MGMT E-6750 Marketing Analytics: a Source of Informational Advantage

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MGMT E-6750

Marketing Analytics: a Source of Informational Advantage

Harvard Extension School – Spring 2013

Wednesday 7:40 pm – 9:40 pm; 53 Church Street, Rm. 203 Instructor: Andrew Banasiewicz, Ph.D.

Email: [email protected] or [email protected]

Phone: 617-620-7235

Course Summary:

The emergence and rapid proliferation of electronic transaction processing systems, coupled with the rise of Internet-enabled communication infrastructure resulted in digital torrents that outstretch many organizations’ data processing abilities. At the same time, convergence of ever-increasing competitive pressures, brisk pace of technological innovation and rapidly changing consumer preferences lead to the heightening of the need for more and better decision-guiding insights. Not surprisingly, data analytic proficiency is quickly becoming one of the key determinants of organizations’ competitiveness.

Nowhere is the opportunity to leverage data more pronounced than in marketing, where robust analytics can be the “difference-maker” in new customer acquisition, current customer retention and customer base value maximization efforts. Yet, although virtually all mid-size and larger organizations have access to essentially the same types of data, there are considerable cross-firm differences in data analytical proficiency – why is that the case? Primarily because teaching of marketing database analytics has not kept pace with the rapidly emerging and evolving practice of marketing analytics. It is the stated goal of this course to offer an introductory overview of database analytics – more specifically, to explain, in easy-to-follow terms, the process of translating large volumes of diverse data into

competitively-advantageous, decision-guiding knowledge.

Course Timeline

Jan. 30: Introduction and Setting the Stage

The Knowledge Bottleneck: Overabundance of Data and Scarcity of Insights.

 Data are vast, both volume- and type-wise: Is everything that is knowable worth knowing?

 Storage is cheap: In1970s, it cost about $1 million per terabyte – it is $50 per terabyte today;

 Growth is explosive: Projected 60%+ compound year-over-year growth in data volume …

But:

 Data sources are often incommensurate: Captured via dissimilar means and structured differently;

 About 95% of all is non-numeric: Deriving insights out of unstructured, textual data is full of difficulties…

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Hence:

 The majority of business organizations are – still – just purveyors of information: The data-information-knowledge continuum;

 Though boasting high potential value, the realized value of data is often quite low: Generic information vs. competitively advantageous insights.

Jan. 30 Readings: Data Explosion

From Information to Audiences

Big Data: Powering the Next Industrial Revolution

Feb. 6 and Feb. 13: Generalizable data types and sources.

 Structured vs. unstructured data: Historically, marketers focused on structured data, but 95% of marketing-usable data is unstructured – the opportunity is knocking…

 The “old” Big Data: The product of electronic transaction processing.

 The “new” Big Data: Open-ended online communications.

 Is data an asset?

Feb. 6 and Feb. 13 Readings:

Intelligence for Everyone McKinsey Big Data Report

Feb. 20: Knowledge creation, competitive advantage and multisource analytics.

 What is knowledge and how is knowledge created?

 Data as a source of knowledge and knowledge as a source of competitive advantage.

 Data-derived, competitively-advantageous, decision-guiding insights.

 Multi-source data analytics: A “must” of transforming data into informational advantage.

Feb. 20 Readings:

BA and the Path to Better Decisions Marketing Database Analytics: Excerpt 1

Feb. 27: Data mining vs. predictive analytics.

 Data exploration vs. hypothesis testing as a source of decision-guiding insights.  Data mining: Exploring the available data for worthwhile insights.

 Text mining as a subset of data mining.

 Predictive analytics: Using yesterday’s facts to estimate the future.

Feb. 27 Readings:

The Power of Predictive Analytics

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March 6: Purpose-driven data exploration - the Marketing Database Analytics (MDA) process.

 The repeatability of marketing decisions: New customer acquisition, current customer retention and customer base optimization.

 Ad hoc insights vs. ongoing informational flows.

 The Marketing Database Analytics process : Approach and philosophy.

