Machine learning seems to be at the top of a lot of people’s minds these days, but most of us only have a faint idea of what it is and how it can be applied to the world around us.
The truth is that it’s one of the biggest leaps in computing which the world has ever seen, and the newer segments of it are getting downright impressive.
Whatever your field of interest, it’s pretty likely that machine learning is going to be affecting it soon.
Defining Machine Learning
Machine learning is a subsegment of artificial intelligence.
Simply defined: machine learning automates analytical processes and is able to improve upon these processes through the continued use of their faculties.
In other words, the idea behind machine learning is to build an analytical “brain” which can learn from it’s pasts.
Essentially, a self-improving algorithm.
There are two major types of machine learning which are currently in usage.
Supervised Machine Learning
Supervised machine learning works best when someone is trying to make sure that their output data matches their predictions and is generally overseen by a “teacher” who guides the program as it learns to formulate the correct data.
This makes it useful for two main purposes: classification and regression.
Most of us are familiar with classification techniques, even if we weren’t aware they’re already in use. One of the most common applications is for e-mails, detecting spam and other unwanted data and throwing it in a box that most of us rarely bother to look at.
This works by enabling the computer to separate images, text, or other data into distinct categories.
In addition to simple quality-of-life programming like e-mails, it’s also used in the medical field, speech recognition, and other vital pieces of infrastructure. While the output still needs to be looked over in vital cases, it can greatly simplify things by learning to recognize specific patterns.
Regression analysis, on the other hand, draws conclusions from a continuous set of data that includes real numbers.
This allows for a continuous output of information based on the patterns which have already been discovered. Essentially this form of analysis can be used to help figure out patterns and predict them in the future.
It’s often used in trading, to discover temperature patterns, or in algorithmic trading. This form is less familiar to most people.
Unsupervised Machine Learning
This form of machine learning occurs without the use of a “teacher”, essentially allowing a machine to build a system for understanding data from the ground up.
Clustering is the most common use of this type of learning, allowing for the machine in question to form sets of data which can find hidden patterns.
Some of the common applications include marketing research, genetic research, and even object recognition. By allowing the machine to largely teach itself, hidden patterns can often be brought up which makes it pretty much essential for exploration of large sets of data.
Applications for Machine Learning in Marketing
One of the most exciting areas where machine learning is shining is for those of us in the world of advertising and marketing.
Already there are a ton of online services being offered to help marketers increase their conversion rates and laser target their campaigns through the use of this technology.
Marketing waste is one of the biggest concerns of marketers everywhere. Each time you have a paid ad put in front of someone who clicks but doesn’t convert you’re going to be losing money, and that will reduce your overall return on investment.
There’s also another simple fact: without machine learning you need exponentially more analysts, and analysts are expensive.
So, with a wise choice of program you can reduce your costs across the board. With employment and advertising costs going down, you’ll be able to make more profit over time.
It can also be used to cut communication costs, by updating customers with information on a regular basis rather than having to hire someone to handle social media posts and e-mail communications on a per post basis.
Real Time Updating
Machine learning allows for actual real time updating of your advertising campaigns. While the moniker of “real time” has been frequently applied in the past, with the proper machine learning algorithm backing you constant minor changes and tweaks can be made continuously to make sure that you’re bringing in customers.
This is especially important for those who want to run campaigns which engage their customers as they’re happening.
It’s also another way to prevent marketing waste when you’re running paid ads on a network which can be modified at will. Some platforms allow for tight audience targeting, but if your variables aren’t just right you may see a lower than expected conversion rate for the lifetime of a campaign.
On the other hand, if it gets adjusted before it becomes an issue then you’ll be good to go.
Data Mining to Discover New Customers
Say you have a product which was targeted towards young males, but ends up with a significant amount of middle aged females purchasing it.
With old-school analytics, you’d probably notice sooner or later. With a machine learning algorithm you might just end up looking at the output and realizing there was an entire audience segment that you weren’t expecting buying up your product.
That can lead to a major change in profit distribution and keep you ahead of the curve if you want to retarget your advertisements to be inclusive of your new market segment.
Data mining can often show segments that simply aren’t readily apparent as well. Finding new audiences and customers is an enormous advantage, and something that most marketers can only dream of being able to find as the new market emerges.
If there’s one thing that marketers love, it’s automation. Whether it’s simple programs which make the occasional social media posts and keep their content marketing schedule updated exactly on time or just being able to generate a list of paid advertising campaigns with just the keyword input for AdWords.
