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제37회 INNOPOLIS AI 세미나 : 생성형 AI를 활용한 콘텐츠 마케팅 자동화의 모든 것

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AI검색, AI에이전트의 시대 ‘마케팅 패러다임’은 어떻게 바뀔까? (강정수 박사)

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[2025년 외식산업 트렌드 1편] 외식업 전망과 대응전략, 인공지능을 활용한 불황극복 비법

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AI와 마케팅의 관계 이해

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AI와 마케팅의 관계 이해

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학생들은 AI와 마케팅 기술이 서로 어떻게 연결되고 활용되는지를 이해합니다.

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데이터 분석 활용법 알아보기

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데이터 분석 활용법 알아보기

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학생들은 AI를 활용하여 어떻게 데이터를 분석하고 해석하는지를 학습합니다.

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학생들이 실제 데이터를 사용하여 AI를 통해 분석하는 실습을 진행합니다.

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Nowadays, it's hard to scroll through your LinkedIn feed without seeing a post about ChatGPT. How to create sales email templates, blogposts, debug code,... The list of things you can do with generative AI tools seems endless.

How about using AI for data analysis? In this article, we'll explore why AI is great to speed up data analysis, how to automate data analysis each step of the way, and which tools to use. Let's jump in.

What is AI data analysis?

As your data grows, it gets harder and more time-consuming to do data exploration. AI data analysis uses different artificial intelligence techniques to get valuable insights from large amounts of data. Here are just a few examples of these techniques.

Machine learning algorithms: extract patterns or make predictions on large datasets

Deep learning: use neural networks for things like image recognition, time-series analysis and more

Natural language processing (NLP): derives insights from unstructured text data

Imagine you work in a warehouse that stores and distributes thousands of packages daily. To run your warehouse more efficiently, you want to know:

How long does inventory stay in the warehouse on average?

How much space in your warehouse is (un)occupied on average?

Which items are low in stock and need to be replenished?

What's the average time needed to replenish stock items per product type?

Which items have been in the warehouse for longer than 1 month/quarter/year?

AI algorithms will search for patterns in large datasets to find an answer to these business questions. By automating this difficult task, businesses can make better, data-driven decisions quickly. Data scientists have been using machine learning for years to analyze big data. And nowadays, a new wave of generative AI tools empowers anyone to analyze data, even if you don't know anything about data science.

Benefits of using AI for data analytics

AI can feel daunting if you're not familiar with it. But if you consider the benefits, it's more than worth dipping your toes into it.

First off, artificial intelligence can seriously reduce your operational costs. 54% of businesses say implementing AI led to cost savings. Imagine you're paying a data scientist to spend 8 hours on manual data cleaning or processing. What if they can complete that task in less than an hour using machine learning models? They have 7 hours left to analyze other data or interpret the results.

This leads us straight to another huge advantage. Time savings. AI can analyze large volumes of data far quicker than humans. It's much easier to scale data analysis, and get insights in real-time. Especially in industries like manufacturing, construction or finance, real-time analytics have an invaluable impact. Just imagine how many life-threatening accidents you could avoid if machine malfunctions were reported before they happened.

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AI analytics: a threat for data analysts?

With the rise of tools like ChatGPT, people immediately started raising concerns about job security. Think of our data scientist again, who now gets the job done 8 times faster. Should they fear AI will take over their jobs?

If you know that 90% of the world's data was generated in the last 2 years alone, and that data growth is expected to increase by 150% in 2025, there's no cause for concern. Data will only become more important in the next coming years, and the world needs data analysts and data scientists to make sense of it. While AI tools may change their job roles and the way they work, these roles will still play a pivotal role in data-driven companies.

On the contrary, if you familiarize yourself with AI tools now, it can be a huge driver in your career. You'll be able to solve more complex problems faster, which is critical for innovation.

How to use AI for data analysis?

From raw data to decisions, let's look at the role artificial intelligence plays in each stage of the data analytics process.