 Analytic roadmap: Business goals, informational needs, data and methods.

March 6 Readings:

Marketing Database Analytics: Excerpt 3

March13: The Marketing Database Analytics process - Understanding the data.

 The data exploratory process.

 Data basics: Data sources, data types and databases

 Numeric vs. text data

 Metadata.

Marketing Database Analytics Excerpt 4

March 27: The MDA process: Describing the structure of the customer base.

 Defining value-based customer segments to guide customer retention efforts.  Segmentation types and choosing the “right” one (or ones)

 Analytical selection process.

April 3: The MDA process: Loyalty analytics.

 Customer loyalty vs. product/service repurchase.  Operationalizing loyalty.

 Enhancing the accuracy of buyer loyalty classification.

April 3 Readings:

April 10: The MDA process: New customer acquisition.

 Predictive analytics as the tool of choice.

 New customer acquisition: Finding high value, retainable customers.

 Pitfalls of self-selection driven customer acquisition: The retention-acquisition link.

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April 17: The MDA process: Promotional mix optimization.

 The growing commercial clutter: Everyone’s talking – is anyone listening?

 Big Data and general advertising measurement: The new frontier of marketing science.  Maximizing the overall benefits by integrating individual elements.

April 24: The MDA process: Measuring the impact of promotions.

 A tale of two campaigns and the problem of impact measurement.

 Response rates and effectiveness: Often confused – rarely aligned.  Treatment-attributable incrementality.

May 1: From analytic findings to better decisions: Results as the beginning – not the end.

 Model is built…now what?

 The science of analysis and the art of communication.

 Analytic insights and decisioning: Dashboards and scorecards.  Aligning analytic results with stakeholder needs and preferences.

May 1 Readings:

May 8: Analytics as an ongoing process – not an isolated event; course wrap-up.

 Rescoring, refreshing and restaging: Keeping the system and results current.  “In God we trust – all others bring data.”

 Overcoming resistance and creating a habit of data reliance.

May 8 Readings:

Course Policies and Requirements

Class Policies

 Assignment Completion & Late Work – Assignments must be turned in by the dates specified in this syllabus; late submissions will be penalized at the rate of 5 point reduction (on the standard 100 point scale) per day, computed based on the previous day’s maximum number of points — for example, if an assignment can earn a maximum of 100 points, being 1 day late will reduce the maximum number of points to 95, being 2

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or a concern regarding your assignment, please see me before or after class or contact me via email.

 Academic Conduct Code – Cheating and plagiarism will not be tolerated in any Metropolitan College course. They will result in no credit for the assignment or examination and may lead to disciplinary actions. Please take the time to review the Student Academic Conduct Code:

 Grading Criteria – All assignments, including the mid-term exam, will be graded on a standard 100-point scale; final grades will be given on the basis of the guidelines provided by the school.

Class Requirements

The overall class performance will be determined as a weighted average of the following:

Class participation 10%

Case analysis (due March 6) 10%

Data exploration exercise (due March 27) 10%

Customer segmentation exercise (due April 3) 15%

Predictive analytics exercise (due April 17) 20%

Final project (due May 13) 35%

Additional details describing individual assignments will be provided in a separate document.

Readings, Data and Software

Readings

 The readings referenced in the Course Timeline section will be made available prior to the start of the class.

Data

 Sample data (for use during class instruction and outside-of-class work) will be provided by the Instructor – however, if you would rather use your own data, you are welcome to do so.

Software

 The hands-on part of the course (i.e., conducting data analyses) will utilize the IBM SPSS data analytical software. If you are interested in being able to gain access to the

aforementioned application outside of the University, you can “rent” it from IBM at a cost of $49 (plus $4.99 download fee) for a period of 6 months – here is the link to an authorized distributor:

http://e5.onthehub.com/WebStore/OfferingDetails.aspx?ws=49c547ba-f56d-dd11-bb6c-0030485a6b08&vsro=8&o=2c77a355-182b-e111-8d82-f04da23e67f6