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Google Unveils Plan to Demolish the Journalism Industry Using AI

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Sourced from Futurism

Remember back in 2018, when Google removed “don’t be evil” from its code of conduct?

It’s been living up to that removal lately. At its annual I/O in San Francisco this week, the search giant finally lifted the lid on its vision for AI-integrated search — and that vision, apparently, involves cutting digital publishers off at the knees.

Google’s new AI-powered search interface, dubbed “Search Generative Experience,” or SGE for short, involves a feature called “AI Snapshot.” Basically, it’s an enormous top-of-the-page summarization feature. Ask, for example, “why is sourdough bread still so popular?” — one of the examples that Google used in their presentation — and, before you get to the blue links that we’re all familiar with, Google will provide you with a large language model (LLM) -generated summary. Or, we guess, snapshot.

“Google’s normal search results load almost immediately,” The Verge’s David Pierce explains. “Above them, a rectangular orange section pulses and glows and shows the phrase ‘Generative AI is experimental.’ A few seconds later, the glowing is replaced by an AI-generated summary: a few paragraphs detailing how good sourdough tastes, the upsides of its prebiotic abilities, and more.”

“To the right,” he adds, “there are three links to sites with information that Reid says ‘corroborates’ what’s in the summary.”

As it goes without saying, this format of search, where Google uses AI tech to regurgitate the internet back to users, is wildly different from how the search-facilitated internet works today. Right now, if you Google that same query — “why is sourdough bread still so popular?” — you’d be met with a more familiar scene: a featured excerpt from whichever website won the SEO race (in this case, that website was British Baker), followed by that series of blue links.

At first glance, the change might seem relatively benign. Often, all folks surfing the web want is a quick-hit summary or snippet of something anyway.

But it’s not unfair to say that Google, which in April, according to data from SimilarWeb, hosted roughly 91 percent of all search traffic, is somewhat synonymous with, well, the internet. And the internet isn’t just some ethereal, predetermined thing, as natural water or air. The internet is a marketplace, and Google is its kingmaker.

As such, the demo raises an extremely important question for the future of the already-ravaged journalism industry: if Google’s AI is going to mulch up original work and provide a distilled version of it to users at scale, without ever connecting them to the original work, how will publishers continue to monetize their work?

“Google has unveiled its vision for how it will incorporate AI into search,” tweeted The Verge’s James Vincent. “The quick answer: it’s going to gobble up the open web and then summarize/rewrite/regurgitate it (pick the adjective that reflects your level of disquiet) in a shiny Google UI.”

Research has shown that information consumers hardly ever make it to even the second page of search results, let alone even the bottom of the page. And worse, it’s not like Google’s taking clicks away from its long-time information merchants by hiring an army of human content writers to churn out summarization. Google’s new search interface, which is built on a model that’s already been trained by way of boatloads upon boatloads of unpaid-for human output, will seemingly be swallowing even more human-made content and spitting it back out to information-seekers, all the while taking valuable clicks away from the publishers that are actually doing the work of reporting, curating, and holding powerful interests like Google to account.

As of now, it’s unclear whether or how Google plans to compensate those publishers.

In an emailed statement to Futurism, a Google spokesperson said that “we’re introducing this new generative AI experience as an experiment in Search Labs to help us iterate and improve, while incorporating feedback from users and other stakeholders.”

“As we experiment with new LLM-powered capabilities in Search, we’ll continue to prioritize approaches that will allow us to send valuable traffic to a wide range of creators and support a healthy, open web,” the spokesperson added.

Asked specifically whether the company has plans to compensate publishers for any AI-regurgitated content, Google had little in response.

“We don’t have plans to share on this, but we’ll continue to work with the broader ecosystem,” the spokesperson told Futurism.

Publishers, however, are extremely wary of these changes.

“If this actually works and is implemented in a firm way,” wrote RPG Site owner Alex Donaldson, “this is literally the end of the business model for vast swathes of digital media lol.”

At the end of the day, there are a lot of questions that Google needs to answer here, not the least being that AI systems, Google’s included, spew fabrications all the time.

The Silicon Valley giant has long claimed that its goal is to maximize access to information. SGE, though, seemingly seeks to do something quite different — and if the company doesn’t figure out a way to compensate publishers for the labour it’ll be gleaning from the journalists, the effects on the public’s actual access to information could be catastrophic.

Updated with comment from Google.

Feature Image Credit: Getty

Sourced from Futurism

 

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6 Things to Look For When Evaluating an Employee Learning Platform

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By Subbu Viswanathan

Leveraging the right learning experience platforms can help you achieve your business goals by enabling you to address company skill gaps, create a skills inventory, and encourage learning and retention.

A learning program that simply ensures employees are compliant–with content that may or may not be relevant to their job role–is a common practice. This is especially true when the learning solution is bundled with your human capital management (HCM) platform. In my experience, an HCM solution usually provides no more than a simple administrative system that tracks learning adoption.

Today more than ever, I think it is vital to provide personalized, role-specific and relevant content for learning and skills-building for your employees.

Advantages of Personalized Learning

Personalized learning takes an employee-centric approach to learning and development. The idea is to create customized learning opportunities that align with a person’s job role, existing skills and interests.

As the leader of a learning experience platform, here are some of the benefits I’ve seen clients experience when they take advantage of personalized learning:

  • Metrics to analyse employee skills and role readiness.
  • A leadership pipeline based on hard and soft skills enhancements.
  • Improved knowledge retention with personalized content.
  • Increased employee engagement and retention.

Six Thing To Prioritize When Evaluating a Learning Platform

There are many learning platform options, but it is important to ensure that they are aligned with your business requirements. Below are the most important features and capabilities that your learning platform should have.

1. Role-Based Skilling

Look for a solution that enables you to access a skill directory that aligns with your L&D strategies. To do this effectively, you must have the capability to identify and map skills for every role in your organization.

2. Skill Assessment

Your employees will have varying levels of skills and knowledge. You don’t want to put all employees through the same learning experience, even if they share the same role.