1. Data collection

If you want to get valuable insights from your data using AI, data collection is the fundamental first step. You'll need to extract data from different data sources to feed your AI algorithm. Otherwise, it won't have input to learn from. You can train AI systems with any type of data, whether it be product analytics, sales transactions, web tracking, or automated data collection through web scraping.

You can also scrape web data using R, a programming language and software environment for statistical computing and graphics. R provides various libraries and packages that allow you to scrape data from websites, extracting the required information for your AI algorithms. This can be particularly useful when you need to gather real-time or updated data from websites for your data analysis.

2. Data cleaning

The cleaner your data, the more valuable your insights will be. However, data cleaning is a tedious process and prone to human error if you need to do it manually. You can use artificial intelligence to do the heavy lifting. For example, use AI to identify outliers, handle empty values, normalize your data, and more.

3. Data analysis

After you've trained AI models with clean, relevant data, you can start analyzing the data and generate insights. AI models can identify patterns, correlations, anomalies, and trends in the data. Doing it manually would cost your data analyst a few days or weeks. Now, they can do it in mere hours!

As with any technology, always be careful about system bias. AI learns from its training data, so if your source data contains biases, it can creep into AI algorithms as well.

4. Data visualization

Once you've found interesting patterns in your data, you want to present them in an easy, understandable format. With the help of business intelligence tools, you can start building visual dashboards to interpret insights. Interactive charts and graphs let you explore your data deeply, and drill down into specific information to improve your workflows.

You can automate data visualization tasks with ChatGPT or other tools. Some business intelligence tools have added AI capabilities to their software. Luzmo's AI-powered dashboard builder is a great example.

5. Predictive analytics

Compared to traditional business analytics, artificial intelligence excels in forecasting. Based on patterns in historical data, it can run predictive models to make accurate predictions about the future. Just think of forecasting inventory based on past stock levels. Or setting sales targets based on past sales and seasonality.

6. Data-driven decision-making

If you've used artificial intelligence in the previous steps, you're bound to get better insights as a result. Leveraging these powerful insights, you'll make better decisions faster and improve your status quo. With powerful predictive analytics, you can even avoid issues before they happen.

The dangers of using AI for data analysis

While AI analytics tools speed up the analysis process significantly, there are some pitfalls too. A tool can streamline the process, but the tool is only as good as the person using it. Here are some of the challenges that may arise with AI.

Data quality

Trash in = trash out. AI data analytics tools use the data you feed into them and give you adequate results. If your data is poorly formatted, has errors and missing fields or has outliers, AI analytics tools won't be able to spot it as many don't have anomaly detection.

In other words, before doing any data analytics, you should invest time and money in data cleaning and formatting.

The decision-making processes only work if the data is accurate and up to date, and this is where a human being comes into play.

Data security and privacy

In April 2023, Samsung employees used OpenAI to help them with writing code. The result? They leaked classified code for measuring superconductor equipment. As OpenAI states on their website, the data you feed into it is used to train the language learning model and teach it more things about the world.

If you ask an AI tool to analyze or summarize data, it means that in most cases, someone else will be able to access that data. Be it the people behind the powerful AI analytics tool or the users wanting to learn, your data is not always safe.

Be mindful of the data you feed into AI analytics tools, especially if you have to comply with strict laws and regulations.

You'll still need personnel on board

Even if you have the best data analysis tools in the world, you're still going to need someone to man the ship.

First, you may need a data scientist to help you with unstructured data, as well as choosing the right ETL and data visualization platform. You may be able to do a lot of the work yourself, by using a tool such as Luzmo's chart generator. However, you'll still need someone to steer you in the right direction.

Best AI data analysis tools

Luzmo

This embedded analytics platform focuses on user-friendly analytics. It helps SaaS products add powerful client-facing reporting to their platform in days, not weeks. With their no-code dashboard editor, anyone can build interactive charts in minutes. Hook up their extensive API to ChatGPT or other AI tools, and use it to autogenerate dashboards with simple prompts.

