How Will Big Data Change the Techniques of Effective Lead Generation?

It is a universally accepted fact that for any business to thrive, customers are of maximum importance. That is why, all companies in the world emphasize on new client acquisition. However, for most companies, the process is not as easy as it sounds. Often, the process of shortlisting and reaching out to potential clients belonging to a particular target segment is an uphill task. Ask any sales professional and he will gladly take the pain of explaining it vividly!

Thanks to technological advancements, it has become a lot easier to find potential clients in an internet dominated world. This is due to the fact that vast amount of data being generated every moment from the interactions of users with websites, marketing teams can chalk out strategies to leverage useful information after careful analysis of data. However, on the other side, this might be the death knell for software generated leads, which are a lot more random in nature and are widely used by most companies around the globe.

This inevitable loss in popularity for thousands of leads that are generated by software can be attributed to many factors, quality and conversion rates being the two most crucial ones. In a scenario when companies are vying with each other to grab a slice of the market pie, no company can afford to engage resources for chasing wild geese. Leads of high quality are the primary requirement for increasing efficiency and reduction of cost arising from misuse of resources.

A high ‘lead to conversion’ ratio can be achieved if teams conducting market research employ big data techniques. At a fundamental level, big data analytics is about designing complex mathematical models and algorithms and converting them into sophisticated programs, so that we can have critical insights about the correlations between different parameters pertaining to the dataset under study. Our understanding of the results need not be supported by causality, as is the case with conventional methods.

For example, in order to maximize the sale of newly launched ‘beauty and health’ magazine, it is sufficient for its publisher to know that upper middle class women forms the majority of its subscribers. The reason for such a phenomenon is, of course, a subject to be studied by social scientists. Such approaches can be replicated and/or tweaked by companies to suit their requirements, but the basics are more or less the same.

When a lead generation software is integrated with big data techniques to filter out results, it can result in a very steady supply of high quality lead. Vaguely speaking, all one needs to do is to synchronize the parameters of the lead churning software with the results obtained from big data based analytics. However, creating such a lead generation software is not everybody’s cup of tea. Such a software to be stable and worth the money, will require algorithms related to artificial intelligence and machine learning. Otherwise, it will be nothing but a futile attempt to synchronize the software and results obtained from data analytics, because every time the analysis will reveal something new and interesting, the codes have to be rewritten, which does not sound like a good business proposition.

Source by Prakash Singh Chauhan

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