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Is Your Brand Trusted Significantly More Than ChatGPT?

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By Chad S. White

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favor, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

Feature Image Credit: Andrey Popov on Adobe Stock Photo

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle. Connect with Chad S. White:  

Sourced from CMSWIRE

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3 Ways Ecommerce Brands Can Use AI for Personalization

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By Shane O’Neill

How AI is revolutionizing ecommerce, from personalized ads to dynamic pricing and enhanced customer support.

The Gist

  • AI powerhouse. AI for personalization enhances individualized ecommerce experiences.
  • Tech advantage. Machine learning dynamically adapts prices, boosting consumer appeal.
  • Customer support. AI-enabled chatbots provide personalized, emotionally intelligent assistance.

Attention ecommerce brands: The days of blanketing consumers with vaguely relevant ads are over.

Seven out of 10 consumers now expect brands to personalize ads and product recommendations, and 76% get frustrated when this doesn’t happen, according to McKinsey research.

In response, nine out of 10 businesses, including Coca-Cola, Netflix and Sephora, are investing in the practice of using artificial intelligence (AI) for personalization to give consumers a one-to-one experience, or something close to it.

In a nutshell, personalization in ecommerce uses data to show customers products and deals tailored just for them. Instead of asking shoppers to sift through a list of products, personalization uses a customer’s purchase history and browsing behaviour with the brand to suggest the most likely item that person would buy.

To return the favour, 78% of consumers are likely to make repeat purchases from companies that personalize, according to the same McKinsey report mentioned above.

Yet personalization will only boost customer satisfaction, brand loyalty and sales if it’s executed precisely. And to do that requires culling insights from droves of customer data that humans simply cannot process and analyse manually.

And this is where artificial intelligence (AI), machine learning (ML) and natural language processing (NLP) come into play for ecommerce brands.

AI for Personalization in Ecommerce

Personalization in ecommerce is still possible without AI, but it relies on grouping customers into “personas” based on shared demographics or interests. While this is an adequate approach, today’s consumer can sniff out when they’re being marketed to as a persona rather than an individual.

AI-based personalization is much more specific, using advanced algorithms to scan volumes of customer data and deliver information to you based on your own specific behaviour.

“AI’s ability to process data in real-time and adapt on the fly to create personalized experiences is a key advantage for ecommerce brands,” said Kristin Smith, managing director and retail commerce lead at Deloitte Digital. “It also helps that AI isn’t prone to human mistakes and can work 24/7.”

With advanced personalization now expected by the majority of consumers, ecommerce brands have a variety of ways to utilize AI to deliver tailored shopping experiences. Here are three of them.

1. Product Recommendations for the Individual

One of the clearest examples of using AI for personalization are the tailored product recommendations we see in emails or when logging on to our favourite ecommerce brand’s web site.

Here, complex machine learning algorithms mine your previous purchases, cart adds, product reviews, and product interactions, and generate personalized product recommendations in real time.

This customer data becomes the basis for training an algorithm that continues to learn and improve on the accuracy of recommendations as it receives new data.

Example to Emulate: Netflix

Netflix is a recommendation trailblazer. The streaming giant’s recommendation engine, called NRE (Netflix Recommendation Engine), uses algorithms to analyse data from each member’s viewing history and generates hyper personalized movie and TV show recommendations.

2. Automated Dynamic Pricing

Constantly adjusting product prices is a necessary but time-consuming task. By incorporating machine learning into pricing, ecommerce brands can automatically adjust prices in real time based on their own manufacturing costs, competitor’s prices, market demand and seasonality.

AI-based dynamic pricing benefits consumers by:

  • Monitoring the competition and adjusting prices to ensure customers get a fair price.
  • Offering real-time personalized discounts based on a customer’s behavior. For instance, if a person continually shows interest in a product, a dynamic pricing algorithm could entice that person with a time-limited discount.

Example to Emulate: Amazon

Amazon is the king of AI-based dynamic pricing. The ecommerce giant uses machine learning to update the prices of millions of products several times every day. Its repricing algorithm factors in product demand, stock availability and customer behavior. This allows Amazon to consistently offer the most competitive prices.

3. Personalized Customer Support via AI-Powered Chatbots

Using NLP and sentiment analysis, today’s chatbots understand not just text but also the emotion behind customer support requests.