Instead, you should evaluate current employee readiness and determine the skill gaps that each employee has. Once you’ve performed the skill assessment, you can tailor a personalized learning experience that will meet individual employee needs for learning and upskilling.

3. AI-Based Content Recommendations

These days, the best learning experience platforms leverage artificial intelligence to provide content and learning recommendations. This technology enables your platform to auto-generate a personalized learning path with even more quality content fetched from renowned and credible sources.

An employee’s learning experience may include AI-based recommendations for a variety of content formats like online articles, videos, etc. Any way that you can include these things can make for a more engaging experience.

4. Self-Paced Learning

One of the issues with many learning programs is the mistaken belief that every course should be taken at the same pace. It’s important to understand that each employee learns at a different pace. Some catch on to certain concepts quickly, while others need a bit more time to digest and truly understand them.

Therefore, I recommend prioritizing self-paced learning. Allow your employees to go through the training at their own pace. They will feel more comfortable and will likely retain more of what they learn.

5. In-Depth Analytics

I also recommend that the platform you choose provide in-depth learning analytics. You can then measure how effective your personalized learning initiatives are. The right analytics allow you to link personalized learning efforts to employee performance and optimize the impact on your business outcomes.

You’ll also be able to determine whether the courses being recommended are working as you planned, or whether you may need to make changes to provide more learning opportunities for some or all employees.

6. Third-Party Integrations

When choosing your learning platform also consider other software applications you are currently using in your business.

You should find a system that integrates well with third-party platforms, so it will be easier for you to adopt. Consider the HRMS, LMS, CMS, MOOCs, business apps and any other software you might be using and determine whether your platform will integrate with them.

A learning experience platform should not have a problem integrating with popular third-party platforms available on the market.

In conclusion, leveraging the right learning experience platforms can help you achieve your business goals by enabling you to address company skill gaps, create a skills inventory and encourage learning and retention on the part of your employees. By prioritizing the right elements of a platform, you can infuse your team with a mindset that makes them want to enhance their skills.

Feature Image Credit: Getty Images

By Subbu Viswanathan,

CEO of Disprz, an enterprise skills acceleration platform, 3-time tech entrepreneur, former McKinsey consultant, ITT and ISB alumni.

Sourced from Inc.

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Vector Databases: Long-Term Memory for Artificial Intelligence

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By Mark Hinkle

Vector databases store data such as text, video or images that are converted into vector embeddings for AI models to access them quickly.

Artificial Intelligence, such as ChatGPT, acts much like someone with endemic memory who goes to a library and reads every book. However, when you ask an AI a question that was not in the book at the library, it either admits it doesn’t know or hallucinates.

An AI hallucination refers to instances where an artificial intelligence system generates an output that may seem coherent or plausible but is not grounded in reality or accurate information. These outputs can include text, images or other forms of data that the AI model has produced based on its training but may not align with real-world facts or logic.

For example, we could use a generative AI for images like the ones Midjourney provides to generate a picture of an old man. However, the prompt (the way you communicate with an AI like Stable Diffusion or others) has to be something that the model understands. For example, you may ask the AI to create a picture of a man who is over the hill. In this case, I used Midjourney, a popular generative AI for images, to do just that. I used an example that I thought might cause it to hallucinate.

Midjourney-generated image of a man over the hill

Midjourney doesn’t understand euphemisms like over the hill, so it generated a picture of a man who was literally over the top of a hill.

How could you inform the AI what you mean by “over the hill,” and other nuances of language it doesn’t know of? First, you could provide training data. The way you would do this is to convert that data into something known as embeddings, and then import them into a vector database.

While this example is a bit far-fetched for effect, many other contexts apply. For example, industry-specific terminology for medical and legal fields would benefit from being able to train AI on their specific terminology and meanings. Enterprises will want to provide their data to AI without introducing public models.

A critical use case for vector databases is large language models to retrieve domain-specific or proprietary facts that can be queried during text generation. Therefore, vector databases will be essential for organizations building proprietary large language models.

Vector vs. NoSQL and SQL Databases

Traditional databases, such as relational databases (e.g., MySQL, PostgreSQL, Oracle) and NoSQL databases (e.g., MongoDB, Cassandra), have been the backbone of business data management for decades. They store and organize data in structured formats like tables, documents or key-value pairs, making it easier to query and manipulate using standard programming languages.

These databases excel at handling structured data with fixed schema, but they often struggle with unstructured data or high-dimensional data, such as images, audio and text. Moreover, as the volume and velocity of data increase, they may face performance bottlenecks, leading to slower response times and scalability issues.

Vector databases, on the other hand, represent a paradigm shift in data storage and retrieval. Instead of relying on structured formats, they store and index data as mathematical vectors in high-dimensional space. This approach, called “vectorization,” allows for more efficient similarity searches and better handling of complex data types, such as images, audio, video and natural language.

Imagine a vector database as a vast warehouse and the AI as the skilled warehouse manager. In this warehouse, every item (data) is stored in a box (vector), organized neatly on shelves in a multidimensional space. The warehouse manager (AI) knows the exact position of each box and can quickly retrieve or compare the items based on their similarities, just like a skilled warehouse manager can find similar group products.

The boxes represent different types of unstructured data, such as text, images or audio, which have been transformed into a structured numerical format (vectors) to be efficiently stored and managed. The more organized and optimized the warehouse is, the faster and more accurately the warehouse manager (AI) can find the items needed for various tasks, such as making recommendations, recognizing patterns or detecting anomalies.

This analogy helps convey the idea that vector databases serve as a crucial foundation for AI systems, enabling them to efficiently manage, search and process complex data in a structured and organized manner. Just as a well-managed warehouse is essential for smooth business operations, a vector database plays a vital role in the success of AI-driven applications and solutions.

The key advantage of vector databases is their ability to perform approximate nearest neighbour (ANN) search, quickly identifying similar items in a large dataset. Using techniques like dimensionality reduction and indexing algorithms, vector databases can perform these searches at scale, providing lightning-fast response times and making them ideal for applications like recommendation systems, anomaly detection and natural language processing.

