This document discusses algorithm programming and predictive analytics. It introduces predictive analytics libraries and functions that can be used for automated predictive insights. Examples of predictive use cases for retail are provided, such as store segmentation, product launch profiling, and customer loyalty analysis. Common predictive algorithms like Apriori are explained. The goal of advanced analytics is to incorporate predictive results into more applications and processes to drive better business outcomes and act in real-time.

1. Algorithm programming made easy
Sander de Wildt, The Next View
Marcel de Bruin, SAP
Nov 2015

2. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 2
Predictive analytics
HANA
AFL_PAL
Application
function library –
predictive analytics
libary
APL
Automated predictive
insight
Predictive functions are everywhere
Automated
(KXEN/SAP Infinite
Insight
Expert
(SAP Predictive Analysis)

3. © 2013 SAP AG or an SAP affiliate company. All rights reserved. 3
Predictive analytics
HANA
AFM
Application
function library –
predictive analytics
libary
APL
Automated predictive
insight
Predictive functions are everywhere
Automated
(KXEN/SAP Infinite
Insight
Expert
(SAP Predictive Analysis)

4. Advanced
Analytics Enterprise
Business Intelligence
Agile
Visualization
Advanced
Analytics

5. How analytics need to evolve to deliver
collective insights
Raw
Data
Cleaned
Data
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?
UserEngagement
Maturity of Analytics Capabilities
Self
Service BI
Generic
Predictive Analysis
End-to-end
Easy adoption
Fast implementation
Business focused
Enable storytelling
CollectiveInsight

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RETAIL
Store Segmentation &
Performance
Returner Segmentation
& Targeting
Product Launch
Demographic Profiling
for Marketing
Markdown
Optimization
Real-time Personalized
Offers
Buyer Classification &
Churn Prevention
Sales and Inventory
Forecasting
Customer Sentiment
Analysis
Customer Loyalty
Analysis
Short-term Check-out
Prediction
Market Basket
Analysis
Predictive Use Cases

7. © 2013 SAP AG. All rights reserved. 7
RETAIL
Store Segmentation &
Performance
Returner Segmentation
& Targeting
Product Launch
Demographic Profiling
for Marketing
Markdown
Optimization
Real-time Personalized
Offers
Buyer Classification &
Churn Prevention
Sales and Inventory
Forecasting
Customer Sentiment
Analysis
Customer Loyalty
Analysis
Short-term Check-out
Prediction
Market Basket
Analysis
Predictive Use Cases

8. © 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 Milk
1 Bread
2 Butter
3 Beer
4 Milk
4 Bread
4 Butter
5 Milk
5 Butter Support Milk & Bread
Support Milk X Support Bread
= 1,6

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Predictive Use Cases

11. 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 Analytics
Confidently anticipate what comes next
to drive better business outcomes
PREDICT

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Thank You
m.de.bruin@sap.com
Solution engineer HANA/ BI
sander.de.wildt@thenextview.nl
Big Data Consultant