Machine learning can not only automate processes but also use the gathered data to make sure that things are released at exactly the right time.
Some hours of the day are going to inevitably be better than others, and keeping track of all of that data and manually altering it is time consuming. Analysing data and making modifications in a prudent matter can take up hours and hours of each and every day.
This is another form of reducing marketing waste, but it’s also one of the most looked forward to advantages offered by these algorithms.
But is It a Miracle?
Machine learning is undoubtedly advantageous, but it’s not exactly the miracle that some people claim it to be.
There are some inherent limitations to using it which you should be aware of before you commit to spending your money on someone’s offer:
- You need a large amount of input data to effectively utilize any form of machine learning. While many people have access to big data who offer the services, the algorithms are only as useful as the data which is being put into them. GIGO still rules the day. Garbage in, garbage out.
- Running a machine learning program still takes someone with experience in analysis. At this point you still need to utilize someone who really knows what they’re doing in order to get the most out of machine learning programs, it’s not really something you can just pick up without a whole lot of work on your part.
- Even the best program will take a ton of time to run in batches for the initial data testing. Machine learning programs have to be “trained” through running through data, this usually means at least initial supervision and they’re still labor intensive initially.
There’s a great anecdotal story which highlights many of the current problems with machine learning:
The United States Army wanted to develop a program which could find camouflaged tanks in the field. The input data was a series of fifty pictures of woodlands without tanks, and fifty pictures which contained heavily camouflaged tanks.
The initial testing was 100% effective at identifying the camouflaged tanks and the program was forwarded to the higher ups in the chain of command.
The program was promptly returned with notes that it was no better than random chance.
Upon further inspection of the data, another pattern was apparent.
The fifty pictures of the woodlands alone were taken in sunny weather, while the fifty with tanks in them were taken on foggy days.
The program developed had learned to tell the difference between sunny and foggy days.
In that case, the input data was good… just not for what was wanted.
When using machine learning the value of the input data is always going to determine the output. Even more than that, you’ll also need a sufficient amount of quality data, and with unsupervised machine learning you’ll also need to make sure that you’re actually getting the conclusions you’re trying to draw.
It’s machine learning after all, and not full blown artificial intelligence.
So, Is It Worth It?
At the end of the day, with all of the limitations which are in place and the initial financial and time costs of machine learning you’re probably wondering if it’s worth it.
Indeed, to hear some people speak there’s an almost mystical aura surrounding machine learning as being so far beyond human capacity that it’s pretty much inevitable it’ll form a complete takeover.
Some of the predictions even point to it as being able to eventually replace all creatives in general.
I’m not entirely sure that’s the case, but it is a powerful tool in the right hands. And, love it or hate it, it’s been released from Pandora’s box and companies with a lot of manpower and money are using it extensively in order to bring great results for either themselves or their clients.
For entrepreneurs with less manpower it can save a lot of time, particularly if they hire someone with access to the required data and the skills to use it properly.
It’s also useful for larger enterprises which can afford to have staff dedicated to making sure their algorithms are being run properly.
That said: you’re probably not going to end up entirely in the red just because you decided not to work with machine learning. The old fashioned methods still work pretty well, and even with machine learning there will still be some trial and error as things get dialed in.
Overall, however, utilizing machine learning can lead to higher profits, less errors, and less market wastage for most businesses.
It’s probably not worth spending the time learning entirely from scratch for a single person operation which doesn’t have the background in computer science, analytics, and data management required to make the most of these techniques.
Machine learning is a revolutionary technology but before you decide to invest in it you need to be aware of both it’s advantages and limitations. While it stands poised to completely take over marketing alongside big data, it’s not quite where it needs to be just yet to be a “plug-and-play” solution to the wide variety of problems which face advertisers and marketers.
On the other hand, for those who can afford it, it’s undoubtedly one of the biggest game changers out there. Just make sure that you pair with a company or individual who can help to integrate that power relatively seamlessly with the rest of your operation and knows how to press the advantages and limit the disadvantages as time goes on.
If you think that your projects could take advantage of machine learning in a big way, then it’s definitely worth a shot. The one sure thing is that with a skilled user on your side you’re definitely not going to end up any worse off, and in the best case scenario the time, money, and manpower saved is just what you need to get a leg up on the competition.