Chapter 16Building the Data Mining

Environment

2

The Ideal Customer-Centric Organization

• Customer is king (not pauper)

• For B2C (business to consumer) - Combination of point-of-sale

transaction data and loyalty cards

• For B2B (business to business) – traditional approaches (purchase

orders, sales orders, etc.), Electronic Data Interchange (EDI) of

same, Enterprise Resource Planning (ERP) software with intranet

access for business partners

• Customer interactions are recorded, remembered, utilized (action)

• Corporate culture focused on rewards for how customers are treated

3

The Ideal Data Mining Environment

• A corporate culture that appreciates the value of

information

• Committed (human and $ capital investment) to

consolidate customer data from disparate data sources

(ECTL – extract, clean, transform, load) which is

challenging and time consuming

• A corporate culture committed to being a Learning

Organization which values progress and steady

improvement

4

The Ideal Data Mining Environment

• Recognize the importance of data analysis and its

results are shared across the organization

– Marketing

– Sales

– Operational system designers (IT or vendor software)

• Willing to make data readily available for analysis

even if it means some re-design of software

5

Reality (where “rubber meets the road”)

• The ideal environments, organizations, and corporate culture rarely exist all in one organization!!!

• Don’t be shocked…it’s hard work!!!

6

Building a Customer-Centric Organization

• Biggest challenge to this is establishing a single view of the customer shared across the entire enterprise

• Reverse of this is also a challenge – creating a single view of our own company to the customer

• Consistency is needed for both the above

7

Building a Customer-Centric Organization

Corp. Culture Data Mining Environment

Single Customer View

Customer MetricsCollecting the Right data

Mining Customer data

8

Single Customer View

• Customer Profitability Model

• Payment Default Risk Model

• Loyalty Model• Shared Definitions:

– Customer start– New customer– Loyal customer– Valuable customer

Figure 16.1 A customer-centric organization requires centralized customer data

9

Defining Customer-Centric Metrics

• Business metrics guide managers in their decision-making

• Selecting the right metrics is crucial because a business tends to become what it is measured by– New customers – tend to sign up new ones without

regard to quality, tenure, profitability– Market share – tend to increase this at the expense of

profitability

• Easy to say customer loyalty is a goal…harder to measure the success of this

10

Collecting the Right Data

• Data collection should map back to defined customer metrics

• Customer metrics often stated as questions in need of answers:– How many times/year does customer contact our

Customer Support (phone, web, etc.)?

– What is payment status of customers (current, 30, 60, 90 days, etc.)?

– Thousands of other questions

11

DM Environment & Mining Data

• Data Mining group (team) is needed

• DM Infrastructure to support is needed

12

Data Mining Group

• Possible locations for such a group include– Part of I.T.

– Outside organization – outsource this activity

– Part of marketing, finance, customer relationship management

– Interdisciplinary group across functional departments (e.g., marketing, finance, IT, etc.)

• Each of the above have advantages and disadvantages

13

Data Mining Staff Characteristics

• Database skills (SQL)• Data ECTL (extraction, cleaning, transformation, loading) skills• Hands-on with Data Mining software such as

PolyAnalyst, SAS, SPSS, Salford Systems, Clementine, etc.)

• Statistics• Machine learning skills• Industry knowledge• Data visualization skills• Interviewing and requirements gathering skills• Presentation, writing, and communication skills

14

Data Mining Infrastructure

1. Ability to access data from many sources & consolidate

2. Ability to score customers based on existing models

3. Ability to manage lots of models over time

4. Ability to manage lots of model scores over time

5. Ability to track model score changes over time

6. Ability to reconstruct a customer “signature” on demand

7. Ability to publish scores, rules, and other data mining

results

15

The Mining Platform (example)

• Lots of architecture strategies – this is just one that includes OLAP also

16

Data Mining Software

Review “Questions to Ask” Side Bar in book on page 533 (2nd edition)

17

End of Chapter 16