When you combine sentiment, access to customer data and speedy responses, it’s easy to see why chatbots are now a personalization tool. Today’s chatbots can greet customers by name, recommend products and discounts based on purchase and browsing data, and even help customers complete online purchases.

Example to Emulate: Sephora

Most ecommerce chatbots can handle rudimentary customer inquiries, but the more innovative chatbots also serve as shopping assistants.

Cosmetics retailer Sephora is a prime example. Sephora’s website chatbot answers questions about returns and exchanges. But it’s also a virtual assistant that asks customers questions about their skin tone and makeup preferences and then gives tailored recommendations.

The Big AI Personalization Challenge: Relevant Data

The benefits of using AI for personalization are clear, but the success of your strategy hinges on your data.

Kristin Smith of Deloitte recommends that ecommerce brands ask themselves the following questions regarding customer data:

  • What is the quality and source of the data your brand is trying to use?
  • Does the brand have permission to collect and use the data they have?
  • How actionable and granular is the data?

“Many organizations have customer data only at a high level,” Smith said. “But high-level, demographic data does not always translate to actionable insights for personalization.”

In addition to having the skilled staff in place to implement and maintain AI tools, the entire marketing and data team should always ensure that the data the AI algorithms are using is unbiased and specific enough to actually help the customer connect with your brand and buy from you consistently.

“There will be a rabbit hole of ideas for data points AI can collect for personalization,” said Derric Haynie, head of demand generation at Pipe17 and co-founder of Ecommerce Tech.

“Maybe you’re going to test new products based on previous purchase history. Or test personalized emails based on when customers last visited the site. There’s a lot to personalize, and the nature of personalization is recognizing each person has a different customer journey, and catering to it.”

Feature Image Credit: Blue Planet Studio

By Shane O’Neill

Shane O’Neill is an award-winning journalist and content marketer with more than 20 years of experience covering digital transformation, content marketing, social media marketing, artificial intelligence, and ecommerce. His work has been recognized nationally, earning an ASBPE Award for Blogging and a Min Editorial & Design Award for Best Online Article. Shane’s experience as both a B2B journalist at CIO.com and InformationWeek and as a content marketing director at tech startups gives him a unique insider/outsider perspective on tech innovation. Connect with Shane O’Neill: https://twitter.com/smoneill 

Sourced from CMSWIRE

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Is Your Brand Trusted Significantly More Than ChatGPT?

No Comments
By Chad S. White

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favour, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle.

Sourced from CMSWIRE

chatgpt,  digital experience, search, email marketing, artificial intelligence, generative ai, artificial intelligence in marketing

 

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Is Your Brand Trusted Significantly More Than ChatGPT?

No Comments
By Chad S. White
Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

The Gist

  • AI disruption. Generative AI is set to disrupt SEO significantly.
  • Content shielding. Brands need strategies to protect their content from AI.
  • Direct relationships. Building strong direct relationships is key.

Do your customers trust your brand more than ChatGPT?

The answer to that question will determine which brands truly have credibility and authority in the years ahead and which do not.

Those who are more trustworthy than generative AI engines will:

  1. Be destinations for answer-seekers, generating strong direct traffic to their websites and robust app usage.
  2. Be able to build large first-party audiences via email, SMS, push and other channels.

Both of those will be critical for any brand wanting to insulate themselves from the search engine optimization (SEO) traffic loss that will be caused by generative AI.

The Threat to SEO

Despite racking up 100 million users just two months after launching — an all-time record — ChatGPT doesn’t appear to be having a noticeable impact on the many billions of searches that happen every day yet. However, it’s not hard to imagine it and other large language models (LLMs) taking a sizable bite out of search market share as they improve and become more reliable.

And improve they will. After all, Microsoft, Google and others are investing tens of billions of dollars into generative AI engines. Long dominating the search engine market, Google in particular is keenly aware of the enormous risk to its business, which is why it declared a Code Red and marshalled all available resources into AI development.

If you accept that generative AI will improve significantly over the next few years — and probably dramatically by the end of the decade — and therefore consumers will inevitability get more answers to their questions through zero-click engagements, which are already sizable, then it begs the question:

What should brands consider doing to maintain brand visibility and authority, as well as avoid losing value on the investments they’ve made in content?

Protective Measures From Negative Generative AI Effects

Brands have two major levers they can pull to protect themselves from the negative effects of growing use of generative AI.

