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Fiverr launches Togetherr: ‘It’s fantasy football meets advertising’

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By Audrey Kemp

Fiver’s new launch, Togetherr, leverages AI to build optimized “dream teams” of creative talent for brands on individual projects.

For brands and agencies, putting together a dream team of talent has never been easier—at least, that’s the idea behind Togetherr.

Popular freelancer platform Fiverr teamed up with Tel Aviv-based advertising veteran Amir Guy to launch Togetherr. The platform’s algorithm, called the Creative Genome, builds virtual teams of highly skilled, independent talent and connects them with brands and agencies on an individual project basis.

Togetherr’s creators have compared its interface to fantasy football. “Togetherr allows brands to build creative teams that are tailored specifically to their needs… They are getting access to world class talent for any project they can imagine,” Guy told The Drum. “Togetherr gives brands what they need, faster, and with exceptional quality.”

The platform also provides freedom and flexibility to creatives by allowing them to choose the types of projects they want to partake in.

In addition to 30 micro-independent agencies, Togetherr’s growing portfolio counts over 1,100 vetted, award-winning creatives and ad industry leaders, who have worked on campaigns for Nike, Coca Cola, Apple and Netflix. The site launches today at Cannes.

Guy has spent over 25 years at creative agencies. Starting out at Young & Rubicam, he eventually climbed the ranks to take the helm of agency Grey, Israel, where he led regional accounts for P&G,Volkswagen and other brands.

It was here, Guy said, where the idea for Togetherr was born. After pitching the idea to Fiverr’s founders, they were happy to make it a reality.

How Togetherr works

When a client uses Togetherr, they’re immediately asked what they need, be it brand strategy and identity, creative concepting or something else. After making that choice, they can specify the channels they’re interested in, such as video, social or experiential.

Finally, the client inputs their industry, budget and brands that inspire them. That data helps Togetherr’s Creative Genome to quickly match the client to three teams of creatives best suited for their project.

Each team at least one creative lead and freelancers who have worked together previously, which ensures compatibility and punctuality among members.

a screen grab of togetherr's interface

A “dream team” built via Togetherr’s algorithm / Image credits: Fiverr + Togetherr

Togetherr’s future milestones

Guy has big dreams himself for this dream team model. Togetherr could also replace the advertising industry’s agency-of-record (AOR) model, which has gone stale over the past 25 years, he says.

“[AOR’s] hefty retainers, bloated head-count and overheads, combined with complex processes, is not meeting today’s client needs,” he saidsays. “Clients need a lot more for less, and faster. Trying to meet these needs without changing our industry’s complex system resulted in broken spirits and a lack of excitement.”

Although the site is officially live, Fiverr plans to continue to build out Togetherr’s platform and improve its AI, as well as add new talent that specializes in different areas, such as media buying and production.

“It’s also important to us to have talent from all over the world We want every team to be as diverse as possible.”

Feature Image Credit: Amir Guy, General Manager, Togetherr / Fiverr + Togetherr

By Audrey Kemp

Sourced from The Drum

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How artificial intelligence ‘blew up’ tennis

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By Chiyo Robertson

Bridie Lynch has been playing and coaching tennis for most of her life.

As her parents run a local tennis club in Wales, she was immersed in the sport from the age of 14.

One aspect she has noticed is the embrace of technology, at all levels of tennis.

“Tennis is such a technical sport. These days, anyone I play or coach is into tech, be it video analysis or longest rally stats.”

She uses a range of apps and techniques for her own matches and coaching including a smartphone-based video system called SwingVision, which breaks down her performance with details such as forehand errors and backhand winners.

“Personally, I like having the tech to enhance my game. I can see a clearer vision of what I can improve, from my swing to my patterns of play,” she explains.

Data analytics has been around a long time in sport. Perhaps the best known in example of its use is from 2002, when the Oakland Athletics baseball team used statistical analysis to choose their squad, rather than the wisdom of coaches and scouts, and their favoured metrics.

Jonah Hill and Brad Pitt speak onstage at "Moneyball" Press Conference during 2011Getty Images
Even Hollywood has taken an interest in data analytics with the movie Moneyball starring Brad Pitt and Jonah Hill

That experience was the core of Michael Lewis’s 2003 best-selling book Moneyball, which later become a film staring Brad Pitt and Jonah Hill.

Tennis has also seen this revolution. “Data blew up our sport,” says tennis strategist and coach Craig O’Shannessy.

For him the 2015 Australian Open was a key moment.

As Novak Djokovic and Andy Murray battled on court, powerful computers crunched the data and grouped rally length into three distinct categories, essentially short, medium and long.

Novak Djokovic of Serbia plays a forehand in his men's final match against Andy Murray of Great Britain during day 14 of the 2015 Australian OpenGetty Images
In tennis the 2015 Australian Open final was a big moment for data analysis says Craig O’Shannessy

“We discovered 70% of all points were each player hitting the ball into the court a maximum of just twice,” he says.

Mr O’Shannessy, who worked with Novak Djokovic between 2017 to 2019, says that insight made him realise that the way players practice was all wrong.