Tableau

Tableau's AI features are aimed at data scientists, so you need a little more experience to get started. Data analysts can do AI-powered predictions, what-if scenario planning, and other data science techniques. For data scientists, they offer statistical modeling directly in Tableau with R, Python, MATLAB, and more.

Microsoft Power BI

Microsoft has infused AI capabilities directly into Power BI to handle text data. You can now enrich your data through for example sentiment analysis, key phrase extraction, language detection and more.

KNIME

KNIME is an open-source data science platform, and a great entry tool if you want to start experimenting with AI tools. Data experts can use an intuitive, drag-and-drop interface to design, train, and apply machine learning models.

Databricks

Databricks unifies data, analytics and AI all under one platform. It combines elements of data lakes and data warehouses, making it a great infrastructure for developers to run AI applications or machine learning algorithms.

AnswerRocket

AnswerRocket is like an AI assistant for data analysis. You can connect data from different data sources, ask questions in plain English, and get proactive insights and recommendations based on the underlying data.

Getting started with AI data analysis

Do you want to unburden data analysts and data scientists from cumbersome, time-consuming data analysis tasks? Then now is the right time to start exploring the power of AI. It can seriously speed up time to insight, and help your team focus on more complex tasks.

The tools above are a great starting point to start fidgeting with AI data analysis. If you're working on a customer-facing analytics offering for your SaaS product, Luzmo is tailor-built for SaaS use cases. Get in touch with our experts for a product demo, or sign up for a free trial to try it yourself!

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Using AI for Data Analysis: The Ultimate Guide (2024) | Luzmo

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What is AI data analysis, and why should you do it? Explore the best AI tools for data analysis and how to use them in each stage of the data analytics process.

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AI와 마케팅에 대한 더 깊이 있는 기사를 읽고 요약하기.

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Evgeny Popov is EVP and GM (International) of Verve Group.
In the realm of advertising and marketing, the evolution toward more sophisticated and personal engagements with consumers has necessitated a shift in focus from mere campaign impact metrics to the underlying reasons driving consumer behavior. Traditional models, while effective in their simplicity, often overlook the complex journey a consumer undertakes, from initial exposure to a brand to the final action of purchase or loyalty.

The introduction of computer vision AI into this landscape offers a promising horizon for not just understanding but actively shaping this journey. For marketers aiming to incorporate AI technologies such as automated content creation, augmented reality (AR) and enhanced personalization/targeted advertising, starting can seem daunting. Here are actionable tips for each area.

* Automated content creation: Begin by identifying repetitive content tasks that can be automated to save time and resources. Experiment with AI tools on a small scale to understand their capabilities and limitations. Ensure that you review and edit AI-generated content to maintain your brand's voice and accuracy.

* Augmented reality (AR): Start with a clear objective for using AR-whether it's for interactive product demos, virtual try-ons or immersive experiences. Focus on the user experience, ensuring it adds real value. Pilot with a small project to gather insights and feedback before a full-scale launch.

* Enhanced personalization/targeted advertising: Leverage customer data to segment your audience accurately. Use AI to analyze behavior and preferences, creating more relevant and personalized campaign messages. Prioritize privacy and ethical use of data, ensuring transparency with your audience about how their data is used.

Before implementing these features, marketers should familiarize themselves with the technology's basics, understand the ethical considerations and set clear objectives aligned with their brand's goals. Testing and learning from small-scale implementations can provide valuable insights for broader applications, ensuring a balance between innovation and responsibility.

Here are some examples of how computer vision AI is transforming the marketing industry.

Automated Content Creation And Optimization

Automation lies at the forefront of this revolution. By harnessing the power of AI, marketers can now create dynamic and responsive content that resonates deeply with consumers on an individual level. This not only elevates creativity but significantly enhances operational efficiency, allowing brands to stay relevant in a rapidly changing market environment.

Augmented Reality (AR)

AR represents another groundbreaking application of computer vision AI. This technology provides immersive experiences that go beyond traditional advertising, engaging consumers in a manner that is both novel and deeply engaging. The potential for AR to influence consumer behavior and shift brand perceptions is immense, offering a tangible bridge between digital engagement and real-world action.

