Posts by jaymarwaha

Learn how Lenovo & others make Adobe & enterprise data actionable in Hadoop

April 1st, 2017 Posted by Analytics 0 thoughts on “Learn how Lenovo & others make Adobe & enterprise data actionable in Hadoop”

What do you need to create actionable customer profiles? Learn how Lenovo and others are applying predictive behavioural analytics to unleash a goldmine of information to target customers effectively and gain conversions. To understand your audience, it’s vital to have a complete view of the entire customer journey. However, when an organisation fails to integrate all its data, it’s robbing itself of the knowledge needed to achieve this and be successful digital marketers.

In this session:

  • Find out how integrating enterprise and clickstream data in Hadoop can enhance Adobe Marketing Cloud
  • Hear stories from leading experts about how they activated this information and started seeing real results

 

Watch now!

Hey, Media: Do you know your content consumers? Try Clustering Your Audience

January 30th, 2017 Posted by Analytics 0 thoughts on “Hey, Media: Do you know your content consumers? Try Clustering Your Audience”

Fake news always seems to be in the news these days.

Media companies are working themselves into a frenzy wondering why so many disaffected readers have turned towards click-baity headlines and conspiracy- monging websites, instead of opting for their own tried and true content.

So, how can a website hold onto an increasingly divided audience when there are so many other – more ideologically tailored — options to choose from?

I think the answer is simple. Mainstream news outlets have done enough solid reporting throughout the presidential campaign to earn the trust of the broader American electorate. But the content needs to be placed in front of the right pair of eyes.

If The Washington Post wants more Trump supporters to visit its website, it should place its vigorous reporting on the failings of Hillary Clinton as a candidate front and center when they visit the website.

If it wants liberals to remain loyal readers, it should send them constant updates on its investigations into Trump’s nonprofit organization and controversial past.

By clustering their audience in a clever way, media companies can hold onto their readership – and even grow it. Even in these contentious times.

And media consumers are rarely uni-dimensional. Once they’re in, they will move beyond the content that drew them in. They will check out other verticals. (Cat videos, perhaps). And eventually, they will warm up to the stories that contradict their world view.

It’s more important than ever for media companies to place an emphasis on targeting readers with the right content.

This should begin with the use of sophisticated tools like behavioral analytics, which allows websites to cluster visitors based on how they act, what they read, how much time they spend on each story, do they read the entire story or skip to the end, do they watch the embedded video, do they watch the entire advertisement or skip etc.  rather than the demographic category they belong to. Knowing these behaviors and more will help companies in the business of content to personalize and monetize their content by serving them advertisements that are relevant to their behavior.

Netflix did this brilliantly, and guess what happened? They are no longer remembered for their “Be Kind, Rewind” days. They rose overnight to become one of the preeminent media companies of our day. Youtube gives you video recommendations without you even knowing that they are recommendation.

It’s time for newspapers and other traditional media companies to shed their old ways. Once we get people on both sides of the political divide to trust real news, then we will start having constructive debates over the future of our country.

IoT and Behavioral Analytics: A Perfect Marriage of Big Data

January 18th, 2017 Posted by Analytics 0 thoughts on “IoT and Behavioral Analytics: A Perfect Marriage of Big Data”

Big data is about to get even bigger.

As the Internet of Things (IoT) grows – connecting everything from our cars to our FitBits and inventory pallets to coordinated networks – so does the need for sophisticated data analytics processes like behavioral analytics.

First, it is important to note that the Internet of Things places the onus on dynamic analysis. What do we like about self-driving car? They process information instantaneously to produce the most efficient, and safe, outcome possible for the rider.

The days of gathering data to inform business decisions to be made in the next quarter are over. Companies now need a direct input-analysis-output vector to satisfy customers or make strategic decisions. And on the other hand, customers also expect quick results. The days of waiting for customer’s next visit to the site are over – companies need to capture their attention and sell a product during their first digital touch.

Additionally, machines are better at predicting not only large-scale outcomes – like the state of the traffic on your local interstate – but also individual human behavior.

That’s what a team of MIT professors proved last year in an experiment that compared how a computer system fared in creating predictive algorithms for unfamiliar dataset.

The computer finished ahead of 615 human teams out of 906 – and worked exponentially faster. It even produced better results in predicting human behavioral outcomes, like dropout rates, by selecting more relevant data than its human competitors.