“Ninety percent of practice is focused on consistency, but only 10% of the match court is in rallies of more than 9 points,” he points out.

“This data changed our sport forever,” he says.

Tennis strategist and coach Craig O'Shannessy with Novak DjokovicCraig O’Shannessy
Craig O’Shannessy worked with Novak Djokovic for two years

That manipulation of data has been taken to a new level.

Coaches now have artificial intelligence (AI), where sophisticated software is fed, or trained, with unimaginable amounts of data. The resulting AI can spot patterns that a human would never be able to see.

“AI can sniff out areas of significances. Humans do a very bad job at layering data, whereas AI can do it in seconds,” says Mr O’Shannessy.

So, for example, if Novak Djokovic hits 50 winners from his forehand those shots could be broken down in multiple ways or layers. Perhaps 40 of them came when he was serving and then 35 came on the first shot after the serve.

Finding a pattern of play where Novak hits 35 out of 50 winners in exactly same way is a first, according to Mr O’Shannessy.

“We’ve stumbled around for decades trying to bring all this together.”

AI requires vast amounts of data to train and build accurate algorithms.

Rafael Nadal of Spain plays a forehand against Felix Auger-Aliassime of Canada during the Men's Singles Fourth Round match on Day 8 of The 2022 French Open at Roland Garros on May 29, 2022Getty Images
Players have access to even more data than ever at this year’s French Open

Raghavan Subramanian is the head of the Infosys Tennis Platform and has been working with the Association of Tennis Professionals (ATP) since 2015 and with The French Open (also known as Roland Garros) for more than three years.

He has access to videos and statistics from around 700 matches every year. “Valuable data that forms the raw material for all our AI and machine learning systems,” says Mr Subramanian.

He said accuracy has improved over the past four years, as more training data has become available.

From the player’s point of view it means they can analyse a match with more precision. Using the Roland Garros Players App, they can see exactly the placement of key shots, such as winners, errors and serves.

Raghavan Subramanian is the head of the Infosys Tennis PlatformInfosys
Raghavan Subramanian says the Infosys AI gets more accurate with each tournament

“We saw a 51% jump in the use of the RG Players App in 2021, compared to the previous year, with 1,100 players and coaches using AI-powered videos,” says Mr Subramanian.

The AI is also speeding up media coverage of the tournament. AI is slicing and dicing data to create video content in seconds, a job that would normally take a multimedia team hours to do.

“Fans are able to access and analyse match highlights and other smart playlists almost immediately after a match.”

Feature Image Credit: Bridie Lynch.  Tennis players are embracing tech says Bridie Lynch

By Chiyo Robertson

Sourced from BBC News

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AI PREDICTS IF AND WHEN YOU MIGHT HAVE A FATAL HEART ATTACK

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A new artificial intelligence-based approach can predict if and when a patient could die of a heart attack.

The technology, built on raw images of patient’s diseased hearts and patient backgrounds, significantly improves on doctor’s predictions and stands to revolutionize clinical decision making and increase survival from sudden and lethal cardiac arrhythmias, one of medicine’s deadliest and most puzzling conditions.

“Sudden cardiac death caused by arrhythmia accounts for as many as 20% of all deaths worldwide and we know little about why it’s happening or how to tell who’s at risk,” says senior author Natalia Trayanova, a professor of biomedical engineering and medicine at Johns Hopkins University.

“There are patients who may be at low risk of sudden cardiac death getting defibrillators that they might not need and then there are high-risk patients that aren’t getting the treatment they need and could die in the prime of their life. What our algorithm can do is determine who is at risk for cardiac death and when it will occur, allowing doctors to decide exactly what needs to be done.”

The team is the first to use neural networks to build a personalized survival assessment for each patient with heart disease. These risk measures provide with high accuracy the chance for a sudden cardiac death over 10 years, and when it’s most likely to happen.

The deep learning technology is called Survival Study of Cardiac Arrhythmia Risk, or SSCAR. The name alludes to cardiac scarring caused by heart disease that often results in lethal arrhythmias, and the key to the algorithm’s predictions.

For the study in Nature Cardiovascular Research, researchers used contrast-enhanced cardiac images that visualize scar distribution from hundreds of real patients at Johns Hopkins Hospital with cardiac scarring to train an algorithm to detect patterns and relationships not visible to the naked eye.

Current clinical cardiac image analysis extracts only simple scar features like volume and mass, severely underutilizing what’s demonstrated in this work to be critical data.

“The images carry critical information that doctors haven’t been able to access,” says first author Dan Popescu, a former Johns Hopkins doctoral student. “This scarring can be distributed in different ways and it says something about a patient’s chance for survival. There is information hidden in it.”

The team trained a second neural network to learn from 10 years of standard clinical patient data, 22 factors such as patients’ age, weight, race, and prescription drug use.

The algorithms’ predictions were significantly more accurate on every measure than doctors, and they were validated in tests with an independent patient cohort from 60 health centers across the United States, with different cardiac histories and different imaging data, suggesting the platform could be adopted anywhere.

