How Customer Analytics has evolved...

Customer analytics has been one of hottest buzzwords for years. Few years back it was only marketing department’s monopoly carried out with limited volumes of customer data, which was stored in relational databases like Oracle or appliances like Teradata and Netezza.

SAS & SPSS were the leaders in providing customer analytics but it was restricted to conducting segmentation of customers who are likely to buy your products or services.

In the 90’s came web analytics, it was more popular for page hits, time on sessions, use of cookies for visitors and then using that for customer analytics.

By the late 2000s, Facebook, Twitter and all the other social channels changed the way people interacted with brands and each other. Businesses needed to have a presence on the major social sites to stay relevant.

With the digital age things have changed drastically. Customer is superman now. Their mobile interactions have increased substantially and they leave digital footprint everywhere they go. They are more informed, more connected, always on and looking for exceptionally simple and easy experience.

This tsunami of data has changed the customer analytics forever.

Today customer analytics is not only restricted to marketing for churn and retention but more focus is going on how to improve the customer experience and is done by every department of the organization.

A lot of companies had problems integrating large bulk of customer data between various databases and warehouse systems. They are not completely sure of which key metrics to use for profiling customers. Hence creating customer 360 degree view became the foundation for customer analytics. It can capture all customer interactions which can be used for further analytics.

From the technology perspective, the biggest change is the introduction of big data platforms which can do the analytics very fast on all the data organization has, instead of sampling and segmentation.

Then came Cloud based platforms, which can scale up and down as per the need of analysis, so companies didn’t have to invest upfront on infrastructure.

Predictive models of customer churn, Retention, Cross-Sell do exist today as well, but they run against more data than ever before.

Even analytics has further evolved from descriptive to predictive to prescriptive. Only showing what will happen next is not helping anymore but what actions you need to take is becoming more critical.

There are various ways customer analytics is carried out:

·       Acquiring all the customer data

·       Understanding the customer journey

·       Applying big data concepts to customer relationships

·       Finding high propensity prospects

·       Upselling by identifying related products and interests

·       Generating customer loyalty by discovering response patterns

·       Predicting customer lifetime value (CLV)

·       Identifying dissatisfied customers & churn patterns

·       Applying predictive analytics

·       Implementing continuous improvement

Hyper-personalization is the center stage now which gives your customer the right message, on the right platform, using the right channel, at the right time

Now via Cognitive computing and Artificial Intelligence using IBM Watson, Microsoft and Google cognitive services, customer analytics will become sharper as their deep learning neural network algorithms provide a game changing aspect.

Tomorrow there may not be just plain simple customer sentiment analytics based on feedbacks or surveys or social media, but with help of cognitive it may be what customer’s facial expressions show in real time.

There’s no doubt that customer analytics is absolutely essential for brand survival.

Want to know how to choose Machine Learning algorithm?

Machine Learning is the foundation for today’s insights on customer, products, costs and revenues which learns from the data provided to its algorithms.

Some of the most common examples of machine learning are Netflix’s algorithms to give movie suggestions based on movies you have watched in the past or Amazon’s algorithms that recommend products based on other customers bought before.

Typical algorithm model selection can be decided broadly on following questions:

·        How much data do you have & is it continuous?

·        Is it classification or regression problem?

·        Predefined variables (Labeled), unlabeled or mix?

·        Data class skewed?

·        What is the goal? – predict or rank?

·        Result interpretation easy or hard?

Here are the most used algorithms for various business problems:

Decision Trees: Decision tree output is very easy to understand even for people from non-analytical background. It does not require any statistical knowledge to read and interpret them. Fastest way to identify most significant variables and relation between two or more variables. Decision Trees are excellent tools for helping you to choose between several courses of action. Most popular decision trees are CART, CHAID, and C4.5 etc.

In general, decision trees can be used in real-world applications such as:

·        Investment decisions

·        Customer churn

·        Banks loan defaulters

·        Build vs Buy decisions

·        Company mergers decisions

·        Sales lead qualifications

Logistic Regression: Logistic regression is a powerful statistical way of modeling a binomial outcome with one or more explanatory variables. It measures the relationship between the categorical dependent variable and one or more independent variables by estimating probabilities using a logistic function, which is the cumulative logistic distribution.