1. Shielding Content From Generative AI Training

Major legal battles will be fought in the years ahead to clarify what rights copyright holders have in this new age and what still constitutes Fair Use. Content and social media platforms are likely to try to redefine the copyright landscape in their favour, amending their user agreements to give themselves more rights over the content that’s shared on their platforms.

A white robot hand holds a gavel above a sound block sitting on a wooden table.
Andrey Popov on Adobe Stock Photo

You can already see the split in how companies are deciding to proceed. For example, while Getty Images’ is suing Stable Diffusion over copyright violations in training its AI, Shutterstock is instead partnering with OpenAI, having decided that it has the right to sell its contributors’ content as training material to AI engines. Although Shutterstock says it doesn’t need to compensate its contributors, it has created a contributors fund to pay those whose works are used most by AI engines. It is also giving contributors the ability to opt out of having their content used as AI training material.

Since Google was permitted to scan and share copyrighted books without compensating authors, it’s entirely reasonable to assume that generative AI will also be allowed to use copyrighted works without agreements or compensation of copyright holders. So, content providers shouldn’t expect the law to protect them.

Given all of that, brands can protect themselves by:

  • Gating more of their web content, whether that’s behind paywalls, account logins or lead generation forms. Although there are disputes, both search and AI engines shouldn’t be crawling behind paywalls.
  • Releasing some content in password-protected PDFs. While web-hosted PDFs are crawlable, password-protected ones are not. Because consumers aren’t used to frequently encountering password-protected PDFs, some education would be necessary. Moreover, this approach would be most appropriate for your highest-value content.
  • Distributing more content via subscriber-exclusive channels, including email, push and print. Inboxes are considered privacy spaces, so crawling this content is already a no-no. While print publications like books have been scanned in the past by Google and others, smaller publications would likely be safe from scanning efforts.

In addition to those, hopefully brands will gain a noindex equivalent to tell companies not to train their large language models (LLMs) and other AI tools on the content of their webpages.

Of course, while shielding their content from external generative AI engines, brands could also deploy generative AI within their own sites as a way to help visitors and customers find the information they’re looking for. For most brands, this would be a welcome augmentation to their site search functionality.

2. Building Stronger Direct Relationships

While shielding your content is the defensive play, building your first-party audiences is the offensive play. Put another way, now that you’ve kept your valuable content out of the hands of generative AI engines, you need to get it into the hands of your target audience.

You do that by building out your subscription-based channels like email and push. On your email signup forms, highlight the exclusive nature of the content you’ll be sharing. If you’re going to be personalizing the content that you send, highlight that, too.

Brands have the opportunity to both turn their emails into personalized homepages for their subscribers, as well as to turn their subscribers’ inboxes into personalized search engines.

Email Marketing Reinvents Itself Again

Brands already have urgent reasons to build out their first-party audiences. One is the sunsetting of third-party cookies and the need for more customer data. Email marketing and loyalty programs, in particular, along with SMS, are great at collecting both zero-party data through preference centers and progressive profiling, as well as first-party data through channel engagement data.

Another is the increasingly evident dangers of building on the “rented land” of social media. For example, Facebook is slowly declining, Twitter has cut 80% of its staff to avoid bankruptcy as its value plunges, and TikTok faces growing bans around the world. Some are even claiming we’re witnessing the beginning of the end of the age of social media. I wouldn’t go that far, but brands certainly have lots of reasons to focus more on those channels they have much more control over, including the web, loyalty, SMS, and, of course, email.

So, the disruption of search engine optimization by generative AI is just providing another compelling reason to invest more into email programs, or to acquire them. It’s hard not to see this as just another case of email marketing reinventing itself and making itself more relevant to brands yet again.

Feature Image Credit: Andrey Popov on Adobe Stock Photo

By Chad S. White

Chad S. White is the author of four editions of Email Marketing Rules and Head of Research for Oracle Marketing Consulting, a global full-service digital marketing agency inside of Oracle. Connect with Chad S. White:  

Sourced from CMSWIRE

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AI Advertising: 9 Ways To Use Artificial Intelligence in Marketing

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Sourced from Droid Men

61% of marketers declare that Artificial Intelligence (AI) is the most critical element of their data strategy.

Marketing is a fast-paced discipline. If you want to succeed in it, you must stay at the bleeding edge of new breakthroughs.

By 2020, 85% of customer relations will be automated. You, therefore, need to position your brand to stay competitive.

AI is probably the biggest technology push of our time revolutionizing every aspect of marketing. To get the most out of automated customer service, marketers are turning to AI advertising for strategies that can deliver higher value.

