sitNL 2015 algorithm programming made easy (Marcel de Bruin, Sander de Wildt)
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Use this title slide only with an image Algorithm programming made easy Sander de Wildt, The Next View Marcel de Bruin, SAP Nov 2015
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Transcript of sitNL 2015 algorithm programming made easy (Marcel de Bruin, Sander de Wildt)
Use this title slide only with an image
Algorithm programming made easy
Sander de Wildt, The Next ViewMarcel de Bruin, SAPNov 2015
© 2013 SAP AG or an SAP affiliate company. All rights reserved. 2
Predictive analytics
HANAAFL_PAL
Application function library –
predictive analytics libary
APL
Automated predictive insight
Predictive functions are everywhere
Automated(KXEN/SAP Infinite
Insight
Expert(SAP Predictive Analysis)
© 2013 SAP AG or an SAP affiliate company. All rights reserved. 3
Predictive analytics
HANAAFM
Application function library –
predictive analytics libary
APL
Automated predictive insight
Predictive functions are everywhere
Automated(KXEN/SAP Infinite
Insight
Expert(SAP Predictive Analysis)
AdvancedAnalytics Enterprise
Business Intelligence
Agile Visualization
Advanced Analytics
How analytics need to evolve to deliver collective insights
RawData
CleanedData
Standard Reports
Ad Hoc Reports &
OLAP
Agile Visualization
Predictive Modeling
Optimization
What happened?
Why did it happen?
What will happen?
What is the best that
could happen?
Use
r Eng
agem
ent
Maturity of Analytics Capabilities
Self Service BI
Generic Predictive Analysis
End-to-endEasy adoption
Fast implementation Business focused Enable storytelling
Col
lect
ive
Insi
ght
© 2013 SAP AG. All rights reserved. 6
RETAIL
Store Segmentation & Performance
Returner Segmentation& Targeting Product Launch
Demographic Profiling for Marketing
Markdown Optimization
Real-time PersonalizedOffers
Buyer Classification & Churn Prevention
Sales and Inventory Forecasting
Customer Sentiment Analysis
Customer Loyalty Analysis
Short-term Check-outPrediction
Market BasketAnalysis
Predictive Use Cases
© 2013 SAP AG. All rights reserved. 7
RETAIL
Store Segmentation & Performance
Returner Segmentation& Targeting Product Launch
Demographic Profiling for Marketing
Markdown Optimization
Real-time PersonalizedOffers
Buyer Classification & Churn Prevention
Sales and Inventory Forecasting
Customer Sentiment Analysis
Customer Loyalty Analysis
Short-term Check-outPrediction
Market BasketAnalysis
Predictive Use Cases
© 2013 SAP AG. All rights reserved. 8
APRIORI Algorithm
LIFT:
Confidence :How often when a customer bought A he also bought B. Milk & Bread = 2/3 because out of 3 transactions that contain Milk, only 2 contain the Milk & Bread
Support:How many times does a article combination
Occur. Milk & Bread = 2/5
Transaction product
1 Milk1 Bread2 Butter3 Beer4 Milk4 Bread4 Butter5 Milk5 Butter Support Milk & Bread
Support Milk X Support Bread = 1,6
© 2013 SAP AG. All rights reserved. 9
Predictive Use Cases
Predictive results incorporated into more applications and processes, for more users
Bridges the skills gap between human experts vs. analytical experts
Predict and act in real-time
Advanced AnalyticsConfidently anticipate what comes nextto drive better business outcomes
PREDICT
© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank You
[email protected] engineer HANA/ BI
[email protected] Data Consultant