In general, regressions can be used in real-world applications such as:

·        Predicting the Customer Churn

·        Credit Scoring & Fraud Detection

·        Measuring the effectiveness of marketing campaigns

Support Vector Machines: Support Vector Machine (SVM) is a supervised machine learning technique that is widely used in pattern recognition and classification problems - when your data has exactly two classes.

In general, SVM can be used in real-world applications such as:

·        detecting persons with common diseases such as diabetes

·        hand-written character recognition

·        text categorization – news articles by topics

·        stock market price prediction

Naive Bayes: It is a classification technique based on Bayes’ theorem and very easy to build and particularly useful for very large data sets. Along with simplicity, Naive Bayes is known to outperform even highly sophisticated classification methods. Naive Bayes is also a good choice when CPU and memory resources are a limiting factor

In general, Naive Bayes can be used in real-world applications such as:

·        Sentiment analysis and text classification

·        Recommendation systems like Netflix, Amazon

·        To mark an email as spam or not spam

·        Facebook like face recognition

Apriori: This algorithm generates association rules from a given data set. Association rule implies that if an item A occurs, then item B also occurs with a certain probability.

In general, Apriori can be used in real-world applications such as:

·        Market basket analysis like amazon - products purchased together

·        Auto complete functionality like Google to provide words which come together

·        Identify Drugs and their effects on patients

Random Forest: is an ensemble of decision trees. It can solve both regression and classification problems with large data sets. It also helps identify most significant variables from thousands of input variables.

In general, Random Forest can be used in real-world applications such as:

·        Predict patients for high risks

·        Predict parts failures in manufacturing

·        Predict loan defaulters

The most powerful form of machine learning being used today, is called “Deep Learning”.

In today’s Digital Transformation age, most businesses will tap into machine learning algorithms for their operational and customer-facing functions.

What is Deep Learning ?

Remember how you started recognizing fruits, animals, cars and for that matter any other object by looking at them from our childhood? 

Our brain gets trained over the years to recognize these images and then further classify them as apple, orange, banana, cat, dog, horse, Toyota, Honda, BMW and so on.

Inspired by these biological processes of human brain, artificial neural networks (ANN) were developed.  Deep learning refers to these artificial neural networks that are composed of many layers. It is the fastest-growing field in machine learning. It uses many-layered Deep Neural Networks (DNNs) to learn levels of representation and abstraction that make sense of data such as images, sound, and text

Why ‘Deep Learning’ is called deep? It is because of the structure of ANNs. Earlier few decades back, neural networks were only 2 layers deep as it was not feasible to build larger networks. Now with big data platforms we can have neural networks with 10+ layers.

Using multiple levels of neural networks in Deep Learning, computers now have the capacity to see, learn, and react to complex situations as well or better than humans.

Normally data scientists spend lot of time in data preparation – feature extraction or selecting variables which are actually useful to predictive analytics. Deep learning does this job automatically and make life easier.

Many technology companies have made their deep learning libraries as open source:

• Google’s Tensorflow
• Facebook open source modules for Torch
• Amazon released DSSTNE on GitHub
• Microsoft released CNTK, its open source deep learning toolkit, on GitHub

Today we see lot of examples of Deep learning around:

• Google Translate is using deep learning and image recognition to translate not only voice but written languages as well. 
• With CamFind app, simply take a picture of any object and it uses mobile visual search technology to tell you what it is. It provides fast, accurate results with no typing necessary. Snap a picture, learn more. That’s it.
• All digital assistants like Siri, Cortana, Alexa & Google Now are using deep learning for natural language processing and speech recognition
• Amazon, Netflix & Spotify are using recommendation engines using deep learning for next best offer, movies and music
• Google PlaNet can look at the photo and tell where it was taken
• DCGAN is used for enhancing and completing the human faces
• DeepStereo: Turns images from Street View into a 3D space that shows unseen views from different angles by figuring out the depth and color of each pixel
• DeepMind’s WaveNet is able to generate speech which mimics any human voice that sounds more natural than the best existing Text-to-Speech systems
• Paypal is using H2O based deep learning to prevent fraud in payments

Till now, Deep Learning has aided image classification, language translation, speech recognition and it can be used to solve any pattern recognition problem, and all of it is happening without human intervention.

Deep learning is a disruptive Digital technology that is being used by more and more companies to create new business models.

