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Syndicated Data … Analysis for… Brand… Scientists.

Syndicated Data … Analysis for… Brand… Scientists. Learning Objective Give students hands-on experience using syndicated data to generate market insights, which in turn drive actionable category and product/brand plans. Some Questions We Often Ask. Score keeping

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Syndicated Data … Analysis for… Brand… Scientists.

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  1. Syndicated Data … Analysis for… Brand… Scientists. Learning Objective Give students hands-on experience using syndicated data to generate market insights, which in turn drive actionable category and product/brand plans.

  2. Some Questions We Often Ask • Score keeping • How are we doing vis-à-vis last year? the competition? the status quo? • Understand “causality” • What factors influence our sales and share? What is their relative influence? • Prescription • What should we do?

  3. What is Syndicated data? • Aggregation of structured or unstructured data from multiple people or companies for redistribution to the market. • Examples and Uses: • Sales: point-of-sale, consumer panel, shopper card • Attitudes & Trends: survey data (Mintel, Simmons, Forrester) • Economic: Repackaged public or government data • Media: TV/Radio/Print measurement, social media (facebook), mobile, internet (Buzz, ads, search), couponing Source: Eric Schmidt

  4. Big Data 15 out of 17 sectors in the United States have more data stored per company than the US Library of Congress *McKinsey Global Institute, June 2011 Source: Eric Schmidt

  5. Market Research Companies by U.S. Revenue Source: http://www.marketingpower.com/ResourceLibrary/Publications/MarketingNews/2012/6-30-12/Hono-Top-50-Chart.pdf

  6. Approximate Schedule • Day 1 (Wed. Feb.27) • Intro; Scanner Data; Intro to Market Response Analysis; Experimentation Lab; Market Response Analysis Lab; Category Analysis Lab; Resource Allocation • Day 2 (Thurs. Feb.28) • Promotion Analysis; Misc. Econometrics Topics; Vendor Perspective (guest speaker); Mktg Mix Models (guest speaker); Web Analytics (guest speaker); Social Media (guest speaker); SAS Programming Lab • Day 3 (Fri. Mar.1) • Brita Case Study; Valuing Customers; Leveraging Customer Databases (guest speaker); Reading Published Studies

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