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IT & InnovationHow Data Science is Shaping the Future of Marketing

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Sunando Das
Posted bySunando Das

Sunando Das is the Head of Marketing Analytics & Data Science at TNS and a founding member of Eurostaff Experts.

The paradigm of marketing is changing enabled by technological advances and necessitated by the digital revolution, channel proliferation, demographic, economic and socio-political changes. This is reflected in trends such as multiple digital media creatives for personalized marketing, in-the-moment campaign optimization, real-time prediction to shape sales, omnichannel experience synchronization.

Data Science has a significant role in enabling this marketing transformation by riding on the large volumes of digital behavioural and attitudinal data (across social media, search, mobile, Internet of things devices, clickstream). Data Science can help identify strategies for conventional marketing issues better, faster and cheaper; enable new opportunities opened-up by the ever increasing connected world such as personalization strategy and programmatic planning for better ROI, real-time optimization, early warning system to shape marketing actions, intervention strategy via automated agents; and develop strategies for new channels and formats like e-commerce and videos respectively.

Data Science can do these by harnessing billions of unstructured text, image, video data into a meaningful business relevant taxonomy, leveraging techniques such as Natural Language Processing (NLP) / phonetics /deep learning algorithms, to help shift from big data to deep data to uncover business opportunities. However, this requires significant people expertise coupled with machine learning as no off-the-shelf SaaS solution can deliver required business/consumer relevance on its own.

Overlaying graph theories on the taxonomy enables to market to the consumer of one i.e. uncover nano-segments/ sub-cultures for personalized content and context strategy. Integration with predictive analytics (such as neural networks, expert recommendation algorithms, dynamic mix modelling) helps move further from personalization to attribution; prediction to shaping opportunities.

This combination of deep NLPs with graph theories and predictive analytics helps in delivering the artificial intelligence (AI) algorithm for marketing applications.

Given the range and power of applications, no wonder AIs have been referred to as the future of CMOs!

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