There’s been a lot of buzz and excitement about machine learning, especially in terms of how it will make marketing pros’ lives easier. While machine learning tactics can make a positive impact on your business, it’s key to understand just what they are and how they can help solve today’s marketing challenges.
Marketing teams will likely want to explore how machine learning can improve content management, personalization, and analytics technologies, specifically around how it will automate day-to-day marketing functions. These include things like audience targeting, content classification, reporting and so on.
What marketers will soon realize is that while many martech companies say they have machine learning technology or are investing in it, they don’t define what these machine learning techniques are or do. Depending on who is offering the technology and how it’s incorporated, what you actually get can differ greatly.
Marketers will need to look past the hype and understand how vendors’ machine learning technologies are incorporated into their solutions and which manual business processes will be reduced or eliminated. Before we get into the weeds on machine learning techniques, we’d like to first define Acquia’s meaning of machine learning, and how that informs our long-term investments.
Defining Machine Learning
What is machine learning? Here at Acquia, we define machine learning as: “a specific set of techniques that enables technology to learn from data and make intelligent predictions.”
We believe that content management and machine learning naturally complement one another. At the heart of every great customer experience is content and data, and the core benefit of machine learning is using data and automation to speed up processes and gain key insights that are actionable.
Addressing Common Marketing Challenges
Marketing teams that want to leverage machine learning techniques should first understand the content and data they have available, and the goals they want to achieve with that information - whether that be making content easier to find, personalization more automated or insights readily available.
While machine learning can help reduce or eliminate arduous marketing tasks like content tagging, most machine learning techniques requires an up-front human element of labeling training data to help inform machine learning algorithms. This can easily become a pretty tedious process, the key is to find ways data can be leveraged up front so less needs to be inferred by marketers. For instance, adding a ratings or feedback option as part of a product review, or tracking engagement data on content assets, will give critical insight to marketers about purchasing behavior and content consumption with reduced manual efforts.
An example of a task that a marketer could reduce or eliminate with machine learning includes personalized content recommendations. Marketers will want to recommend content to each of their audiences based on what’s already been consumed and is resonating. Ideally, a machine learning engine should be able to read and identify both structured and unstructured content data, and use a pre-trained machine learning model. This would allow customers to avoid heavy classification or tagging so they can get started with recommendations straightaway.
Investments in Machine Learning at Acquia
By treating content as a data, we can solve multiple content challenges for our customers to make their own processes more efficient. Words that make up blogs or product descriptions can’t be understood by machines. Words must be aligned with numerical representations so that machines can understand their meaning, and marketers can leverage machine learning to help them with basic marketing tasks like classifying content or recommending content.
For Acquia, this means leveraging pre-trained word embeddings for natural language processing. We’ve built a team of data scientists and engineers focused specifically on machine learning, and are constantly looking at new ways to invest further. Our team is actively using machine learning techniques internally and incorporating them into our products to make our solutions even easier and faster for marketers to use.
There are many types of machine learning techniques out there, and that is why it’s key for marketers to ask questions and understand how companies incorporate machine learning into their products, and really, how these techniques will help solve their challenges. There are multiple types of machine learning techniques we use at Acquia to treat content as data to tackle marketing challenges and help solve customer problems.
Ordinary machine learning algorithms require labeled data in order to learn. However, the reality is that not all customers have readily labeled data to use, and yet they need techniques that can overcome this hurdle. Some of the machine learning techniques our team is working on at Acquia to address this includes transfer learning, few-shot learning, and human-in-the-loop leaning.
Transfer learning often involves applying learnings from a really large dataset to tasks with smaller datasets. Specifically in our case it means learning from pre-trained word embeddings (large dataset) that can be applied to a smaller datasets ( i.e. our customers’ content).
Few-shot learning often goes hand-in-hand with transfer learning; transfer learning is about learning from very rich representations of data, and it’s this very richness that makes it possible for few-shot learning to learn from just a few examples.
Human-in-the-Loop learning often takes the form of crowdsourced labeling of training data such as leveraging data from reviews or ratings. Since we have a strong UX team, we can use strategies that involve a human element in the data training process, allowing us to further increase the accuracy of learnings.
Learn more about these machine learning techniques and how Acquia uses content as data to inform our machine learning strategy.
We look forward to further enhancing our solutions with machine learning capabilities to help speed and automate the creation of omnichannel customer journeys. We’re extremely excited about the applications of machine learning and future possibilities.