AI News, AI in Marketing: How to Optimize Your Marketing Delivery artificial intelligence

The Nuts and Bolts of Creating Personalized Content in Marketing Cloud

By using tools that help you automate personalization, make data-driven decisions, and streamline the process of creating dynamic content, you can quickly build personalized, omni-channel experiences at scale.

As it pulls information into each personalized message, marketing automation technology can update your data extensions from your landing pages, making future interactions even more personalized for that customer based on their engagement, user behavior, or information they provide.

With the help of AI, you can identify new prospects and follow them through the sales funnel, deliver personalized ads and customer journeys, and deliver deep data insights that your sales and marketing teams can use to personalize customer experiences.

With dynamic content, your developers could create a custom content block that displays real-time weather data in a subscriber’s geographic area, and then directs them to seasonal content or products with personalized recommendations.

With features like automation, artificial intelligence, and dynamic content, your developers and marketing teams can quickly create personalized content that scales using tools already in Marketing Cloud.

Why Artificial Intelligence (AI) Is The Future of Video Marketing

The profound impact of artificial intelligence (AI) on digital marketing efforts to date is something no marketer will dispute.

AI has revolutionized how effectively brands can target their users online by offering businesses an abundance of actionable user insights, tangible campaign results, and robust data.

AI analytics have enabled efficient digital marketing tactics such as ad targeting and search optimization and made it possible for brands to make data-driven decisions like never before.

With all evidence clearly pointing to the continued growth of video consumption and engagement, it shouldn’t take much for any marketer to convince your business to invest in video marketing.

Thanks to artificial intelligence analytics, brands now know more about their customers than ever before.AI tools and algorithms have provided them with increased access to data and browsing habits.

To put it simply, using the information that brands can gather about their customers through these tools, they can create a video marketing strategy and content that is completely shaped by tangible customer insights.

By gaining a clear picture of their target audience wants and needs, video marketing can be relevant and developed to cater to a specific customer profile interests.

Personalized video marketing may not be a tactic that many brands have adopted as yet, but as more companies see the evidence of tailored video content leading to much higher levels of engagement, you’ll be likely to see a lot more of it in the future.

When it comes to video marketing, many smaller brands and businesses are hesitant to invest a huge amount of budget in a relatively new channel unless they are completely confident about its return on investment.

AI clearly impacts the future of video marketing by offering brands the opportunity to tailor their content and shape their entire video marketing strategy through analytics, creating a win-win situation for both brands and consumers.

9 Applications Of Artificial Intelligence In Digital Marketing That Will Revolutionize Your Business

In a survey taken by over 1,600 professionals dedicated to marketing, 61% of those surveyed (without considering the size of their company) mentioned that both artificial intelligence and machine learning will be the most important data initiatives next year (source: MeMSQL).

This represents the highest expected year-after-year growth of all emerging technologies that marketers are considering adopting next year, surpassing even the Internet of Things (IoT) and marketing automatization.

And, while the amount of information on potential consumers grows, computer sciences related to AI (like machine learning, deep learning and natural language processing [NPL]), will be of utmost importance when making data-based decisions.

We’ve carefully analyzed which AI applications are already revolutionizing the digital market, and you’ll definitely see more than one that probably never even crossed your mind…

And even though AI still can’t write its own political opinion for a newspaper column or a blog post on the best and most practical advice for a specific industry, there are certain areas in which content created by AI can be useful and help attract visitors to your website.

This technology is commonly used to make personalized content recommendations that the user may find interesting, such as the typical, “people who buy X also buy Y,” like we constantly see on Amazon.

Machine learning or automatic learning can analyze millions of data about the consumer to then determine the best times and days of the week to contact the user, the recommended frequency, the content that catches their attention the most, and which email subjects and titles generate more clicks.

For example, Facebook and Google ad platforms already use machine learning and artificial intelligence to find people more prone to making the advertiser’s desired action.

This means that, in addition to testing different audiences on each ad (up to 480 everyday!) to detect the one most likely to make the desired action or conversion, it’s able to identify and learn which platforms are most profitable and then channel the investment toward them.

Its intelligent algorithms adjust and optimize budgets every hour so that the advertiser can obtain the highest sustained conversion rate(which is guaranteed under contract, or else the software is free) andon average, its increasing ads performance by 83% in just 10 days!

In its blog, Google revealed that around 70% of the searches Google Assistant receives are in natural, conversational language, and do not use the typical key works that you would type out in a classic Google search.

It interprets the user’s voice searches and, using the power of AI, provides the user with the best results according to what it interpreted from the user’s language and context.

After analyzing thousands of data on a single user (including location, demographics, devices, interaction with the site, etc.), AI can display offers and content that are more appropriate for each user type.

2017 Everygage survey on personalization in real time showed that 33% of the marketers surveyed used AI to provide personalized web experiences.

Chatbots are making the process of automating responses to potential buyers’ frequently asked questions even easier by providing them with a way to search for the product or service they’re looking for.

Sephora, for example, is a brand that uses a chatbot to give beauty advice to its users and offer them the best cosmetic products according to their needs.

This AI application will transform marketers from reactive to proactive planners, thanks to the data that serves as a forward-thinking element or guide to make the correct decisions.

Models generated by machine learning can be trained to rank prospects or leads based on certain criteria that the sales team defines as “qualified purchasers.” This way, the sales team won’t lose any more time on leads that will never convert and can focus on those that will.

Here are some articles that talk about both topics in more detail: We recommend that you subscribe to our blog to read the most innovative posts on artificial intelligence and digital marketing &

AI Marketing in Action: Delivering Perfectly Timed Content on the Right Channel Improves Engagement 4X

While you invest significant resources to create rich content and experiences tailored to specific customers, if your message is delivered at the wrong moment or context, it’s a wasted opportunity.

But as engagement and sales are no longer limited by time of day or to any one device or channel, how can marketers determine the optimal time and channel on which to engage each customer as they switch fluidly between a growing number of devices and channels throughout the day?

Traditional send time optimization requires manually analyzing historic trends and running A/B test campaigns at various times of day to determine optimal send times.

If you’re not learning from customers’ behavior patterns as they use different devices, apps, and channels for different purposes at different times of the day, you’re taking a guess as to how to best engage with them.

Today’s perpetually connected customers are much more likely to have many frequent bursts of activity around the clock than a recurring habit of opening their emails at certain times of day or clicking onto sites or apps at specific hours.

Unlike traditional Send Time Optimization, Predictive Engage Time Optimization uses AI to examines campaign clicks, website browsing behavior, total engagement time, engagement depth and transactions to determine windows of time in which each user is most likely to engage.

It similarly uses AI to learn on which channel each individual customer is most likely to engage at a given moment by performing an ongoing, deep analysis of customer engagement and transaction behaviors.

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#209: Artificial Intelligence (AI) in Marketing

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