Generative AI Use Disclosure:
This blog post was edited and optimized with AI, but the final output was generated by me, a human.
The primary {robot} image in this post was generated with Midjourney.ai, but the content surrounding the image and the final output was generated by a human.
Whether you’re a Marketer working at a Fortune 500 company, a Data Scientist creating algorithms for advanced financial analysis, or an Analyst working at a Mom-and-Pop shop, we’re all dealing with more data than ever before. We’ve all seen the stats - data is growing faster than ever. Statistica estimates our current earthly data volumes at 147 zettabytes, which is a 22% increase from last year, and expected to grow another 22% this year. Let’s try to visualize this amount of data, to put it into perspective. Imagine an average laptop with 500 gigabytes of storage, we would need 294 trillion laptops to contain all of the world's data. If we were to line all of these laptops up together, that’s enough laptops to wrap around the earth 2.2 million times!
Data isn’t slowing down anytime soon. However, there is something new in the arsenal of Marketers, Data Scientists, and Analysts alike and that’s Artificial Intelligence. Honestly, it couldn’t have come at a better time because we desperately need it to help us parse through and make sense of all these zettabytes of data. Yet these same business professionals who are grappling with trillions of laptops worth of data are struggling to understand AI. According to the 2024 Data and AI Leadership Executive Survey that interviewed more than 100 Chief Data Officers (CDOs), 62% of organizations are making investments in Generative AI a top organizational priority for their businesses this year. Yet according to the study, 60.4% of organizations surveyed are in the early experimentation and testing stages, and only 4.7% of organizations have implemented Generative AI in production at scale.
So you’re probably asking yourself, why? With all this data, and all this new artificial intelligence to sort through it all, why isn’t adoption greater? Our hypothesis is that there’s still a massive amount of confusion surrounding what AI is, how it relates to Machine Learning, what purpose Large Language Models (LLMs) serve and how to make sense of it all.
This post will guide you through the different types of generative AI, their relevance in big data and analytics, and how they can revolutionize all that data within your digital marketing strategies. So, buckle up – we're about to take AI from 'what the heck?' to 'hell yeah!' in your daily marketing grind.
[Robot Image created using Midjourney]
Machine Learning (ML)
We’ve all heard of Machine Learning, but there’s a confusion around what ML means in the context of AI. In today's digital age, the marketing landscape is evolving at an unprecedented pace, and at the heart of this transformation lies a powerful catalyst: Machine Learning (ML). But what does this mean for the modern marketer? ML is not just a buzzword but a real game-changer in enabling Marketers to dive deeper into their data when crafting campaigns that resonate, engage, and convert.
What is ML?
Machine learning is a part of AI technology that allows computers to learn and make decisions by themselves by analyzing data and identifying patterns, much like how humans learn from experience.
Why is ML valuable to Digital Marketers?
In analytics, ML can identify patterns and insights in data that might be missed by human analysis. For instance, ML algorithms can predict customer behavior, optimize pricing strategies, and forecast sales trends.
ML is a cornerstone of predictive analytics, allowing marketers to forecast future customer behavior, market trends, and campaign outcomes based on historical data, thereby enabling more accurate and targeted marketing strategies.
Practical ML Applications:
At Seer, we recommend leveraging the prowess of machine learning in practice, not just in theory, by integrating it into campaign types you are either already using or considering for deployment. Machine learning algorithms act as the powerhouse behind several key campaign types, each meticulously designed to significantly enhance your marketing effectiveness:
- Dynamic Search Ads (DSA): These campaigns use ML to adjust your ads based on what potential customers are searching for in real-time. By analyzing search queries, ML dynamically generates ad headlines and landing pages tailored to match user intent, ensuring your ads are not only relevant but also timely.
- Programmatic Advertising: Here, ML's role revolutionizes traditional ad buying. It's about optimizing placement and bidding in real-time across diverse channels, ensuring maximum visibility and engagement. This dynamic adjustment, based on continuous performance analysis, has helped small businesses and global brands alike optimize their ad spend for the best ROI.
- Customer Segmentation Campaigns: Through deep data analysis, ML identifies nuanced customer segments, enabling campaigns that speak directly to specific preferences and behaviors. Retail giants, for example, leverage ML to provide personalized shopping experiences, using customer data to tailor marketing messages, offers, and even in-store experiences, revolutionizing the way we think about personalization.