“This has the potential to significantly shape clinical decision-making regarding arrhythmia risk and represents an essential step towards bringing patient trajectory prognostication into the age of artificial intelligence,” says Trayanova, co-director of the Alliance for Cardiovascular Diagnostic and Treatment Innovation. “It epitomizes the trend of merging artificial intelligence, engineering, and medicine as the future of healthcare.”

The team is now working to build algorithms to detect other cardiac diseases. Trayanova says the deep-learning concept could be developed for other fields of medicine that rely on visual diagnosis.

Source: Johns Hopkins University

Feature Image Credit:  Olivier Collet/Unsplash

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Turning point for artificial intelligence: Will the large cloud providers dominate?

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Could the tech giants take control of the AI narrative and reduce choices for enterprises? Experts weighed the pros and cons in a recent online conference.

Artificial intelligence and machine learning requires huge amounts of processing capacity and data storage, making the cloud the preferred option. That raises the specter of a few cloud giants dominating AI applications and platforms. Could the tech giants take control of the AI narrative and reduce choices for enterprises?

Not necessarily, but with some caveats, AI experts emphasize. But the large cloud providers are definitely in a position to control the AI narrative from several perspectives.

That’s part of the consensus raised at a recent webcast hosted by New York University Center for the Future of Management and LMU institute for Strategy, Technology and Organization, joined by Daron Acemoglu, professor at MIT; Jacques Bughin, professor at the Solvay School of Economics and Management; and Raffaella Sadun, professor at Harvard Business School.

There’s more to AI than cloud. The complexity and diversity of AI applications go well beyond the cloud environments where they are run — and therefore reduce the dominance of a few cloud giants.

Certainly, “AI will require more capacity in storage, of the information flow,” says Bughin. At the same time, “cloud is only one part of the total pie of the platform. It’s part of infrastructure, but the platform layer is what you develop in house and through a third party. This integration is going to be hybrid, even more important than the cloud itself. Let’s be very clear, it’s not about operation, it’s a lot of algorithms, it’s a lot of different data, that integration piece, that will require system integration, architecture and design. That means that different types of firms will be involved in that work.”

What Bughin worries about more is the innovation potential from AI startups that may be squashed by larger players gobbling up smaller companies and startups through mergers and acquisitions. “Companies like the big internet or AI guys are going and buying a lot of very small and very clever AI firms.”

At the same time, Sadun points out that smaller companies may be in a better position to leverage AI innovations — but need help with training and education to prepare them. “This issue of who benefits from AI is really important,” she says. “On the one hand, we might think the smaller firms may be able to use these technologies more effectively, because they are more nimble, more agile. Companies that have already digital can exploit and scale AI.”

Where the large cloud providers may also make their dominance felt is in the monopolization of the data that feeds AI systems, says Acemoglu. Cloud architecture itself can be based on price-sensitive and competitive cloud services, he explains. “But the cloud architecture will not enable you to exploit data. The area, where I worry about the future of AI technologies are those that enable firms to monopolize data. That’s where firms have an oversized effect on the future direction of technology. That means a few people in a boardroom are going to determine where a technology’s going to go. We want more people focused and people-centric AI. That’s not going to be possible if a few firms that have a different business model dominate the future of technology. ”

The value of an AI-driven enterprise “does not reside in the cloud that enables it,” Bughin believes. “I think there’s enough of competition for the price point not to destroy the value. The value will come from the fact that you have integrated these technologies where you work, and the way your company works, in your own back end. The back end is not going to be the battlefield. The value is from generating productivity and revenue, at a rate faster than what we’ve seen in traditional digital transformations.”

And, for the first time, we see the terms traditional and digital transformation used together in the same sentence. As these thought leaders relate, such transformations are moving to the next phase, enabling autonomous, software-driven operations and innovation through AI. It’s a question of whether large tech vendors control the momentum, or if it remains a market and practice with a diversity of choices. Stay tuned.

Feature Image Credit: Joe McKendrick

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How Covid broke supply chains, and how AI and blockchain could fix them

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When the coronavirus crisis erupted in 2020, it became apparent that the medical emergency was accompanied by severe shortages, especially in some medical devices.

The pattern was first observed for ventilators: demand spiked everywhere and the supply chain was disrupted. This was because production of the devices spanned multiple countries, with each part dependent on other parts manufactured in different locations. The longer the chain and the more complex the dependence, the greater the exposure of any point to the disruption of another one, and to mandated shutdowns.

Now, two years since Covid first hit, this pattern has affected almost every sector of the global economy. “Supply chain issues” have become so widespread that they are now a running joke, affecting everything from furniture to groceries. But why has Covid had such a severe effect on how we receive products and goods?

In recent decades, supply chains became lean, and they lengthened as they became more cost-efficient: more and more steps were added in the manufacture and transportation of any given product in the name of speed and cost. This means there are more and more places where something can go wrong between you ordering something online and it arriving to your door.

Scandi living room interior with grey, big sofa in the center and modern picture on the wall
The supply chain crisis started with ventilators and ended with sofas. Photographee.eu/Shutterstock

Today, downstream suppliers – such as those who provide vehicle control systems to your car manufacturer – depend on upstream suppliers – such as chip manufacturers – to deliver on time so they can in turn deliver on time to you.