Embeddings — Turning Words, Images and Videos into Numbers

Embeddings are techniques that convert complex data, such as words, into simpler numerical representations (called vectors). This makes it easier for AI systems to understand and work with the data. Probability helps create these representations by analysing how often certain pieces of data appear together.

Probability helps quantify the similarity of two pieces of data, allowing the AI system to find related items. Probability-based techniques help AI systems quickly find similar data points in large databases without examining every item. Probability helps AI systems group similar data points together and reduce the complexity of the data, making it easier to process and analyse.

Popular Vector Databases

While there are an ever-growing number of vector databases, several factors contribute to their popularity. These factors include efficient performance in storing, indexing and searching high-dimensional vectors, ease of use in integrating with existing machine learning frameworks and libraries, scalability in handling large-scale, high-dimensional data, flexibility in offering multiple backends and indexing algorithms, and active community support with valuable resources, tutorials and examples.

Vector databases that are more likely to be popular among users are ones that provide fast and accurate nearest-neighbour search, clustering, and similarity matching, and that can be easily deployed on cloud infrastructure or distributed computing systems. Based on popularity among users and the number of stars on Github, here are some of the most popular vector databases.

  • Pinecone: Pinecone is a cloud-based vector database designed to efficiently store, index and search extensive collections of high-dimensional vectors. Pinecone’s key features include real-time indexing and searching, handling sparse and dense vectors, and support for exact and approximate nearest-neighbour search. In addition, Pinecone can be easily integrated with other machine learning frameworks and libraries, making it popular for building production-grade NLP and computer vision applications.
  • Chroma: Chroma is an open source vector database that provides a fast and scalable way to store and retrieve embeddings. Chroma is designed to be lightweight and easy to use, with a simple API and support for multiple backends, including RocksDB and Faiss (Facebook AI Similarity Search — a library that allows developers to quickly search for embeddings of multimedia documents that are similar to each other). Chroma’s unique features include built-in support for compression and quantization, as well as the ability to dynamically adjust the size of the database to handle changing workloads. Chroma is a popular choice for research and experimentation due to its flexibility and ease of use.
  • Weaviate: Weaviate is an open source vector database designed to build and deploy AI-powered applications. Weaviate’s key features include support for semantic search and knowledge graphs and the ability to automatically extract entities and relationships from text data. Weaviate also includes built-in support for data exploration and visualization. Weaviate is an excellent choice for applications that require complex semantic search or knowledge graph functionality.
  • Milvus: Milvus is an open source vector database designed for large-scale machine-learning applications. Milvus is optimized for both CPU and GPU-based systems and supports exact and approximate nearest-neighbour searches. Milvus also includes a built-in RESTful API and support for multiple programming languages, including Python and Java. Milvus is a popular choice for building recommendation engines and search systems that require real-time similarity searches. Milvus is part of the Linux Foundation’s AI and Data Foundation, but the primary developer is Zilliz.
  • DeepLake: DeepLake is a cloud-based vector database that is designed for machine learning applications. DeepLake’s unique features include built-in support for streaming data, real-time indexing and searching, and the ability to handle both dense and sparse vectors. DeepLake also provides a RESTful API and support for multiple programming languages. DeepLake is a good choice for applications that require real-time indexing and search of large-scale, high-dimensional data.
  • Qdrant: Qdrant is an open source vector database designed for real-time analytics and search. Qdrant’s unique features include built-in support for geospatial data and the ability to perform geospatial queries. Qdrant also supports exact and approximate nearest-neighbour searches and includes a RESTful API and support for multiple programming languages. Qdrant is an excellent choice for applications that require real-time geospatial search and analytics.

As in the case of SQL and NoSQL databases, vector databases come in many different flavours and address various use cases.

Use Cases for Vector Databases

Artificial intelligence applications rely on efficiently storing and retrieving high-dimensional data to provide personalized recommendations, recognize visual content, analyse text and detect anomalies. Vector databases enable efficient and accurate search and analysis of high-dimensional data, making them essential for developing robust and efficient AI systems.

Recommender Systems

In recommender systems, vector databases have the crucial function of storing and proposing items that best match users’ interests and preferences. These databases facilitate fast and effective searches for similar items by representing items as vectors. This feature allows AI-powered systems to provide personalized recommendations, thus improving user experiences on social networks, streaming services and e-commerce websites.

One commonly used AI-powered recommendation system is the one used by Amazon. Amazon uses a collaborative filtering algorithm that analyses customer behaviour and preferences to make personalized recommendations for products they might be interested in purchasing.

This system considers past purchase history, search queries and items in the customer’s shopping cart to make recommendations. Amazon’s recommendation system also uses natural language-processing techniques to analyse product descriptions and customer reviews to provide more accurate and relevant recommendations.

Image and Video Recognition

In image and video recognition, vector databases store visual content as high-dimensional vectors. These databases empower AI models to efficiently recognize and understand images or videos, find similarities, and perform object recognition, face recognition, or image classification tasks. This has applications in security and surveillance, autonomous vehicles and content moderation.

One commonly used image and video recognition system powered by AI is the TensorFlow Object Detection API. This open source framework developed by Google allows users to train their own models for object detection tasks, such as identifying and localizing objects within images and videos.

The TensorFlow Object Detection API uses deep learning models, such as the popular Faster R-CNN and SSD models, to achieve high accuracy in object detection. It also provides pre-trained models for everyday object detection tasks, which can be fine-tuned on new datasets to improve performance.

Natural Language Processing (NLP)

Vector databases play a critical role in NLP by storing and managing information about words and sentences as vectors. These databases enable AI systems to perform tasks such as searching for related content, analysing the sentiment of a piece of text or even generating human-like responses. By harnessing the power of vector databases, NLP models can be used for applications like chatbots, sentiment analysis or machine translation.

