Can machines be creative? Can they empathise?
Last night, I was sober enough to whip my phone out and type things.
Obviously, littered a lot of spelling errors.
Here is what I typed,
But Google showed me what I wanted to see,
Good lord! That’s definitely the machine learning! Let’s begin with…
Today’s marketing and analytics ground
First of all, let’s stop calling it as web analytics.
Web analytics is generally about the metrics that webmasters care about — website load time, time on the page per view and so on.
But marketers need marketing analytics.
We look for no. of. — sign ups, downloads, leads, sales and the total traffic.
Our focus mainly fixates on social media, emails, and even on offline campaigns, that are generally customer centric.
And without an analytics tool, this data cannot be obtained. Perhaps, most marketers nowadays are data-driven.
And ML for marketing almost feels like success for eternity!
But before getting into all that unexplainable bliss, know what silicon valley thinks marketers are!
They call us ‘Mediocre Marketers’, without knowing the naked truth that marketers nowadays are soothsayers and are bloody DATA-DRIVEN. Which means, that marketers are good at managing and employing data.
We also cannot deny the reality that our digital world has shifted fromGUI (Graphical User Interface) to CUI (Conversational User Interface) where web-centric marketing is out of date and everything now ispeople centric.
Thus, to stay on track and be able to catch up with other leading marketers, understanding how users interact with your service is more than just important.
With the rise in the number of distribution channels and complex user interactions (which definitely is not the same way how it was a decade ago), the production of SaaS tools is proliferating wildly to clear this muck.
This is how agitated our marketing stream looks like today!
How would we tackle this situation now? By being data-driven (not just data informed)
Considering the perspective of how a startup works,
It is essential to use several SaaS tools to employ one’s immediate ideas and to figure out the pattern of how users are using their service.
This is where data plays a pivotal role in most cases.
More the volume of the data, the more tools are needed to understand user patterns to support production activities with the understanding gained from data sets.
So far we have known and been following these 3 steps,
But this cannot bring us the cohesive success at all.
There is more to do with data before setting on to the action pace.
The actual and semantic application of data looks something like this,
As we know that the amount of SaaS tools that we use to acquire data from is humongous, hence comes the necessity to identifying the most effective tools.
Though we have come crossing decades since the evolution of the digital world, the utility of data didn’t see much difference yet.
Also, the procedures we use to extract, merge and cluster the data remains to be the same. I would consider saying that this isn’t the crucial factor at all.
What is it,
“To predict patterns from valuable insightsand make right decisions”
Take a dive into this TED Talks by Sebastian Wernicke, who was in the data specialists team of Netflix, a time when they included House Of Cards to the list and saw the speedy rise in the number of subscribers.
“House Of Cards skyrocketed Netflix’s subscribers from 33 million to 98.75 million”
At Netflix, data scientists used data to gauge their user actions as well as user journeys.
In specific, they nabbed the data about — the type of videos they played, the places they paused, the videos they resumed and more.
Collectively, by looking at how users used their service as a whole, they identified a way to build ultimate converting user pathways and thereby brought in varieties of likable content.
With Machine Learning, businesses stay within the Habitable Zone
When brilliant datasets meet mind blowing algorithms, that’s when the magic happens!
You being a brand, compiling a good amount of user data, at some stage would want to implement machine learning into your service.
This is how the Habitable zone looks like,
Ways Machine Learning can help marketers by,
- Giving clear vision over our day to day audience behavior.
- Joining the dots between what users say they do and what they do in reality.
- Understanding the marketing techniques, data, and users at the deeper level.
- Analyzing user data for hyper/personalized targeting.
- Deriving patterns from the consolidated user behavior.
- Taking strategic actions.
- Converting data into something actionable.
What will happen to customers if ML takes the lead?
CX (customer experience) is the most important factor that every business must focus on. A little deviation from it can lead to monstrous failure. Be it, product wise, user experience wise or in anyway.
“Forgetting CX will lead to catastrophes you cause for yourself”
Don’t we know that customer experience is the one that drives sales? Of course, we do.
We also know that our customers are no longer playing the blind game.
That is, before they choose or switch from any platform/product, they do tonne of research and then discover the suitable ones for them.
“Customers always go with the product that they feel are solely made for them”
Thus being the marketer, we need to care more about how to establish, sustain and expand relationships with our customers.
With the help of ML, understanding and delivering more appropriate content to our customers would be much easier and constructive.
“ML can also keep up with our tech fad, since it’s all about learning”
Ways Machine Learning can help customers by,
- Recommending relevant options based on users’ previous activities which absolutely can save a lot of users time.
- Customizing user experience, ML can suggest things that they never thought they wanted.
- Gathering data from customer’s past behavior, it can deliver content pertinent to them as individuals.
- Learning from other mediums, it can open up information that we wouldn’t have known by any other way.
Hey, if you are worrying of your gig because of all the things I said,
Better don’t and think of just this,
“Can machines be creative? Can they empathize?”
They can’t but we can. And we can teach them through ML…
Thus, for machines to replace humans completely, they need to haveinnate human characteristics, which is critical.
Thereby, we are in the Habitable zone too! Safe and sound. 🙂