VIBER
Viber is a free app that allows users to message and call others, and is available on many platforms including iPhone, iPad, iPod touch, Windows, macOS, Linux, and Android. It has over a billion users worldwide and is considered one of the most popular instant messengers in the world.

TELEGRAM
Telegram is a free, cloud-based app that allows users to send messages, photos, videos, and files, as well as make voice and video calls. It’s available on smartphones, tablets, and computers, including Android, iOS, Windows, macOS, Linux, and web browsers.
Telegram is known for its end-to-end encryption, which makes communication secure. It also offers optional private chats called Secret Chats, which are also encrypted. Telegram has its own cloud, so messages and files are stored until the user deletes them, and there’s no limit to how much can be saved. However, files sent through Telegram can’t be larger than 15 GB

QZONE
Ranked at 13 with 615 million monthly users, Qzone is a social networking website and blogging service based in China that was created by Tencent in 2005. It’s integrated within QQ, Tencent’s instant messenger product: every QQ user in China automatically gets a Qzone page.

DOUYiN
Douyin is a Chinese short-video app and social media platform that is similar to TikTok, its international counterpart. Both apps are owned by ByteDance and allow users to create and share short videos, often in a vertical format. Douyin also includes features like editing tools, challenges, and a large music library and is only available in China.

BETA
BORN 2025-2039

ALPHA
BORN 2011-2024
They are the first generation to be born entirely in the 21st century and are expected to be the largest generation in history, with over 2 billion people. Gen Alpha is often the children of millennials and are considered to be the most technologically aware generation because they grew up with digital innovation

GEN Z
BORN 1996-2010
These kids were the first born with the Internet and are suspected to be the most individualistic and technology-dependent generation. Sometimes referred to as the iGeneration.

MILLENNIALS/ GEN Y
BORN 1981-1995
People born between 1981 and 1996 are generally considered to be part of the Millennial generation, also known as Generation Y. Millennials are the generation that comes after Generation X and before Generation Z. Most Millennials are the children of Baby Boomers and older Generation X, and they are often the parents of Generation Alpha.

GENERATION X
BORN 1966-1980
Generation X is sometimes called the “middle child” generation because it comes after the baby boomer generation and before the millennial generation. They are also sometimes called the “baby bust” generation because they were born when the high birth rates of the baby boomer era began to decline. This decline is partly attributed to the introduction of the birth control pill in the early 1960s.
Generation X grew up during a time of rapid technological advancement, but technology wasn’t as widely available as it is today. This generation is said to straddle both the digital and non-digital worlds, and to understand the importance of both. Some say that Generation Xers are resourceful, logical, good problem-solvers, self-sufficient, individualistic, and value freedom and responsibility.

BABY BOOMERS
BORN 1946-1965
Baby Boomers were the largest generation in U.S. history until the Millennials slightly surpassed them. Baby Boomers have had a significant impact on the economy and make up a large portion of the world’s population, especially in developed countries.

THE SILENT GENERATION
BORN 1928-1945
This generation is known for their civic and conformist instincts, which may be a contrast to the more rebellious Baby Boomers. The Silent Generation also includes soldiers who fought in the Korean War, which some call the “Forgotten War” but which had a significant impact on their lives

GREATEST GENERATION
BORN 1901-1927
They were born and came of age in the “American Century” of economic growth, technological progress, and mostly military triumph

A retail media network is a platform created by retailers that allows advertisers to buy advertising space on the retailer’s digital assets, utilizing the retailer’s own customer data to reach shoppers at various stages of their purchasing journey.

By leveraging first-party customer data, advertisers can connect with potential customers directly through a retailer’s channels, reaching them at crucial points in their decision-making process, including the point of sale (POS).

The goal of a successful retail media network is to effectively monetize all available channels in a manner that is brand-safe and compliant with privacy regulations, ultimately reaching the end consumer with targeted advertising.

On a retailer’s website, advertisers have the opportunity to showcase their ads on prominent pages such as search result pages. For example, a sponsored post from a laptop manufacturer may appear on a department store search result page for “laptop,” targeting shoppers who are specifically searching for that product. This targeted approach allows advertisers to tailor their messaging to shoppers who have already expressed interest in a particular item, increasing the likelihood of conversion.

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The term “Made for Advertising” refers to websites that are created primarily for the purpose of making profit through exploiting the advertising ecosystem. These sites are easily recognizable by their subpar content and overwhelming number of ad placements, ultimately wasting advertising dollars and providing limited engagement opportunities for users.

After conducting an analysis of various advertising channels, Adalytics discovered that a Fortune 500 brand had inadvertently spent more than $10 million into MFA websites. This alarming revelation serves as a stark reminder for advertisers to carefully monitor where their ads are being placed.

