Big Data Analytics

Big data is the new buzzword within the data warehousing and business analytics community.

According to TDWI recent report on BIG data, there are 3 Vs of big data – Volume which is multiple terabytes or over petabytes, Variety which is numbers, audio, video, text, streams , weblogs, Social media etc & velocity which is the speed with which it is collected.

Today, enterprises are exploring big data to discover facts they didn’t know before. This is an important task right now, because the recent economic recession forced deep changes into most businesses, especially those that depend on mass consumers. 

Using advanced analytics, businesses can study big data to understand the current state of the business and track customer behavior.

Here are few examples of Big Data to get the idea:

• Twitter produces over 90 million tweets per day
• Wal-Mart is logging one million transactions per hour
• Facebook creates over 30 billion pieces of content every day ranging from web links, news, blogs, photos etc.
• 72 hours of videos are added to Facebook every minute

Big Data Analytics usability - think about the possibilities of real-time location data with regard to promoting coupons or customized offers to consumers who pass by a retailer’s location, Insurance companies can analyze the data collected by electronic toll transponders to accurately determine a driver’s speed, location, and mileage – and adjust insurance rates accordingly.

Because it's early on, big-data technologies are still evolving and haven't yet reached the level of product maturity.

Discovery analytics against big data can be enabled by different types of analytic tools, including those based on SQL queries, data mining, statistical analysis, fact clustering, data visualization, natural language processing, text analytics, artificial intelligence, and so on.

Solutions getting most advantages by Big Data Analytics:

• Customer analytics – Churn, segmentation, Cross Sell & Behavior analytics
• Fraud detection
• Risk analytics
• Advanced data visualization

Today various technology platforms are becoming available for big data analytics – Hadoop-Mapreduce, Teradata, Greenplum, Kognitio.

Hadoop has become more popular amongst all the tools as it is open source with less total cost of ownership & allows combination of any form of data without needing to have any data types or schemas defined.  

With massively parallel processing using MapReduce functionality it gives power to get the results quickly.  It can scale up & out by adding new nodes. This also allowes fail safe mechanism and all time availability.

Big players like Google, Yahoo, Facebook, Linkedin  have already proved the Hadoop usability.

Analytical tools selection made easy

Almost every large organization today is jumping onto the analytics bandwagon.  Given the continued presence of economic pressures and cutthroat competition, all are keen to use analytical tools to maximize competitive advantage. Unfortunately, the field of analytics can be complicated and confusing, with an overabundance of terms to understand and myriad options to select from. Starting with text analytics and predictive analytics, the list goes on to social media analytics, data analytics, mobile analytics and possibly many more in the future. 

So, do you really need all of the analytics tools to get ahead of the pack, or will just one or two suffice? Let’s take a brief look at each of the options in order to decide........

check the post below :

http://searchbusinessintelligence.techtarget.in/news/1280098454/Analytical-tools-selection-made-easy

Cross/Up-Sell & Next Best Offer

 Today global markets are within reach of every organization & there is a lot of pressure in increasing the revenues and market share.  Business leaders demand to know how customers and prospects are most likely to transact with them in the near and long-term.  

The explosive growth of various mediums to reach customers has made marketing an easy but costly affair.

Typical sales & marketing analytics will involve the following:

• Customer Segmentation – Grouping of customers based on demographics data, buying behavior or some other patters which are further used in marketing
• Customer Acquisition – Finding new customers with qualified sales leads
• Cross-Sell / Up-sell – Existing customers present an opportunity to grow revenues through cross-selling and up-sell strategies. Banks can use cross-selling to push credit/debit cards, checking account, investment banking services to savings account holders. Credit Card Company can offer Gold or Platinum cards to Silver cardholders. Use of Social Media Analytics is very useful.
• Channel – Optimize channel performance for sales success by identifying the best leads and channel partners to pursue across channels.
• Churn / Loyalty – Define the right targeting criteria and messaging mix to improve customer retention and loyalty.

Let us look at next best offer in more detail.

Next-best offer is the personalization & optimization of cross/up-sell. It is the use of analytics to identify the products or services your specific customers are most likely to be interested in, for their next purchase.

Here is the typical process for next best offer: 

• Consolidate & create a holistic view of the customers with the vast and growing amounts of transactional, demographic data.
• Apply predictive analytics techniques to customer data & identify trends in purchase behaviors and product affinities.
• Segmentation
• Decision trees
• Neural networks
• Use product affinities/associations to recommend other products or services customers may be interested in.
• It’s very important to close the loop by applying customer responses to future targeting that addresses customer needs.

This coupled with social media analytics of competition offerings or speech analytics of call center data will make cross/up-sell & next best offers more fruitful since they receive critical and indispensable feedback about the issues that matter most to customers, product improvement, service requirements and spending patterns.

Predicting the next product or service, customers are most likely to be interested in, not only improves customer lifetime value but also supports profitable and long-term customer relationships. 

With increasing global competition, optimal customer experience is of prime importance  !!!