Did you know that companies can predict your next purchase, what you will likely buy, and whether or not you’ll like something, all through the use of machine learning? While you may not realize it, ML has already begun to influence your daily life in all sorts of ways – from TV commercials to online ads to the news you read about – and it’s only going to become more prevalent in our everyday lives in the future. There’s no doubt that in the future of marketing, machine learning will be an important asset to have. Whether it’s using an algorithm to write sales copy or analysing the success of your ad campaigns and refining them accordingly, machine learning can truly give you an edge in the increasingly competitive world of online marketing. This article explains how to get started with machine learning today and what it means for your business tomorrow.
What is machine learning?
Machine learning is a field of computer science that gives computers the ability to learn without being explicitly programmed. In other words, it allows them to grow smarter by experience and exposure. Machine learning algorithms take advantage of data, enabling computers to automatically learn from it as well as adjust their behaviour and performance in response to new information. And unlike traditional computer programs where every piece of information has to be written into code, machine learning algorithms can handle ambiguity and make sense of incomplete or incorrect data.
Machine learning algorithms are commonly used in data analytics to predict future trends based on historic information. It has been applied to fields as diverse as genomics, business, education, and search engines. Given a set of data, these algorithms look for patterns that allow them to identify past and future trends.
How can it improve your marketing strategies?
You might think that you don’t need machine learning to improve your marketing, but think again. For instance, you can use machine learning to identify patterns in user behaviour so that you can make relevant recommendations and increase customer retention. This feature is often present in platforms like Amazon and Netflix which help users discover new products based on past behaviours.
It’s algorithm then takes into account factors such as previous purchases and demographic information in order to calculate an individual’s probability of making another purchase. This allows brands to better predict who will buy their products, which can make marketing more effective overall.
What are some examples of it being used today?
There are many examples of machine learning being used today to increase and improve our digital marketing results. In email marketing, one example is spam detection. Spam will make up about 80% of all your email, but you’re not going to manually inspect every single email that comes in. However, a machine learning algorithm can detect spam with higher accuracy than any human ever could—making sure your legitimate emails aren’t tagged as spam accidentally and increasing deliverability rates.
Another example of machine learning in digital marketing is personalization. If a user visited your website multiple times, had very high bounce rates, and interacted with a lot of pages but didn’t convert—machine learning algorithms can interpret these signals to identify them as a cold lead who should be converted into a hot lead through personalized messaging. They can also use machine learning to optimize email marketing templates and landing pages to increase conversions.
These are just two examples of machine learning in digital marketing; if you want to learn more about it, check out this “17 Machine Learning Examples Your Industry Needs to Know Now” post.
Where should you begin?
Where do you start? It starts with finding a business case, identifying your goal and determining what data you’ll need to achieve that goal. If you’re still in research mode, study your competitors (big and small) to determine whether they are using machine learning to achieve their goals. That can help guide your research path and make sure you get on board before everyone else does.
Once you have identified your business case, think about what you hope to achieve. Do you want to optimize a given process? If so, can your end goal be achieved in some other way that doesn’t require machine learning? Do you want to predict an outcome based on historical data? Is there an important metric that’s hard to measure with traditional methods? Identify how machine learning could help.
If you need help determining whether machine learning could help achieve your goals, contact InnResearch experts. We can help determine whether it’s worth pursuing and identify potential data sources, including hidden ones such as customer reviews or even offline data.