Oracle Using Oracle Advanced Analytics to Target the Right Customers with the Right Oracle Products

Transcription

1 January 28, 2015 San Francisco Oracle Using Oracle Advanced Analytics to Target the Right Customers with the Right Oracle Products Sumbo Ajisafe, Snr Business Analyst Oracle Frank Heiland, Snr Business Specialist Ops Oracle

2 Speaker Bio Sumbo Ajisafe, MSc Senior Business Analyst Oracle Ireland, Dublin Lob: Applications, Technology and Systems Team: Oracle Direct EMEA Sales Programs at Oracle since: December 2010 Education studied: Business Analytics at: University College Dublin, Ireland

3 Speaker Bio Frank Heiland, MSc Senior Specialist Business Operations Oracle Germany B.V. & Co. KG, Munich Lob: Global Sales Operations Team: Sales Tools & Reporting at Oracle since: April 2008 Education studied: Information Systems and Managment at: University of Applied Science, Munich

4 Agenda Data Mining Customer Segmentation Frank Heiland Data Mining Experiences - Oracle Direct EMEA Sumbo Ajisafe

5 Objectives Overview on how we integrate and implement Oracle Advanced Analytics/Oracle Data Mining within the business How the data and predictive analytics process flow functions inside Oracle Share the experiences and lesson learned regarding the use of an Internal Oracle Data Mining process

6 Key Takeaways Learn how to integrate and implement Predictive analytics within the business Knowledge on Data Mining Process that can be replicated Example of use case Improvements from Lesson Learned

7 Oracle Using Oracle Advanced Analytics to Target the Right Customers with the Right Oracle Products Part 1 - Data Mining Customer Segmentation Frank Heiland Senior Business Specialist Operations Global Sales Operations January 28, 2015 Copyright 2015, Oracle and/or its affiliates. All rights reserved.

8 The customer to purchase next luxury shoes what are typical characteristics??? or?

9 Data Mining Platform Predict High Propensity Customers SOURCE SYSTEMS STAGING AREA OPERATIONAL DATA STORE DATA WAREHOUSE Data Mining Segmentation + Flatfile Flatfiles 3 Data Mining Cassification Models: > Decision Tree (DT) GSRT, GSI, DNB, Territory-List,... Data Feed: -Install base -Pipeline -Support -Service Requests -Marketing Responses -D&B Customer Information Relational Online No Relationship 3rd Normal Form Analytical Processing between Data Tables Mining Platform (ROLAP) Oracle Warehouse 12c + Advance Analytics Option + Oracle R Enterprise Automatic discovery of patterns Prediction of likely outcomes Oracle SQL Developer 4 + Oracle Data Miner + RStudio > Support Vector Machine (SVM) > Generalized Linear Model (GLM)

10 Oracle Data Miner DM Workflow Workflow begins with Install Base and Potential Customer

11 Oracle Data Miner Identify the best performing classification model

12 Oracle Data Miner Generalized Linear Model Customer Profile

13 Simplified Customer Profile Top20 out of 420 Characteristics Validation with Product Experts and Sales Program Management

14 Oracle Data Miner Generalized Linear Model - Prediction Probability split into buckets: Gold / Silver / Bronze

15 Target List Sample output Note

16 Target List Sample output Note

17 Oracle Using Oracle Advanced Analytics to Target the Right Customers with the Right Oracle Products Part 2 - Data Mining Experiences: Oracle Direct EMEA Use Case Oracle Direct EMEA Sales uses Oracle s own Oracle Advanced Analytics Database Option to build predictive models to help Oracle Sales target the right customers with the right products. Sumbo Ajisafe Business Analyst Oracle Direct EMEA Sales Programs January 28, 2015 Copyright 2015, Oracle and/or its affiliates. All rights reserved.