- Email Personalization Campaigns: ML takes email marketing beyond generic blasts, analyzing interaction and conversion histories to personalize every aspect of communication. This strategy has been pivotal for e-commerce sites, where personalized emails significantly boost open rates and conversions by offering timely, relevant product recommendations.
- Content Optimization: ML not only helps in deciding whom to show your ads to but also in optimizing the content of the ads themselves. By analyzing A/B tests of which ad creatives, headlines, and descriptions perform best, ML continuously refines your content strategy, ensuring your messaging resonates with your audience.
Incorporating these ML-powered campaign types into your marketing strategy doesn't just mean staying ahead of the curve—it means actively shaping the future of how brands connect with their audiences. With each campaign, ML offers insights and optimizations that are not merely reactive but predictive, allowing you to craft marketing strategies that are both innovative and deeply resonant with your target market.
Expanding Horizons: Beyond Marketing Campaigns to Broader ML Applications Across Industries
- Netflix uses machine learning algorithms to analyze viewing patterns and provide personalized content recommendations. This approach keeps users engaged and improves their overall viewing experience.
- Spotify uses machine learning to curate personalized playlists and music recommendations for its users. This personalized approach keeps users engaged with new and relevant content tailored to their music tastes.
- How could this help a local restaurant owner? ML can analyze sales data to predict busy times and popular menu items. This can help in efficient inventory management, staffing decisions, and menu optimization to reduce waste and increase customer satisfaction.
Neural Networks and Deep Learning
What are Neural Networks and Deep Learning?
We now know that Machine Learning, a branch of AI, enables computers to learn from data and make predictions. But what drives ML's capabilities?
Neural networks, a key component of machine learning, are inspired by the human brain's structure. They function as a computer's simplified brain, consisting of multiple layers of small processing units that collaborate to identify patterns and interpret data. By analyzing numerous images of cats, for example, neural networks learn to recognize common features, enabling them to identify a cat in a photo. This process mimics the way we teach computers to understand complex concepts through pattern recognition.
Deep Learning takes neural networks a step further by incorporating numerous layers, enabling the processing of highly complex patterns. This advanced approach allows computers not only to recognize a cat but also to distinguish between different breeds or discern a cat's mood from its expression. Such depth of understanding equips deep learning with the power to perform sophisticated tasks, such as translating languages and enabling autonomous driving, by mimicking human-level perception and decision-making.
Why are Neural Networks and Deep Learning valuable to Digital Marketers?
They are powerful in image and speech recognition, which can be applied in digital marketing for brand monitoring and audio-visual content analysis. Deep Learning can also enhance personalization in marketing by understanding customer preferences at a granular level.
Neural networks enhance descriptive analytics by processing complex visual data from digital campaigns, providing detailed insights into customer interactions and engagement with visual content. Alternatively, Deep learning excels in prescriptive analytics by not only predicting outcomes but also suggesting actionable marketing strategies based on data-driven insights, thereby optimizing decision-making processes.
Practical Neural Network and Deep Learning Applications:
Leverage these technologies for advanced customer segmentation and to create highly personalized marketing content.
- Google uses neural networks and deep learning in various applications, including Google Photos for image recognition and categorization. This technology helps users search and organize their photos more efficiently.
- Tesla integrates neural networks and deep learning in their Autopilot and Full Self-Driving (FSD) features, for advanced image and pattern recognition crucial in autonomous driving technology.
- Google and Tesla out of your league? Neural Networks and Deep learning could also be applied to a family-owned salon. Deep learning can be used for image recognition to create a virtual hairstyle try-on feature on the salon’s app or website. Customers can upload their photos to see how different hairstyles would look on them, enhancing customer engagement and appointment bookings.
Natural Language Processing (NLP)
What is NLP?
NLP involves teaching machines to understand and interpret human language. It's a subset of AI that focuses on language-based data.
Why is NLP valuable to Digital Marketers?
NLP can analyze customer feedback, social media conversations, and market trends. For instance, sentiment analysis can help brands gauge public opinion about their products or services, enabling them to make data-driven decisions.