Enhanced Personalization And Targeted Advertising

With enhanced personalization and targeted advertising, the capabilities of computer vision AI can go even further, allowing for a granular understanding of consumer emotions, preferences and behaviors. This enables the crafting of messages that not only capture the attention but also speak directly to the individual's mental market share (MMS), fostering a deeper and more meaningful connection with the brand.

Challenges With Implementing AI

There's no doubt that the advancements in AI technology are paving the way for the creation of more effective marketing strategies. However, this new frontier also brings challenges, particularly concerning privacy and the ethical use of AI. Balancing innovation with responsibility in marketing extends beyond privacy and ethical AI use to include the challenges of content saturation, consumer skepticism and regulatory compliance. Marketers face the difficulty of cutting through the noise in a crowded digital landscape, where consumers are increasingly wary of advertisements and content. This skepticism is compounded by evolving regulations around data use, digital advertising and consumer protection, which vary significantly across regions and are constantly changing.

To overcome these challenges, marketers should focus on building genuine relationships with their audience through authentic and transparent communication. Leveraging data responsibly to gain insights into consumer behavior can help create more personalized and meaningful engagements. Additionally, staying informed about and compliant with global and local regulations will not only protect the brand but also build trust with consumers. Embracing a strategy that prioritizes ethical practices, respects consumer privacy and delivers value through innovative but responsible marketing can turn these barriers into opportunities for deeper engagement and brand loyalty.

In conclusion, the advent of computer vision AI presents a transformative opportunity for the advertising and marketing industry. By embracing these technologies, marketers can unlock deeper insights into consumer behavior, craft more resonant and engaging content and ultimately drive stronger brand attachment and business outcomes. The future of marketing is not just about reaching consumers but connecting with them on a profoundly personal level, and computer vision AI is the key to unlocking this future.

Forbes Technology Council is an invitation-only community for world-class CIOs, CTOs and technology executives. Do I qualify?

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Council Post: How AI Is Transforming The Marketing Industry

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The introduction of computer vision AI into this landscape offers a promising horizon for not just understanding but actively shaping the consumer journey.

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작은 그룹으로 나뉘어 AI가 마케팅에 미치는 다양한 영향을 토론합니다.

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AI가 마케팅에 어떻게 기여할 수 있을까요?

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The biggest impact of AI on marketing? The Customer Centric Gateway. by Steven Van Belleghem

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AI 도구에 대한 소개

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AI 도구에 대한 소개

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AI 기반의 마케팅 도구를 학습합니다.

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Some of the biggest brands like Google, IBM, and Airbnb are using AI marketing tools to gain a competitive edge. We've uncovered some of the best AI marketing tools on the market today and listed them below.

The arcane art of marketing has been irrevocably changed by the invention of AI software. Hundreds of these tools have sprung up in recent years, so it can be trick to know which of them will provide best value.

It's important to zero in on what you want your AI marketing tool to achieve, so let's start by identifying what artificial intelligence is especially useful for, and what it can do for your business.

What is AI used for in marketing?

In 2024, marketing is all about data - information on your competitors, on market trends, and on what your customers (and potential customers) are saying about your product. There's a lot of information out there but it's hard to manually source. AI makes this process much simpler. It does quickly, and at scale, what would be onerous to achieve by hand.

Techniques including sentiment analysis can be used to aggregate positive and critical product reviews. Competitor intelligence can keep you up to date with what rival brands are doing. You can improve the ROI of your marketing campaigns with better targeting. There are even AI-driven systems to write creative copy, helping you keep up with the internet's incessant demand for content (SEO optimization).

It's likely that you'll use more than one AI-powered software solution. In fact, most data manipulation tools now use the power of machine-learning and automation to achieve impressive results at scale. In this rundown of some of the best AI-driven apps and platforms, we'll explain exactly what each one does, what its benefits are, as well as any limitations it might have.