This is all good news, because not only will IoT make behavioral analytics processes stronger by increasing the data pool by several orders of magnitude. It will also make it more valuable.

Take your FitBit. Imagine, after the company has gathered ten years of data on millions of users, what a sophisticated algorithm will be able to infer from your heart rate, monitored hour after hour, day after day.

FitBit will have enough information to create advanced counterpart identification models to not only diagnose users’ health problems – but also estimate what they are at risk for.

Tesla Motors has over 1 billion miles of customer driving behavior data so they can design a better car and feed the data into a smarter autonomous car.

That’s the beauty of behavioral analytics, synced to IoT. It should make your heart race with excitement.

How Trump Beat the Pollsters, and What You Can Learn From It

December 21st, 2016 Posted by Analytics 0 thoughts on “How Trump Beat the Pollsters, and What You Can Learn From It”

After Donald Trump was elected president in a historic upset last month, many pollsters had to eat their words.

In fact, one prominent expert, Princeton professor Sam Wang, went on live TV to follow through on a pledge he had made pre-election: that he would eat a bug if Trump won.

“After all I was wrong. A lot of people were wrong,” said Wang, before munching on a cricket.

Bets aside, the election results were a jarring reminder of the limitations of polling as a science.

Polling is a small numbers game. In order to predict the outcome of an election, pollsters divide up voters into demographic categories — age, geographic location, ethnicity, socioeconomic status, and so on.

Based on responses from a small subset of people in each group, they infer what the population at large is going to decide.

In the era of big data, that sounds a little backwards.

If your company is basing its online sales strategies on similar calculations, it’s got it all wrong.

The best way to determine the outcome of a consumer’s visit on a website is to watch what he or she does, and map that onto past behaviors observed on the site. The more data you have on prior customer behavior, the more accurate the result will be.

This type of demographics-agnostic calculus is what we call behavioral analytics. Try it, and you’ll never have to eat a bug on live TV.

This is Going to be “Yuge”

December 15th, 2016 Posted by Analytics 0 thoughts on “This is Going to be “Yuge””

Behavioral analytics is great, let me tell you. I am firm believer in behavioral analytics. This is going to be Yuge!

If you want to make your business great again, you gotta know who your customer is. It’s a huge problem when you don’t use behavioral analytics to know your customers. We need to take our customer back to being great again. It’s gonna be massive.

Your customers are good guys. But when they’re shopping online, they want good deals. They want you to give them good deals.

Now, I know what customers want. Trust me, I do. The reason why I know what customers want is that I get them.

I get them because when they behave in a certain way on your site, I’ll know they’re not alone. It’s a movement. And I know if you take your time you’re one type of customer, and if you immediately click on a certain type of product you’re another type of customer.

And once I know what type of customer you are, I’ll know what you want. Because of what the other customers before you have wanted.

Now, this is not about identity. Black, white, young, old, rich, poor. When you go onto that website and you know what you want, you’re going to want the same deals, no matter who you are.

It’s so easy. For me, it is. It’s so simple, once you know that you gotta target your deals to your consumers based on their behavior.

It’s what they want, that’s what matters. We are all winners and we will make you great again. Knowing what they want will make you a winner. The best part is we’ll even tell you who your “rotten” visitors are and believe me there are many. It’s a ‘uge problem for companies.

And I’ve got so many wins already, I want you to win too.

How do you win? By knowing who you’re talking to. By segmenting. And trust me, behavioral analytics is the way to go if you want to reach people.

Now I’m a businessman, you’re a businessman too. You gotta be successful. That’s why you gotta use behavioral analytics. Let me tell you.

F*** Black Friday & Cyber Monday

November 24th, 2016 Posted by Analytics 0 thoughts on “F*** Black Friday & Cyber Monday”

While retailers lick their chops over the hordes they will attract this Black Friday, I have a PSA to make.

Scrap Black Friday. No, I mean it. Burn it like that first turkey you made 20 years ago that your family will never let you live down.

Just throw it all away, like the dull green bean casserole that your cousin insisted on bringing this year, just like he has every year prior.

Heck, if I were you I would even do away with cyber Monday — and treat it like the cranberry jelly that really adds nothing to the turkey-and-carbs fest, if we’re being honest.

