Imagine when you are scrolling through Instagram many times a day and suddenly you see an ad. It doesn’t appear anything unusual however when the display locks your gaze once you realize that it is exactlythe same graphics you googled earlier. You simply wonder “how is that possible?”
Machine Learning (ML) is the school of computer science that enables computers to learn without programming them exclusively. The development has been happening since long and generally it has been discreet that we forgot noticing. Social media being the main instance.
As a natural human behavior, we prefer to see ads of new stuff that suits out taste and in a regular normal way. It’s annoying when ads interfere with our scrolling experience. One of the instances of machine learning enabled activity created in the past few years is about the things that we interact with all the time.
Here’s some subtle examples of ML that we have not even observed but not acknolwedged:
- Browsing History
Google follows you where ever you go, irrespective of the time and date you browsed.
The most characteristic feature of Google’s machine learning engine is that it picks up your browsing preferences to tailor your experience better. Though the idea of internet recording your entire browsing data is rather intimidating, however, Google opines that users expect the most optimized experience possible, and machine learning is the only way to go.
Giving recommendation from retailers of relevant products you have browsed or shopped in the past is one of the ways that Google implements to optimize your experience. Another example is YouTube videos, it is machine learning at work.
Our online behavior is the best approach for companies to get to know you better, and in return our online interactions have become all the better because of it.
As marketers, it’s imperative that you use these search preferences while creating better targeted ads. Search data can be one of the best tools in marketing. This data should not be ignored as you may figure out that your most interested and potential customers have been reading articles about your product or service, just waiting for you to reach out.
- Siri and Cortana
The progress Siri and Cortana have made in the past few years is unbelievable. ML is used to understand how to mimic human interactions.As they continuously learn and grow, the two apps will be capable of understanding the simple nuances of just about every language in the future.
As per a remarkable observation made by Forbes, marketers should adopt tactical approach in their search engines interactions since the introduction of Siri and Cortana. Brands need to work around their keyword strategies moreconversationally, considering how individuals communicate with their voice-activated friend. These programs continue to become “smarter” with each passing day and so should be marketers’ more than ever need of constant creativity in their solutions.
- Facebook and Beyond
Manual tagging of friends in your posted photos is story of the past since the introduction of facial recognition program. If you post a picture of an event posing with a group of ten people, Facebook is enabled with a program to scroll through your contacts, match them to the corresponding profile photo, and tag them in the photo while you still upload the photo. Facial recognition is an interestingML approach that social media is usingto enhance our experience as users.
Another interestinginteraction with social media: memes. Memes taking the internet by storm involves machine learning at its most basic form. With time ML has really become powerful and computers will become even more smarter as time goes on.
Modern Marketers must show ardent readiness to harness the power of social media and participate in these fun trends to both showcase what your brand does and get people talking.
- TV and Music
There are times when you find yourselves aimlessly scrolling the Netflix.Same as YouTube, Netflix also learnsyourlikes and recommends similar titles, and really knows you better than a lot of companies can claim.
If you like watching Comedy sitcom then Netflix would recommend similar sitcoms or even scientific documentaries that you might find interesting. Often, the suggestions are spot-on, and you are immersed in the Netflix binge-watching experience.
Besides movies, music apps also require a lot of machine learning to enhance your experience. If you like listening to your favorite soft romantic melodies in loop, then Saavn and Wynk might recommend songs similar to your choices. They might even go as far as creating a playlist for you that includes a medley of old classics and latest trending. These apps use ML algorithms to analyze your activity and music taste, curating more specific content.
Marketers should be aware of what Netflix, Saavn and Wynk are doing right. Dynamic content suggestions are all about building a “profile” of sorts for the customer.
Different versions of a site change based on who is viewing your site at any given time which is a great way to keep your customer at the focal point in all your decision making. Getting to know your customer is the absolute goal you must intend at for which they will be thankful in the long term.
So, in a nervous moment before you decide to erase your footprints from everywhere in the internet and wipe your hard-drive clean, here’s what you should be pondering at firstly. The amazing features that ML as a technology has given which continues to grow as time progresses.
It’s obvious that there will be more precise recommendations online in the future with lesser inaccuracies and better immersive experience. We definitely will see computers getting better at talking like humans, able to communicate seamlessly with us.
Even the most complex and insanely sophisticated technologies like self-driving cars are powered by machine learning, so as we move into the future, the experience is only going to get more and more exciting.