21 best AI marketing tools to grow your business in 2024

Here are 21 AI marketing tools you need to try in 2024:

Okay, let's dive a bit deeper into each one.

1. Jasper AI (for copywriting)

The people behind Jasper, the best-known of the various AI-powered copywriting systems, quickly became a victim of their own success. Marvel sent them a cease-and-desist letter after they had secured more than 350,000 users. Their AI was previously known as Jarvis, cheekily inspired by Tony Stark (AKA Iron Man)'s virtual assistant. And so, Jasper was reborn.

Jasper, this human writer must grudgingly accept, is remarkably good at creating copy, in a range of tones and styles, on any topic you can throw at it. Its creators say it has "read" 10% of the internet. How well does Jasper work? Using a free trial, I asked Jasper to write 40 words about AI in business automation. It came up with:
AI is the next step in business automation. AI had been instrumental in improving business processes. AI can be deployed to an organization's business process for a particular strategic purpose to increase efficiencies, cut costs and improve customer service.
This is certainly a decent start, although you still require human intervention to make sense of the flow of the copy, and there's an element of repetition. Jasper's natural language processing is perhaps best seen as a tool to create early drafts for later polishing by human copywriters.

Whether you're looking to write copy for email campaigns, product descriptions for ecommerce products, blog posts, or landing page copy, Jasper can do it all.

It has a simple and intuitive dashboard, is inexpensive to implement, and will certainly speed up the content creation process. Jasper, which boasts over 5,000 5-star product reviews, won't turn you into Tolstoy, but it will keep those vital SEO page scores high.

2. Lexica Art (for blog thumbnails)

Lexica Art is a high-quality AI image generator that's one of the best I've seen. It creates some of the most realistic AI images and you can generate marketing content for almost anything. I've personally used this generative AI-powered tool to create blog thumbnail images for my SEO (search engine optimization) clients. There are different prompts you can use and save so all the images it generates are "on brand" with whatever brand guidelines you have. Some brands even use it for their social media posts.

If you're looking to move away from standard stock images for your blog thumbnails, this is a tool you definitely need to check out.

3. Surfer SEO (for content writing)

Surfer SEO is a content optimization tool that helps you create copy for ranking on search engines - a worthy goal for any content strategy. While you do the writing, Surfer assesses and scores your content according to its keyword density, readability, length, use of headers, and other aspects which push content up the rankings.

To use Surfer SEO, first you choose your domain, niche, and target audience. The system will then give you actionable insights by highlighting the top-ranking keywords, suggest a content outline structure, and even define image density for your piece. You can work directly on Surfer SEO, which has its own text editor, or copy-paste for quick analysis. As you edit your work, you'll see the SEO improving in real time.

Integrations are available with other content marketing tools like Jasper, WordPress, Google Docs, and more. Surfer SEO promises hit your organic growth metrics for Google search, and satisfied clients include FedEx, Shopify, Quantas, and Viacom.

4. Content at Scale (for generating SEO blog posts)

Content at Scale is a platform I've been experimenting with for the past few months and I have to say, I'm pretty impressed. Out of all the AI writing tools that I've used, this is one of the few that passes AI detectors. Any time I generate an article with this tool, and run it through an AI detector, it usually comes out at least 70% human-written - which is quite impressive.

The platform is still quite new and the UI can be a bit buggy. But the content it generates is some of the best I've seen when it comes to AI content generation. It's not as flexible like Surfer is in terms of being able to label H2 and H3 headings in your articles, but the actual content it generates is a bit more higher quality than that of Surfer and even Jasper. I definitely recommend playing around with this tool if you're looking to generate SEO blog posts. Of course, you always want a real human to review the final draft.

5. Originality AI (for AI content detection)

Originality AI is an AI content detector and plagiarism tool. I run almost every piece of content through this tool these days to make sure it passes has being written by a human. Of course, I won't run actual human writing through it. But I'll generally run AI generated content, from a tool like Content at Scale, through this just to double check what areas need some work.

