AI in DIGITAL MARKETING

Influencer Marketing is big business. In 2023 it is estimated at $21.1 billion worldwide, and that’s set to continue growing in 2024.  As traditional advertising finds it increasingly difficult to reach today’s tech-savvy, ad-resistant consumers, influencers provide an additional way to connect with potential customers. And, as brands see higher levels of trust and engagement from smaller (micro) influencer partnerships, the need to streamline the multi-influencer marketing model increases. 1. Target Audience Segmentation It’s vitally important to understand your target audience before you begin to select influencers to partner with. That’s why most social media management and social listening tools have AI built in to streamline this process.  AI tools such as Audiense, Vista Social, and Hootsuite can help identify and segment your target audience by analyzing data from various sources, such as demographics, interests, social media behavior, browsing history, and purchase patterns.  By understanding the preferences and characteristics of your audience through buyer personas, you can ensure you find influencers who resonate with them. 2. Influencer Discovery Finding and selecting influencers can be time-consuming. You want to ensure a great fit with your brand and its values, that the influencer’s followers match your specified target audience, and that their inclusion will result in a wider reach and impact for your activity.  An AI-powered tool such as Hype Auditor makes influencer discovery easy by identifying the most suitable influencers for your brand.  It takes into consideration the following factors. Demographics – This makes sure your influencers’ followers and engaged community match the demographics of your target audience. You don’t want to pay for access to consumers who won’t be able to transact with your business. Brand mentions – By looking for influencers who already talk about your brand you can ensure they are interested in you and more likely to want to get involved. This is particularly useful for finding creators to collaborate with or to identify your most loyal customers who might become brand influencers. Interests – By searching for relevant keywords and hashtags, you can find influencers in a specific niche particularly relevant to your audience. Growth rate – A steadily increasing number of followers is a great sign that the influencer has the potential to be a long-term partner.  Similar influencers – When you know which influencers work out well for you, the look-a-like algorithm finds other influencers with similar audiences, interests and locations to your best performing partners Authenticity and quality – The most useful aspect of the tool and one which is hard to replicate manually is the authenticity and quality score which checks that the influencers are followed by real people and have high-quality engagements. 3. Content Creation and Optimization There are so many ways that AI-powered tools can help influencers save time and improve the overall quality of their content it’s impossible to include them all here. However, some good options to start with include: Content generation – ChatGPT can produce text, images, videos, or audio based on a given prompt or input to create a video or voice script, captions, hashtags, or keywords for an influencer’s post. Caption creation – ScripAI is great for helping influencers write innovative and engaging captions for their content that generate more likes, comments, and shares.  Storytelling – Yarnit is a fantastic ideation tool that uses AI to generate text, images, and audio based on influencers’ inputs  Hook generation – Interesting or intriguing comments that encourage audiences to click-through or engage with an influencer’s video, blog or audio content can be easily created using Tribe Scaler. Content optimization – AI can also optimize content by using predictive models, such as BERT, that can analyze the content and provide suggestions or feedback on how to improve it. 4. Performance Analytics AI-driven analytics tools are used both by social listening tools such as Brandwatch and influencer marketing platforms such as HypeAuditor to analyze real-time data on the performance of influencer campaigns.  Brands can track engagement, click-through rates, conversion rates, and other key performance indicators. All crucial data for making informed decisions and optimizing future campaigns. 5. Influencer Fraud Detection Influencer marketing is not immune to fraudulent activities such as fake followers and engagement. AI algorithms built into most influencer marketing platforms can detect these fraudulent accounts and behaviors, helping brands partner with authentic and trustworthy influencers.  The Power of AI in Influencer Marketing Artificial intelligence is a powerful partner in influencer marketing, but as with any technology, there are challenges and ethical considerations that can’t be ignored. Issues related to privacy, transparency, and the need for clear guidelines are vital.  Brands need to be mindful of the potential for bias in AI algorithms and ensure that they select influencers in an ethical and transparent manner.  While the technology continues to evolve, it’s crucial for brands to adapt to stay ahead, and by harnessing the power of influencer marketing and AI, businesses can drive their online success and build strong, lasting relationships with their customers. For More Such Content Visit: DCS EDUCATIONAL INDIA

