More is always better, isn’t it? But does it always holds true, especially when it comes to customer data? Maybe not, because business is all about extracting meaningful insights from data, and if that cannot be acted upon then it is of no good.
Recently, Accenture concluded that one of the greatest challenges that marketers face nowadays is to discover the right ways to turn data into productive insights and then into action. For that, you would need analytics professionals who do know how to collect, store and integrate information, while mastering the technology aspect.
Now, this is basic. In this massively competitive world, to shine bright you have to ask your executives to put more emphasis on data science – more attention need to be put on analysis and acting upon zone than on data scientists. Just like Peter Fader, the Frances and Pei-Yuan Chia Professor of Marketing at the Wharton School of the University of Pennsylvania and author of Customer Centricity: Focus on the Right Customers for Strategic Advantage said, “Data scientists are technicians who are very good at managing and manipulating data, but data science is about looking for patterns, coming up with hypotheses, testing them, and acting on the results.”
This magical tool of technology can boost analysis and intensify your analytics team’s work by zillion times – by cramming large chunks of data. A specific sort of AI puts into work an algorithm program that has the ability to churn out insights without involving too much effort. Machine Learning makes it easier to crunch huge volumes of data, pointing out snags before anyone finds them and solving questions before you could even think of them. Such speedy processing of insights leaves more room for improvement among data professionals and analysts who can now witness overall customer journey.
However, organizations, instead of focusing their resources on building data science capabilities should stress on hiring analytics specialists – though good professionals are strength of any organization, undeniably. But, such conflict of interests can result into a data ‘Firehose’, as quoted by Fader.
The devised data science approach enables companies enjoy a certain competitive advantage. One of the companies that falls under this category is Caesars Entertainment (formerly known as Harrah’s Entertainment) – this company made analytics its strongpoint where all the factors turned unfavorable. With data analytics, Harrah was able to find out who its loyal customers were and what kind of activities would enhance customer experience with the casinos. As a matter of fact, Harrah’s ascertained that its most loyal customers were not the expected high rollers, but mere retired professionals, mainly lawyers and doctors.
As a closure, to frame an effective data analytics strategy, experts need to determine business problems, and understand the questions that analytics need to answer. If they fail to do so, the risk of maneuvering data towards wrong direction increases, which again won’t be good for the company success.
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