Predictive Analytics Incorporating Predictive Analytics as Part of the Innovation Process in the Food Industry

Transcription

1 Predictive Analytics Incorporating Predictive Analytics as Part of the Innovation Process in the Food Industry 6 th May 2012 Barry McIntyre

2 What is DataMining? Mining A Why user-centric, do we need interactive process a Process-based which leverages Approach analysis technologies to data mining? and computing power to find relationships that have not previously been discovered

3 What Data Mining is not Data mining is not Blind application of analysis/modeling algorithms Data mining is not Brute-force crunching of bulk data A difficult to understand technology requiring an advanced degree in computer science

4 There s Reporting and then there s Predictive Analytics 4

5 5

6 Data Mining versus OLAP Data Mining versus OLAP OLAP - On-line Analytical Processing Provides you with a very good view of what is happening, but can not predict what will happen in the future or why it is happening

7 7 What is Predictive Analytics?

8 8 What does it involve?

9 9

10 Data Sources Enterprise Data Sources Marketing Attitudinal Interaction Web Call-center Operational Interaction data - Offers - Results - Context - Click streams - Notes Descriptive data - Attributes - Characteristics - Self-declared info - (Geo)demographics Attitudinal data - Opinions - Preferences - Needs - Desires Behavioral data - Orders - Transactions - Payment history - Usage history Customer Contact Channels Website Phone Mail Branch ATM Agent Mobile

11 3. Capture Feedback Through Any Channel 2008 SPSS Inc. 11

12 3. The Value Of Attitudinal Data in more effective Targeting BUYING BEHAVIOR Married Males < 30 DEMOGRAPHIC SEGMENTS Single Males < 30 Married Men > 30 Single Men > 30 Product A Product B Same demographics 69 but 950 different 92 behavior?? 22 Product C

13 3. The Value Of Attitudinal Data in more effective Targeting BUYING BEHAVIOR Married Males < 30 DEMOGRAPHIC SEGMENTS Single Males < 30 Married Men > 30 Single Men > 30 Product A Product B Product C Same 69 behavior and different demographics??

14 3. Improving Predictive Accuracy Enterprise Data Sources Marketing Attitudinal Interaction Web Call-center Operational Interaction data - Offers - Results - Context Web data Up to 20% - Click streams - Notes Descriptive data - Attributes better - Characteristics - Self-declared info predicti - (Geo)demographics ons Text data Up to 40% better predicti ons Attitudinal data - Opinions - Preferences - Needs - Desires Attitud es Up to 30% Behavioral data - Orders better - Transactions - Payment history - Usage history predicti ons Customer Contact Channels Website Phone Mail Branch ATM Agent Mobile

15 An example in the Commercial Sector 15

16 16

17 Why a Standard Process Model? Guide for beginners and project managers can be confident they re doing it the right way Framework for recording project experience allows projects to be reported and replicated Aid to project planning and management Comfort factor for new adopters

18 Requirements for A Standard Project Process Model Evolution/rationalization of existent best practice based on experience of practitioners Sufficiently powerful to genuinely add value Sufficiently lightweight to minimize project overhead Needs to cover business/organizational aspects of project not just technical steps

19 Project Structure Business Objectives Business Results DM Goals DM Results

20 Process Standardization CRISP-DM: CRoss Industry Standard Process for Data Mining Initiative launched Sept.1996 SPSS/ISL, NCR, Daimler-Benz, OHRA Funding from European commission

21 CRISP-DM Phases Business Understanding Data Understanding Deployment Data Data Data Data Preparation Modelling Evaluation 21

22 22 Advanced Analytics is a Journey!

23 Thank you Data House 79 Old Kilmainham Road Kilmainham Dublin 8, Ireland TELEPHONE +353 (0) info@presidion.com 23 Formerly SPSS Ireland