With long chains, risks are now shared between multiple entities all around the world.

Using AI and blockchain to protect trade

Supply chain problems have a knock-on financial effect known as trade credit contagion. This is where firms delay payments to suppliers because their customers delay payments to them. The pay-on-delivery model can lead to cancelled or delayed shipments which can in turn lead to bankruptcies.

While a high proportion of trade credit risk remains uninsured today, a post-pandemic world may see insurance and reinsurance firms fill in this protection gap.

Researchers are currently working to develop methodologies to identify vulnerabilities in global supply chains and to understand their trade credit contagion risks. The goal is to make these systems more robust overall.

How can we design ways to design insurance and reinsurance contracts in order to effectively share the risk and mitigate vulnerabilities? How can reliable trade credit lead to fewer delays in supply chains and replace the familiar predicament we face now, of paying for something in advance with an unknown delivery date?

Artificial intelligence and complex network theory are helpful in identifying the structures that could pose systemic risk. They help us ask: which patterns of connections are likely to lead to delay and trade credit contagion and which are more robust?

Using these tools, we can create large-scale simulators of global supply chains responding to a wide variety of shocks and then use machine learning techniques to detect the problematic parts of the chain. This knowledge can then be used in market designs that strengthen the system before another pandemic or disaster occurs.

Other novel technologies such as blockchain bring the promise of using high quality data to analyse supply chain dependencies. blockchain technology uses real-time data and transparent verification carried out by multiple parties. In combination with other features, such as smart contracts, this could lead to timely resolution in cases of disputes along the supply chain.

An aisle in a warehouse with shelves stacked with boxes
We need to insure each link in the chain. dreamnikon/Shutterstock

My research involves

using blockchain to streamline record-keeping and payments. This problem is challenging because the adoption of blockchain depends both on the specifics of the technology and the cost.

The problem of adopting technology in the presence of positive externalities (whereby firms adopting the technology in turn improve the operations of external parties) is an old one in economics, but now these externalities are systemic in nature: the effects propagate along the chains. The cost of the technology depends on how many firms adopt it, and each one faces business specific costs based on its position in the supply chain, its risk tolerance and its costs to insure these risks.

Real-time recording keeping, the traceability of transactions, and the immutability of blockchain can all help supply chains become more efficient. This is all the more true if we consider the full length of the chain, where transactions need to be verified by several parties: participants in the supply chain, insurance and reinsurance firms.

The future of supply chains

Trade credit insurance is likely to grow after the pandemic. It may rely on private-public partnerships – the pandemic has shown that governments become important players when they impose shutdowns in certain areas.

These funds can be used to make up for payment delays, reduce losses and jump-start critical production where necessary. But not all links in a chain can be insured, and an important challenge is to identify the most important stages under different shock scenarios.

Supply chains can also be rewired – large-scale algorithms can identify which suppliers need to be replaced and which new ones need to emerge.

In a few years, supply chains may look different, as the overall goal shifts from minimising costs, as was the case before the pandemic, to minimising delays and trade credit risks. The end consumer will drive the need to rewire the network, as demand shifts. Ultimately, the flexibility of the customer determines the resilience of the supply chain.

Feature Image Credit: Studio concept/Shutterstock

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2 Easy Ways to Use Artificial Intelligence at Your Small Business

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By Peter Roesler

A.I. is at the forefront of the latest and greatest technology developments. Learning how to make the most of this technology for your brand is necessary.

Feature Image Credit: Getty Images

By Peter Roesler

Sourced from Inc.

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The Role of AI and ML in Improving Digital Performance

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By Anil Gupta

Artificial intelligence and machine learning are among the top marketing buzzwords we come across in the field of digital marketing. These technologies have already become an integral part of digital marketing and are being leveraged to make campaigns more personable and efficient.

For instance, artificial intelligence can make personalization easy and quick by creating accurate buyer personas. These personas are auto-generated to deliver a holistic audience segmentation, thereby improving the effectiveness of the campaigns. In addition, Netflix, Google, Uber, Spotify, Pinterest, and other apps use machine learning to personalize individual accounts and make relevant recommendations to their users.

The ever-improving algorithms and the exponential growth of data are encouraging business leaders and marketers to use AI, in the form of machine learning, natural language processing (NLP), deep learning, and other technologies. These technologies are helping them improve customer experience and conversions.

A Gartner survey shows that 37% of organizations are applying AI in some form or the other to boost their digital performance.

This post highlights how AI and ML are proving to be game-changers in the digital marketing realm.

1. Offer a Better Understanding of the Audience

Great content starts with knowing the audience well. When a business knows its target audience, the connection feels more natural and relevant. That genuine connection goes a long way in building lasting relationships with customers.

In recent years, AI and ML have opened up a whole new world of possibilities for understanding audience behavior. AI tools and data-driven insights are helping businesses understand who they are reaching, what the customers want and need, when to communicate, and where to reach them.

Artificial intelligence helps marketers instantly define buyer personas. Then, platforms like Socialbakers auto-generate these personas to deliver more holistic audience segmentation in the form of actionable insights. These insights help content marketers share inspiring stories that convert.