One commonly used NLP system is the Natural Language Toolkit (NLTK). NLTK is a comprehensive platform for building Python programs to work with human language data. It provides easy-to-use interfaces to over 50 corpora and lexical resources and a suite of text-processing libraries for classification, tokenization, stemming, tagging, parsing, semantic reasoning and more. Researchers and practitioners widely use NLTK in academia and industry, and it is a popular choice for teaching NLP concepts and techniques.

Anomaly Detection

Vector databases can help detect unusual activities or behaviours in various areas, such as cybersecurity, fraud detection or industrial equipment monitoring. These databases can quickly identify patterns that deviate from the norm by representing data as vectors. AI models integrated with vector databases can then flag these anomalies and trigger alerts or mitigation measures, ensuring timely and effective responses.

Microsoft Azure Anomaly Detector is a cloud-based service that allows users to monitor and analyse time series data to identify anomalies, spikes and other unusual patterns. Azure Anomaly Detector uses advanced AI algorithms such as Seasonal Hybrid ESD (S-H-ESD) and Singular Spectrum Analysis (SSA) to automatically detect and alert users when anomalous behaviour is caught in the data. It also provides a simple REST API for developers to integrate the service into their applications and workflows efficiently.

Summary

Vector databases are critical to many artificial intelligence (AI) applications, including recommender systems, image and video recognition, natural language processing (NLP) and anomaly detection. By storing and managing data as high-dimensional vectors, these databases enable efficient and accurate search and analysis of large datasets, leading to enhanced user experiences, improved automation, and timely detection of anomalies. In the realm of recommender systems, vector databases allow for the quick identification of items most relevant to users’ preferences.

At the same time, image and video recognition enables efficient object and face recognition. Vector databases play a crucial role in NLP by storing and managing information about words and sentences as vectors. In anomaly detection, they enable quick identification of unusual patterns or behaviours. Overall, vector databases are essential for developing robust and efficient AI systems across various domains.

Feature Image Credit: tikisada from Pixabay

By Mark Hinkle

Mark has a long history in emerging technologies and open source. Before co-founding TriggerMesh, he was the executive director of the Node.js Foundation and an executive at Citrix, Cloud.com and Zenoss where he led their open source efforts.

Sourced from THENEWSTACK

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The 7 Most Successful Business Models Of The Digital Era

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By Bernard Marr

The first two decades of this century are characterized by digital entrepreneurs upending traditional business models in search of new ways of creating revenue and serving customers.

This has been made possible by the emergence of several new waves of technology – from desktop computers to the internet, mobile devices, and the cloud. Going forward, these waves of disruption seem certain to continue as new breakthroughs such as artificial intelligence (AI) continue to redefine the way we shop, work, play, and live our lives.

Often these business models are used in combination – for example, a software provider might make a “freemium” version available, supported by advertising revenue, while also offering a premium, ad-free service to those that are willing to pay. Or e-tailers like Amazon may make revenue from e-commerce while also acting as a marketplace where other sellers can offer their goods in exchange for a cut of the profits.

Anyone wanting to do business today – or understand how money is going to be made tomorrow – needs to understand the fundamental models underpinning the digital economy. So here’s my overview of some of the most successful and important and an explanation of how technology has made each of them possible.

The ad-supported business model is among the most successful of the digital era. It is behind the rise of companies like Google and Facebook, which match users to products and services using AI and analytics. This has become possible due to the sheer amount of user data that can be captured from online users. The success of these businesses is due to the concept that “if you’re not paying, you’re the product.” In the days of newspaper, radio, and television advertising, the data that could be collected was limited to information gleaned from audience and market research surveys. Today, every click, follow, like, and share – as well as the information we directly give to sites and services – can be used to learn about us. This data is collected from audiences and users and sold to advertisers who use it to predict what products and services we might want to buy.

As it’s simplest, this simply refers to companies that offer products and services online directly to the customer. This can describe the giants such as Amazon and Alibaba that sell products directly to consumers themselves but also operate as marketplaces. It also describes thousands of smaller and niche businesses that exist today, generally operating via platforms and marketplaces such as Amazon, Shopify, Etsy, or Alibaba. E-commerce offers a super-convenient and affordable way for just about anyone to start selling their products globally without having to worry about the logistics and expense of setting up bricks ‘n’ mortar stores. Platforms and marketplaces make the job of setting up a storefront and listing products a one-person job, and e-commerce operators will often leverage the power of advertising platforms such as Google or Facebook to reach customers in their niche. The value of global e-commerce was estimated to be around $10 trillion in 2020 and is expected to grow to $27 trillion by 2027.

Freemium

The freemium business model generally involves offering a basic, no-frills version of a product or service for free but charging users if they want to access premium features. Examples include Spotify, which puts limits on how users can listen to music unless they are subscribers, Dropbox, which offers limited storage and transfer speeds to free users, LinkedIn, which lets anyone browse job adverts and list vacancies, but enables advanced analytics functions to subscribers to help with job searches and hiring, and Zoom, which limits the length of meetings and the number of participants for free users.

Productivity and workplace software-as-a-service providers also frequently use the freemium model, then offer individual or corporate licenses to users who want to access the full feature set without limitations. It’s also popular with games publishers, who use a free version to get players hooked before enticing them to either take out a subscription or buy individual features or benefits on a “pay-to-play” basis.

Marketplace/ Platform

This model covers both the e-commerce providers like Amazon and Alibaba, which have grown into marketplaces where anyone can set up their own business. It also covers more specialized platforms like eBay, Uber, or AirB’n’B. Users benefit from the prominence and financial clout of these platform providers, which will often use analytics and advertising campaigns to drive traffic to their customers’ stores or listings. For the marketplace or platform owner, the benefit is that they do not even have to provide a product or service themselves, and they can simply take a cut from every business that sells through them. We can also include “gig economy” sites like Fiverr, Freelancer, and Amazon’s Mechanical Turk in this category, as they offer platforms for individuals to offer their own one-to-one services to businesses.