This is also a good tool to run your content through if you're working with freelance writers and want to make sure they're not using ChatGPT to generate large portions of text. Of course, take these detectors with a grain of salt. There have been reports of it falsely accusing human writing as AI writing. But, out of most of the AI content detectors out there, I found this one to be one of the best.

6. Writer (content writing for teams)

First off, Writer has secured a high value URL, indicating lofty ambitions. They position their platform as a writer's assistant for marketing teams. Writer is a collaborative efficiency tool which takes some of the features of a traditional text editor like Word and turbocharges them.

Features include autocorrect, autocomplete, grammar and clarity checks, and there are often-used snippets to paste in. There's also a suite of intelligence tools to maintain house style, including a database of approved terminology (vital for tech, legal and financial firms). Their AI is light touch, sitting in the background making recommendations rather than rewriting block of copy.

When you have a virtual or hybrid team working with minimal in-person supervision, this AI writer could provide all the reassurance you need that your copy remains professional and accurate across all uses. Clients include Deloitte, Accenture, Twitter and Vistaprint.

Undetectable AI is another AI content detector similar to Originality AI. What makes this one different is that it can actually rewrite AI generate content from ChatGPT to make it sound like a human. I've tested this by creating a paragraph of text in ChatGPT, running it through Undetectable AI to rewrite it, then double checking it with Originality AI. To my surprise, it does a pretty good job.

Again, you want to take these AI detectors and rewriters with a grain of salt. Sometimes this tool will rewrite things that don't totally make sense. Or it will purposely mess up in terms of grammar. So, you'll still want to read everything it generates to make sure there are no errors.

8. Fullstory (for digital experiences)

By digital experience, Fullstory mean the journey a site visitor goes through from their first visit to conversion (or dismissal). They make the valuable point that you can learn a huge amount from the unpredictable things users do, which couldn't have been anticipated.

To make this level of insight possible, Fullstory employ an AI to track every cursor move, click, and page visit across a visitor's journey to create their "story". This can then be compared with thousands of other visitors' stories to derive insight. Fullstory leverage the processing power and scalability of AI automation to discover opportunities and errors much more quickly than any human observer would.

Fullstory claim users will make cost savings, retain more customers, and improve the UX of their site significantly. Their current client roster includes GAP, Zipcar, Icelandair, and Forbes.

9. Zapier (for automating tasks)

Zapier is really the Lego of tech stack and process integration. Using it you can build connections and marketing automations between thousands of different systems, saving time, promoting efficiency, reducing repetitive tasks, and making cost savings. You build customized workflows to link actions in one system to automated processes in one or more others. The salient data is pulled from the right place each time.

These automations can be written without coding, and there are templates to speed things up. Zapier calls its automations zaps, and it features over 3,000 integrations at time of writing. You can even create branching workflows, dependent on logical criteria you set.

The AI is at work behind the scenes interpreting signals which trigger processes with a speed and efficiency no human team could master. Clients rave about the time savings these zaps create. There's excellent support too, including a blog, webinars, online no-code community, and Zapier University for training.

Even if you only want to use human writers to create your copy, AI can at least help edit it for maximum readability and clarity. Named after a writer who was famously sparing with words, Hemingway highlights aspects of "poor" style including overlong sentences, passive voice, and excessive adverb use.

Hemingway also awards a readability score, based upon US educational grades. To reach the largest audience, grade 9 or lower is considered ideal. It's exceptionally easy to use - just copy and paste your text into the online app and hit return.

Best of all - it's free! The app was created by Ben and Adam Long in emulation of their favourite writer. Although there's a $20 version including PDF exports, offline usability and instant publish features, the free version is still immensely useful.

That said, a perfectly optimized Hemingway piece might feel too simplistic for some purposes, such as annual reports, white papers, opinion pieces and other types of writing where personal style carries more weight than raw readability.

11. Chatfuel (for chatbots)

Vital AI assistants or irritating nuisance - whatever you think of chatbots, they are here to stay. Of course, a chatbot's usefulness depends upon how you build it, which is where Chatfuel comes in. Rather than buying an off-the-shelf solution, the platform allows you to create a bespoke virtual personality yourself, using an intuitive drag and drop interface.

