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Online retailing is growing very fast, with Forrester Research predicting a 17% increase in 2008. As more consumers switch to online retailing as their primary channel, data analytics can help you be more nimble and gain a larger market share. Behavioral targeting and personalization requirements are driving up the need for larger data sets and more atomic-level data analysis. You can now segment the long tail of customers and products at a more atomic level to provide the right product to the right customer at the right time.

Aster provides the analytic horsepower required on a massive scale for e-tailing applications such as:

  • Recommendation engines increase average order size by recommending complementary products based on predictive analysis for cross-selling
  • Cross-channel analytics sales attribution, average order value, lifetime value (e.g., How many in-store purchases resulted from a particular recommendation, advertisement, or promotion)
  • Event analytics what series of steps (golden path) led to a desired outcome (e.g., purchase, registration, etc.)
  • Segmentation loyalty campaigns and offers to distinct segments and individuals

Web analytics path analysis, attribution, user engagement, and more. This is critical in making relevant e-tail site design decisions to deliver the right content and advertisements to the most relevant user segments, improving their shopping experience.

Common issues impacting e-tailing data warehouses:

E-Tailing Issue Technical Causes
The recommendation engine rules are stale Loading Performance, Query Performance
Accounting for seasonality in sales analysis - not enough historical detailed data Data Growth
Path analysis and other complex queries are difficult to run Query Performance
There is simply too much data to load into warehouse quickly Data Loading


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