While AI in Marketing has been proven to enhance our daily consumer lives—with the adoption of autonomous vehicle technology, fitness devices, voice assistants, and smartphones—it has also served to improve the digital marketing efforts of businesses across many industries. Since erupting onto the scene, ChatGPT, for example, has already made a significant impact in the digital marketing arena, with its innate ability to produce targeted AI-powered content on an ever-expanding range of subjects. A clear indication of how widespread AI is in our current landscape.  AI Social Media Examples Sephora’s AI chatbot-powered shopping service Understanding the power of AI early on, cosmetics brand Sephora has been using chatbot technology to offer its audience the most personalized, informational customer experience possible. After conducting consumer research, the brand realized that many customers were overwhelmed by Sephora’s extensive choice of products, having to browse through a host of pages to find something suitable in certain categories. In response to this, Sephora launched an interactive quiz to help guide its users through the shopping process, led by chatbots that offer tailored tips and suggestions based on specific answers or responses. Ada’s AI-driven customer support and social media engagement platform Ada is an innovative AI technology provider based in Toronto that offers a fully tailored platform designed to plug into a business’s existing tools or tech stack and offer automated customer support. In addition to automating essential tasks and offering human-like customer support communications, Ada’s AI-powered technology can also adopt a conversational tone to spark more engagement across social media channels. To date, Ada’s autonomous technology has helped brands including Air Asia, Indigo, and Zoom with their CX as well as social media marketing activities. Working with Malaysian ride-hailing service Grab, Ada’s AI-powered innovations helped the brand expand its reach into international markets throughout its various social media channels using multilingual chatbot technology. Not only did the bot-powered help broaden Grab’s brand offering (extended to grocery deliveries, bill payment assistance, and ordering takeaways), but it also engaged consumers from six new international markets through Facebook Messenger. As a result of adopting an AI-powered approach to its social media marketing comms, Grab reduced its customer query backlog by 90% while It is also enjoying growth in each of its six new target markets. AI Marketing Automation Examples Starbucks’ AI voice-powered barista services Voice technology is on the rise, and those who embrace its possibilities stand to reap great rewards. Starbucks is no exception. The coffee colossus integrated one of its most subscribed apps with Amazon’s Alexa technology to offer its audience an additional layer of consumer value. In doing so, its customers can now use their very own ‘My Starbucks Barista’ service to place and modify orders, as well as confirm a pickup location for maximum convenience—a seamless blend of voice and chatbot innovation in action. Due to the success of its ‘My Starbucks Barista’ app, the brand has broadened its AI innovations by adding AI-powered Mastrena II espresso makers (machines that essentially complement the pre-ordering capabilities of the app) to thousands of its outlets to offer a more consistent and streamlined coffee-making experience to its customers. Lowe’s AI-powered personal shopping assistants A forward-thinking brand that has successfully enriched its in-store shopping experience, hardware giant Lowe’s introduced its very own AI development –  to offer its consumers personalized suggestions and tailored information as they browse its colossal US stores. Not only does its AI-driven technology help give its customers a seamless shopping experience, but by automatically tracking stock and inventory in real-time, the store’s rolling kiosks provide its marketers with invaluable insights on consumer shopping trends. Speaking about the brand’s technological triumph, Josh Shabtai, Director of Lab Productions at Lowe’s Innovation Labs, explained: “We utilize our stores as living labs to rapidly test our prototypes and gather real-world feedback on the new experiences we’re delivering to demystify home improvement.” Netflix’s AI-centric user content suggestion tool Netflix, the world’s leading SVoD content streaming service, needs little introduction. However, not many people know that it’s possible to attribute a great deal of the unstoppable success of Netflix to its cutting-edge approach to AI. As we know, UX is paramount to the ongoing success of any business, regardless of its industry—and when a customer has a positive experience with a brand, they’re likely to share it with their peers online. To enhance its overall UX and tailor its service to the individual user, Netflix uses AI-driven machine learning technology to provide personalized content recommendations based on a user’s preferences in addition to what they’ve enjoyed in the past. Moreover, Netflix uses an AI development to automatically optimize streaming quality and avoid any quality or buffering issues. Over 80% of the shows people