Subscription

This refers to any business which charges customers a regular payment. Initially, it would generally refer to service providers – such as Netflix offering movies on demand, or Microsoft and Adobe offering software-as-a-service subscription packages such as Microsoft 365 or Adobe Creative Cloud. Increasingly, however, product retailers and manufacturers are offering goods and consumables through subscriptions as well. This includes home fresh food delivery businesses such as Hello Fresh and Gousto. Amazon is an example of a business that covers the whole spectrum – offering digital services like video, music, and cloud computing infrastructure, and also product subscriptions that deliver physical goods directly to customers’ doors. This business model enables organizations to generate a regular income while also developing ongoing relationships with customers, meaning they are able to offer different products and services as their customers’ requirements change. Niche and independent businesses might also choose to generate revenues through subscriptions by taking advantage of a platform such as Substack, which allows audiences to connect with individual creators.

Aggregator Sites

This business model involves scraping the web for companies offering products and services, then aggregating them into a handy portal where shoppers can compare prices, features, and benefits. Some well-known examples include PriceRunner, PriceGrabber, and Shopping.com. Other aggregators specialize in particular markets such as comparethemarket and moneysupermarket (insurance and financial services) and Expedia (holidays and travel). Rather than charging a fee to businesses that advertise their products on their sites, these businesses generate revenue from referrals they are paid when we buy products through them.

Crowdfunding

The final digital era business model we can’t ignore is crowdfunding. The big crowdfunding sites – such as Kickstarter, Indiegogo, and Gofundme, are also platforms offering other businesses the opportunity to raise funding via small donations from a large number of individuals. Crowdfunded businesses themselves are those that use money generated through these platforms as a source of revenue, often to launch niche or prototype products. Other sites like Patreon allow creators to build personal relationships with their audience, often allowing them to create ongoing products or services such as music, videos, or writing.

To stay on top of the latest on new and emerging business and tech trends, make sure to subscribe to my newsletter, follow me on Twitter, LinkedIn, and YouTube, and check out my books ‘Future Skills: The 20 Skills And Competencies Everyone Needs To Succeed In A Digital World’ and ‘Business Trends in Practice, which won the 2022 Business Book of the Year award.

Feature Image Credit: Adobe Stock

By Bernard Marr

Bernard Marr is an internationally best-selling author, popular keynote speaker, futurist, and a strategic business & technology advisor to governments and companies. He helps organisations improve their business performance, use data more intelligently, and understand the implications of new technologies such as artificial intelligence, big data, blockchains, and the Internet of Things. Why don’t you connect with Bernard on Twitter (@bernardmarr), LinkedIn (https://uk.linkedin.com/in/bernardmarr) or instagram (bernard.marr)?

Sourced from Forbes

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Confessions of a social media entrepreneur on the pressure to constantly create content

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By Kimeko McCoy

Social media fragmentation, the rise of TikTok and social media’s expedited pivot to video has upped the ante for client expectations. Agency partners in public relations and social media say they’re feeling the impact as clients are increasingly asking for more content, feeding platforms like TikTok and Instagram Reels in hopes for a viral moment.

The surge in workload has pushed one social media marketing entrepreneur to remove social media management service offerings to focus on content creation. In this latest edition of our Confessions series, in which we exchange anonymity for candour, we hear from that social media entrepreneur about client expectations in the fast-paced, ever-changing social media landscape.

This conversation has been edited and condensed for clarity.

As a content creator and social media strategist, what’s your experience in today’s current digital landscape? 

One thing I’ve noticed, for a change, [is] clients don’t necessarily understand what it takes to get the results that we do. They’ll drop a lot of ideas on you at once, or they have a lot of different ideas that they want to do at once, but not necessarily know that it takes a lot to execute it. Then they want you to get it out very timely. This is a process. Sometimes, the process seems a little rushed now because of how fast paced everything is happening — new features and everyone wants to keep up with everybody else. It just seems like people don’t necessarily appreciate the process anymore when it comes to social media experts.

So there’s pressure on you to put out good content fast? How does that impact the way you work? 

It definitely does. My agency recently had 12 clients at once. That was a hard thing to manoeuvre, even now, because of how burnt out I felt in dealing with that. I literally started changing my business activity. I used to say that we specialized in social media management and content creation. Now, I’m saying that we specialize in just content creation.

Why did you do that?

I was making 30 posts a month for my clients. When I got a little bit more experience, I changed my lowest [service] package to 15 posts a month and a few Reels a week. Now, you have to create Reels. It’s just videos. Now, we have to force the clients to get that content. Before, it was just me making the content. I didn’t necessarily need them. But now, I need those Day in the Life videos. I need you to show your expertise, go live and collaborate with others. You have to do these different things now to thrive on these different platforms. [But] they’re busy too. That’s why they hired me. So that has definitely become a struggle within itself too — just being able to connect with my clients for them to get me the content that I need.

What social media platforms are taking up the most of your time and energy? 

Instagram, definitely. TikTok, I see as the least amount of effort. With all of my clients, we’re able to have fun on TikTok. But with Instagram, everything has to be so technical because some of my clients have different [product] features that some of my other clients don’t. If a client sees something, they’re like, “I want that. Can I do something like that?” And it’s like, “You don’t even have that feature [available on your account].” Then they feel upset and we have to manage expectations. [Clients asked for more] when Reels came out. When video content literally took over, because everybody wanted to be seen. When Reels dropped, that’s the only way people saw people’s content. [It] was through video content.

You said Instagram Reels is a heavier lift for you, in terms of content production than TikTok. Why? 

Everybody wants to be perfect on Instagram. TikTok thrives off of authenticity. You can literally do a video of you in bed, talking about whatever and it will blow up because people love you, relate to you… As far as Instagram, you may not see a post for three days that somebody posted. Or you may not see somebody’s story because Instagram is only showing 10% of their followers’ posts. There’s so many technical things with Instagram now that’s just drawing people away.

By Kimeko McCoy

Sourced from DIGIDAY

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How To Scale Your Business Online

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By Markus Hetzenegger

As a business owner, you’re always looking for ways to grow your company. Scaling up can be a daunting task, but one strategy that has proven effective for many businesses is digital marketing. Specifically, performance marketing can help you reach new customers, increase sales and grow your bottom line. This is something we help our clients with.

