學門類別
哈佛
- General Management
- Marketing
- Entrepreneurship
- International Business
- Accounting
- Finance
- Operations Management
- Strategy
- Human Resource Management
- Social Enterprise
- Business Ethics
- Organizational Behavior
- Information Technology
- Negotiation
- Business & Government Relations
- Service Management
- Sales
- Economics
- Teaching & the Case Method
最新個案
- A practical guide to SEC ï¬nancial reporting and disclosures for successful regulatory crowdfunding
- Quality shareholders versus transient investors: The alarming case of product recalls
- The Health Equity Accelerator at Boston Medical Center
- Monosha Biotech: Growth Challenges of a Social Enterprise Brand
- Assessing the Value of Unifying and De-duplicating Customer Data, Spreadsheet Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise, Data Supplement
- Building an AI First Snack Company: A Hands-on Generative AI Exercise
- Board Director Dilemmas: The Tradeoffs of Board Selection
- Barbie: Reviving a Cultural Icon at Mattel (Abridged)
- Happiness Capital: A Hundred-Year-Old Family Business's Quest to Create Happiness
Analytics for Marketers
內容大綱
Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are further refined, managers may struggle with when, where, and how much to incorporate machines into their business analytics, and to what extent they should bring their own judgment to bear when making data-driven decisions. In general, humans are better at decisions involving intuition and ambiguity resolution. Machines are far superior at decisions requiring deduction, granularity, and scalability. How can you find the right balance? There are three common approaches to analytics: descriptive, where decisions are made mainly by humans; predictive, which combines aspects of the other two; and prescriptive, which usually means autonomous management by machines. This article describes when and how to use each approach and examines the trade-offs and limitations. Although the focus is on marketing and sales, the principles may be applied more broadly.