Whether you're looking to replace a mundane FAQ page or drive potential leads down the funnel by offering discount codes, Chatfuel's bots are remarkably good at interpreting even misspelt or non-grammatical responses. The AI here is doing a lot of sophisticated linguistic processing and it can easily spot keywords which trigger appropriate and helpful responses.

They're an official partner with Meta (formerly Facebook), which should provide some reassurance that their AI is top-flight.

12. Grammarly (for content editing)

Like Hemingway, Grammarly will also analyze your content for ways in which it can be improved. Unlike Hemingway, it doesn't make so many assumptions about style, and focuses its attention instead on traditional rules of syntax and grammar.

One great feature is that you can use Grammarly whilst working in many different apps, including Gmail, Word, Twitter, Facebook and more. Its AI will highlight errors and suggest corrections, which you are free to adopt or ignore. Going beyond mere grammar and punctuation, Grammarly can spot redundant words, inconsistencies in style and offer word choice alternatives.

It works by highlighting and color-coding errors or potential improvements (similar to Hemingway) and has been featured in such august publications as the Wall Street Journal, the New York Times and Forbes.

13. Albert.ai (for digital advertising)

Advertising is inherently hit-and-miss, or at least it was prior to the dawn of AI. Albert personalizes and optimizes ad content at scale, across social media and paid search platforms, including Facebook, YouTube, Google Ads, Bing and more. Albert call their methodology " data-powered creativity ", and founder Om Shani believes in using AI and automation to free up human creatives to make campaigns that connect on a human level.

Albert.ai functions as a laboratory for testing and tweaking campaigns to take advantage of unseen channel opportunities, unthought of demographics or new markets. It helps makes ad campaigns more relevant and lest wasteful.

Albert's adopters include Crabtree & Evelyn, Telenor, and Harley Davidson. The latter enjoyed a five-fold swell in site traffic and an enormous 2,930% increase in leads per month.

14. Headlime (for landing pages)

Landing page copy is vital - it's the storefront of the digital realm. Headlime is an AI-powered content-writing system designed to perfect the art of the landing page.

Powered by the deep learning abilities of GPT-3 from OpenAI, Headlime uses machine learning to help predict and complete text you type, potentially saving significant time. It suggests high performing subject lines, optimizes word count, and can write copy in multiple tones and languages.

Like Jasper, it can write blog content, but it's integrated with a simple landing page builder so you don't have to copy-paste constantly. Most importantly, it's constantly focused on the task at hand - optimizing time on page and conversions. With over 1,700 copy templates and 11 languages, you could have a global landing page up and running in no time.

15. Userbot.ai (for conversation management)

At first Userbot might seem like just another chatbot but it's much cleverer than that. When Userbot can't parse a customer's query, it hands over to a human operator but continues to monitor and learn from the rest of the conversation. Userbot then uses what it learns to improve and add the new user query to its repertoire.

Over time, therefore, Userbot should become more and more intuitive and effective, although you may still have to reply on human operators a fair bit when you first implement it. I tried it with the question "who are your top clients?" and it was unable to provide a meaningful response. For the record, they include Aboca, SSG, and Vivactis Group.

The system provides useful customer data, which you can use to monitor the effectiveness of your sales team or customer support department (it also integrates with many popular CRM platforms).

16. Browse AI (for scraping web pages)

Competitive Intelligence (CI) is one of the most powerful weapons in the digital marketer's armory. It lets you to assess competitor brands and spot new trends, pricing strategies, reviews, and product launches. To do CI at scale, you need to use data scraping, employing algorithms (aka "spiders" or bots) which crawl competitor sites and extract useful data.

Browse AI allows you to quickly train a bot to source data for you, automatically filling in a spreadsheet with everything you need. You might get it to look for one- or two-star reviews of competitor products in order to spot possible product improvements, or check the current price of similar products on ecommerce sites, for instance.