NLP is crucial for diagnostic analytics, as it helps in interpreting and analyzing customer feedback and social media data to understand the 'why' behind consumer behaviors and market trends.
For marketers, the rise of Natural Language Processing (NLP) technology represents a revolution in understanding and engaging with audiences. Envision the ability to swiftly sift through customer feedback, social media conversations, and product reviews, unlocking a deep understanding of your audience’s desires and sentiments towards your brand. NLP stands at the forefront of this capability, enabling the extraction of trends, sentiments, and insights from vast amounts of text. This facilitates smarter, data-driven decisions, empowering marketers to fine-tune their strategies to more closely align with customer needs, create more compelling content, and enhance overall communication with their audience.
However, while NLP provides transformative opportunities, it's essential to recognize its inherent limitations. The effectiveness of NLP technologies hinges on the quality and diversity of the data they are trained on, leading to potential challenges in interpreting nuances, sarcasm, or culturally specific expressions accurately. Furthermore, to remain relevant, NLP systems demand ongoing updates and training to adapt to the dynamic nature of human language and emerging slang. Recognizing these challenges is vital for marketers to set realistic expectations and apply NLP solutions judiciously, ensuring that human insight complements automated strategies for optimal outcomes.
In essence, NLP offers a pathway to deeply understand and connect with your market at scale, sidestepping the need for deep technical expertise. Yet, it underscores the necessity for human involvement, blending the best of technology with the irreplaceable nuances of human judgment and creativity.
Practical NLP Applications:
Implement NLP tools to monitor and analyze customer feedback across various platforms, helping in tailoring marketing messages and improving customer service.
- Amazon leverages NLP in its customer service chatbots and Alexa voice assistant. This technology enables them to understand and respond to customer inquiries effectively, enhancing the customer support experience.
- Walmart employs NLP in its customer service interfaces and voice-activated shopping assistants. This technology helps in accurately understanding and processing customer queries and shopping lists, improving the shopping experience.
- What if you’re not Walmart, but instead manage a small retail business? NLP can be used to analyze customer reviews on social media or the store’s website. By understanding customer sentiments and preferences, the store can make informed decisions about inventory, store layout, and customer service improvements.
Large Language Models (LLMs)
What are LLMs?
Large Language Models like ChatGPT-4, Claude, and Llama are advanced AI systems trained on vast amounts of text data. They can generate human-like text, answer questions, and even create content.
Why are LLMs valuable to Digital Marketers?
Let’s face it, marketing is not just about selling a product; it's about telling a story that connects with the audience on a personal level. This is where LLMs shine, transforming marketing from a battle for attention into a symphony of personalized engagement. They are not just tools but collaborators that help marketers create content that captivates, chatbots that understand and empathize, and analyses that unveil the pulse of market trends and consumer sentiments.
Picture a world where every piece of content a brand puts out is not just seen but felt, where every customer interaction is not just a transaction but a conversation, where market insights are not just data points but narratives waiting to be told. This is the promise of LLMs in marketing—a promise that is not just about automation, but augmentation; not just efficiency, but creativity; not just personalization, but connection.
In this evolving landscape, LLMs are the beacon that guides marketers towards a future where the power of language bridges the gap between brands and consumers, creating experiences that are not just memorable, but meaningful. As we stand on the brink of this new era, the question for marketers is no longer if they will adopt LLMs, but how they will use them to transform their stories, their strategies, and their success. This is the revolution in marketing, powered by the art and science of language, where every word counts and every interaction matters.
Practical LLM Applications:
Marketers can use LLMs to automate routine writing tasks, freeing up time to focus on creative and strategic aspects of marketing campaigns.
- Airbnb uses LLMs for content creation, particularly in generating property descriptions and communicating with users in multiple languages. This enhances user experience and streamlines the booking process.
- Microsoft integrates LLMs into its Office suite using its CoPilot functionality, particularly in Word and Outlook, to improve writing suggestions, grammar corrections, and email composition assistance. This application enhances user productivity and writing quality.
- What if you’re a small business owner? Suppose you own and operate a local bakery, an LLM can be used to automatically generate engaging social media posts and blog entries about new bakery items, recipes, or baking tips. This can help in maintaining an active online presence with minimal effort, attracting more customers through engaging content.