Performance marketing is a data-driven approach to advertising that focuses on achieving specific business goals, such as driving website traffic or increasing sales. Instead of paying for ad impressions or clicks, you only pay for the desired action, such as a purchase or a lead. This makes performance marketing a highly cost-effective way to reach your target audience and achieve your business objectives.

So, how can you use performance marketing to scale your business? Here are a few key strategies to consider:

Set Clear Objectives

Before you start any performance marketing campaign, it’s important to set clear, measurable goals. What do you want to achieve? Do you want to increase website traffic, generate more leads or drive sales? Whatever your objectives, be sure to define them in specific, measurable terms. This will help you track your progress and optimize your campaigns for maximum performance.

Identify Your Target Audience

The success of your performance marketing campaigns depends on your ability to reach the right audience. To do this, you need to understand who your ideal customer is and what motivates them. Use data and analytics to create detailed customer profiles, including demographics, interests and behaviors. This will help you create highly targeted campaigns that speak directly to your audience and drive conversions.

Select The Right Platforms

There are countless digital marketing platforms available—from social media networks to search engines to display advertising networks. To get the most out of your performance marketing campaigns, it’s important to choose the platforms that best align with your goals and target audience. For example, if you’re targeting younger consumers, you may want to focus on social media platforms like Instagram and TikTok. If you’re targeting professionals, you may want to focus on LinkedIn or Google Ads.

Develop Performance Design

To stand out in a crowded digital landscape, you need to create compelling, attention-grabbing creative. This includes everything from ad copy to images and videos. Your creative should be designed to capture the attention of your target audience and communicate the unique value proposition of your product or service. Make sure your messaging is clear, concise and compelling, and that your creative aligns with your brand image and voice.

Test And Optimize

One of the key advantages of performance marketing is the ability to track and measure your results in real time. This allows you to test different campaigns, creative, and targeting strategies and optimize for maximum performance. Use A/B testing to compare different versions of your ads and landing pages, and use data to identify the highest-performing elements of your campaigns. Continuously refine your strategies and adjust your campaigns based on performance data to achieve the best results.

Conclusion

Performance marketing can be a highly effective way to scale your business and reach new customers. By setting clear objectives, identifying your target audience, using the right platforms, developing compelling creative, and testing and optimizing your campaigns, you can achieve your business goals and drive growth. But remember, success in performance marketing requires a data-driven, iterative approach. Stay focused on your objectives, be willing to experiment and adapt, and use data to guide your decisions. With the right strategies in place, you can multiply the total revenue of your business and take it to beyond imaginable heights.

Feature Image Credit: getty

By Markus Hetzenegger

Founder & CEO of NYBA AG ⎜A Leading Performance Marketing Agency. Read Markus Hetzenegger’s full executive profile here. Follow me on LinkedIn. Check out my website.

Sourced from Forbes

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AI’s Role in Digital and Retail Personalization, Part 1: The Big Picture

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By Justin Racine

Now more than ever, the convergence of retail and digital commerce has never been more important.

The Gist

  • Dreams and the path. Desire leads to personalized journeys and trajectories, as seen in my this author’s own story of buying a Mustang and getting a marketing internship.
  • The evolution. Personalization has been around for decades, evolving from traditional relationship marketing to email marketing and now AI-powered personalization in almost all aspects of life.
  • Three-part series. This series will explore three main components of personalization in retail, digital, and the intersection of dissonance and action.

When I was in high school, I pined for a fast, cool-looking muscle car to be my first vehicle. At age 16, all I could think about was a convertible Ford Mustang — and I was really determined to buy it. My parents, being the traditional people they were said, “Son, if you want this — you need to work for it — that’s how life goes.”

So, I did what any 16-year-old would do — I decided to find a job at a local golf course as I played quite frequently with friends and thought it would be something I would enjoy. (It was.) Sure enough, I saved up the money and was able to buy the Mustang I wanted so desperately.

But what happened next is what would forever alter the course of my life in a personalized way.

An Internship and a Transformation

While working at the golf course — I also was attending classes at a university, specifically for a degree in marketing and advertising strategy. Part of the course curriculum required me to get an internship; and, well, working at a golf course didn’t quite cut it. I gave my notice and told the owner of the course what was going on and he said, “Justin, we loved having you here and hate to see you go — you know, I also work as a general manager of a medical product distribution company — we could use someone like you this summer.”

Eager to upgrade my Mustang, I accepted and spent the summer learning marketing and advertising within the medical space.

We All Have Our Personalized Journeys

This story is important to set the stage of this article. We all as humans have our own journeys, personalized to what’s important to us. For me, it was a Mustang convertible — my desire to have that car set me off on my own personalized journey and trajectory. I later accepted a full-time position at the above-mentioned medical products company and spent the next 13 years learning and absorbing as much as a I could, which provided me with the path to that I’m still on today.

The same holds true for consumers today who demand experiences built on AI-powered personalization. Consumers want and demand things in their lives, and marketers and advertisers must provide personalized journeys to help them intuitively find what they want, and ultimately — change the trajectories of their lives. But to do so, requires a little help from our computer conscious friends.

Personalization, From the Start

The term personalization is somewhat new, thanks to the vast and wide adoption of technology and AI that allows brands to display products and services that we desire; that being said, personalization has been around for decades and traditionally took on another term, Relationship Marketing.

The ANA (Association of National Advertisers) describes “relationship marketing” as “a strategy of Customer Relationship Management (CRM) that emphasizes customer retention, satisfaction and lifetime customer value. Its purpose is to market to current customers versus new customer acquisition through sales and advertising.”

This holds true, to a point.

At its core, relationship marketing has really always been around. A customer visits your business, purchases a service or product, consumes that service or product, then hopefully if you as a business gave them an exceptional experience — the cycle will repeat. This of course has been prevalent since businesses have been around — however, relationship marketing also made a massive step forward during the 1990s, thanks to the rise of the internet and email marketing.