Browse's creators say their AI can mimic human behavior to fool Captcha and other bot-spotting protections. The product is used by over 2500 companies including Adobe, Amazon, Salesforce, and HubSpot.

17. Algolia (for search and recommendation APIs)

Search fields can either prove really helpful or endlessly frustrating. Rather than plug-in a "powered by Google" field, why not create a bespoke search facility of your own?

This is what Algolia enables, proving especially popular with entertainment companies or ecommerce firms with large inventories. The less time visitors have to spend fruitlessly scanning your catalogue the more likely they are to buy from you.

Create your own search filter to enable your customers to find exactly what they want at lightning speed. Algolia is already incorporated into the sales portals of Staples, Gymshark, NBCUniversal, Decathlon, Lacoste, and many more.

18. PhotoRoom (for removing image backgrounds)

Here's a neat (and free) specialist tool - it selectively removes the background of any photo, leaving the human subject highlighted on a transparent background you can incorporate into other graphics. I tried it with several photos of my own and it worked beautifully.

This is a design tool which uses AI and machine learning to identify the subject of a portrait and separate it from the background. While it might previously have taken a designer ten minutes to patiently define the outline of an image, PhotoRoom does it in seconds. You can also drop in a colored background of your choice.

It's an absolute blessing for anyone tasked with creating a "meet the team" page or creating an avatar to use in multiple locations. There's a premium batch version, and an app for mobile users.

Constructing standard email responses are another marketing task which can quickly become repetitive and labor-intensive. Reply.io's AI Sales Email Assistant aims to remove much of that drudgery. But it also has many different use cases, describing itself as a "sales engagement platform."

While the conversion rate for email marketing campaigns may be a lowly 1.22%, that can translate to a lot of promising leads when you scale up. To make the most of this technique, which can be fully automated, you need an AI-powered email marketing tool such as Reply.io.

Build cold email drip campaigns across multiple channels and Reply will automate the rest, applying AI-powered response scoring to help identify leads which hold potential. There are a host of CRM integrations and predictive analytics tools to track the progress of your campaigns.

20. Brand24 (for media monitoring)

If you want to find out how your brand is being mentioned all over the net where do you start? Brand24 scours news sites, social media, blogs, forums, video, and other locations to aggregate mentions. It then applies sentiment analysis to identify topics of conversation and the underlying emotions of reviewers and users.

You can respond quickly to criticism and intervene with customer support issues, improving customer retention and potentially spotting product and service flaws before they become crises. Much more efficiently than a Google keyword search, Brand24 will highlight problematic mentions and great reviews, weeding out irrelevancies.

There's a great hashtag trend-spotting feature and good customer support including blogs and masterclasses to sharpen your marketing strategy and customer service skills. Clients include Uber, Stanford University, and Intel.

21. Influencity (for influencer marketing)

The use of influencers in social media marketing has been a major trend in recent years and isn't showing signs of slowing. Whether its hauls, product reviews, sponsorship or traditional advertising, social media influencers are big business for brands, especially those aimed at niche or younger demographics.

Influencity bills itself as the "most complete influencer marketing platform" on the market, and it's used by such giants as WPP, Kellogg's and Samsung. It helps brands assess and contact influencers, collaborate on campaigns and then track their effectiveness.

All the major social media platforms are covered. There are plenty of stats and the ability to work at scale across multiple brands, making this a great agency solution.

AI-powered marketing is here to stay

Given the many benefits that AI algorithms can provide - scalability, reach, efficiency, cost savings, decision making, analytic power, better customer experience, and more - it's clear that we are still only at the dawn of this AI technology revolution. Marketers have more exciting tools at their fingertips than ever before, allowing the tiniest startup to compete for global success alongside the most established brands.

Many of the above tools have freemium versions available, so be open-minded, give them a try, and they may become your best friend for future marketing efforts.

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21 best AI marketing tools to include in your stack in 2024 | Marketer Milk

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Discover the 21 best AI marketing tools used by some of the biggest brands today. Learn how teams are using artificial intelligence to grow fast.

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