Upon entering the big data era, the volume of data has been sky-rocketing at an amazing rate. The mind-blowing statistics shows 2.5 quintillion bytes of data created each day, AND that pace is only accelerating with the growth of the Internet of Things (IoT) (Ref. 1). With 90 percent of the data in the world was generated in the most recent two years, many organizations especially large companies have a vast volume of data which represents a valuable asset for marketing. The availability of massive amount data has been changing the modern marketing landscape. As an emerging trend, big data digital marketing is the practice of transforming consumer data into actionable insights, with the goal of creating highly targeted advertising campaigns (Ref. 2). However, there are significant challenges facing marketers who want to rapidly analyze the data, gain insights, and then bring those insights to market via marketing interactions tailored to what’s relevant for each customer (Ref. 3).
In order to take a full advantage of the data, it is critical for companies or organizations to set up a database to include all the data about their customers that are available. This include all types of data such as identity data, descriptive data, behavioral data, and attitudinal data (Ref. 4). The data could be sourced from in-house data or third-party input such as resellers (e.g. Amazon is partnered as a reseller) and Google Trends. Both online data and offline data should be included. Only from a comprehensive data silo one can draw out a 360 degree view of the relevant information needed for a specific marketing campaign or project. Because the crude data silo is full of unstructured, complicated and even ‘toxic’ data, the first step should be untangling the silo and extracting the most relevant data set out of the whole database.
Indicated by a survey, the real roadblock is the lack of data analytics skills and a reliance on unrefined processes (Ref. 3). With that said, digital marketers should learn to improve their data analytics skills and adopt advanced analytics tools. Many people struggle to read and interpret the big data that they have access to while some still rely on usage of Excel only. Tableau is a powerful tool to achieve data visualization. Other examples of analytics tools include Hadoop tools or similar software options (Ref. 5). Many organizations are turning to Artificial Intelligence (AI) to sift through the data and complete the necessary vetting to return usable insights. AI uses algorithmic processes including machine learning, deep learning, and natural learning processing, to automate the overwhelming amount of information into something that companies can use to their advantage (Ref. 2). Moreover, marketers together with the IT department of organizations or companies should push hard to set up an automated and systematic analytics process. The establishment of a robust analytics process can ensure the quick yield of holistic and actionable business insights for digital marketing.
The applications of the business insights include many aspects of modern data-driven digital marketing, such as Planning Marketing Strategies, Recommending More Relevant Products, Audience Targeted Advertising, Personalized Messages/Experience, and Trend Forecasting (Ref. 6-8). The feedback, outcome, and measures of the data-driven marketing campaigns also generate new data, which should be incorporated into the ever-updating database.
This cycle of data analytics shall be optimized over the time by continuously adopting new technologies such as robotics, AI and blockchain. This will help marketers to take a full advantage of Big Data in practice of digital marketing and business management.
- Bernard Marr. May 21, 2018. How Much Data Do We Create Every Day? The Mind-Blowing Stats Everyone Should Read. https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#641ac34760ba
- Melyssa Blucher. What is Big Data Analytics? https://www.mni.com/phone/future-of-big-data-digital-marketing.html
- Thor Olavsrud. Aug 9, 2013. Marketers See Value in Big Data Analytics, But Face Hurdles. https://www.cio.com/article/2383427/data-management/marketers-see-value-in-big-data-analytics–but-face-hurdles.html
- Jim Roberts. 12 Nov, 2013. A comprehensive checklist for auditing different data types in a CRM or Email marketing system. https://www.smartinsights.com/customer-relationship-management/customer-privacy/types-customer-data/
- Mitesh Patel. January 5, 2018. Three Ways Big Data Will Impact on marketing in 2018. https://insidebigdata.com/2018/01/05/three-ways-big-data-will-impact-marketing-2018/
- James Paine. Nov 25, 2017. Big Data in Marketing: 5 Use Cases. https://www.inc.com/james-paine/5-ways-big-data-is-changing-marketing.html
- Chris Richardson. August 21, 2017. 7 Great Benefits of Big Data in Marketing. https://www.smartdatacollective.com/benefits-big-data-in-marketing/
- Jean Spencer. Nov 19, 2014. 5 Ways Marketers Can Actually Use Big Data. https://www.salesforce.com/blog/2014/11/5-ways-marketers-can-actually-use-big-data-gp.html