What I mean by all this is that it doesn’t make sense to pick out one day each year to draw in customers with huge deals.

If you have the right data analytics program to monitor what customers are doing every time they visit your website, you can create a Black Friday-like experience all year long.

You can increase your conversion rates both online and in-stores, 365 days a year, 24 hours a day.

SYNTASA’s Customer360 Data App allows companies to tailor how their website responds to each incoming user, based on their prior behavior on the website.

That gives you the ability to influence the outcome of each visitation. The algorithmic nature of SYNTASA’s Data Apps is very powerful will automatically recommend the next best action that needs to be taken.

You won’t even need to create gigantic sales to improve your retention rate and overall sales results.

So this year, scrap Black Friday. And instead of stuffing your data into static analytics programs, start using dynamic behavioral analytics tools to make the best of each customer visit.

If you would like to find out how you can make every day of the year as Black Friday using our Customer360 Data App you can contact me at jay.marwaha@syntasa.com

How SYNTASA Came to Be

November 14th, 2016 Posted by Analytics 0 thoughts on “How SYNTASA Came to Be”

The story of SYNTASA begins with the worst terrorist attack in U.S. history.

On September 11, 2001, I was working in New York as an executive at American Express. I was only a block away from the World Trade Center when the towers came crashing down. I witnessed the tragedy of 9/11 unfold in front of my eyes.

The experience shook me deeply. But, it also motivated me to work in the defense sector, to help improve the country’s defenses and prevent anything like this from ever happening again.

In 2013, I moved from New York to Washington D.C. to found ABSc, a company that would focus on providing security services to the government. I landed a project with a federal agency to help their cyber infrastructure.

I started out recruiting a couple of smart, patriotic minds who had already built a name for themselves within the federal government for their advanced analytics work. We soon grew to a staff of several hundreds of people.

Because of budget cuts and drawing down from the war starting in 2009, our company saw ups and downs due to a decline in demand. But still, our management team was strong enough to quickly build the company back up again.

Some of our staff has been around since the company was run out of a small closet of an office. Now, we are a well-known partner of the federal agencies.

Still, my entrepreneurial bug was not going away. So, I started SYNTASA about 4 years ago to build a product to perform Predictive Behavioral Analytics for large commercial enterprises.

SYNTASA’s Predictive Behavioral Analytics software provides enterprises insights into their customer behavior, to help improve business outcomes.

The software analyzes, in real time, the behavior of users. This allows for the creation of dynamic websites that will change according to the customer’s needs, and improves the customer experience.

SYNTASA Predictive Behavioral Analytics is at the forefront of the technological and data revolution, adapting the very latest in machine learning technology to help enterprises identify actions and outcomes. In some cases the software can be used to identify bad guys who could potentially harm our nation.

There are a lot of problems in the world that I’d like to work hard to solve in order to make the world a better place. I also realize that there are many very smart and successful people who can also solve these problems. I feel fortunate that I was given an opportunity to serve our nation in a small way.

The Future of Data Analytics: Predictive Behavioral Analytics™

November 10th, 2016 Posted by Analytics 0 thoughts on “The Future of Data Analytics: Predictive Behavioral Analytics™”

We’ve come a long way since the days of horse-drawn carriages. Not only do we have fast, powerful cars at our disposal – we can also see glimpses of an era when everyone will be riding in self-driving automobiles.

You can say the same for data analytics. Computers and digital data have done much to revolutionize the processing of information, but here too artificial intelligence is expected to change the game in the decades to come.

The cutting edge data analytics tools today don’t just process static data. Instead, they analyze data in-motion — based on a dynamic model that evolves with each new addition of data – and coordinate an immediate and appropriate response.

This type of program – what we call Predictive Behavioral Analytics™ or PBA – is already being used by businesses in a wide range of industry. Think about websites like Amazon, and how they tailor results to a customer’s behavior.

Predictive Behavioral Analytics™ is also an important weapon in the arsenal of several government entities seeking to improve intelligence production capabilities and strengthen their cybersecurity. It provides the analytics capacity to identify suspicious users – based on their immediate behavior on the network – and shut them out instantly. It can identify content most relevant to the analytics in an effort to produce targeted intelligence.

