Application of Data Science in Digital Marketing

The exponential growth of Big Data has transformed digital marketing radically. Forbes¹ presents some mind-boggling data about Big Data which truly indicates its potential. The report of NGData² brings for the value of Big Data in various aspects of business. As per one of the studies done by CapGemini³ , big data is acting as a driver to boost revenues, as per 61% of respondents. The report also concludes that Big Data has the capacity to offer competitive advantage to any company.

Major part of data, in raw form, has been generated in the last few years, with the major sources being unstructured data and social media. The advent of software like Spark and Hadoop, and availability of high speed computing at an affordable price has facilitated the potential to synthesize bulk amount of data that was earlier only available to enterprise companies and large institutions.

Amid such data-driven environment, Data Science aids digital marketers in building actionable conclusions that would affect positive outcomes. In today’s hyper connected world, data is one language that conveys experiences and ideas through an array of technology devices. For data scientists and digital marketers, analyzing the world through critical data is vital because it is how modern technology interprets real world interactions and experiences. So, how can you apply data science to digital marketing?

Make Marketing Analytics More Actionable

Gone are the days when data analytics merely provided rear view of the mirror. Today, data science provides in-depth insights from data, helping you understand which specific element in your marketing mix have direct impact on your revenues. To start with it, you should be able to define your KPIs more specifically – do you want to double your email open rate, increase number of video views or order value, or improve rate of conversion. Now, use Data Science to determine how critical business or consumer data is driving your decision to take measurable actions against the KPIs. 

Data-driven Marketing with Automated Personalization

Every digital marketer has the dream to build the next killer campaign that would help maximize revenues. However, for this, they have to take the risk. According to a study, 54% of marketers realize that they ought to take more risks, but lack of trust and confidence in data available continues to be a major drawback to rigorous and systematic testing, thus, affecting personalization and optimization of digital marketing strategy⁴. This is where Data Science helps to take calculated and smart risks from existing data, insightful experimentation, well-defined KPIs, and tactic knowledge. It aids in automated personalization that is a powerful tactic to allow digital marketers reduce their control on content decisions, and subsequently, deliver more personalized experiences.

Deliver More Data-driven Personalization

No matter whatever is your digital marketing strategy, data science facilitates data-driven personalization that delivers measurable results. In a scenario where nearly 80% of consumers expect certain level of personalization as one of the key features in a product or service, data-driven approach is undoubtedly an integral part of digital transformation. For instance, in the health industry, 90% of all patients in America would like to share their personal data, if there is robust privacy control in place⁵. This is where data science plays an integral role in refining digital marketing strategies and offering more personalization.

Tools to Apply Data Science to Digital Marketing

To enhance your digital marketing analytics from traditional web analytics to more powerful and resourceful statistics and predictive analysis, there are certain tools that help a marketer leverage the potentials of data science in the most efficient manner. Some of the best tools are:

· R Studio

· IBM Watson Analytics

· Excel

· BigML

These tools facilitate effective content management like managing the blogs, Pinterest images, videos and tweets; predicting the success of marketing campaigns with paid search optimization, digital order prediction, email and banner optimization; to analyze customer data with mix prediction, and customer segmentation and clustering.

Sources:

1. http://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#26c2415b6c1d

2. http://www.ngdata.com/101-big-data-marketing-tips/

3. https://www.capgemini.com/resource-file-access/resource/pdf/big_fast_data_the_rise_of_insight-driven_business-report.pdf

4. http://www.valassis.com/top-5-reasons-omni-channel-personalization-guide.aspx through https://twitter.com/valassis/status/659739032302329856

5. http://www.businesswire.com/news/home/20140424005203/en/90-Americans-Ready-Share-Personal-Health-Data and http://medcitynews.com/2014/04/comes-sharing-personal-healthcare-data-survey-shows-comfort-level-rising/