1. Customer Analytics POC for a global retailer, using Oracle Advanced Analytics
Magnus Perman, Senior Consultant/Solution Architect SIGMA IT Consulting

2. Abstract
The aim of this presentation is to show how a global retailer in a POC project was able to see how it possibly could increase its analytical capabilities by the OAA option in the existing Oracle RDB.
Testcases for descriptive, predictive and prescriptive analytics were verified with the available data and enabling of the OAA tools. The goal is to be able to know the customer better, deliver more personalized communication and a better customer experience.
The presentation will elaborate on the process of building models to test different hypothesis and the learnings along this process. Also what kind of people that were needed in the process of building models. Business analyst, knowing the business needs and processes best. Data engineer, knowing what kind of data and integrations are available. Data scientist, knowing how the data can be used to create code for predictive and prescriptive analytics. DBA, optimizing the db for purpose.
Process for testing the different types of analytics: 1) Hypothesis 2) Data exploration phase (can drive hypothesis) 3) Data quality (stop, filter or improve source) 4) Model development 5) Validation and refinement.
Lessons learned by running the more advanced analytics processing inside the database with ORE instead of on local machines R sessions. This is a great improvement in terms of both performance, stability and security. Potential of running analytics for many countries on the same platform with possibility of building a common ground for collaboration around analytics.

3. Speaker Bio Magnus Perman, SIGMA IT Consulting, Sweden
M.Sc. Computer Science, LTH, Lund Sweden
Senior Consultant/Solution Architect
Global clients within Retail, Industry and Media
Projects within DW, BI and Analytics since 2004
Mail:
Mobile:

4. Agenda SIGMA IT Consulting Customer Analytics POC, background
Team Setup
Oracle Advanced Analytics
POC setup
Test cases
Evolution of analytics
Process
Quick wins
Lessons learned, key takeaways
The agenda of this session, start with a short presentation of the company I work for and then move into the Customer Analytics POC background etc.

5. WHY SIGMA
The Sigma vision - EXPECT A BETTER TOMORROW. With our vision we want to create a better future for our customers and the society at large.

6. Swedish IT consultant with operations in Scandinavia and UK
Part of the Sigma consulting group
850 employees
Specialists within IT & Management
Oracle Gold Partner
SIGMA IT Consulting part of the SIGMA group, we are specialists within IT & Management.

7. Different companys within the SIGMA group.
Sigma IT Consulting – IT focused consulting..
If someone asks:
Sigma IT Consulting – Consulting in Strategy, Technology, Communications and Recruitment, with a focus on IT
Sigma Technology – a global supplier of product information, software and embedded solutions, and “offshore” development opportunities.
Sigma Civil – fast growing consulting firm within social structure with a focus on the infrastructure and construction planning, such as roads, geotechnical, environmental, sanitary engineering, etc.
Sigma Industry – provide the industry with competitive engineers within technology and development.
Sigma Connectivity – Internet of Things, advanced mobile technology and connectivity (formerly Sony Mobile)
Sigma Software – production and support of software “offshore” (mainly Ukraine and Poland)

8. SIGMA WORLDWIDE LOCATIONS SWEDEN DENMARK FINLAND NORWAY GERMANY
HUNGARY
POLAND
UKRAINE
UNITED KINGDOM
CHINA
USA
We present not only our company – it’s the whole Sigma as we are highlighting
Focus on: world leading expertise and local roots with global resources.

9. SIGMA FACTS 3000 2000 SIGMA 11 27 CONSULTANTS IN OUR NETWORK
COUNTRIES
A LEADING SWEDISH CONSULTING GROUP 27
LOCATIONS IN
SWEDEN
The whole Sigma in numbers
Pay attention to updates. Latest version of this presentation is always available on our intranet.
SIGMA GROUP: SIGMA IT CONSULTING, SIGMA TECHNOLOGY, SIGMA CONNECTIVITY, SIGMA CIVIL, SIGMA INDUSTRY, SIGMA SOFTWARE

10. Customer Analytics
Let’s move into the Customer Analytics POC

11. Customer Analytics POC - background
POC for a global retailer around customer analytics to prove the value of analytics and evaluate Oracle Advanced Analytics based on requirements from a pre-study.
Requirements included outcome from pre-study interviews with future users and stakeholders and also included customer centric use cases.
Customer Analytics POC for a global retailer. A LOT of Loyalty members in an existing DW.

12. Customer Analytics POC - background
Why customer analytics?
Customers of today expect retailers to understand their needs and provide meaningful and relevant communication and offers.
Customer centric approach.
Relationship building with the customer.
Needs to be maintained, can quickly turn bad.
Builds customer trust, advocacy and loyalty.
Leads to higher customer lifetime value and attracts more loyal customers.
So what is then customer analytics and why is it needed?

13. Customer Analytics POC - background
Customer centric decisions
Pesonalised communications
Why customer analytics?
Gearwheels of customer analytics.

