Big Data and Analytics : Fundamentally changing the marketing landscape


Big data and analytics become an integral part of marketing since the inception of internet in the late 1990’s and the introduction of smart phone and other internet enabled smart devices in recent times. Big data usually refers to the data sets with sizes beyond the scope of commonly used software tools to collect, clean, manage and process data within an acceptable time frame. With the rapid advancement in technology and increasing use of smart phones and other internet enabled devices, organizations today face overwhelming volume of data through multiple channels and devices. The main roadblocks while using big data are:

  • Capture high volume of data from multiple sources and consolidate the data
  • Data cleaning (i.e. removing redundant or unnecessary data – internal data, data generated due to testing, deletion/ blocking of cookies)
  • Data storage
  • Data sharing/ transfer
  • Data visualization
  • Data analysis

Now the important question is why should organizations use big data and analytics. The answer lies in decision making. Organizations cannot afford to make any decision just by guess or gut feeling. The use of predictive analytics and certain other advanced statistical methods to extract value from data leads to more confident decision making which in turn results is greater operational efficiency, cost reduction and reduced risk.

Big data enables marketers to increase the size and range of information sources while speeding up reporting, enabling real-time forecasting and more informed and accurate decision-making.

marketers benefit from a large volume targeting options when it comes to online advertising. The growth of cookies and information-rich social media, means that the data is there to go beyond simple demographic, geographic, psychographic and time-based targeting options.

Big data analytics helps optimizing campaigns and improve results in a scalable, real-time manner.

Big data transformed the last-click conversion attribution model to the multifaceted attribution model which takes into account all touch points across consumer purchase life-cycle. It was complicated earlier to capture these different, interrelated factors and their relative weights. But big data makes it easy to track, analyze and evaluate activities to see what is actually driving customers to engage.

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