into the development process.

Integration and Interoperability

Integrating generative AI with existing IT infrastructure and ensuring compatibility across different systems.

Managing the technical complexity of integrating AI models into applications and workflows.

Cost and Return on Investment (ROI)

Justifying the cost of acquiring, developing, and maintaining generative AI systems against potential benefits.

Balancing technical feasibility with cost-effectiveness and demonstrating clear ROI for proposed AI projects.

Skill Gap and Workforce Management

Identifying and acquiring the necessary talent to manage, operate, and maintain generative AI solutions.

Addressing potential job displacement and reskilling existing personnel to adapt to a changing technological landscape.

Vendor Management and Long-Term Support

Evaluating and selecting reliable vendors for AI solutions and ensuring ongoing support and maintenance.

Ensuring the chosen technology stack can be sustained and adapted to future advancements in the field.

Ethical Considerations and Regulatory Compliance

Mitigating potential bias in AI outputs and ensuring ethical and responsible use of the technology.

Addressing evolving regulatory frameworks and complying with data privacy regulations in different jurisdictions.

Of course, those include the typical concerns CxOs have about any proposed new information technology. 


CxO Role

Concerns

CEO

Strategic Fit: Does the technology align with the overall business strategy and objectives? Return on Investment (ROI): Can the technology demonstrably improve profitability, growth, or other key metrics? Competitive Advantage: Can the technology create a sustainable edge over competitors? Risk Management: What are the potential risks associated with deploying the technology, and how can they be mitigated? Change Management: How will the technology impact the organization's culture, workforce, and existing workflows?

CIO

Security and Data Privacy: Can the technology be implemented securely and meet data privacy regulations? Integration and Interoperability: Can the technology integrate seamlessly with existing IT infrastructure and systems? Scalability and Performance: Can the technology handle the current and future needs of the organization? Total Cost of Ownership (TCO): What are the upfront and ongoing costs associated with acquiring, deploying, and maintaining the technology? Vendor Management: Does the chosen vendor have a good reputation, strong support infrastructure, and a clear roadmap for future development?

CTO

Technical Feasibility: Can the technology be implemented effectively given the organization's technical capabilities and resources? Technical Debt and Complexity: Will the technology introduce technical debt or create additional complexity for the IT team? Standardization and Maintainability: Can the technology be easily standardized and maintained within the existing IT environment? Performance and Scalability: Can the technology meet the organization's performance and scalability requirements? Future-Proofing: Is the technology based on a future-proof architecture that can adapt to evolving industry standards?

CMO

Customer Impact: How will the technology impact the customer experience and overall marketing effectiveness? Data and Analytics: Can the technology generate valuable customer insights and improve marketing ROI? Brand Reputation: Could the technology potentially damage the brand's reputation if not implemented or managed effectively? Agility and Time to Market: Can the technology help the marketing team be more agile and bring new products or services to market faster? Measurement and Attribution: Can the impact of the technology be effectively measured and attributed to marketing efforts?


Saturday, March 23, 2024

Can GenAI Replace Search?

Many seemingly believe a Gartner analyst opinion that AI queries will replace search, an obvious enough conclusion for those who use generative AI engines routinely.


Some might even agree that “by 2026, traditional search engine volume will drop 25 percent, with search marketing losing market share to AI chatbots and other virtual agents,” according to Alan Antin, Gartner analyst. 


Somewhat obviously, such a shift--at some scale--will potentially reshape organic and paid search as venues for marketing spend. But ask yourself: does GenAI fundamentally change the experience of “lean forward” media as compared to “lean back?” Does GenAI turn “lean forward” into “lean back” or vice versa?


It remains true that each successive wave of electronic media has shifted marketer spending, from radio to broadcast TV to cable TV to the internet, search engine marketing, social media advertising and mobile advertising. 


As virtual environments such as the metaverse are commercialized, advertising will migrate there as well.


To some extent, these shifts were zero sum games: what one emerging industry gained, the legacy media lost. The issue with generative or any other form of AI is the degree to which hybrid use models will emerge, where AI-assisted ad placement and formats develop as part of all existing venues. 


