Predictive Analytics — To Paint The Holistic Customer Picture

Originally Published On TechRevolve.

Predictive analytics helps businesses be wary of customer’s future issues and fix them at the earliest.

Life of a storyteller within the tech industry exists more around data miningand data analysis. With all these being taken care of in a day or a week (based on the size of the company), the storytelling kicks off from here.

Having heard several TED talks about human behavior and all those insights we were exposed to, it essentially boils down to a single yet solid theory, which is…

• Why storytelling matters?
• How can data do it well?

Watch Joseph Pine taking us through the functioning style of today’s consumer minds.

Few such speeches from data experts and the day-to-day storytellers had changed our perception overall. Ideas and belief we had upon data.

Now, to us,

“Data are not just the numbers but pages from each of our online biography.”

Also, it’s not only the work of a data scientist to understand humans/customers. It is the job of a marketer too. The more a marketer indulges oneself into dissecting data sets, the more h/she learns human psychology.

That’s quite deep.

Essentially, with enough amount of data we deal with on a daily basis, predicting the next set of user behavior becomes more natural. Understanding the purpose of specific human behaviors shall help us identify each of our quests.

“Having someone along, to deal with one’s quest is what every human wish they had.”

The quest is always unknown.

Hence, here we’re trying to understand what users might need for a better future and a better life.

With unique data sets, we can help each other fulfil our purposes. Visualizing the trail our users leave online, we can come up with a potential solution.

For keyword/jargon maniacs,

• Do predictive analysis (just use data)
• Circle out the problems customers might face in the future
• Design the wireframe of your solution/product
• Run it with the existing customers (do it with no fuss created)
• Establish 95% statistical significance (can’t bid on the rest 5%)
• Go global

2 Steps To Doing Sensible Predictive Marketing Analysis

In 2 points, performing predictive analysis means,

• Decoding user behavior
• Optimizing features/products/campaigns

Decoding User Behaviour

Understanding user behaviour will help build a better product

We no more live in a world where just the primary user behavior analyticsseems enough. Right from identifying user’s demography to examining one’s clicks and focus region, everything has grown old.

Now it’s time we derive insights from data and with the help of machine learning (ML), deliver solutions before the problems may arise.

Which has now grown into deep personalization techniques like,

• Advanced segmentation
• Hyper-personalization
• Giving omnichannel experiences
• Cross-device betting
• Predictive segmentation

Will be more relatable if shown an example. Let’s take a look at Adobe and its personalization knacks.

Usually, a new user on the Adobe website will see a homepage like this,

Adobe’s webpage before playing with the inside features

And after watching enough of videos, scrolling through multiple interest based features, blogs, and tutorials, something highly personalized like this shows up.

This is how user behavior analytics work.

By predicting the user’s interest like how one browses across the website, the features one opts for, videos they spend more time on, products can visualizespecific user map. And plot down each of their niche interest addressing their online activity.

It’s called web analytics intelligence just in case if you think it’s creepy AF!

Optimizing Features/Products/Campaigns

Make changes with the derived insights/data

Based on the insights acquired from real-time tools, change the poorly performing content. And if the data describes more about product’s dysfunctionality or feature wise blunder, go for internal agile testing and arrive at some real fix.

Further with the advanced tools like RapidMiner, GraphLab, IBM predictive analytics and similar, extract precise user behavioral data and tweak the existing model.

It applies to enterprise level services to B2C products.

Many times, conducting external user analysis for BETA products shall work wonders. For this to happen, pick out the most reliable customers from your user base and ask them to try out your product.

Sending out smaller tweaks to the free trial users may work too.

Investment Tip:

Report from recent Gartner’s survey says, by 2018, 50% consumer product investments will be relocated to customer experience innovations.

Hey,

Users are not going to settle down for copied innovations or boring products anymore. To fall off such saturated radar, we must build something straightforward, coupled with problem-solving pills.

Always build tools that may solve user’s complex issues, instead of making product itself as one heck of a thing to deal with.

GOOD LUCK.

Originally Published On TechRevolve.