watch on Netflix are now based on its personal recommendations platform—, a testament to its power in a time where people demand more value than ever from the brands they’re willing to invest in. AI Digital Advertising Examples The Economist’s targeted AI ad content delivery innovation The publication leveraged the process of AI-driven programmatic advertising to its advantage, buying and selling targeted adverts autonomously. By using this process to capture data and analyze consumer data in detail, The Economist was able to identify a segment of its audience that it considered to be reluctant readers. By analyzing web and app usage autonomously, they drilled down into specific reading habits or preferences and, as a result, found better ways of approaching their prospects online. Also, by focusing on matching cookie, subscribers and additional data sets to discover new segments and create lookalike audiences, the publication managed to yield a host of positive results that have served to boost its readership levels once again. In summary, this forward-thinking campaign encouraged 3.6 million new readers to engage with the publication, achieving an overall return on investment of 10:1 from the initial wave of revenue generated from these prospects. That’s no small feat in an ultra-competitive digital environment. Heinz uses AI image generator to advertise its iconic ketchup In the wake of its successful ‘Draw Ketchup’ campaign,

How to Get Started in AI and Machine Learning AI and machine learning aren’t just passing trends in an ever-evolving digital landscape – they are critical aspects of the future of our planet. Rapid development in AI has already transformed many industries, and it will continue to do so, with Forbes projecting that AI will be very big. This technology is here now, and a lot of people want to get involved. What may surprise you is that it’s possible to get a job in AI or machine learning without a specialized degree. 5 Core Skills for Getting a Job in AI and Machine Learning The idea of an AI career may sound uber-cool in a slick, futuristic kind of way, but behind the scenes, AI and machine learning jobs entail tasks and abilities that may not have everyone leaping out of bed on a Monday morning. To truly succeed in an AI career, you must have some core skills. Moreover, you should have a genuine passion for the work that is involved. Consider the following: Probability and Statistics: To say you should have a head for numbers is an understatement. Ideally, you should be excited to read about statistical theories and get giddy on probability problems. Applied Math: It’s mostly about high-level mathematics. When you’re learning about complex things like algorithms and gradient descent, you should only yawn because it’s too easy. Programming Proficiency: Python is the best language to learn when you want to start a career in machine learning or AI. Build your skills in languages like C++ and Java too. Distributed Computing: AI runs on data, and often that data is spread over many machines. Learn to handle massive datasets efficiently across distributed systems. Unix: Most AI work happens on Linux systems. Get familiar with Unix commands and tools if you’re not already. What to do Before You Apply for an AI Job As mentioned, a degree is not essential for an AI career. Of course, it would certainly boost your chances, especially as competition for artificial intelligence jobs increases in the years ahead. 1. Online Study If you’re truly motivated to get an AI career, you can undertake self-directed study. There is a plethora of fantastic options online now. Here are a few gems: Learn with Google AI: Free and beginner-friendly intro to machine learning and AI. Machine Learning by Stanford University: A Coursera course by Andrew Ng. Comprehensive and respected. Fundamentals of Deep Learning for Computer Vision by Nvidia: Learn how to build visual recognition systems. 2. Personal Projects To gain experience without a formal job, contribute on platforms like GitHub. Employers value real projects more than certificates. Tips for your GitHub project: Pick a project aligned with your current skills. Keep it under a month to finish. Ensure clean, commented, modular code. Include a README.md with dependencies, tech stack, and resources used. How to Find a Job in AI or Machine Learning You get the chance to work alongside experienced engineers, data scientists, and programmers, gaining valuable experience on smaller projects before securing a long-term role. 1. Build Your Network LinkedIn: Connect with peers, industry experts, and potential employers. AI Communities: Get active on GitHub, Kaggle, etc., to network and find jobs. Events: Attend AI meetups and tech conferences, especially in major cities. 2. Create a Shortlist As you explore the field, compile a list of 20–25 companies you’d love to work for. Research their websites and socials to see if their mission aligns with yours. 3. Judge Yourself Study job descriptions to learn the required skills and tools. Compare your abilities with what the roles demand. Close the gap through learning and practicing. 4. Continued AI Career Development Portfolio Website: Display your projects, resume, and skills. Blog: Write about AI/ML topics and share your learning process. Freelancing: Gain real-world experience through platforms like Upwork and Freelancer.