18 Data Mining Business Goals Increase pipeline creation and potential conversion to revenue, by: Identifying Target Customers with higher potential to buy specific products for Demand Generation Activities (Data Mining Classification - Generalized linear models) Delivering Quick Wins Analysis for Up- and Cross-Sell Opportunities (Data Mining Market Baskets Association models) Delivering increased Insight into Territory and Account Potential for Demand Generation Planning (Benchmarking)

19 Data Mining Project Team Oracle Direct Sales Manager, Sales Reps Campaign execution Campaign review Sales Progam Management Program Manager DemGen Planning & Mgts DM Analyst Type2 Gathering Requirements DM analysis Communicator Sales Operations DM Analyst Type1 DM Analysis DWH maintenance Data Intelligence

20 Data Mining Process Flow Gathering Requirements Preliminary Study Problem Definition Data Gathering and Preparation Model Building and Evaluation Plausability Check Market Basket Analysis Building the Case Table Accuracy Simulation Model Definition Avg. 5days Knowledge Deployment List Completion Quality Ensurance Oracle Data Mining Documentation 12c [ Campaign Execution Tracking Results

21 DM Outcome by prior Campaigns 3x Data Mining yielded three times as much opportunities than the traditional Segmentation Better conversion rate from Data Mining compare to the traditional Segmentation High Propensity Ranking split into Gold / Silver / Bronze 90% Sales Reps would like to receive Data Mining Ranking in FUTURE campaigns

22 Key Success Factor & Lesson Learned Use of Oracle Data Miner and Data Mining techniques Sales Enablement Identified high propensity accounts & ranking Systematic Data Mining Process in place

23

24 APPENDIX

25 What is Data Mining? Oracle Documentation: Data Mining Concepts 12c R1 Data mining is the practice of automatically searching large stores of data to discover patterns and trends that go beyond simple analysis. Data mining uses sophisticated mathematical algorithms to segment the data and to predict the likelihood of future events based on past events. Key properties: Automatic discovery of patterns Prediction of likely outcomes Creation of actionable information Focus on large data sets and databases [

26 Target List Sample output Note

27 ODM Prediction Gold / Silver / Bronze comparison Predictive Customer Profile Telecom company Telecom company Asset Mgmt Bank Typical Customer Profile

28 SR and SPM Feedback for DM Sales Campaign Support I have to be honest; this is one of the most comprehensive and extensive analysis I have ever seen within Oracle I want to say a big thank you as the data you provided was fantastic,.. We got 8 opportunities on the day for the ODA campaign Using the data mining information (note and scoring in fusion) and reps think it s very useful and positive feedback, using the scoring to priorities Quite Satisfied with the Account, the install base information was helpful. I am happy with the propensity to buy, use for future campaign Good and accurate data, right customer to target for this campaign Like the segmentation, probability to purchase is very positive For the reps that used the 20;20 plan with data mining and great sales plays from Gerhard, this was a winning formula for success in terms of opening and closing pipeline! Very much in favour of the data mining approach, want to use for future Over 90% of the Sales Reps would like to receive Data Mining Ranking in FUTURE campaigns, indicating satisfaction with the quality and results of the target accounts

29 Data Mining activities Preliminary Analysis (Benchmarking) Cluster Industry # of of Employees Annual turnover TopN Products Year Started Pipeline by Sales Channel Where is the Whitespace? What s the best product sold? Are we able to achieve the expected campaign value with a specific target product?

30 Data Mining activities Customer Classification (Data Mining Classification - Generalized linear models) Here: Ranking of customers buying propensity for RAC into categories: Gold, Silver and Bronze What are the preferred customers to target within a campaign or territory? Which customer to prioritise for a specific target product within a campaign? What is the typical customer profile for an oracle product? Does a specific target product have enough potential to successfully drive a campaign?

31 Data Mining activities Quick Wins (Data Mining Market Baskets Association models) Product Win rate % Database Vault 31 Database Vault+ Advanced Security 43 Database Vault+ Data Masking Pack 51 Database Vault+ Advanced Security+ Data Masking Pack 60 An increase of the win rate of DB Vault Ops can be achieved by adding Advanced Security or Data Masking Pack What Oracle products are frequently sold together? Which products are potential up- and cross sell Opportunity? Quarterly Spenders analysis

32