Keeping your audience at the centre of your online strategies is critical to business success. AI can help by offering unique audience insights, enabling businesses to deliver an integrated brand experience through relevant content. It also helps in selecting the most trustworthy and effective influencers for the brand.

2. Help with Lead Management

Big data, predictive analytics, and machine learning are being increasingly used in business intelligence these days. Machine learning, with its ability to bring out valuable hidden insights from large data sets, can create tangible value for businesses.

Leads are the driving force for businesses. They are the ones who will soon contribute to the organizational revenue. Hence, business leaders spend a significant amount of time in lead management. ML can be leveraged to improve and scale a firm’s approach to lead management, thereby boosting the bottom line. It helps firms generate better leads, qualify and nurture them, and ultimately monetize them effectively.

For instance, ML can help you create an ideal customer profile (ICP) to reach the best customers. ICP takes a structured look at the demographics and psychographics of an individual and determines their purchase intent and the content that matters to them. Thus, ICP can be used for lead scoring, allowing marketers to prioritize targeted accounts.

ML can also help firms generate more qualified leads from the traffic already coming to the site. For example, check out how Drift, a revenue acceleration platform, uses conversational AI to recognize quality from noise, learn from the conversations, and automatically qualify or disqualify website visitors. These qualifiers help the sales team focus on leads that are ready for conversions.

3. Curate and Create Better Content

AI is changing the game for content marketers. The technology is being used to automatically generate content for simple stories like sports news or stock market updates. AI also allows social channels to customize user new feeds.

But one content field where AI is increasingly applied is content curation. AI algorithms make it easier to collect target audience data to create relevant content at each stage of the marketing funnel.

For instance, the algorithms collect data on what the audience prefers to read, the questions they want answers to, or any specific concerns. Using this data, content marketers can curate and create relevant content that boosts customer experience and ultimately leads to conversions.

The North Face uses an AI-powered technology like IBM Watson that recreates shopping experiences. The AI tool uses cognitive computing that brings the online and in-store experiences closer together.

Besides, machine learning feeds content strategies by discovering fresh research-based content ideas, identifying the top-performing topic clusters, showing the most relevant keywords in a specific niche.

For instance, Google Analytics and SEMrush operate on machine-learning algorithms that are useful in keyword research and discovery, and content distribution. In addition, these tools can discover industry trends and show you ways to rank higher in SERP.

AI and ML-enabled tools improve the overall reception and performance of online content. In addition, the tools allow marketers to offer relevant and personalized digital experiences that positively influence engagement.

4. Help with Competitive Search Engine Ranking

Search engines are already using AI-enabled algorithms to deliver the most relevant SERP results. These algorithms rely on AI to understand the context of the content and spot irrelevant keywords. No wonder SEOs are constantly striving to understand these algorithms and coming up with strategies to create contextual, conceptual, and accurate content.

The placement of your business in the SERPs can make or break your online reputation and performance. AI technologies make it easier to create compelling content that answers the target audience’s queries, keywords, and phrases.

SEO isn’t a day’s job. It’s challenging, and the results of one’s efforts can only be seen after months. Fortunately, AI-based SEO tools help alleviate this stress. SEO optimization tools like Moz, WooRank, BrightEdge, and MarketMuse heavily rely on AI to offer SEO solutions like:

  • Keyword research
  • Search terms to make the content more relevant
  • Link-building opportunities
  • Trending topics
  • Optimum content length
  • User intent and more.

Tools like Alli AI can instantly optimize your website regardless of the CMS and your web development expertise. The platform performs a site-wide content and SEO audit, automatically optimizes the content, and resolves duplicate content issues. All this makes it easier for content creators to avoid poor-performing content and boost their online ranking.

5. Improve Page Speed

Google has put an exact value on fast user experience by including page speed as one of its ranking signals. That’s why boosting page speed is one of the top priorities for all businesses, especially ecommerce firms. As a result, Webmasters take all sorts of measures to improve page speed.

For instance, WordPress site owners may speed up WordPress by optimizing background processes, keeping the WP site updated, using a content delivery network (CDN), or using faster plugins. Of course, they also use various tools like Page Speed Insights, load time testers, and CMS plugins for the purpose. But now, there’s another ML-powered solution available for boosting the page speed – the Page Forecasting Model.

This model predicts user behaviour using machine learning and predicts the next page visitors will click on in real-time. This allows Webmasters to preload the page in the background, thus improving the overall experience.

The algorithm is trained with historical data from Google Analytics.

For instance, user patterns like going from home page to category page or product page to the shopping cart are recognized, understood, and included in update algorithms. If the user behaves similarly, the algorithm is automatically prepared with the next page.

However, the prediction accuracy is dependent on the amount of data available to train the algorithm and the website structure. So, the models will vary according to these factors. For instance, if yours is an ecommerce website that combines industry news with product pages, it’s better to use two or more models that can predict the behaviour per section.

