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http://www.a4everyone.com
DESIGNING MACHINES FOR DATA MINING
Alexander Efremov
Challenges in Retail Industry
http://www.a4everyone.com
 Data Science, Data
 Data Mining Workflows – Specifics
 Analytics for Retail
Agenda
http://www.a4everyone.com
Data Science?
http://www.a4everyone.com
Data Science?
http://www.a4everyone.com
Data?
Data Science?
http://www.a4everyone.com
Data Mining Workflows – Specifics
 Use Data
 Use Business Logic
 Reduce/Remove human intervention
http://www.a4everyone...
Analytics for Retail
 Why A4Retail?
Demand forecast, Storage planning, Price Optimization, etc.
 Retail System – Spec.
D...
……
Analytics for Retail
………
Retail
System
product1:
price, disc., adv., disp...
sale1
salen
…
weather
social events
other
...
Analytics for Retail
Data Prep Modelling Validation
Business knowledgeData
no yes
Sales
Forecast
& Stats
http://www.a4ever...
Analytics for Retail
Data Prep
Cleaning
- Miss, Outs, LowVar
Preprocessing
- Vars decomp. /detrend,
deseas./
- Transform
-...
Analytics for Retail
 Wrong Interconnections
http://www.a4everyone.com
Analytics for Retail
 Multistage Modelling
Modelling 2
Modelling 1
Fact. Set 1
Product
Modelling 3
Fact. Set 2
Category
F...
Analytics for Retail
A4Retail – Results
www.a4everyone.com
alexander.efremov@a4everyone.com
Thank you!
http://www.a4everyone.com
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Designing machines for Data Mining

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Data analytics challenges in Retail

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Designing machines for Data Mining

  1. 1. http://www.a4everyone.com
  2. 2. DESIGNING MACHINES FOR DATA MINING Alexander Efremov Challenges in Retail Industry http://www.a4everyone.com
  3. 3.  Data Science, Data  Data Mining Workflows – Specifics  Analytics for Retail Agenda http://www.a4everyone.com
  4. 4. Data Science? http://www.a4everyone.com
  5. 5. Data Science? http://www.a4everyone.com
  6. 6. Data? Data Science? http://www.a4everyone.com
  7. 7. Data Mining Workflows – Specifics  Use Data  Use Business Logic  Reduce/Remove human intervention http://www.a4everyone.com
  8. 8. Analytics for Retail  Why A4Retail? Demand forecast, Storage planning, Price Optimization, etc.  Retail System – Spec. Dynamic, Time-Varying, Non-Linear, MIMO system  Retail Data – Spec. Missings, Outliers, Uninformative vars, Seasonality, Trend, etc. http://www.a4everyone.com
  9. 9. …… Analytics for Retail ……… Retail System product1: price, disc., adv., disp... sale1 salen … weather social events other productn: price, disc., adv., disp... http://www.a4everyone.com …
  10. 10. Analytics for Retail Data Prep Modelling Validation Business knowledgeData no yes Sales Forecast & Stats http://www.a4everyone.com
  11. 11. Analytics for Retail Data Prep Cleaning - Miss, Outs, LowVar Preprocessing - Vars decomp. /detrend, deseas./ - Transform - Standardization - Remove multicol., etc. Modelling & Validation Multistage modelling SWR - Model dev Model val Sales forecast & Stats Model destandardization Detransformation Add trend & seas http://www.a4everyone.com
  12. 12. Analytics for Retail  Wrong Interconnections http://www.a4everyone.com
  13. 13. Analytics for Retail  Multistage Modelling Modelling 2 Modelling 1 Fact. Set 1 Product Modelling 3 Fact. Set 2 Category Fact. Set 3 Rest y e1 e2 Depend. http://www.a4everyone.com
  14. 14. Analytics for Retail A4Retail – Results www.a4everyone.com alexander.efremov@a4everyone.com
  15. 15. Thank you! http://www.a4everyone.com

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