Dean Scaduto is CEO of Dinos digital. Dean is also an entrepreneur, digital consultant, author and digital marketer.
An enthusiastic digital marketer with a passion for search engine optimization (SEO) and machine learning, I have continued my education with some great artificial intelligence related programs from the University of Oxford and MIT.
Through this experience I discover an exciting intersection between these seemingly separate fields. Therefore, this piece aims to shed light on some fundamental principles of machine learning and their potential impact on the digital marketing landscape.
Contents
Machine Learning: An Introduction
Machine learning (ML) is the scientific discipline that focuses on how computers can learn from data without being explicitly programmed. There are three main categories: supervised learning, unsupervised learning, and reinforcement learning.
Supervised learning is similar to a supervised learning experience, using data with known outcomes, much like having a solution guide when solving a series of problems. In terms of SEO, this parallels recognizing trends between site optimization strategies and the resulting increase in traffic.
Rather, unsupervised learning is about raw, unlabeled data and deciphering inherent patterns independently. This can be compared to conducting keyword research without any initial preconceptions about what effective keywords might be.
Finally, reinforcement learning functions based on a feedback loop of rewards and punishments, similar to how A/B testing methodologies in digital marketing are used to determine which landing page or element produces better results.
Machine learning and digital marketing: an intersection
I believe machine learning has the transformative potential to revolutionize digital marketing and SEO in ways we don’t fully understand yet. As digital marketers, our day-to-day activities involve managing a deluge of data, from user engagement metrics and conversion rates to SEO analytics. The role of ML is paramount here. It can automate data processing and analysis, which not only increases efficiency, but also provides deep insights that may escape manual scrutiny.
Consider a scenario where a company launches multiple campaigns on different platforms. A digital marketer should track each campaign and note performance indicators such as click-through rates, bounce rates, and conversions. An ML algorithm can help monitor these campaigns, instantly highlighting patterns, anomalies and trends.
Delving deeper into the realm of SEO, keyword analysis and content optimization are integral components. These tasks often require painstaking manual effort and expertise. However, I see how supervised learning can significantly improve this process. Imagine we have data on a range of keywords and associated content that have led to high traffic and engagement in the past. We can train an ML model on this data; the keywords and content can serve as inputs and the engagement metrics as outputs.
Once the model is trained, we can provide it with new content and potential keywords. The model can then predict how these might perform based on what it has learned from the historical data. For example, if your blog post on “Machine Learning in Digital Marketing” attracted a lot of traffic with keywords such as “AI in marketing” and “SEO automation”, a trained model should be able to predict the performance of a similar blog post using related keywords. .
This predictive ability can lead to more targeted content and keyword strategies that can increase the likelihood of attracting relevant traffic and therefore improve return on investment (ROI). In an increasingly competitive digital landscape, such strategic, data-driven approaches can make all the difference.
Unsupervised learning, one of the pillars of machine learning, offers remarkable opportunities to uncover hidden customer segments. During my career as a marketer, I have regularly used analytics to define target group clusters. However, conventional methods sometimes fail to detect subtle, latent groupings that do not correspond to preconceived categories.
Consider an e-commerce website with customers with different interests, browsing habits and buying patterns. Traditional analytics can segment users based on explicit factors such as age, location, or gender. But what if there are hidden segments like “weekend shoppers who buy electronics and also have an interest in home decor”? Unsupervised learning, especially techniques like clustering, can help identify such complex segments by analyzing multifaceted patterns in browsing and purchasing data.
Once these nuanced customer segments are revealed, they open up a whole new realm of opportunities for targeted marketing. For example, a segment of customers interested in both electronics and interior decoration could be targeted with marketing strategies that showcase smart home products. Likewise, the “weekend sporting goods shoppers” can be targeted with special weekend offers or recommendations for additional products such as fitness accessories.
Essentially, unsupervised learning enables a level of personalization beyond superficial level segmentation, enabling digital marketers to deliver a truly individualized customer experience. This, in turn, increases conversion potential as marketing messages are better tailored to unique customer behaviors and preferences.
The power of reinforcement learning
Reinforcement learning brings an element of trial-and-error to the table, enabling an AI-driven advancement to mainstream digital marketing techniques. Instead of relying on the static nature of something like traditional A/B testing, we can harness the power of AI to consistently learn from consumer interactions, leading to a dynamic adjustment of marketing tactics.
Looking back at my own journey in SEO, A/B testing has been a crucial tool in my arsenal, used for fine-tuning elements such as web page layout, content rendering, among others. But implementing reinforcement learning in this mix goes one step further. Decisions regarding web page changes or content improvements do not need to be made and tested manually. Instead, the learning algorithm can automate this process and make optimal decisions based on previous user interactions.
Suppose two headline variants are being tested for a blog post. Traditional A/B testing would measure which headline generates more traffic. However, with reinforcement learning, AI can go beyond just identifying the better headline. It can analyze why a particular headline works better. Maybe it’s the use of a specific keyword, the feeling it portrays or the length of it. It can then use these insights to create future headlines, gradually refine its approach, and drive user engagement and conversion rates over time.
This continuous learning and optimization process demonstrates the potential of reinforcement learning in shaping future digital marketing strategies.
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Janice has been with businesskinda for 5 years, writing copy for client websites, blog posts, EDMs and other mediums to engage readers and encourage action. By collaborating with clients, our SEO manager and the wider businesskinda team, Janice seeks to understand an audience before creating memorable, persuasive copy.