In a 2021 survey, 86% of businesses said that AI was becoming a “mainstream technology” at their company, while another poll showed that adoption rates of machine learning methods were at 45%. Digital marketing is one of the industries that can benefit greatly from this technology, harnessing it for advanced segmentation, personalization, and SEO optimization.
The advantage is that machines can process big data quickly, learning from it in order to draw conclusions and making accurate predictions about customer behavior. Deep learning and continuous learning take things a step further—and that’s what we’ll explore in this post.
Deep learning is a subset of machine learning (ML) in which computers are taught to think in a human-like way. It uses algorithms called artificial neural networks (ANNs) containing several layers of neurons or decision-making units—that’s why it’s called “deep” learning.
Deep learning models are able to make assumptions, test them, and learn autonomously, without being explicitly programmed. They are already used for things like self-driving cars, virtual assistants, and image recognition.
This technology is capable of managing and learning from large amounts of structured and unstructured data. The model improves its learning as it receives more data, leading to more accurate outcomes.
Artificial neural networks are complicated algorithms, capable of learning from each new piece of data. They are designed to replicate the neurons and synapses in the human brain. However, machines using ANNs can process huge amounts of data and learn new skills much faster than humans can, no matter how many explainer videos we watch!
Deep learning algorithms can arrive at a particular outcome based on the inputs they receive. The neurons are able to change their own connections based on what they’ve learned, which means they can make predictions about customer behavior.
The models are trained using data that has already been classified, enabling the system to classify new data. For example, the Databricks TensorFlow integration can help you train and run deep neural networks.
Although these machines can learn autonomously, they still need some assistance from their creators—if you want them to learn new words, you’ll have to tell them what each word means. You can use data virtualization to find the most relevant data for training purposes.
The most advanced machines are now capable of acquiring new knowledge throughout their lifespan. They reproduce the human ability to learn incrementally by refining and transferring their knowledge and skills.
In AI terms, continuous (sometimes called continual) learning means that computer models learn and evolve as they receive increasing amounts of data. Crucially, they can absorb new information while retaining the things they’ve already learned.
Most ML models are trained offline with input feeds. But the latest development is to get the machines to learn and retrain by themselves, by feeding off ever-changing data and trends.
One of the advantages for digital marketing teams is that AI models can save time by automating repetitive processes.
For example, they could automatically add captions to pictures on your website, using image recognition. Or they could create different language versions of the site using automatic translation. ML is also used in sales proposal and contract generator software, creating documents based on client data.
As well as freeing up marketers to focus on human interactions with customers, continuous learning models also help to improve customer engagement, loyalty, and revenue. Here’s how:
Deep learning models find subtle patterns in data, which makes them ideal for advanced segmentation. Marketers can then identify the target audience for a particular campaign, while machines can also predict potential leads based on past behaviors.
ANNs are able to make decisions based on large datasets, so digital marketers don’t have to use guesswork. For example, a continuous learning model can tell you which channel or platform you should focus on for advertising.
Machines can also identify customers who are on the verge of leaving, giving you the opportunity to take action. Plus, they can spot potential advocates and help you incentivize them to promote your brand.
71% of consumers expect customized interactions, but it’s not easy when there’s so much data to analyze and infer from. Continuous learning models process that data quickly, identifying each customer’s interests from their behaviors.
Deep learning is used to develop personalization engines that help marketers deliver hyper-personalized content. Think dynamic websites that display different content depending on who’s browsing, or push notifications for customers who leave without buying.
It’s even possible to recommend solutions before the customer searches for them. Rather than a generic email about Christmas gifts, the machine could make personalized recommendations based on social media activity.
As well as making it easier to personalize the content itself, continuous learning tools also help you identify the optimum times and methods for delivery. If you can target the right people with the right message at the right time, you have a much better chance of engagement.
For example, the AI might detect that a certain customer always checks their emails at 8:00am, and often clicks through from special offers. Another customer spends most of their time on Facebook but rarely uses email. In both cases, the marketing team will know how best to engage those customers.
Of course, you’re going to need the right contact details for all customers—email finder software uses machine learning to find up-to-date addresses so your marketing efforts aren’t wasted.
Another useful aspect of continuous learning is that it helps companies predict what customers will do next. From tracking how people navigate through your website to how often they make a purchase, the AI models can learn and draw accurate conclusions from the data.
This not only helps with personalization, but also tells companies which products or services are likely to be in demand. Businesses can use this knowledge to ship popular products to their warehouses in advance (like Amazon does) and better allocate their marketing budgets.
You can also use Rule-Based Optimization to automatically scale your marketing campaigns and make adjustments to your advertising, based on the amount of traffic and people’s viewing behavior, without you needing to make any manual changes.
It’s worth remembering that bias may exist in datasets, such as when there’s less data for a certain group. You can ensure your ML model isn’t biased by using the SageMaker bias reduction tool.
Thanks to natural language programming (NLP), machines can be trained to respond to customer queries in a natural, non-robotic manner. For example, continuous learning chatbots analyze data from human conversations—and, over time, they learn to sound more human.
As well as being appealing to customers, they gather more data while they’re chatting. And they can identify customer moods and reactions through sentiment analysis, giving further insight to marketers.
Deep learning tools can use heat mapping to analyze the performance of specific areas of a website. By tracking mouse clicks and movements (and even users’ eye movements) they identify which areas get the most “heat” from visitors. Marketers can then make sure they position key elements like CTAs in those places, which increases click-through rates and improves the user experience.
Heat mapping is one of the tools marketers can use to optimize SEO, while continuous learning models will also scan online content and customer search terms to identify trending keywords and topics. Automatic website generation software can optimize website design and content in real-time, which is more efficient than manual testing and experimentation.
The rise of social media has provided a ton of data for marketers to analyze, so continuous learning tools are required to help marketers make sense of it all. By scanning the keywords and sentiment in posts, comments, and reviews, machines can produce reports on how users perceive and communicate with the brand.
ML does require some investment in terms of data management and tech infrastructure, such as tools that can run and analyze massive numbers of simulations. You might have a significant amount of data in a data lake, but you need sufficient computational power to process and analyze it. Large volumes of data are required to train a neural network, so new businesses might not have enough customer details to feed the machines.
AI machines are smart, but not infallible. You could see a decrease in performance while new data is integrated or even an overwrite of previous knowledge with the new data. There’s also a glitch called catastrophic forgetting, where deep networks fail to recall what they’ve learned and have to be constantly reminded.
It’s expensive and time-consuming to retrain the model each time there’s new data. To be sure that your new model works properly, you’ll need to test it on old data before and during deployment. MLOps with Databricks is a way to maintain and monitor ML models.
Two-thirds of customers would be happy to share their data, or would consider sharing data, if they got something of value in return—such as personalized offers. However, companies must be careful not to alienate customers by giving off a stalker vibe! And it’s also essential to comply with privacy laws.
Adopting any new technology comes with challenges, but in the case of continuous learning, the benefits tend to outweigh them. It processes huge datasets quickly, saving time and helping marketers identify their target audience.
The technique also provides deeper insights into customers’ needs and behaviors—both now and in the future—which you can use to personalize messaging and improve experiences. If you harness this technology to its full potential, you’ll see more engagement and customer loyalty, and an increase in revenue.