sitnl 2015 algorithm programming made easy (marcel de bruin, sander de wildt)
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Algorithm programming made easy
Sander de Wildt, The Next ViewMarcel de Bruin, SAPNov 2015
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© 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)
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© 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)
![Page 4: sitNL 2015 algorithm programming made easy (Marcel de Bruin, Sander de Wildt)](https://reader035.vdocument.in/reader035/viewer/2022062523/58ee7c021a28ab60348b45c1/html5/thumbnails/4.jpg)
AdvancedAnalytics Enterprise
Business Intelligence
Agile Visualization
Advanced Analytics
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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
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© 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
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© 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
![Page 8: sitNL 2015 algorithm programming made easy (Marcel de Bruin, Sander de Wildt)](https://reader035.vdocument.in/reader035/viewer/2022062523/58ee7c021a28ab60348b45c1/html5/thumbnails/8.jpg)
© 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
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© 2013 SAP AG. All rights reserved. 9
Predictive Use Cases
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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
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© 2014 SAP AG or an SAP affiliate company. All rights reserved.
Thank You
[email protected] engineer HANA/ BI
[email protected] Data Consultant