Watch these Movies for Big Data Analytics & Machine Learning

Business analytics & Big Data has not only got the business and technology industry excited, but have influenced many movie-makers across the last few decades. It would be a big miss for data scientists and business analysts alike if they don’t know these good references to their field of work and passion.

Here are some interesting movies with Big Data, predictive analytics, and machine learning embedded in the story line.

Ex Machina :

Plot: Caleb, a 26 year old programmer at the world's largest internet company, wins a competition to spend a week at a private mountain retreat belonging to Nathan, the reclusive CEO of the company. But when Caleb arrives at the remote location he finds that he will have to participate in a strange and fascinating experiment in which he must interact with the world's first true artificial intelligence, housed in the body of a beautiful robot girl.

21 :

Plot: The movie is based on a true-story of five highly intellectual Ivy League students who get trained by their professor in card counting at blackjack. Using a combination of code talk and hand signals, these students manage to win hundreds and thousands of dollars at casino after Casino in Las Vegas.

Moneyball :

Plot: Oakland A's general manager Billy Beane's successful attempt to assemble a baseball team on a lean budget by employing computer-generated analysis to acquire new players.

With the use of historical data and predictive modeling to build a champion baseball team, this true story is sure to delight both data scientists and sports enthusiasts.

I, Robot :

Plot: In 2035 a technophobic cop investigates a crime that may have been perpetrated by a robot, which leads to a larger threat to humanity.

A super computer called VIKI, which stands for Virtual Interactive Kinetic Intelligence, uses truckloads of data and computational powers to take control of the world’s robots. VIKI and its evil plans are eventually defeated by the protagonist with the help of a friendly robot.

Minority Report :

Plot: In a future where a special police unit is able to arrest murderers before they commit their crimes, an officer from that unit is himself accused of a future murder.

Crucial to this movie’s story are the PreCogs, a small team of humans who can see into the future to predict when the murder will be committed, by whom it will be committed, and who will be the victim.

The PreCogs are the source of future data for the PreCrime police unit, who are really the super-smart “data scientists” doing the hard work. It’s down to this team to solve the time-bound challenge of sifting through visual data and piece together information that nails down the final details in order to prevent the next crime. 

It is fun watching these movies while learning the use of Big Data Analytics & Machine Learning.

What is Artificial Intelligence ?

Just over ten years ago, the IBM supercomputer program Deep Blue beat world chess champion Garry Kasparov—the greatest chess mind alive. That moment marked a turning point in the relationship between man and machine.

Later in 2011 IBM Watson supercomputer defeated Brad Rutter and Ken Jennings in US TV show Jeopardy.  

Recently Google’s AlphaGo beat the 2500 year old Chinese game Go.

Machines have topped the best humans at most games, including Chess, Scrabble, Othello, and even Jeopardy!! But the future of artificial intelligence (AI) is about way more than games is used in Digital Transformation.

Artificial Intelligence is the capability of a machine to imitate intelligent human behavior

There’s definite signs that machines with artificial intelligence will soon be taking over skilled manual work that now is typically handled by humans.

Amazon awarded $20,000 to the creators of a robot that uses Artificial Intelligence to fill orders most like a human, but it’s not going to replace employees yet.

Online services like Google, Facebook, and Microsoft, already use deep learning to identify images, recognize spoken words, and understand natural language and translate in other.

Today these biggies are making their machine learning - deep learning software freely available to people.

Last year Google open sourced its artificial intelligence engine TensorFlow, which the company uses for many of its own applications, including voice recognition in Android and even its flagship search engine.

Facebook open sourced designs for custom hardware, designed to run the latest AI algorithms. 

China’s largest search engine, Baidu, open sourced its the artificial intelligence training software.

Microsoft’s Brain is now available for anyone to use in their apps., The company has open sourced the artificial intelligence framework it uses to power speech recognition in its Cortana digital assistant and Skype.

BMW, Tesla, Google are using AI for self-driving cars. Apple is also getting into this so the idea of buying a car from the same company that made your iPod doesn't begin to seem all that far-fetched.

Many experts in the field of artificial intelligence say that it will eventually evolve far beyond all human physical and intellectual capacities.

Recently Elon Musk, the CEO of Tesla, raised a concern about how AI can be threat to humanity. Even Professor Stephen Hawking warned that AI could spell the end of human race. 

But unlike Terminator movie series with Skynet, we hope that these AI champions will be used to solve real world tough problems like climate modeling to disease analysis and betterment of humanity.