What is Machine Learning?
Machine Learning (ML) is a branch of AI that is concerned with the use of machines, or algorithms, to iteratively make better decisions and perform tasks progressively better without the involvement of humans.
It does this by taking inputs and filtering them through a neural network (an artificial collection of nodes that act like a real brain), testing out strategies for success, receiving feedback in the form of results from this testing, and using this feedback to ‘learn’ how to do things better.
A (very) brief history
This process means that ML gains the ability to perform tasks by adjusting to information it receives, rather than being programmed to perform those tasks.
The first person to coin the term Machine Learning was Arthur Samuel, who created a machine that could play checkers in the 1950s. Back then, ML was largely indistinguishable from AI, but through the 1960s and ‘70s, the two fields started to branch off from each other, until, by the 1980s and ‘90s, they had become very separate disciplines.
AI was largely concerned with solving logical problems, whereas ML, with its use of algorithms and neural networks, had become a more practical field of study, with the possibility of real-world applications becoming more and more apparent.
Today, ML is pervasive throughout the technology sector, with everything from driverless cars to fraud detection making extensive use of machines’ ability to learn from multiple data points and make complex decisions on a scale that is beyond the capabilities of the human brain.
This is especially true of eCommerce, where ML is driving innovation and providing online brands with personalization capabilities that were impossible even a few years ago.
What is Advanced Machine Learning?
The ability of an artificial network to learn was a huge innovation in its time, but traditional ML models require a lot of setup and oversight by human teams to produce usable results.
A reinforcement model of machine learning, where the ML algorithm is fed a data set, makes decisions, and then learns how to make better decisions by the feedback it receives (rather than being guided by human teams) reduces the need for oversight.
This self-correcting model of ML means that it is a scalable technology, allowing marketers to take a hands-off approach, while at the same time providing valuable insights into what personalization strategies work best.
Advanced ML has several benefits for marketers:
- More accurate decisions faster – The reduced set-up and oversight required means you can input more data faster, resulting in accurate decisions in a fraction of the time. Advanced ML reduces time to value.
- Greater control over merchandising – While the decisions that ML makes will greatly improve your marketing and personalization, you’ll still want to step in and make business decisions that are beyond the MLs remit. A sophisticated ML solution will allow you to slot in your own merchandising recommendations alongside algorithmic choices according to business needs.
- Visibility into ML decisions – In contrast to deep learning models of ML, reinforcement learning gives you much greater transparency into how ML decisions are made. These insights let non-technical teams (marketers not coders) understand and work with the ML decisioning engine, tweaking and overriding where necessary and, once again, learning along the way.
- Scalability – Genuine 1-to-1 personalization requires weighing up multiple variables (your customers’ age, gender, location, past behavior, etc). Doing this at scale is beyond the capacity of even the most talented and well-resourced human marketing teams. Advanced ML systems can evaluate and action huge data sets derived from hundreds of thousands of customer interactions (or more), meaning you can deliver individualized experiences to every customer.
- Application across the customer journey – As well as being able to scale upward, advanced ML is generalizable across all aspects of the customer journey. It doesn’t just provide you with product recommendations, you can also apply smart ML to messaging, search, social proof, re-engagement, and more.
- Greater potential for integration – As personalization technology gets more advanced, so does the number of tools and solutions proliferate. This can be a problem when different teams have access to different pieces of software and multiple logins are required. Advanced ML technology is capable of providing an integrated solution that encompasses everything from analytics and reporting to merchandising. Having all these capabilities under one roof democratizes access to data and empowers your teams to make informed joined-up decisions.
Why does Advanced ML matter to 1-1 personalization?
Personalization makes a big difference to the customer experience and, as a consequence, to conversions.
With an advanced ML solution, you can go beyond customer segmentation, and provide 1-to-1 personalized experiences to every customer. Segmentation still matters: you still need to know the different groups your customers fall into and what messaging these groups respond best to.
But to be able to create genuinely individualized experiences, you need to be able to combine general insights into how customer segments behave with data points on individual customers.
Advanced ML solutions are clever enough to do this, producing an experience that visitors will connect with and feel valued by. To create digital experiences that make each and every site visitor feel like your brand ‘gets them’ you need to:
Make unique decisions about every customer – Pool all your customer data to craft a bespoke experience. Understand what your customers’ past interactions are (if they’ve added items to cart before abandoning, or purchased from a certain product line, etc) and what messaging customers like them tend to respond well to. This will give you a much greater chance of maximizing revenue per session and lifetime value.
Create a personalized experience across the whole customer journey – The integration capabilities and read-across advanced ML means you can join the dots between touchpoints and interactions, providing a consistent journey from landing to checkout.
Go deeper into Advanced Machine Learning
If you want to find out more about ML, its use cases, and its benefits, take a look at our downloadable guide for more information.