CRM and data mining Day 01

The update time: the length of time it takes for a change in the operational data to reflect in the data warehouse. A 24 hour 'wrinkle of time" is recommended for the update time.

low-level granularity  vs. high-level granularity
The lower the level of granularity, the more versatile the query that can be issued.

high-level of detail (the details of every phone call made by a customer for a month) vs. low-level of detail (the summary of phone calls made by a customer for a month)


Granularity: is the level of detail or summarization of the units in the DW. E.g. details of every phone call made by a customer for a month vs the summarized phone calls made by a customer for a month. You can always aggregate detailed data by summarizing, but cannot disaggregate data that is stored only as summaries. This is the benefit of low-level granularity.

Customer relationship management (CRM) promises

1. Faster customer service at lower costs
2. Higher customer satisfaction
3. Better customer retention

Ultimately, these lead to customer loyalty .

Essential questions for business that can be solved by computers (decision support systems).

1. Who are my customers and what products are they buying?
2. Which customers are most likely to go to the competition?
3. What impact will new products/services have on revenue and margins?
4. What product promotions have the biggest impact on revenue?
5. What is the most effective distribution channel?
6. Which are our lowest/highest margin customers?

Organizations need to think about finding products for the customers rather than customers for their products

A key focus of business intelligence is optimizing the lifetime value of customers. To do this we need to

• Get to know the customers better -> create customer profiles
• Interact appropriately with the customers ->

Customer intelligence or 'Analytical CRM' is the process of gathering, analyzing and exploiting information of a company's customer base.

There are 3 main steps:

1. Build a data warehousing for customer intelligence.
2. Use decision support tools (OLAP and reporting)
3. Data mining

Data warehouses enable organizations capture, organize and analyze the information in order to make decisions about the future based on the past. The steps are as follows.

1. Identify the data that needs to be gathered from the operational business systems,
2. Place data in the data warehouse
3. Ask questions of the data to derive valuable information.

Operational information: questions about situations right now

1. How many unfulfilled orders are there?
2. On which items are we out of stock?
3. What is the position of a particular order?

Strategic information is concerned with sales of products over time. A 'mail order wine club' may wish to ask

1. Which product lines are increasing in popularity and which are decreasing?
2. Which product lines are seasonal?
3. Which customers place the same orders on a regular basis?
4. Are some products more popular in different parts of the country?
5. Do customers tend to purchase a particular class of product?

Data warehouse is "A subject-oriented, integrated, time-variant and non-volatile collection of data in support of management's decision making processing" (W.H. Inmon)

Data warehouse is "A copy of transaction data, specifically structured for query and analysis" (Ralph Kimball)

Data warehouse data is a series of snapshots, each snapshot taken at one moment in time.

Data warehouse (DW) data always contains a time element-with entries for different values of time.

References

1. FIT5158 Monash Lecture Notes, 2011

Glossary of terms

Fact table vs. dimension table
Data mart
Star join is an ideal structure for a data mart.
The centre of the star join is called the 'fact table'.
Agility
Integrated computerized decision support
Analysis, decisions, predictions
Margin customers
The lifetime value of customers
Customer-centric businesses
Customer-centric organisations
Elaborate: work out in detail (e.g. elaborate a plan)
Blanket marketing campaigns vs. targeted marketing campaigns
Monetary
OLAP: online analytical processing
OLTP: online transaction processing
A single unified data repository
Repetitive
Decision support system data (DSS data)
Clerical functions vs. managerial functions
Primitive data vs. derived data
Collective time horizon
The volume of data in the warehouse