AI in Digital Marketing – The Ultimate Guide AI has evolved from a fantastical notion to a game-changer for digital marketers today. It’s become the secret engine driving personalized ads, predictive analytics, customer experiences, and much more. What is AI in Digital Marketing? AI in digital marketing uses artificial intelligence capabilities such as data collection, natural language processing, machine learning, and data analysis to get customer insights and automate marketing tasks and decisions. The advantage of AI is that it will help you gain insights into customer behaviors at speed by harvesting vast amounts of data. These can be used to respond to and tailor a customer’s needs depending on their actions and preferences. AI is also able to adapt and improve over time using feedback and new data. Artificial intelligence offers great opportunities for marketers to be more effective and personalized. This means customers get relevant content that can be triggered by actions on a site, an ad, or a branded message. But AI doesn’t replace the roles and efforts of humans! It is a technology that complements and enhances those but it requires human skills to use it effectively. AI Digital Marketing Tools While ChatGPT may be the most talked-about generative AI tool, there are so many more out there that are helping marketers in their daily roles. They can help marketers in a variety of functions from content generation to automation to SEO and design. Here are some of the most effective AI tools out there: Jasper – A content generation and optimization tool designed for augmenting content production Writer – A highly-rated content generation tool that can repurpose content and includes an AI writing assistant Optimove – A customer data platform with AI-based marketing functions Zapier – Helps automate workflows by connecting apps and services that your company uses Surfer SEO – A cloud-based on-page optimization tool that analyzes and compares your pages against what currently ranks on the SERPs Examples of AI in Digital Marketing The power of AI means it can be leveraged across many digital channels. Companies use it for social media campaigns, to automate tasks and processes, and for digital advertising. It can also be used in any industry. Beauty brands use it to deliver a tailored customer experience such as Sephora’s AI chatbot-powered shopping service, while publishers like The Economist use AI-driven programmatic advertising to target and influence ‘reluctant readers’. How to Use AI in Digital Marketing As AI technologies have become more accessible and easier to use, companies have started to use them in daily tasks such as creating content for PPC ads or setting up triggered email workflows. When we asked our membership community at the Digital Marketing Institute about AI, over half felt their roles are changing due to the technology while 44% already use it. Of those who use artificial intelligence, 45% believe it has helped increase their productivity. Content and image creation Marketing teams are often under pressure to create content quickly for many purposes and channels. However, creating good content takes time and effort, and a lot of marketers are time- and resource-poor. AI software such as ChatGPT and Jasper has exploded because it helps people generate content: that’s why they’re called generative AI tools! This could be email subject lines, ad copy, landing pages, blogs, or anything else you need for your content marketing. You can also use AI writing prompts to create catchy headlines and customize content for your audience which drives personalization. Using these tools saves time and money. However, make sure to read over any content before publishing it live to ensure it has personality and fits in with your brand voice. Customer service and support AI-driven chatbots have been around for a while as a way to communicate with and provide information to customers. When people want to get instant answers to simple queries or interact with a brand, they first go to a chatbot. However, AI has a bigger role when it comes to customers. It can improve customer experience by personalizing content and messaging, anticipating customer needs, streamlining purchasing, and directing queries or concerns to the right department or agent. Customer segmentation Customer segmentation and targeting help you identify and influence your most valuable customers. However, doing this manually can be time-consuming and costly. AI can use algorithms to segment your prospects and customers into groups or segments of people with similar interests, needs, or behaviors. You can then communicate with and target these segments with relevant and customized messaging that can be adapted and adjusted in real-time. Recommendation systems can suggest products, services, or offers that match customer preferences while Natural Language Processing (NLP) can analyze customer feedback and reviews to improve your products, services, or customer experience. Pay-per-click advertising Pay-per-click (PPC) advertising is a form of advertising where you pay when someone clicks your ad. As a strategy, PPC marketing is important as it can attract new customers through targeted campaigns, get quick results, and be easily tweaked and optimized. The Pros of AI in Digital Marketing Boosts productivity – Automate a variety of jobs so your time and effort can be redirected to more difficult and strategic tasks. Drives efficiency – Handles mundane and repetitious activities such as data input, separating marketing campaign leads, and replying to consumer queries. Reveals insights – Eliminate guesswork and provide data-driven capabilities to develop a more effective marketing approach and open up new marketing opportunities. Increases ROI – Predict buying behavior and choices by evaluating data, improving the UX, and providing consumers with the content they require to complete their buyer journey. Create and optimize content – Automate content creation to produce quality content at scale for all online channels. Personalize at scale – Analyze customer data and generate personalized recommendations or content to enhance customer experience. Predictive analysis – Forecast consumer behavior or market trends based on historical data to help more accurate and effective planning. Automate tasks – Handle routine tasks such as data analysis, customer