By Justin Racine

Sourced from CMSWIRE

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The 4 Marketing Strategies Your Shopify Store Needs to Drive Traffic

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By Eric Netsch

A successful Shopify store is built on a solid marketing strategy. Read on if you want to discover the secrets of Shopify marketing.

After taking the leap into the ecommerce world and launching your Shopify store, you’re probably looking for ways to attract new customers. Even with a great design and solid product offering, standing out in a crowded market can be challenging. Fortunately, you can take several practical strategies and actions to promote and drive traffic to your business. Depending on your needs and goals, these can include anything from social media marketing to paid advertising to email marketing campaigns.

Shopify is currently the leading platform for ecommerce brands, and it powers nearly two million businesses’ online stores. It goes without saying, but competition in the ecommerce world is fierce; retailers looking to remain relevant and stand out need to establish a solid marketing strategy that will actually move the mark.

Whether you’re just starting out or looking to take your business to the next level, it’s essential to prioritize marketing and make it a crucial part of your overall ecommerce strategy. Doing so can lead to increased sales, greater brand recognition and the development of a loyal customer base.

1. Boost your search engine appeal

To draw more traffic and give potential customers the best chance of finding you when they search for similar products, it’s essential to prioritize Search Engine Optimization (SEO). This strategy requires patience, time and ongoing effort, but it’s worth the investment.

There are several key steps to optimizing your Shopify store. These include optimizing your store’s architecture, conducting thorough keyword research to identify relevant keywords for your business, refining your on-page optimization over time and implementing an effective link-building strategy.

Remember that SEO is an ongoing process that requires continuous effort. Fortunately, Shopify offers built-in marketing tools, including SEO optimization features, to help you optimize for search engines. Moreover, you can enhance your SEO efforts and maintain a steady traffic flow by using various third-party SEO tools and Shopify apps.

If you prioritize SEO and take the essential measures to optimize your Shopify store for search engines, you can gradually attract a larger audience, increase your sales and expand your business.

2. Amplify your reach with digital ads

Investing in paid ads is a highly effective strategy, particularly if you’re just starting out or facing challenges in gaining visibility through organic search marketing. Pay-per-click (PPC) campaigns, like Google Ads, can be an excellent option for driving more traffic to your site as ads appear at the top of search results. In a PPC campaign, you set a maximum bid and only pay when someone clicks on your link. A well-targeted and well-written PPC campaign can immediately bring traffic to your Shopify store. However, it’s worth noting that your competitors and the ads platform determine the amount you pay per visitor, even though you control your overall spending.

Another option for paid advertising is launching campaigns on social media platforms, such as Facebook, Instagram, Twitter and TikTok, the most common networks for paid advertising. To determine which platform works best for you, conduct different tests and analyse your ad’s performance.

Having attractive visual content and persuasive copywriting that appeals to potential customers is essential to creating successful ads. Be creative! Successful advertising campaigns need that creative appeal to capture the attention of shoppers.

Before launching your ads, consider the most relevant keywords for your business and target your ads to specific audiences to ensure your campaigns are effective. Paid advertising campaigns can inform potential customers about your Shopify brand and retarget previous site visitors, making it an effective tactic for attracting new customers and guiding shoppers back.

3. Utilize influencer marketing

In recent years, influencer marketing has gained immense popularity and has proven to be an effective way to market your Shopify store, especially for brands in the fashion, beauty and CPG industries. In essence, influencer marketing is a partnership between a brand and an influencer, and it is becoming an increasingly popular choice for brands’ marketing budgets. Finding an influencer whose niche audience aligns with your product or brand is crucial to begin with influencer marketing. You must also establish a relationship with the influencer, who will create their own content to help position your brand.

Influencer marketing is highly effective for ecommerce businesses. Seventy-one percent of marketers find that it generates better quality customers and traffic than other sources. Consumers trust influencer recommendations because they create a direct communication channel and leverage the credibility and trust of public figures. In fact, 64% of marketers agree that influencer marketing is an enhanced form of word-of-mouth marketing, which is the most effective form of marketing.

This personalized approach is particularly effective in today’s world, where so many brand choices inundate consumers. Influencer marketing is a highly reliable and direct marketing strategy, as recommendations from real people carry more weight than conventional paid advertisements.

4. Master your customer referral program

Referral programs can be an effective strategy where satisfied customers become brand advocates and receive rewards such as discounts or free products. You can reach a wider audience by incentivizing past customers to recommend your products to their network. This approach is particularly effective because people tend to trust recommendations from friends and family more than advertising. Word-of-mouth marketing is such a powerful tool that it can generate up to five times more sales than traditional paid advertising.

Unlike customer reviews, a referral program involves direct word-of-mouth marketing where people share their positive experiences with your products with those close to them, generating new leads that are highly likely to convert. Several platforms and tools are available to create a customer referral program, such as Talkable, which offers an in-app referral program to help find brand advocates.

To attract new customers to your Shopify store, you can use several strategies to promote your business and drive traffic, such as Search Engine Optimization (SEO), paid advertising, influencer marketing and customer referral programs. Implementing these tactics can attract more customers, increase sales and develop a loyal customer base. Remember to be creative and invest time and effort into continuously improving your marketing strategy to stay ahead in the competitive ecommerce industry.

By Eric Netsch

Sourced from Entrepreneur

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Gen Alpha Loves Eating McDonald’s, Watching YouTube and Disney+, and Telling Their Parents What to Buy at the Store

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By Dominick Reuter

With birth years starting in 2013, Generation Alpha is already the most plugged-in generation of children yet, developing some strikingly powerful brand affinities before they reach age 9.

They’re not old enough to open their own checking account or drive to a store, but they are steering some important spending decisions — and companies are taking notice.

With birth years starting in 2013, Generation Alpha is already the most plugged-in generation of children yet, developing some strikingly powerful brand affinities before they reach age 9, according to a recent survey from Morning Consult.

Among the findings: kids love McDonald’s. Like, they’re really lovin’ it.