14. Customer Analytics POC - background
Higher CLV
Why customer analytics?
Personalised communication
Customer trust
Customer advocacy
$$$
More customers
That buy more
And stay longer
Sustainable growth

15. Team setup
Business Analyst, knowing the business needs and processes best.
Solution Architect, responsible for the solution design.
Data Engineer, knowing what kind of data and integrations are available. Enables the data for analytics.
Data Scientist, knowing how to create models for predictive and prescriptive analytics with the data available.
Data analyst being able to use the models to gather insights from the data.
DBA, monitoring and optimizing the database for purpose.
People and roles that were part of the project

16. Oracle Advanced Analytics
A licensed option in the Oracle Database Enterprise edition.
Extends your Oracle db into an analytics platform.
Consists of two major components
Oracle R Enterprise
Oracle Data Miner
Enables Business Analysts and Data Scientists to perform advanced analytics through the Dataminer GUI (part of SQL developer), with analytics SQL or through any R IDE which will use the ORE transparency layer to run R code and translate it to run directly in the database.
R #1 and SQL #4 top rated analytics languages by KDNuggets.
This is the first session today about Oracle Advanced Analytics. Will briefly go through the product.

17. Oracle Advanced Analytics
SQL developer Dataminer GUI
RStudio
- ”Drag-and-drop analytics”
- Easy to use interface for data analysts
- No need to know SQL or R
- ”Data scientist playground”, coding
- R code runs directly in the database (ORE)
- Endless capability for modelling and statistics through the R community
Different ways of working for different type of users.

18. POC setup Oracle 12 Rdb with Advanced Analytics Option enabled.
AIX server with Oracle R Distribution (ORD) installed.
Clients running Oracle SQL Developer with Dataminer GUI and RStudio connecting to the db through Oracle R Enterprise (ORE).
Additional R packages installed on client/server from the CRAN library. For example ROracle for adhoc analytics running locally on client.
Platform setup. A LOT of Loyalty members in an existing DW, +100M, years of transaction data.

19. Client/Server setup.
Focusing on a couple of selected countries. Same schema setup in all countries, which makes models reusable. +100M loyalty members. Years of profile, transaction and interaction data->Goldmine. Github used as collaboration tool.

20. Test cases Test cases around three different types of analytics
1) Descriptive
Traditional BI, segmenting, KPI’s etc. Understanding historic data.
2) Predictive
Based on known historic data, predict future values or behavior.
3) Prescriptive
Business rules on top of predictions suggesting actions.
Descriptive: segmenting, demographics, KPI’s, visualizing data on maps for example.
Predictive: Recommendation engine. Member lifetime value.
Prescriptive: Next best action.

21. Test cases
Test cases around three different types of analytics, examples.
1) Descriptive
Segment customers based on what articles they bought.
2) Predictive
Create a recommendation engine that recommends articles based on what they bought / browsed / etc.
3) Prescriptive
Next best action. For example “serve” instead of “sell”.
Descriptive: segmenting, demographics, KPI’s, visualizing data on maps for example.
Predictive: Recommendation engine. Member lifetime value.
Prescriptive: Next best action.

22. Evolution of analytics
Data volumes have of course increased over time which also enables more advanced analytics.

23. Analytics is a hot topic
In a big company there might be more than one analytics project going on at the same time right now. Customer Analytics, Corporate Analytics, Web analytics, HR analytics, etc.
Differentiate how your analytics project deliver unique value in it's own domain. In our case, customer analytics.
Work with storytelling around the insights gathered through analytics and the value this creates. Adapt the message for different audience, if the audience is business managers skip the technical language.
Align and collaborate with the other ongoing analytics projects so that you don't overlap or have conflicting goals.
Decide on how you will visualize the insights gathered and/or how these will feed into other systems.
Platform setup. A LOT of Loyalty members in an existing DW, +100M, years of transaction data.

24. MOVE FAST
Platform setup. A LOT of Loyalty members in an existing DW, +100M, years of transaction data.

25. Business Understanding
Process
Business Understanding
Data Understanding
Data Preparation
Modeling
Evaluation
Deployment
CRISP(-DM)
Hyphothesis driven
Data driven
Data Exploration
> 50 % of time
Data exploration 50% here the process might stop due to dataquality issues etc. Filter, correct or stop.

26. Process Hypothesis driven Data driven
Strong business background and domain knowledge
Start with a hypothesis and try to prove it through modelling
Data driven
Unprejudiced, find new unexpected patterns and correlations
Letting the data create the hypothesis
Watch out for data quality issues
Two approaches to select what is to be modelled.

27. Quick wins
For existing Oracle DW/DB’s for BI/CRM etc. there is a short time to market to deliver advanced analytics compared to implementing a new analytics platform from start.
There is a big gain in starting to deliver the “not-so-advanced” analytics like segmentation etc. to deliver business insight. Start with low-hanging fruits and build on that.
Data quality issues becomes very visible and this puts a focus on solving them.
1) Huge gain to start open up the data in DW for BI/CRM systems etc. for analytics with R code and Dataminer. Compared to perhaps only having a reporting tool.

28. Key takeaways
ORE will improve security, stability and performance of the models implemented. Ease of use to productionize analytics.
Data scientists can directly process the database with the R language and leverage the latest developments in the huge R community, inside the database.
Different channels combined will give great predictive power.
Analytics data repository, one part static read-only for consistence and one part very flexible for importing new type of experimental datasets, aggregations etc.
R libraries are always precompiled for Linux and not AIX.
1) Data scientists running code on data locally, security risk etc.
2) Opens up to the R community, run code locally.
3) Channels: Web, Customer Support, Transactions.
4) Nature of data science, flexible with new datasources etc.