6. Automate Website Analytics Process

Web analytics isn’t new. Businesses have been assessing user behaviour and tracking key performance metrics since the mid-’90s. But thanks to AI and machine learning, web analytics tools now have robust capabilities that allow businesses to automate the process. These tools can offer auto-generated reports and on-demand insights that feed marketing strategies.

Within a single visit to a webpage, each user generates hundreds of data points like the time spent on a page, the browser details, its location, and others. It is practically impossible to analyse all this data manually. AI and ML make such analysis faster and accurate by speeding up the data processing.

AI-based tools can help you track each visitor’s online behaviour, understand user journeys, and how customers move through the marketing funnel. They also point out issues, if any.

Let’s say you have a blog post that gets a lot of traffic, but visitors just read the post and leave without taking action like subscribing to your newsletter or sharing your post on social media. AI-based tools can flag such issues, allowing you to take the necessary corrective action like adding internal links or improving your CTA.

Google Analytics (insights section), Adobe Analytics, and Kissmetrics are among the top web analytics tools that help firms see patterns in customer behaviour and predict future trends.

7. Improve Site Navigation

Site navigation is another critical area in digital performance where AI and ML can help is site navigation. Though it may sound negligible, the importance of having organized and easy-to-follow navigation cannot be ignored. Well-planned navigation improves the visit duration, reduces the bounce rate, and boosts user experience. It also enhances the overall aesthetic appeal of the website design.

AI can help Webmasters create a user-friendly website structure that’s easy to navigate. AI-powered chatbots can guide users through the pages and help them find what they are looking for within the first few clicks. This significantly improves the user experience and sends good signals to search engines, indicating that your content is useful and relevant.

Thus, Google and other search engines will rank your page higher than any other website offering similar content.

8. Design Better Websites

AI applications can improve the usability and experience of a website by enhancing the site’s appearance, strengthening its search abilities, managing inventory better, and improving interaction with website visitors. No wonder a growing number of designers and developers are moving towards AI-based design practices.

AI is slowly becoming an indispensable part of modern web design and development. Take the field of artificial design intelligence (ADI) systems, for instance. ADI has triggered a sudden shift in the way web designing is done. It allows designers to combine applications into the website for better user experience and functionality.

Check out The Grid website platform that automatically adapts its design to highlight the content. The platform uses ML and constraint-based design and flow-based programming to dynamically adapt the website design to the content.

Today, we have several entrants in this space that are taking AI in web design to a whole new level. Brands like Adobe, Firedrop, Bookmark, Wix, Tailor Brands, and many others are leading the segment and leveraging the capabilities of AI in web design. In addition, most of these ADI platforms can learn and offer suggestions for optimizing the website for better user experience and SEO performance.

The Way Forward

Artificial intelligence and machine learning are proving to be awesome technologies when it comes to improving a firm’s digital performance. However, it is essential to remember that these ML models are only as good as the data that’s used to train them. Therefore, it’s critical to ensure that your marketing team has access to high-quality and accurate data.

So, before applying these technologies to your digital efforts, there are specific steps that you need to take.

  • Set up tags to track and capture on-site user behaviour.
  • House all the data from different sources in one central place like Google BigQuery, a Big Data analytics platform.
  • Invest in data deduplication to eliminate duplicate copies of repeating data from multiple sources.

Once your data is in place, you will be in a great position to start deploying AI and ML for boosting your digital performance. In addition, the information shared above will prove to be useful as you start building machine learning solutions for improving your business’s online presence.

By Anil Gupta

Anil is the CEO & Co-Founder of Multidots, one of the top WordPress development agencies on the planet. He is a technopreneur with over 13 years of experience coding, thinking, and leading the business with mind and people with heart. He and his team are seasoned in delivering secure and feature-reach WordPress services for businesses big and small.

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7 Tips to Use AI to Acquire and Retain Customers

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By Kevin Payne

Artificial Intelligence (AI) mimics the cognitive functions of the human mind, particularly in learning and problem-solving. Many of the apps that we use today are powered by AI. From voice-activated virtual assistants to e-commerce, AI applications are everywhere.

With the advancements in AI technology and access to big data, companies across different industries are integrating AI into their processes to find solutions to complex business problems.

The application of AI is most noticeable within the retail and e-commerce space. Websites and apps can interact intelligently with customers, creating a personalized approach that enhances the customer experience.

No matter what industry your business operates in, these seven tips can help you acquire and retain customers more efficiently at a fraction of the time it takes to do things manually.

How to Use AI to Get and Keep Customers

1. Identify Gaps in Your Content Marketing Strategy

If you’re just starting with content marketing, you’ll need to know what type of content to create.

By using AI, you can identify the gaps, find fixes, and evaluate the performance of your content marketing campaign.

Take Packlane, a company that specializes in custom package designs, for example. They came up with high-quality content like helpful blog posts that provide valuable information. At the same time, the content they publish makes it easier for their target market to understand their brand and services.

If you’re in the retail or e-commerce space, you can use AI to identify the gaps in your content marketing. Your content may be focused on your products and their features, but through AI, you can determine the relevant content that addresses your audience’s needs and pain points.

