An Introduction to Retail Big Data & Predictive Analytics
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An Introduction to Retail Big Data & Predictive Analytics

An Introduction to Retail Big Data & Predictive Analytics

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New York Marriott East Side

525 Lexington Ave

New York, NY 10017

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This workshop is a perfect opportunity for an architect, technology evangelist or manager to gain practical knowledge of best practices for Retail Big Data & Predictive Analytics. Offered by practitioners with decades of experience in Retail, the workshop emphasizes learning in a hands-on environment.

Attendees who complete the course will receive a digital Certificate of Completion. We also provide verification of attendance and successful course completion.

The key take-aways from this workshop include:

Recommendations on Technology Architecture

  • Cloud vs. on-premise
  • Key components of a big-data environment for Retail

Big Data Extraction, Transformation & Cleansing (PostgreSQL/Redshift & Hadoop)

  • Collecting data from corporate, competitor and syndicated data sources
  • Real-time event processing
  • Job scheduling and error handling in a cloud environment
  • Data 'cleansing' for in-stock, promotions & pricing effects

Scalable Analytical Tools & Techniques (R, Python)

  • K-means clustering to identify store/customer segmentation based on customer, market and store factors
  • Collaborative Filtering to identify item-item similarity as part of a marketing recommendation engine
  • Information Gain Analysis, PCA/SVD and Correlation Analysis to select product & assortment attributes that influence sales
  • Multi-variate regression modeling of temperature and precipitation to understand seasonality by product category & store
  • Self, cross-product and event elasticity to inform price and promotions
  • Decision Tree (CART, Random Forest) models to predict expected sales based on product & assortment attributes
  • A/B Testing Methods

Data Visualization Tools & Techniques (Tableau)

  • Executive dashboard: Understanding retail sales drivers (Traffic, Conversion, UPT, AUR)
  • Floor set frequency analysis: shopper cohort by revenue/margin vs. visit frequency
  • Size selling & case-pack buying
  • A real-world example: A buying tool for merchants
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Date and Time

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New York Marriott East Side

525 Lexington Ave

New York, NY 10017

View Map

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