Here are some ways in which you can incorporate AI into your marketing to keep up with the times.

1. Chatbots

A Chatbot is an AI software that is capable of simulating a conversation (chat) with a user in natural language.

Brands have taken to using chatbots to interact with their clients on messaging apps like WhatsApp, Slack, and Facebook Messenger.

Through these bots, brands can answer queries customers frequently ask in a speedy fashion.

Since they retain a customer’s data after the interaction, they can build on that information to deliver more personalized experience during the next interaction.

That reinforced learning pattern only makes the experience better for the customer.

2. User Experience (UX)

When you have a website, the user experience will significantly influence whether the customer will return to it or not.

You can use AI to collect information on customers and understand their likes, intent, and desire. Data points to gather here include location, the devices they use to visit the website, demographics among others.

As the user keeps browsing the site, you get to gain more insights about them and deliver appropriate offers and content that resonates with their needs.

AI marketing that helps shape your user experience for the better has the potential to increase your conversion rate.

3. Search Engines

People today take it for granted that they can search for anything on Google and find a relevant result.

Such a scenario is the result of decades of research and analysis on how to create and deliver a more intuitive search experience for customers.

After Google deployed RankBrain, its machine-learning based algorithm, many businesses saw the value of such an application.

Nowadays consumer companies like Amazon take advantage of artificial intelligence marketing tactics that can help them deliver relevant results to you.

Innovations like natural language processing and semantic search determine the relationships between products.

When you run a search, they help recommend similar items and auto-correct mistakes so that you can find the right products.

4. Predictive Analysis

Predictive analysis is the use of data, machine learning techniques, and statistical algorithms to draw conclusions on future actions based on the data.

Using predictive analysis you can determine the probability of a prospect becoming a client.

Thus, depending on the conclusion your draw you can determine how much resources you will dedicate to converting the prospect.

Another area predictive analysis is useful in is pricing. Using this tool, you can more accurately determine which price point will deliver more sales for you.

That information can then contribute towards your value proposition marketing.

5. Email Marketing

Email marketing is a crucial part of any brand’s marketing mix as it is one of the few digital assets they fully control.

But with the rise in sources of data from 10 in 2017 to 15 in 2019, marketers may struggle to personalize these emails.

AI can help you unify the piles of information on a subscriber and learn how to reach more effectively.

For example, it can help you determine how many times to send the email per user and what time of the day is best to send it.

6. Digital Marketing

Pay-per-click (PPC) advertising is a cornerstone for any digital campaign. Typically, PPC ad campaigns are usually managed by an in-house team or a large agency.

AI can help you discover new channels your competition may be unaware of.

Machine learning techniques can help you optimize the layout, bids, targeting, and copy for your campaign.

You will be able to realize a higher return on advertising per campaign by using AI in marketing online.

7. Social Listening

Every brand needs to have a presence on social media to extend its customer service to where its customers are.

Consequently, it is essential that brands have their finger on the pulse of what users are saying about them.

Natural language processing innovation has made it possible for brands to hear what users and the public at large like or dislike about them.

Therefore, they can get ahead of any potential issues before they blow up.

You can also use AI in social listening to identify potential purchasers and nudge them towards a sale.

8. Audience Targeting

Customers today have come to expect a certain level of personalization, and as a marketer, you can’t fail on this expectation.

To help you create more accurately personalized campaigns, you will need to segment your customers as finely as possible.

AI can draw on the data you have on your customers and identify a common variable that can help shape your communication with a specific audience.

For example, if your data shows you that a significant number of your customers are into destiny power leveling, you can set up banner ads to effectively reach them.

9. Voice-Based Services

In the past few years, voice-based services have gained quite some traction.

Voice assistants such as Alexa, Siri, and Cortana have made it infinitely easier for consumers to search and place an order by speaking at their devices.

Natural language processing technology helps improve speech recognition so that customers can successfully issue commands.

AI can help you deploy voice-based services to provide your customers with an avenue for easier sales and interaction.

Beef up Your AI Advertising Strategies or Get Left Behind

Artificial Intelligence is making a big impact on marketing. High automation levels in what once used to be human job roles call for precise AI advertising strategies by brands.

Are you concerned with the impact technology will have on your business? Thumb through our content to learn more about how you can use innovative breakthroughs to power your business forward.

Sourced from Droid Men

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