Philanthropy Index of Social Media!

Over $30.7bn revenue last year from social media sends out a clear message that the globe is no more a closed door opportunity.  Over 17+ most talked social media driving companies leveraged by over 78.5% of sales people in the industry today. Social media has played a very major role to drive awareness, manage online expectations, and magnify all marketing programs and PR events for the company. 

A successful strategy integrates tightly with outsourced PR and Marketing firms, developing a two-way communication system to improve results through information, metrics, and creative execution. The Media industry and the commodity market has comparatively started taking advantage over social media and helped several companies to revamp their product branding. Wherein the biggest institutional players have already started taking advantage of it. HealthCare and Pharma Industry comparatively doesn’t seem to have leveraged it as yet, nevertheless they are paying bulk and volumes to data players to fetch records which they possibly can bank upon, if used social media and social proof carefully.

Thanks to investor’s latest Philanthropy Index derivative by Data Science and Big Data, companies are now looking for counts on “Tweets”, “Likes” etc. Sales revenue today is not anymore the only parameter for your forecast; if you are still doing it, you are already lagging behind in the market. Let me talk a little more about social proof here.  You board a bus and sitting in the middle row. You co passenger looks behind repetitively couple of times, it generates a reflex amongst others. First it’s you who looks behind (a normal reflex), then the other co passengers, and finally the one who’s sitting at the first row, probably reading a book, but the movements around forces his sub cortex to act and look behind! The same logic is used in “Tweet” and “Like” or “+1”s. If you do “Tweet”, and you do have a popularity quotient (of course to your followers); there is close to 78% of probability that your followers would tweet about the same!

This is indeed a chain of events that follows. Now let’s talk about this proof of Social Media Bridge to healthcare. Imagine of a Patient – Patient network that brings you the best of the observations, and experiences for a specific philanthropy and medical condition. A patient tweets, other patient follows, and so do the Pharma companies or providers. Statistics says that of the 400,000 Americans with multiple sclerosis, 300,000 of them are probably on Facebook, while 30,000 are on Google Plus. Theoretically if the behavior of all these users could be tracked to identify those with MS, it can be in a

position as the largest registry of MS in US. Social proof is not a panacea. But as shown, those who have been using it are quickly gaining a competitive advantage. Statistically valid, it’s clear, Social proof can positively affect sales quota – which impacts revenue – which leads to better growth opportunities for business. That also means that LinkedIn, Twitter, Facebook, Foursquare, Google Plus, a blog, etc. are no longer nice to haves, they are salesperson must haves.

The Wisdom Tooth - Sentiment Analytics!

"Not everything that can be counted counts, and not everything that counts can be counted." 
-- Albert Einstein

The arena of sentiment Analytics is not new though the most popular buzz word! Every person who holds a strategic position in the industry, needs an insight about various KPIs.  Excellent tools and technologies available for fetching information. Now we have data, meaningful information, dashboards KPIs we need all across. Loads and Loads of data, but what we do with it. As an Entrepreneur, I wanna see how the market talks about my product, with respect to my products, and the increasing industry needs? The booming answer comes here: Social Network!

Social networking gives you the instrumentation to explore sentiments. Generally companies are getting better at creating 360 - degree view that incorporates all of a company's KPIs in comparison with their Vision and target. With such comprehensive data, one can see the strategies those would have really worked! You can analyze the benefits of detailing by itself. But, hold on, you are only talking about yourself and still have a blind spot how the market sentiments are.

Here goes a solution for The Executives - a predictive analytic using a big data set up to answer some of the most crucial business questions like:

1. Which leads should I focus on for immediate closure?
2. Who are the "Influence-rs" among my Customer Base?
3. How do i personalize my campaigns to each customer & segment based on Social media, unstructured and structured data?
4. What data syndication is relevant for me to optimize my customer experience?
5. How am I using the leading indicators from these insights to increase the demand for our offerings?

This tool uses the most advance technique of data science and empowers the users to know the blind spot, comparative analysis of your own product, and other product lines in the market. Gives you Knowledge to increase your understanding and empower yourself with enabled intuition . Most often effective decision making requires "sensing" data in different views, identifying patterns and getting insights. Any reporting tool will give you the reports that you need, a predictive model gives you the power you want. However only if you feed it with data those make sense to be processed.

Business Analytic - Gaining Business Insight - Thru the Eyes of the Business Users

The field of business analytics has improved significantly over the last few years, providing business users with better insights, particularly from operational data stored in transactional

systems.

As an illustrative example, analysis of e-commerce data has recently come to be considered a killer-app for data mining. The data sets created by integrating clickstream records generated by web sites with demographic and other behavioral data dwarf, in size and complexity, the largest data warehouses of a few years ago, creating massive databases that require a mix of automated analysis techniques and human effort in order to provide business users with critical insight about the activity on the site and the characteristics of the site’s visitors and customers. With many millions of clickstream records being generated on a daily basis and aggregated to records with hundreds of attributes, there is a clear need for automated techniques to find patterns in the data. In this post I would discuss about the technology and enterprise-adoption trends in the area of business analytics.

The key consumer of these analytics is the business user, a person whose job is not directly related to analytics per-se (e.g., a merchandiser, marketer, salesperson), but who typically must use analytical tools to improve the results of a business process along one or more dimensions (e.g., profit, time to market). Fortunately, data mining, analytic applications, and business intelligence systems are now being better integrated with transactional systems creating a closed loop between operations and analyses that allows data to be analyzed faster and the analysis results to be quickly reflected in business actions. Mined information is being deployed to a broader business audience, which is taking advantage of business analytics in everyday activities. Analytics are now regularly used in multiple areas, including sales, marketing, supply chain optimization, and fraud detection. I would be talking about these Business Users and their challenges in my upcoming posts!!!

Future of BI and DW

The source data that drives this intelligence is everywhere and pushing it into a traditional data warehouse is becoming less and less feasible.

I happened to read about the future of BI somewhere and a few questions popped onto my mind. I am in search of these, however putting them together for the readers to contribute too:

1. The call to action today is around real-time analytics – why this push now and how are companies accomplishing this task?

2. Data warehouses have walked with business intelligence for years. Are they reaching their limits? What will replace them?

3. How can companies extract more value from their data today? Are their commercialization opportunities?

4. Hadoop or MapReduce are all the rage today but its complicated to program and leverage. What movements are afoot to help bring these new data analysis models to the masses.