Enhancing Customer experience with Big Data Analytics

The era of Big Data is upon us.

Today, most organizations are still struggling to unlock the full value of this Big data that is available to them. From internet to mobile and social, the amount of customer data is continuously growing.

Company’s ability to extract value from big data through smart analytics will be the key to their business success.

Big data by definition is:

• Volume - Unmanageable volumes by traditional databases
• Variety - Combination of all the internal structured business data (CRM, ERP, POS and all the internal system data) and external unstructured data ( Social media data, feedback surveys, Audios, Videos, streaming data, Call center data, images)
• Velocity - enormous speed at which it comes into the organization.

Channel based marketing is the least priority now. The increased amount of data available at individual customer level has allowed companies to do a personal marketing.  But all this customer data out there is worthless if you can’t process it & turn it into actionable intelligence.

With Big data platforms helping in collection, integration, and transformation of large volumes of data, companies can conduct complex and varied analysis on much larger data sets and reduce the time to action and reaction to customer needs.

Organizations can now impact the entire customer lifecycle and every interaction by being well prepared for each interaction, shaping the interaction in real time as it happens and driving the huge improvements across all the channels for next interaction.

By listening to the data as a signal from customers and working to personalize the experience for the customer, creates the value for the customer as well as business.

Some examples of enhancing the customer experience using Big data Analytics:

• Retail giants are using Big data to personalize the offers to enhance the customer experience
• Healthcare companies are using it for improving the patient care in hospitals
• Banks are using it for cross-up selling and bringing the new products to market
• Insurance companies are making tailor-made policies for their customers in real time
• Manufacturing companies are using Big data to improve their products, predict the failures in their product lines ahead of time to make every customer interaction very smooth

Using Big data to address customer inputs before they become problems is extremely important to ensure they stay loyal and more profitable to the company.

Customers expect to have the best possible experience from their vendors/service providers. They want to be recognized as individuals and not as a part of a segment.

Big data Analytics – A disruptive technology !!

Big data is the most talked term these days in the analytics world. It will have a big transformative impact on all the aspects of the business.

Most of the companies now have realized that there is a huge competitive advantage in analyzing the humongous data quickly & effectively for future insights.

Big data analytics is the disruptive technology bringing the 4^th aspect of Value to the already published TDWI’s 3Vs – Volume, Velocity & Variety.

• It enables business users to process every granular bit of data in a quicker way removing the traditional need for sampling & then applying the models
• It encourages an investigative approach in users for data analysis since they get access to whole data
• It can reveal insights hidden in the data, which were previously too costly due to large data movements
• As per Gartner report, Big data is a priority of SMB & it will drive $232 billion in spending through 2016.

Some of the technology platforms which are used for big data:

• Distributed file based: Hadoop-MapReduce (Cloudera, Hortonworks, MapR)
• Appliance-based: Greenplum, IBM Puredata(Netezza), Oracle, Teradata
• Columnar databases: HP Vertica,  ParAccel, 1010data
• In-memory databases/tools: SAP Hana, Qlikview, Tableau
• Nonrelational/NoSQL: Cassandra, MongoDB, Splunk, Hbase

Hadoop is at the top of the list of technologies used for dealing with Big data due to its ultra-high scalability & low cost compared to other platforms.  It is a suite of products linked together, which breaks up the large datasets into smaller chunks on commodity servers, and data processing is done in a distributed cluster environment to quickly return the results.

Some of the probable Big data use case in various industries:

Insurance: Collecting data from monitoring devices fixed in cars & providing the personalized insurance policies based on driving habits, Underwriting price optimization for insurance products, Claims fraud with social network link analysis.

Retail: Market basket analysis for entire merchandising instead of sample data, Sentiment analysis based on social media for improving brand perception, customer service, competition analysis, Customer & market segmentation, Weblog analysis for customer behavior.

Banking & Finance: Fraud detection with entire history data for better detection, Trade surveillance in capital markets, the More accurate risk score to customers, Text mining on call center data.

Healthcare: Improve patient care by analyzing electronic Health Records (EHR) & reduce insurance payer costs, Reduce hospital readmission rates by analyzing information from discharged cards.

Manufacturing: Forecasting warranty costs & detecting issues in spare parts of finished goods, text mining to understand the complaints from customers for product improvements.

Because of the nascent stage immaturity of Big data initiatives, there are many views of what is it & how it can be applied.  Organizations need to focus on Big data processing while avoiding the movement of large volumes of data which is very costly.

Big data help make better decisions – faster, more efficiently with higher quality.

so what is prescriptive analytics?

Today every business is surfing in the ever-expanding sea of data & using analytics for getting the edge over their competition.

With the explosion of unstructured data on social media, audio-video steams, companies are rushing to use this for big insights.

There are mainly 3 types of analytics & it is based on the company’s maturity in analytics as to which one to adopt….Descriptive, Predictive & Prescriptive analytics.

Let me explain with examples.

	Descriptive Analytics	Predictive Analytics	Prescriptive Analytics
What questions are answered?	What happened? How many customers? Where revenue is less? Why it is so?	What will happen next? What trends will continue? What if we change pricing?	What is the best course of action for a given situation? What is the impact of seasonality?
How it is done?	Use of KPIs, dashboards, charts	Use of statistical methods to understand the relationships in input data & predict the outcomes. Use of data mining, forecasting, predictive modeling.	Use of advanced statistical optimization & simulation techniques with inputs & constraints to recommend what actions to be taken.
General examples	How many customers have churned? Why did they churn?	How many customers will churn in the next few months?	What actions to be taken to retain these predicted churners?
Some of the Industry examples	Netflix uses data mining to find out correlations between different movies that subscribers rent & then recommend the one which you are most likely to watch	ING using personalized campaign offers in real time by predicting who will respond, to increase 30-40% response rates & reduce direct marketing costs by 35% per year.	Amazon.com using price optimization based on demand to increase the online shopping revenues.

  How various industries are using prescriptive analytics?

·    Consumer product companies are using it to maximize the marketing dollar spend

·    Transportation & Logistics companies are using it to find the best route for their deliveries & backhaul

·    Healthcare service providers are using it to decide how many beds they should increase in the hospitals

·    Manufacturing giants are using it for inventory optimization to decide how much safety stock they should keep of each item, where to stock it based on the demand

·   Telecom business is using it for providing on the spot offers to customers when they call the customer service centers

One daily life example - Imagine you are driving a car with a built-in GPS, which analyses all the data it collects from the satellite about traffic, accidents, weather etc. It tells you which routes will have heavy traffic (prediction), but also recommends you the alternate routes (prescription) with less traffic.

So Prescriptive analytics is where you know what the future is, but also know what to do with it, with alternatives of best outcomes J