Or… What have the Ro
mansbots ever done for us?
Machine learning is one of the most exciting new technologies to have emerged in recent years. It is a branch of Artificial Intelligence that gives computers the ability to learn without being explicitly programmed.
This article will explore some of the ways that machine learning can be applied to digital marketing.
Why should marketing care about machine learning?
Over the last couple of years, the world of digital marketing has been revolutionised by machine learning. This has been a profound change in the way that we use data to make decisions and has changed the way that we approach marketing.
Machine learning can be applied to online marketing to analyse customer behaviour and predict future patterns without the need for input from a human (and considerably faster than a human!) In this way, organisations can more easily develop strategies based on changes in consumer needs and tastes.
As a form of AI, machine learning can also help companies anticipate how customers might react to new products or services. Machine learning could help predict customer demographics and psychographic profiles, along with their likely purchasing habits. The company could then use this information to tailor their marketing strategies to certain customer groups.
In fact, Google uses machine learning in almost everything it does. From analytics to ads to voice recognition, it utilises the masses of data it gets to make informed decisions and analyses this on our behalf.
The great thing is, we can also use the same approaches to leverage data ourselves. This brings opportunities beyond what Google can offer us in its tools and also greater ability to tailor our efforts to any goal we choose.
Applications of Machine Learning in marketing
Here’s a brief overview of some key areas in which machine learning is benefiting marketing.
Machines are great at working with data that would be either tedious or incomprehensible to humans. By applying algorithms to large data sets, we can reveal all kinds of patterns that would otherwise go unnoticed by manually observing spreadsheets. This could include features such as:
- Correlations: These can reveal relationships within data you hadn’t considered.
- Trends: Beyond simple 2D and 3D plots, there can be hidden trends that are difficult to spot.
- Clustering: Given that most data is too complex to be visualised all at once, it can be almost impossible to see similarities between multiple, disparate variables.
- Outliers and anomalies: Again, this can be a very difficult thing to establish manually when the data is complex.
- “Big data”: While some data analysis can be done in a spreadsheet with clever use of formulae, when the data set gets above a certain size, this approach will stop being practical. A machine learning approach allows us to cope with extremely large data sets.
Automation and personalisation
By learning as it goes along (or “online” in machine learning speak), an algorithm can automatically adjust settings based on external events (think: user behaviour).
Applications could include:
- Suggested content on a website based on similar users (think: movie recommendations)
- Responding dynamically to how long a browsing session has been active (think: visitor attention span!)
- Advertising relevant products dynamically based on weather conditions without having to tag every product you sell (think: UK summer & umbrellas!)
This kind of behaviour and response system can be constantly updated without human intervention.
Optimisation and ROI
Want to know which of your many marketing spends gives the most bang for your buck? Given the right data, an algorithm can learn which streams give the best ROI and under what conditions.
The beauty of this is in the model of the data that machine learning can produce. Using this model, you can feed new data into it in any configuration you care to try and it will tell you the likely outcome based on past events.
This opens up the ability to forecast ROI for any combination of spend and circumstances you wish to choose. In addition to this, the more good quality data you put in, the better the forecasts will be, so predictions should improve over time.
Some of the greatest recent advances in the machine learning world have been in natural language processing (NLP). With the release of OpenAI’s GPT-3, it’s now entirely possible to include machine learning directly into the workflow of copywriters, SEOs and marketers – in fact, anyone who works with text can probably get some benefit from interacting with this kind of AI. (Having said this, I promise that the person writing this is human and not a machine – honest!)
To make the most of an AI like this involves taking time to understand how to interact with it – and, often, an experienced eye to edit it. Obviously an understanding of the subject you are working on is also hugely beneficial, but there’s an argument to say that the overall time-savings and subsequent results could be worth it.
Is Machine Learning right for my business?
This is a really important question and one that every marketing business will need to tackle at some stage.
While machine learning can bring a huge advantage when applied appropriately, it isn’t something you can just drop into an organisation and reap the benefits. It will be worth taking the time for careful consideration of the data you have to work with and what can be done with it.
Getting high-quality data in the right format is hugely important. But processing and cleaning data takes time, resources and a lot of know-how.
So the final decision comes down to:
- Do we have appropriate data?
- Do we have a clear use-case?
- Will it be worth the time and effort involved for our business?
My opinion? ML is the future of marketing
There is no denying that machine learning brings incredible capabilities to digital marketing. With big companies like Google, Facebook and Amazon all vying to dominate the machine learning arena, it has already become an essential part of how we think about strategy.
Smaller businesses, however, are still very much in the early stages of adoption. From a marketing and operations point of view, this means the field is very much open for gaining the advantage over competitors by streamlining and identifying opportunities.
While implementing machine learning solutions in-house might be possible for some companies, it is not something that most companies can focus on enough to make it worthwhile. This is where working with a company such as Hallam can help to integrate machine learning into current workflows. Our data-centric approach means that you get to concentrate on what you know best – your data – while we handle the processing technology.