While industry initiatives aim to combat MFA websites, these sites still persist and major brands continue to have their ads displayed on them, whether through programmatic or non-programmatic means. As the digital advertising landscape evolves, advertisers must remain vigilant against deceptive practices to protect their investments and brand reputation.

The measurement of attention metrics typically employs three main approaches, as explained by Angelina Eng,  at the IAB:

1. Biometric data: This approach involves tracking neurological-physiological signals like facial recognition, brain waves, heart rate, and blood pressure to measure attention levels in-depth. However, concerns regarding privacy risks and the need for special devices for data collection pose challenges to this method.

2. Data signals: This method involves the capture of signals from publishers or devices, such as dwell time, scroll speed, cursor position, and completion rates. The lack of attention metric standardization and differences in metrics across platforms can lead to varying interpretations.

3. Cognitive and emotional data: This approach examines how an ad impacts a user’s mindset and consideration. Brands gather such data through surveys on brand consideration, awareness, sentiment, and biometric methods.

While attention metrics are becoming increasingly popular for measuring ad effectiveness, challenges such as lack of standardization and controversial data collection methods hinder universal adoption. Marketers are encouraged to incorporate attention metrics alongside other metrics like viewability to gain a comprehensive understanding of ad performance.

The earlier versions of the AI applications that we commonly interact with were based on traditional machine learning models. These models rely on learning algorithms created and managed by data scientists, requiring human intervention to process new information and perform tasks beyond their original training.

With the introduction of artificial neural networks,  machines are able to learn through reinforcement and mimic the information processing of the human brain.

With the continuous evolution of AI, the field is in a state of constant change and rapid development. Our comprehension of both realized and theoretical AI is constantly shifting, leading to variations and overlaps in AI categories and terminology across different sources. Nonetheless, a comprehensive understanding of AI can be gained by examining two overarching categories: AI capabilities and AI functionalities.

The four types of AI based on functionality are:

Reactive Machine AI:

Reactive machines refer to AI systems that lack memory and are programmed to carry out specific tasks based solely on present data without the ability to remember past decisions or outcomes. These AI systems utilize statistical mathematics to analyze large volumes of data and generate intelligent outputs.

Examples of Reactive Machine AI:

• IBM Deep Blue: This supercomputer AI defeated chess grandmaster Garry Kasparov in the late 1990s by evaluating the current chessboard configuration and predicting potential move outcomes.
• The Netflix Recommendation Engine: Netflix employs models that analyze viewing history data to suggest content tailored to individual preferences.

Limited Memory AI:

In contrast to Reactive Machine AI, Limited Memory AI can retain past events and outcomes for a certain period, allowing for decision-making based on historical and current data. While it improves with additional training data, it does not store past experiences for long-term use.

Examples of Limited Memory AI:

• Generative AI tools: ChatGPT, Bard, and DeepAI utilize Limited Memory AI to predict and generate content.
• Virtual assistants and chatbots: Siri, Alexa, Google Assistant, Cortana, and IBM Watson Assistant incorporate Limited Memory AI to comprehend user queries and provide relevant responses.
• Self-driving cars: Autonomous vehicles employ Limited Memory AI to navigate their surroundings and make real-time driving decisions.

Theory of Mind AI:

Theory of Mind AI falls within the General AI category and encompasses AI systems capable of understanding the thoughts and emotions of others. This would enable AI to interact with individuals based on their emotional needs and intentions, forming human-like relationships. Emotion AI is a concept within this category, focusing on analyzing data to recognize and appropriately respond to human emotions.

Self-Aware AI:

Self-Aware AI, a theoretical form of functional AI, would possess superintelligence capabilities, including an understanding of its own internal states and human emotions. This AI class would exhibit emotions, needs, and beliefs, similar to human behavior but remains a concept rather than a realized technology.

As we approach the open enrollment period for Dual-Eligible Special Needs Plans (D-SNPs), marketing to prospects turning 65 years old has never been more crucial. Utilizing direct mail campaigns can be a highly effective strategy to reach this specific demographic and guide them through the enrollment process.

Our company offers the unique ability to identify individuals who are about to turn 65 based on their birth month and birth year. We then narrow down our target audience, single individuals and married individuals earning less than the federal poverty level guidelines along with net worth.

Through a three-touch direct mail strategy, we can engage these prospects at key moments leading up to their 65th birthday. The first touchpoint involves sending them an introduction piece, providing valuable information about your D-SNP plans and the benefits they offer. The second touchpoint is a personalized birthday greeting, along with specific calls to action to start the open enrollment process. Lastly, the third piece includes a comprehensive overview of your plans, helping to seal the deal and convert these prospects into new business.

By planning a year-round open enrollment marketing strategy with our team, you can ensure that your message reaches the right people at the right time. Contact us today to learn more about how we can help you effectively market to D-SNP prospects and drive success during the upcoming enrollment period.