Thirty-seven percent of Gen Alpha parents said the restaurant with the Golden Arches was their kids’ favourite, six times as many as those who said runner-up Chick-fil-A was their top choice. Morning Consult says these were open-ended, unaided responses — in other words, not chosen from a list.

And while Gen Alpha parents (who are largely millennials) are rather health-conscious adults, 43% still said their kids eat fast food at least once a week.

The other strong favourite in the findings was YouTube: kids watch a lot of YouTube.

Fifty-four percent of Gen Alpha kids own tablets – and they watch a lot of streaming video content, mostly on YouTube, Disney+, and Netflix, the survey found.

What they see online — particularly in unboxing videos and other shopping content — directly influences their retail choices, according to 56% of the parents in the survey.

Still, digital influence is a distant follower to the top driver of Gen Alpha brand and product selection: seeing stuff on store shelves. Nearly three-quarters of kids 4 and under, and 85% of kids 5 to 9, have asked their parents for something they saw during a shopping trip.

“Parents know that one of the best ways to avoid impulse buys is to leave the kids at home, not keep them off digital devices,” the report’s authors write.

The Morning Consult results are consistent with prior research that found companies were spending over $16 billion on marketing to tap into young children’s $286 billion influence on adult spending, according to the 2009 book “Simplicity Parenting” by Kim John Payne.

Payne’s book also highlighted findings that children as young as 2 can recognize brands on shelves and that they have recognition of 300-400 brands by age 10.

“When seeking to connect with Gen Alpha, brands can look to established leaders like McDonald’s and Disney,” the Morning Consult researchers conclude. “Not only does each brand have decades of experience catering to families’ needs, but they’ve also managed to maintain relevance and make a connection with Gen Alpha already.”

This story originally appeared on Business Insider.

Feature Image Credit: Valera Golovniov/SOPA Images/LightRocket via Getty Images

By Dominick Reuter

Sourced from Entrepreneur

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‘Mini gold rush’: How ad buyers are handling video podcast inventory

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By Sara Guaglione

As publishers’ podcast executives ramp up their experimentation with accompanying video, podcast ad buyers are starting to have more conversations about what opportunities this format can provide to brands — and which investment teams handle the buys, according to four agency executives who spoke with Digiday.

“I think that you’re seeing a little mini gold rush here that people are responding to. Word got out that people actually like consuming podcasts… on YouTube and now the marketplace is reorganizing around that,” said Dan Granger, CEO of audio ad agency Oxford Road.

Molly Schultz, svp of integrated investment at UM, said she’s recently had more discussions with publishers about video podcast opportunities. These types of conversations started picking up in the fourth quarter of last year, according to Adam Arnegger, managing partner and executive investment director at Wavemaker US.

With the recent fervour around video podcasts, ad buyers discussed the advertising opportunities for brands in video podcasts and how they are organizing their investment teams to manage the crossover from audio to video.

Audio teams handling video ads

Despite the fact that technically ads in video podcasts are in digital video, audio investment teams at ad agencies are continuing to handle the media plans. That’s because video podcast ads are typically tied to audio podcast ad deals, agency execs said.

“If you have a show that has 100,000 impressions and 50,000 are on YouTube and 50,000 are audio, we’ll take the 100,000 impressions that include that video because we’re going to see that [see and say] effect have a larger impact on ad response. So the units are valuable and we don’t need to artificially limit ourselves on the definition of a podcast,” Granger said. “It probably should be a both-and model.”

At Wavemaker, pitches are going to the audio investment team and then brought over to the video team.

“We’re still in the process of figuring out where this lives,” Arnegger said. “We’re handling it from the core, which is audio and then we’ll start to evolve that as … part of our video buying consumption.”

However, this won’t happen until video podcasts become “more mainstream” and more of their podcast partners start to produce them, he said. “It is something that we are looking at and considering for each and every one of our clients now,” Arnegger said.

UM has integrated investment teams that oversee all channels, so there’s no delineation there, Schultz said.

Video podcasts can also help advertisers that are “still hesitant when it comes to believing audio can be enough on its own” to “feel more comfortable that they’re able to get the full sight, sound and motion,” she said.

However, cross-device attribution is not possible through a platform like YouTube, Maria Tullin, vp and managing director of advanced and digital audio at Horizon Media, noted in an email — meaning it’s difficult to track a podcast listener on a mobile device to a desktop viewer on the platform.

“For clients that are measuring success across upper funnel KPIs, I think it’s a great opportunity to come up with creative ideas, brand integrations and larger sponsorship opportunities,” Tullin said. But for lower funnel clients focused on cost per action, customer acquisition cost or return on advertising spend, “it can be a harder sell,” she said.

A Twitch model?

The typical video podcast ad format is similar to the backbone of podcast audio advertising: personal endorsements, agency execs said.

Video “opens up a lot of branding opportunities” like unboxings, product placement and brand logos, Tullin said. It can also be as simple as having a Coke can on the table near a podcast host during a recording of an interview show, Granger said.

Schultz likened it to the way advertisers sponsor content on the livestream platform Twitch. “It’s a different format and content, but sort of similar in the fact that there is a personality who’s hosting the content, which gives you more opportunity to weave the brand in,” she said.

Pre-roll and mid-roll ads on a video platform like YouTube are “table stakes” and add further “frequency” for the more integrated advertising opportunities in video podcasts, she added. “Because there is a host and because it has roots in podcasts, we would be interested in how we can… work ourselves into the content itself,” Schultz said.

Bleacher Report sells sponsorships for its live-streamed podcast recordings of shows like “The Voncast” and “Taylor X” aired on its B/R app, said Tyler Price, vp of content development and production at Bleacher Report. The resulting video assets are then distributed on social media and YouTube, where — depending on the commitment level of the advertiser — they can monetize the video with standard ads. State Farm and Chase have sponsored B/R’s podcast livestreams, Price said.

“If you don’t have a video asset attached to your audio format now, you’re leaving audience on the table and you’re leaving money on the table,” Price said.

By Sara Guaglione

Sourced from DIGIDAY

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