In other words, if AI becomes a core feature of search, social media, gaming, productivity apps, digital content venues and shopping, does GenAI necessarily disrupt, or might it disrupt and shift some amount of activity, but also reinforce existing venues and methods? 


In other words, does GenAI used “as an app” develop as a “new medium” or does it mostly remain a feature of existing media? 


To be sure, some might believe GenAI could revolutionize media by creating entirely new forms of storytelling, entertainment, and information dissemination. 


Others might see that as a remote possibility, with the more likely impact being the reshaping of all existing media. 


For example, GenAI might enable new forms of “interactive fiction,” where users experience narratives that adapt to user choices, generating personalized storylines and branching paths in real-time. Keep in mind that this also was expected for legacy media, by analysts considering the rise of interaction itself. Not so much has really changed, save for gaming use cases, though. “Interactive TV” has flopped, for example. 


AI-powered characters in games whose behavior is personalized for each user are more likely to happen, as is the application of GenAI to create metaverse and augmented reality experiences. But none of those are examples of media replacement. 


In other words, some of us would not agree that “search marketing” is exposed to replacement by use of GenAI. 


GenAI is most likely to modify existing media formats, making them more personalized, interactive, and immersive. Based on what happened with interactive TV (or storytelling in general), it seems unlikely that a brand new medium will emerge from GenAI. 


To the extent that GenAI becomes a core feature of search, social media and nearly all other experiences and apps, GenAI might not actually be a “threat” to search. 


Think of the established categories of “lean forward” experiences such as interacting with a PC or smartphone to the “lean back” experience of video, television, movies or music. GenAI as a feature will be used mostly to create those experiences, but might not change the fundamental “lean forward” experience of work, learning, search or shopping and gaming.


Likewise, the “lean back” nature of entertainment might not be desirable for movies, video, TV or musical experiences and storytelling in general. 


The way we consume media can be categorized into two main types: lean forward and lean back.


Lean forward media require active engagement and focus. Examples include:

Playing video games

Browsing the web or using search

Using social media platforms

Reading e-books

Working on a computer

Mental State: Engaged and alert, requiring concentration.

Physical Posture: Can vary, but often involves sitting at a desk or holding a device.


Lean back media requires minimal user effort and is largely a passive experience:

Watching television

Listening to music

Watching movies

Reading a physical book

Attending a concert or play

Mental State: Relaxed and receptive, focused on enjoying the content.

Physical Posture: Often involves sitting or reclining comfortably.


If GenAI were not tightly integrated with all “lean forward” experiences, one might have a better argument for replacement. But that is unlikely to be the case. Likewise, it is not clear that GenAI changes the fundamental “lean back” experience of storytelling in the form of books, TV, video, movies, music, concerts and plays.


Even if one assumes both search and GenAI chatbots are forms of "lean forward" experience, it is very hard to see a permanent stand-alone role, as GenAI already is rapidly being incorporated into all enterprise and consumer software and experiences.


So GenAI becomes a feature of search; not a replacement.


Friday, March 22, 2024

Can AI Replace Apps?

Almost by definition, artificial intelligence devices beyond the smartphone (pins, pendants, other form factors) will need to operate “beyond apps” as the primary interface will be the spoken word and screens might or might not be used. The interface will take the form of commands or questions that generate responses that are not confined or based on use any one installed app. 


Mobile World Congress, for example, T-Mobile showed an AI phone concept created with Qualcomm Technologies and Brain.ai featuring an AI assistant that replaces apps used on the smartphone. 


But one can argue the more-logical devices to be affected are smart appliances; smartwatches; smart TVs or auto infotainment systems, where screens are small or non-existent; cannot be used for safety reasons; have high context-related interface requirements or where the range of outputs is commonly related to a few parameters: on-off; higher-lower; short answer or sensor output results. 


Voice assistants such as Google Assistant and Amazon Alexa are already replacing the need for dedicated mobile apps to control smart home devices like thermostats, lights, and appliances. 


Smartwatches and other wearable devices might rely less on touchscreens and buttons, using voice commands and gestures.  


AI-powered interfaces on smart TVs and streaming devices could become more intuitive and personalized, reducing the need for navigating through structured traditional app interfaces.