Just like self-driving cars – and I can relate to some of the functions offered by Tesla – can also be used as standard human-operated machines, SYNTASA’s Predictive Behavioral Analytics™ offers more traditional data analytics functions in addition to these cutting-edge abilities.

The program can make recommendations based on the data accumulated over time, in areas such as internal management, customer-facing applications and effective information sharing.

In other words, with SYNTASA® you get the best of both worlds: traditional data processing, and an AI program working for you around the clock.

Why Dynamic Analytics Are The Way of the Future

October 12th, 2016 Posted by Analytics 0 thoughts on “Why Dynamic Analytics Are The Way of the Future”

Is there such a thing as too much data? Modern businesses are finding themselves sitting atop a treasure trove of intelligence, from web statistics to internal figures and customer logs. There are several different ways to exploit this data for different outcomes.

As we wrote last week, the three main types of analytics tools available to businesses today are enterprise business intelligence and digital analytics, which comprise web/app analytics and what we call Predictive Behavioral Analytics™.

With web analytics, businesses can evaluate the success of their websites, computed in page views, clicks, unique visitor numbers and purchase amounts. They can also gauge their best sources of traffic based on referral sources; and verify which pages are the most effective at keeping customers engaged, with bounce page data.

All this information provides great insight into how a business is faring online and allows businesses to tweak their websites/apps in order to draw and retain more customers by improving customer experience.

Enterprise business intelligence operates similarly to web analytics – it helps businesses make smart decisions based on the data available to them – but the focus, this time, is internal.

There is an abundant amount of metrics that describe how a company operates — from sales forecasts and revenue/ costs data, to inventory levels, project progress logs and HR data.

All this data, processed through the right analytics system, can help business leaders quickly identify where the inefficiencies lie. A judicious use of enterprise business intelligence will allow business leaders to ensure they run a tight ship.

But both web analytics and enterprise business intelligence are static. That is, they allow the business to exploit the data only after-the-fact — once the customer has made their purchase online and internal business decisions have been made.

In an era where data is an instantaneous, fluid source of essential business information, such a lag is not only inefficient and costly, but also fully preventable.

That’s where Predictive Behavioral Analytics™ comes in. This analyses a customer’s behavior and responds by creating a bespoke user experience as-it-happens — thus ensuring an optimal sales outcome each and every time.

Plus, the data trail left behind by each single customer interaction is also fed into the program, making the dynamic modeling even sharper next time around.

Three Types of Analytics to Leverage Big Data

October 6th, 2016 Posted by Analytics 0 thoughts on “Three Types of Analytics to Leverage Big Data”

In an era of Big Data, businesses are finding more and more ways to fine-tune products for their customers. There are several layers of information to glean insights from: web analytics, enterprise BI, and behavioral analytics.

With web or app analytics, a company will gather up data on clicks and page views, as well as a number of other metrics that indicate what kind of traffic the website is attracting. Those include the number of unique visitors; downloads; referral sources; and page bounces.

Using this data, the companycan for instance aggregate data on hot topics and on the most valuable pages on its website. This basic data serves as the blueprint for a website that is pleasant to navigate and attractive to customers.

Like a judicious accountant, web analytics allows firms to get a clearer view of what is driving sales performance.

But companies nowadays have an opportunity to go even deeper. With enterprise business intelligence, they can turn the lens back on themselves to reveal the inefficiencies within.

Companies nowadays have a dizzying array of internal data at their fingertips, from sales forecasts to inventory levels and revenue or cost data. Slicing and dicing this data with flexible reporting and ETL processes allows businesses to learn valuable things about themselves.

Enterprise business intelligence is like the eagle-eyed consultant who swoops in to identify how the firm could improve.

But Big Data has also given rise to an entirely new dimension of data analytics based on the real-time processing of customer patterns. Now, companies can observe customer behavior as it happens and respond instantly through dynamic webpages.

The company’s own past data on customer journeys – where users go on the website or mobile devices and what they end up doing – can help create different types of behavioral profiles, called customer segments. Once the website learns to recognize a hesitant buyer, it will respond accordingly – directing the customer to a discount offer, for instance.

This form of analytics, we call it Predictive Behavioral Analytics™, will allow companies to offer a user experience that is as personal as interacting with an affable customer service representative. In other words, Predictive Behavioral Analytics™ is like having Donald Draper assist every single customer that passes through your website!

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