2. Pre-Qualify Prospects and Leads

Not every visitor to your site will become a paying customer. If you’re not getting sales despite the massive traffic, it means you’re generating low-quality leads.

Some reasons why this happens includes:

  • Targeting the wrong audience
  • Poor content marketing strategy
  • Using the wrong type of signup form
  • Promoting in the wrong social media platforms
  • Ineffective calls to action

These explain why 80% of new leads never convert into sales. The mistakes can be rectified with the help of artificial intelligence.

AI tools can extract relevant data to help you learn more about your target audience. These tools also provide predictive analytics on your customers’ behaviour. They, in turn, help improve your lead generation strategy because you’ll know which leads to pursue, where to find them, and how to effectively engage them.

3. Provide Personal Recommendations

According to a report by the Harvard Business Review, even though there are privacy concerns when consumers’ personal information changes hands, people still value personalized marketing experiences.

Brands that tailor their recommendations based on consumer data boost their sales by 10% over brands that don’t.

Recommendation systems’ algorithms typically rely on data on browsing history, pages visited, and previous purchases. But AI is so advanced that it can analyse customers’ interactions with the site content and find relevant products that will interest the individual customer. This way, AI makes it easier to target potential customers and effectively puts the best products in front of the site visitors.

Because of AI, recommendation engines are able to filter and customize the product recommendations based on each customer’s preferences. It’s a cycle of collecting, storing, analysing, and filtering the available data until it matches the customers’ preferences.

This is an effective way of acquiring and retaining customers because there’s an element of personalization.

4. Reduce Cart Abandonment

A high cart abandonment rate is the bane of e-commerce business owners. According to a study by the Baymard Institute, online shopping cart abandonment rate is close to 70%.

Users abandon their online carts for various reasons:

  • high extra costs
  • complicated checkout process
  • privacy concerns
  • not enough payment methods, or
  • they’re not ready to buy yet.

Using AI-powered chatbots is one way to reduce cart abandonment. AI chatbots can guide the customers through their shopping journey.

AI chatbots can have a conversational approach and give the customer a nudge to prompt them to complete the purchase. These chatbots can also act as a virtual shopping assistant or concierge that can let a customer know about an on-the-spot discount, a time-sensitive deal, a free shipping coupon, or any other incentives that will encourage them to complete the checkout.

With AI, lost orders due to cart abandonment are recoverable and can lead to an increase in conversion rate for e-commerce businesses.

5. Increase Repurchases With Predictive Analytics

Predictive analytics is the process of making predictions based on historical data using data mining, statistical modelling, artificial intelligence, machine learning, and other techniques. It can generate insights, forecast trends, and predict behaviours based on past and current data.

In marketing, predictive analytics can be used to predict customers’ propensity to repurchase products as well as its frequency. When used to optimize marketing campaigns, AI-powered predictive analytics can generate customer response, increase repurchase, and promote cross-selling of relevant products.

It’s all part of the hyper personalized marketing approach, where brands interact and engage with customers and improve their experience by anticipating their needs and exceeding their expectations.

With predictive analytics, you can focus your marketing resources on customer retention and targeting a highly motivated segment of your market that are more than happy to return and repurchase your products. This approach is less expensive than advertising or implementing pay-per-click campaigns.

6. Improve Your Website User Experience

Every business—big or small—knows the importance of having a website, where visitors can interact with the brand, respond to a call to action, or purchase products. But it’s not enough to just have an online presence; it’s important that visitors to the site have a great experience while navigating through your site.

What makes for a great user experience? Users have different expectations. Some of them want faster loading time, while others want a simple and intuitive interface. But most important of all, they want to find what they’re looking for. It could be a product, content, or a solution to a problem. Whatever they may be, it’s up to you to meet their expectations.

With artificial intelligence, you can improve your website user experience tenfold. Here are some of the ways AI can be used to improve user experience.

Search relevance

This pertains to how accurate the search results are in relation to the search query.  The more relevant the results are, the better search experience the users will have. This means they are likely to find relevant content answering their queries or finding products that solve their problems.

Personalized recommendations

Content that is tailor-made for the user tends to have greater engagement which increases the likelihood of conversation. Amazon has perfected the product recommendation system using advanced AI and machine learning. AI gets data from customers and uses it to gain insights and apply predictive analysis to recommend relevant products for cross-selling opportunities.

AI chatbots

The presence of chatbots contributes to a great user experience because they provide 24/7 assistance and support in the absence of human customer service.  Users can get accurate answers to their inquiries quickly and efficiently, compared to scrolling through a text-based FAQs.

7. Social Listening for Potential Customers

Social listening is the process of analysing the conversations, trends, and buzz surrounding your brand across different social media platforms. It’s the next step to monitoring and tracking the social media mentions of your brand and products, hashtags, industry trends, as well as your competitors.

Social listening analyses what’s behind the metrics and the numbers. It determines the social media sentiment about your brand and everything that relates to it. It helps you understand how people feel about your brand. All the data and information you get through social listening can be used to guide you in your strategy to gain new customers.

Social media monitoring and listening can be done much more efficiently with the help of artificial intelligence. It’s an enormous task for a team of human beings to monitor and analyse data, but with AI-powered social media tools, all the tedious tasks can be automated. They can be trained to leverage data to provide valuable insights about your brand with high accuracy.