Generative Adversarial Networks (GANs)
What are GANs?
While there are many types of Deep Learning models, GANs involve two neural networks contesting with each other to generate new, synthetic instances of data.
Why are GANs valuable to Digital Marketers?
GANs can create realistic images and videos, opening avenues for innovative advertising and product visualization.
GANs contribute to prescriptive analytics by creating realistic simulations and visualizations, which can be used for A/B testing and optimizing marketing content before actual deployment.
Practical GAN Applications:
Use GANs for creating high-quality, realistic visuals for marketing campaigns.
- L'Oréal has used GANs for creating virtual makeup try-on experiences. This technology allows customers to see how makeup products would look on them virtually, enhancing online shopping experiences.
- Adidas has explored using GANs for creating virtual models of footwear and apparel. This allows customers to see realistic renderings of products, helping them make informed purchasing decisions.
- Adobe incorporated GAN technology into its creative software suite. For instance, in Photoshop, the "Content-Aware Fill" feature utilizes GANs to seamlessly fill or remove objects from images, streamlining tasks for graphic designers and photographers.
- Facebook uses GANs to identify photos with far less human help of labeling objects. For instance, Facebook's AI Research team developed a GAN-based system called "SEER" (Self-Supervised GAN for Image Editing at Scale) trained on 1 billion public images on Instagram and can outperform the most cutting-edge self-monitoring image-recognition system despite poor image quality. It mimics human learning by analyzing relationships between objects in images, uncovering key information.
- So how does this apply to you? Imagine you operate a local art gallery… GANs can create realistic images of how art pieces would look in different settings, like living rooms or offices. This can be used on your shop’s website to help customers visualize the art in their space, aiding in their purchase decision.
Retrieval-Augmented Generation (RAG)
RAG combines retrieval of information with generative models, enhancing the output's relevance and accuracy.
What are RAGs?
RAG can improve the quality of AI-generated content, ensuring it is both relevant and accurate.
Why are RAGs valuable to Digital Marketers?
RAG-enhanced systems for generating accurate, informative content that resonates with your audience.
RAG supports diagnostic analytics by efficiently retrieving and synthesizing relevant historical data, helping marketers to understand the context and causes of current campaign performances and consumer behaviors.
Practical RAG Applications:
Utilize RAG-enhanced systems for generating accurate, informative content that resonates with your audience.
- Meta has been exploring RAG for enhancing the relevance and accuracy of information presented in its content, including news feeds and targeted advertising. This ensures users receive more pertinent and engaging content.
- LinkedIn might use RAG for enhancing the relevancy and precision of job recommendations and content in users' feeds. By combining retrieved information with generative models, LinkedIn can offer more accurate and personalized user experiences.
- Okay, so you’re not Meta or LinkedIn, how could this work for you? RAG can be used to develop an informative chatbot on the store’s website. The chatbot can retrieve information from product catalogs and user manuals to provide customers with detailed product information, usage tips, and troubleshooting help.
Preparing for the Future of AI in Analytics and Digital Marketing
In the dynamic world of analytics and digital marketing, AI technologies like LLMs, NLP, ML, Neural Networks, Deep Learning, GANs, and RAG are not just theoretical concepts but practical tools for everyday challenges. From enhancing customer engagement to optimizing operations, these technologies offer you, the digital marketer, an edge in understanding and connecting with your audience.
Now is the time to dive in. The true value of AI in digital marketing can only be realized through hands-on experience. No blog post, interactive guide or YouTube video can replicate the insights gained from directly experimenting with these tools. This is how we’re looking to apply AI in Analytics everyday at Seer. You should be doing this too. Your creativity and strategic thinking, combined with AI's capabilities, are key to unlocking innovative marketing solutions.
Start small, learn by doing, and watch as your marketing strategies evolve and your insights grow better and more prescient with AI integration. Remember, the future of digital marketing is shaped by those who actively embrace and experiment with AI. I encourage you to begin this journey today – your next big marketing breakthrough awaits.
Want to learn more? Contact us to talk to someone on our Analytics & Insights Team about incorporating AI into your digital marketing analytics workflows.
