To procure meaningful customer insight and strategic planning for the personalised marketing – Customer, Operations, Analysis and Technology (COAT)

Business houses are reaping the benefits of digital marketing nowadays. They are leveraging the advantage provided through emerging technology like Big Data, Data Lake etc. is supported by powerful cloud computing which is not only allowing them to study consumers’ buying patterns however also allowing them to make an educated prediction about exact consumer needs.

Digital marketing allows the business of any size easy access to the mass market at an affordable cost, unlike television and print advertisement, which allows personalized marketing. Apart from being cost effective, digital marketing has opened avenues wherein businesses can measure the effectiveness of their marketing strategy through web analytics which helps to define the success quotient of the said campaign. Moreover, it allows them to engage customers optimally by studying their needs and providing suitable options at the right time.
The pertinent question here is not to judge how well business houses know their customer, however, the onus is now to enable technology make that judgment based on predictive analysis thereby fostering business growth. It remains interesting to think that is marketing data helping business to gather all around customer information- ranging from customer demography, physiography like interest, purchase history, purchasing power, etc. The technology that is currently used for prediction analysis and campaign management, is that leveraging business revenue?   These are the fundamental objectives that business houses seek an answer to in modern day market and this is exactly where technology evaluation in the form of big data has emerged which is aiding integration of raw data, files, system logs, contents and structured data, thereby changing a support framework to becoming a business enabler. It needs to be noted that though business houses look to store customer information in their databases, research conducted by various agencies demonstrate prediction analysis is still not mature enough and require significant improvement to enable business houses to make comprehensive decisions that would help them to beat the fierce market competition. It is known that correct data will make a big difference however the challenge lies in scavenging and cleaning all data in order to procure meaningful insight on customer buying pattern.
Technology helps to process a huge set of data for analytics, perform reporting based on filtering and or pattern matching. One part of this entire process is definitely related to tracking customer footprint on social media, accessing feedback shared by them, the interest shown, traveling details, current location, and posts. At the very best that might help to assess any customer at their basic intent level, however important is to create a  holistic 360-degree characteristic analysis, that should provide reports regarding thought process, purchase power, needs, interest, hobbies etc. The crux lies in being able to offer the customer the exact product they are looking for or at best offer a competitive alternative if any. 
A separate aspect is machine learning, which offers unlimited potential to reach customers in new and exciting ways. Machine learning helps in patterns identification by zooming into it to ensure pattern consideration that can be ignored by human eyes. 
It can therefore be reflected that the need of the hour is to build a platform very specific to customers and their data which can be supported by emerging technology to analyze consumption predictions based on cognitive study, demography, psychograph, purchase history and current buying capacity analysis which would enable business houses to reach right customer at the right time through the right device/channel. More importantly, this customer data platform is entirely different than data management platform and needs to be integrated with other data platform for procuring referential customer information. These data platforms would form a single source for all customer data and remain entirely different than CRM, MDM or any data platform. These are solely designed to cater to digital marketers by providing customer purchases prediction analysis. Therefore it aids real-time buying pattern research conducted by marketers to gain them better insight into individualistic needs and wants.

Now these days, technology evolved and enabled to achieve the level of data analysis which was not possible earlier. Amazon Web Service really enabled business houses to think to build such a data platform which can hold all customer dimensional data, and provide extreme computing power to analyze as well. 

Solution architects from business houses are working of robust, very high scalable and reliable solution based on AWS could products, Amazon Kinesis, Lambda, EMR, a Redshift based solution that really helps Business houses to achieve desired success in this predictive analysis, however a long way to go.


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