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Learn about the tools and skills your organization needs to build successful big data-driven marketing campaigns.
This course, taught by Professor Johannes Gehrke of the Johnson School of Management at Cornell University, introduces you to the world of big data and data scientists. You explore some of the ways that data can inform your marketing strategy and open the door to targeted, customer-aware advertising. Hands-on activities and downloadable templates help you bring the ideas back to your own organization and apply them immediately.
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|Section 1: Course Introduction|
Big Data is revolutionizing business: Companies are using data to drive strategic decisions about product design, marketing, and customer acquisition. In addition, connections through social media are providing a unique insight into the customer base. This course, from the Johnson School of Management at Cornell University, teaches you the language and tools of data-driven marketing research on social media platforms, providing the guidance you need to ask the right questions and put the right people in place to manage your data stream.
|Section 2: Drive Your Social Marketing Efforts with Big Data|
Social platforms provide a massive stream of data. Knowing your goals before you begin analyzing that data will help you focus on strategic patterns and behaviors. The major goals of your social media activity fall into two "conversion" categories: engagement and sales. Define the goals carefully and concretely in those two areas and then decide what metrics define success.
The value of your social media visitors lies in their "connectedness" to others. By introducing you into their networks via "following" or "liking" your content, people influence how others in their network engage with you. By knowing who your connectors are, and nurturing your relationship with them, you can increase the power and reach of your marketing efforts. These social media relationships are also assets for your company. Their lifetime value lies in the long term, as the company recoups initial acquisition costs and retains the income stream over many revenue periods. This sum of their discounted future revenues (derived from the present value of the anticipated future cash flows) is used to calculate the Customer Lifetime Value.
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|Lecture 9||2 slides|
|Section 3: Understand How Big Data Works|
Most organizations have had "big data" for years, but it has usually been scattered among many databases, often with duplicated or contradictory records. Organizational structures, such as business units or divisions, have led to this partitioning of the organization's data and limited the insights that can be gained from looking at data from across the company.
Examining longitudinal data -- tracking customers, products etc. over time -- reveals patterns in consumption and needs that marketing departments can respond to strategically. Cumulative data stores require massive data storage and management systems, but allow marketers to leverage nuanced profiles of their target audiences.
The ability to capture transactions on many types of devices means that large amounts of data are being generated, stored and analyzed every minute. The millions and billions of data points can be analyzed at both micro and macro levels to give you a close understanding of not only your general audience, but of individual customers.
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|Section 4: Learn How Data Scientists Use Testing Data to Support Decision Making|
Quality decisions come from quality data. When transactions are created, data may be missing, recorded incorrectly, duplicated, corrupted or have any number of other issues occur. It is vital that the data manager verify the validity and reliability of the data prior to running algorithms against it to uncover patterns and meaningful customer behaviors.
|Lecture 24||2 slides|
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|Section 5: Introduce Your Marketing Effort to Big Data|
SQL-based databases are powerful when handling structured, tabular data. Unfortunately, big data is often messy and irregular, and therefore difficult to analyze using a structured language like SQL. The last few years have seen the emergence of data processors that, while they lack some of the data rigor of SQL, can handle the ambiguous, amorphous data gathered from social media sources.
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