BIG DATA

Big data is an evolving term that describes a large volume of structured, semi-structured and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications.

An Overview Of Big Data’s Many Contributions To Marketing And Sales

Increasing the quality of sales leads, improving the quality of sales lead data, improving prospecting list accuracy, area planning, win rates and decision maker engagement strategies are all areas where big data is making a contribution to sales today.

In marketing, big data is providing deep understanding into which content is the most effective at each stage of a sales cycle, how Investments in Customer Relationship Management (CRM) systems can be improved, in addition to strategies for increasing conversion rates, the possibility engagement, conversion rates, revenue and customer lifetime value. For cloud-based enterprise software companies, big data provides insights into how to lower the Customer Acquisition Cost (CAC), Customer Lifetime Value (CLTV), and manage many other customer-driven system or standard of measurement. Essential to running a cloud-based business.

1 Differentiating pricing strategies and optimizing pricing using big data are becoming more achievable.

McKinsey found that 75% of a typical company’s revenue comes from its standard products and that 30% of the thousands of revenue decisions companies make every year fail to deliver the best price. With a 1% price increase translating into an 8.7% increase in operating profits, assuming there is no loss of volume, pricing has significant upside potential for improving  for profits.   

2  How companies attain greater customer responsiveness and gain greater customer understanding.

A Forrester study found that 44% of B2C marketers are using big data and analytics to improve responsiveness to 36% are actively using analytics and data mining to gain greater insights to plan more relationship-driven strategies. 

3 Forrester found that big data analytics increases marketers.

The ability to get beyond campaign execution and focus on how to make customer relationships more successful by using big data analytics to define and guide customer development, marketers increase the capacity to develop or  creating greater customer loyalty and improving customer lifetime

4 optimizing selling stragies and market plans using geoanalytics.

McKinsey found that biopharma companies typically spend 20% to 30% of their revenues on selling, general, and administrative If these companies could more accurately place their selling and go-to-market strategies with regions and territories that had the high sales potential, go-to-market costs would be immediately reduced.

5 Customer Value Analytics (CVA) 

This is based on Big Data is making it possible for leading marketers to deliver consistent denoting customer experiences across all channels. Customer Value Analytics emerging as a viable series of Big Data-based technologies that accelerate sales cycles while retaining and scaling the personalized nature of customer relationships. The bottom line is that CVA is now a viable series of technologies for orchestrating excellent omnichannel customer experiences across a selling network.

 Big Data phenomenon is characterized by the 5Vs:

1 Volume

2 Velocity

3 Variety

4 Veracity

5 Value

1)Volume: 

Volume is the quantity  of information produced every second. In 2000, 20% of the data was digital and some of them was analog. In 2015, 98% of the data was  digital and the rest was analog. These data are produced by personal computers, smartphones, tablets and other devices.

Every min. we produce:

216000 pictures on instagram

270000 tweets

30 billion instant text messages

200 million emails

We all know that most of this data is collected by two companies that are:

Google with Gmail, the Google search engine, Android and Youtube

Facebook with instagram and WhatsApp

These two companies accumulate this data in order to process it with the aim of retaining users in order to accumulate the maximum amount of data to monetize with their advertisers.

2) Velocity

Velocity refers to the speed of development and deployment of new data occur in market.

3) Variety

Variety refers to different types of data such as pictures or images , videos, texts, voices , and others. In all of this data, 80% of this data is unstructured and the remaining 20% is structured data that is stored in relational data tables.

4) Veracity

Veracity denotes  the credibility and reliability of the data collected. Very  large amount of data is collected, not all content is authentic.

5) Value

Value is the profit and loss that can be derived from the use of Big Data.

To understand about Big Data, we can mention two companies like Uber and Netflix that will select only the Right Data among the large mass of data.

CONCLUSION:

The availability of Big Data, low-cost commodity hardware, and new information management and analytic software have produced a unique moment in the history of data analysis. The convergence of these trends means that we have the capabilities required to analyze extremely  surprising data sets quickly and cost-effectively for the first time in history. These capabilities are neither theoretical nor trivial. They represent to a clear opportunity for  realizing  very much  gains in terms of efficiency, productivity, revenue, and profitability.

The Age of Big Data is here, and these are truly revolutionary times if both business and technology professionals continue to work together.