With AI and machine learning, your social listening can easily determine your audience, brand sentiments, shopping behaviour, and other important insights. By having this information within reach, you’ll know how you can connect with them more effectively and turn them from prospects to paying customers.

Key Takeaways/Conclusion

More companies across different industries are using the power of artificial intelligence and machine learning to significantly increase brand awareness, enhance customer engagement, improve user experience, and meet customer expectations.

  • AI can identify gaps in your content marketing strategy so that you can create content that’s relevant to your target audience.
  • AI can help you generate high-quality leads that are likely to buy your products.
  • With AI, you can personalize and tailor-fit your product recommendations based on your customers’ preferences, increasing repeat purchases.
  • AI can be integrated into your e-commerce site to reduce shopping cart abandonment.
  • AI significantly improves website user experience by making it intuitive, accessible, and easy to navigate.
  • AI-powered social media tools can help you monitor and gain valuable insights about your brand. You can then use this to develop a social media marketing strategy to gain new customers.

Achieve these milestones, and you’ll be sure to acquire new customers and retain existing ones.

Feature Image Credit: iStock/monsitj

By Kevin Payne

Sourced from Black Enterprise

 

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Social media influencer/model created from artificial intelligence lands 100 sponsorships

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Sourced from allkpop

With the advancement of technology, AI influencers and virtual human models are becoming the new trend. It has recently emerged as a blue-chip in the advertising industry because there are no privacy scandals and there are no time-space restrictions with these virtual humans. In particular, the use of virtual humans seems to be gaining more momentum in the COVID-19 pandemic, where there are many restrictions on travel and limitations on the number of people gathering.

On September 10, Baek Seung Yeop, CEO of Sidus Studio X that created ‘Rozy,’ the newly rising blue-chip in the advertisement industry, explained, “These days, celebrities are sometimes withdrawn from dramas that they have been filming because of school violence scandals or bullying controversies. However, virtual humans have zero scandals to worry about.” 

‘Rozy’ is a virtual human that was created Sidus Studio X last year in August. Her age will forever be 22, and she has been keeping an active presence online as a real human since December of last year. In particular, this virtual human began gaining much attention as she appeared in an advertisement for Shinhan Life in July.

According to CEO Baek Seung Yeop, Rozy currently has advertising contracts with companies and a significant amount of sponsorships. CEO Baek said, “We have advertised twice already this week alone and now we have eight exclusive contracts,” and continued to explain, “She has landed more than 100 sponsorships now, but we have not been able to process them yet.”

He then added, “We have achieved our goal profit now, and I think Rozy will be able to make more than 1 billion KRW (~$854,007) by the end of this year.”

In regards to Rozy’s visual, CEO Baek explained, “We didn’t use a specific person as the model to her look. The MZ generation does not like to hide their flaws nor reveal their flaws. We didn’t take western beauty as the beauty standard either.”

CEO Baek Seung Yeop also shared Rozy’s future plans. He explained that the company plans to expand Rozy’s scope of activities, moving on to movies, dramas, and entertainment shows.

As CEO Baek said, the reason for the popularity of virtual humans is that there is no fear that advertisements will be suspended due to unsavoury privacy scandals after the AI model is selected as the advertising model. In addition, the location and scene can be created through computer graphics, so the virtual model is not limited in time and space, and unlike real people. The other advantage is that period in which the model can be active is very long or eternal because the virtual human doesn’t get sick or grow old.

Sourced from allkpop

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Google’s AI division shocks world with claim it’s now just 5 years away from deciphering Geordie

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Sourced from News Thump

Experts in artificial intelligence have responded with amazement, and some scepticism, to Google Brain’s recent assertion that before the decade is up, it will have cracked the linguistic Holy Grail of understanding what the residents of Newcastle are talking about.

Professor Simone Williams, a neurolinguistics expert working for the project, was adamant the prospect of being able to translate Geordie into English was no longer a pipe dream.

She went on, “After we bought AlphaGo we hooked it up to looped episodes of Geordie Shore. It went dark and after two full years, we were about to give up. But six months ago it finally made a breakthrough and conclusively proved that ‘scran’ was a phoneme used to denote a condition of hunger.”

Professor Williams admitted the project was always seen as a moonshot, particularly by financial backers.

“A lot of people didn’t believe in it. We had to go against decades of conventional thinking that Geordie wasn’t technically ‘speech’ but a method of echolocation gone horribly wrong due to alcohol abuse. And we were constantly being told there was no commercial value in knowing what a ‘canny broon’ is.

“But for linguists like myself, Geordie is the last great frontier. Once we crack it, the prospect of a sci-fi universal translator becomes very real.”

Professor Williams did say it would be at least three years before simple messages like texts could be fully translated and another two years to reach a B2 CEF level.

Until then, trade with Geordies would still have to rely on basic object recognition or getting surly residents of Gateshead to act as interpreters by pretending to agree with their ridiculous claim that they’re not a suburb of Newcastle.

Sourced from News Thump

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