Voice assistants and AI-powered interfaces are already being integrated into car infotainment systems as well, to control various car functions or access information. 


The logic for using AI to replace apps arguably could appeal more on such devices than AI phones, even if voice interaction becomes more common on smartphones. That might especially be true when the use case requires actions using personal and financial information and permissions specific to each individual.


How Much Value Can Connectivity Providers Actually Produce Using AI with Their Data?

It is inevitable that connectivity executives will talk about the ways they can employ generative AI (large language models) to support their businesses and products, ranging from the obvious (customer support or internal information queries) to some that might not be so obvious. 


While it might be easy to envision how marketing; advertising; media; content; e-commerce; education and training; financial services or healthcare could use GenAI, it likely is harder to envision how telcos can use GenAI in areas outside customer service interactions, especially in ways directly related to revenue generation. 


The standard answers might include generation of data sets that preserve the statistical properties of real customer data but are completely anonymized, then sold to customers wanting to use such data. 


At least in principle, anonymized reports could illuminate behavioral trends such as:

  • Foot traffic analysis

  • Traffic patterns and congestion analysis

  • Device usage patterns

  • App usage trends

  • Network usage

  • Mobile money transactions


Some forms of network performance data could be useful in some B2B situations as well. 

  • Network performance insights might be useful for infrastructure suppliers

  • Benchmarking of service providers and geographic areas likewise could be useful for infra providers


In practice, it is a bit unclear how valuable such data is, given that other data sources exist that also can provide insight on location; mobility; device usage; app usage or network usage. And most connectivity providers are not suppliers of mobile money services. 


Industry

Potential Value

Examples

Source

Marketing & Advertising

- Generate personalized marketing content (e.g., ad copy, social media posts) - Create targeted advertising campaigns based on audience insights - Personalize customer experiences across channels

- Generate product descriptions, headlines, and social media content tailored to specific customer segments. - Create personalized marketing materials with high engagement potential. - Develop dynamic pricing strategies based on real-time market analysis.

- Gartner

Media & Entertainment

- Generate personalized content recommendations - Create new forms of interactive entertainment experiences - Personalize news feeds and content curation

- Recommend movies, music, or articles based on individual user preferences. - Generate personalized story outlines or even short stories based on user input. - Create interactive narratives that adapt to user choices.

- Cognite

Customer Service & Support

- Automate routine customer inquiries with chatbots - Generate personalized troubleshooting guides and FAQs - Enhance customer service interactions with natural language processing

- Develop chatbots that can answer common questions and resolve basic issues. - Create personalized knowledge base articles and FAQs tailored to specific customer situations. - Improve sentiment analysis and personalize customer interactions.

- NBER

E-commerce & Retail

- Generate personalized product descriptions and recommendations - Optimize product search and discovery - Automate content creation for marketing and advertising

- Create unique and engaging product descriptions that resonate with target audiences. - Personalize product recommendations based on individual browsing behavior and purchase history. - Generate automated social media posts and marketing materials.

- Paul Prae

Education & Training

- Personalize learning experiences for individual students - Create interactive and engaging educational content - Develop adaptive learning systems that adjust to student needs

- Generate personalized study guides and practice problems tailored to individual learning styles. - Create interactive simulations and role-playing scenarios for practical learning. - Develop adaptive learning platforms that personalize content and difficulty based on student performance.

- Pro IO

Healthcare & Life Sciences

- Generate personalized reports and insights from medical data - Assist with drug discovery and development - Analyze medical literature and identify potential treatment options

- Generate personalized healthcare reports summarizing medical history and treatment options. - Analyze large datasets of medical research to identify potential drug targets. - Assist with summarizing and synthesizing medical literature for researchers.

- Investopedia

Financial Services

- Generate custom financial reports and investment strategies - Automate financial data analysis and risk assessment - Personalize financial advice and recommendations

- Create personalized financial reports summarizing investment performance and financial health. - Analyze market trends and generate investment recommendations based on individual risk tolerance. - Develop chatbots that can answer common financial questions and provide basic financial advice.

- Jon Cooke


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