ML driving change
The field of digital marketing has seen a significant shift over time with the widespread incorporation of machine learning (ML) technologies. The open availability of customer data coupled with the sector's need for constant resource optimization have made traditional marketing strategies insufficient in today's landscape. As a result, marketers are implementing ML algorithms to reduce manual tasks and gain a competitive edge among their peers. From personalization to predictive analytics, SEO automation, campaign management, and even content creation, ML has revolutionized the digital marketing process.
What do we want? Data! Why do we need it?
To take full advantage of ML algorithms and insights, we must first have access to a large enough dataset. The exact size depends on the complexity of the model and the desired end performance, but the key here is the accuracy of the data, as low-quality entries would hinder the proper establishment of patterns and relations. For small models, the 10-fold rule is a common starting point, meaning that the input data should be ten times greater than the model's degrees of freedom.
Where this isn’t possible however, we still have workarounds - users can take advantage of data augmentation or transfer learnings from what is known to fill the gaps and still achieve satisfactory performance. For digital marketing, the best-case scenario would be having a well-organized historical dataset on which to train a model and current running campaigns, for which the model's accuracy can be then evaluated.
Nowadays, advertisers have come to have higher expectations from the advertising campaigns that reach them. Here is where AI tools come in handy, as you can analyse existing customer data to offer a personalized experience per user, including, but not limited to, content creation, email campaigns, and product recommendations. Examples for this would be loyalty rewards, targeted discounts, or timed reminders, such as abandoned cart journeys.
The human-like attention to detail (ironically) that ML provides, can enhance the connection between customer and brand. ML technologies have also revolutionized the way businesses conduct A/B/n testing. Companies can now leverage such testing at scale with ease and drill-down into more sophisticated targeting based on custom events or customer site interactions, returning the exact elements driving improved conversions. Other testing methods, such as multivariate and multi-armed bandit testing, solve a wider scope of problems and, depending on the use case, return results much quicker than conventional means.
Another use case for ML is in the realm of predictive analytics and the more data we have, the better we can leverage it. More data, means more experimentation enabling us to optimise and apply changes across numerous levers like creative, messaging, product etc. to any given audience. You can also run predictive targeting, using all possible combinations of existing user activity data points to look for the probability of engagement, purchase or churn.
An applied example of this that many marketers already use is Google’s Smart Bidding strategies that automate bid adjustments in real time via ML, allowing you to target and optimize campaigns for specific parameters such as CPA, ROAS or conversion rate. Nearly all ad platforms these days incorporate ML in their campaign management tools and we can use these – or our own custom models, to improve campaign performance.
ML Powered Content
One of the most discussed areas where ML has made strides has been in image and content generation. You no longer need to hire a graphic designer to get a high-quality brand logo or digital banner. As controversial as the subject of AI art is, nobody can deny the consistency and scale at which it can be leveraged to improve your campaign and landing pages' visuals. Even Google has introduced AI-powered search ads that would automatically generate ad assets based on the unique context.
Other tools can do the heavy lifting when it comes to generating engaging content, solving for human issues such as writer's block or the need for consistent updates. In my opinion though, the most exciting is Visual AI, which fundamentally changes how computers read images (breaking them down by visible element regions instead of pixel by pixel), enabling machines to interpret context, nuance and understanding beyond just recognition.
Lastly, we cannot discuss digital marketing without touching on SEO strategies as well. Information retrieval algorithms can be used to rate user queries and return successful keywords according to their respective relevance scores through the application of ML. As an example, we came across a tool that allowed you to replicate any search engine environment and predict ranking changes based on different scenarios.
Other AI technologies can directly generate an SEO-optimized page based on the analysis of better-performing competitors, eliminating the need for manual research and maintenance and more is to come. Exciting times for marketers!
A New Dawn?
No article on ML would be complete without a little fear mongering on whether we will be replaced by machines, so I’ll finish with this. There are already technologies on the market that offer to automate the work of marketers, but it’s not just limited to our field - artists, copywriters, even musicians to name a few, are also seeing their industries disrupted by ML. However disruption is not something we should fear. We should treat these advancements as opportunities and continue to adopt new ML technologies that serve us to drive performance as that is what we ultimately are aiming for as marketers and data professionals.
Understanding how to maximise and leverage we have at our disposal, is they best way to counter any of these concerns.