ANALYTICAL CRMS Sudhindra

ANALYTICAL CRM

Analysis of customer data for a variety of purposes including : Design and execution of targeted marketing

campaigns to optimize marketing effectiveness. Design and execution of specific customer campaigns,

including customer acquisition, cross-selling, up-selling, retention.

Analysis of customer behaviour to aid product and service decision making (e.g. pricing, new product development etc.)

Management decisions, e.g. financial forecasting and customer profitability analysis.

Prediction of the probability of customer defection (churn).

MORE DEFINITIONS

A bottom-up perspective, which focuses on the intelligent mining of customer data for strategic or tactical purposes.

Refers to the information management processes that rotate around the collection, accumulation, and analysis of customer information from customer interfaces.

Provides analysis of customer data and behavioral patterns to improve business decisions. This includes the underlying data warehouse architecture, customer profiling/segmentation systems, reporting, and analysis.

CUSTOMER KNOWLEDGE

Knowledge about customers, which includes knowledge about potential customers, customer segments and individual customers.

Knowledge possessed by customers. Different from customer data and

information. Customer knowledge can be explicit, the

structured customer information in databases, or in tacit customer knowledge –knowledge in mind of employees and customers.

VALIDITY OF CRM CLASSIFICATION

Strategic, Operational, and Analytical CRM : Attributes and Measures, Reiny Iriana and Francis Buttle (2006).

Research to establish whether these three forms of CRM are conceptually and operationally different.

Generated an exhaustive set of 32 attributes that had been used in literature to describe and discriminate between different types of CRM initiative. The study narrowed down the factors to 13.

Results suggest that the Strategic, Operational, and Analytical CRM classification is a valid conceptual model, and that it is possible to measure an organization’s orientation towards these three types of CRM.

IMPLEMENTATION

Analytical CRM is typically implemented as a combination of data-warehouse, business intelligence (OLAP) and data-mining systems.

The data required for ACRM typically comes from data-warehouses which in turn consist of data extracted from transactional databases.

The outputs are available for both strategic and operational CRM systems.

TECHNIQUES

NEURAL NETWORKS/PATTERN RECOGNITION

A credit card company has 3,000 records, 100 of which are known fraud records.

The neural network understands the difference between the fraud records and the legitimate ones and learns the patterns of the fraud records.

The network is run against company’s million record data set and the network spits out the records with patterns the same or similar to the fraud records.

MEMORY BASED REASONING

MBR looks for "neighbour" kind of data, rather than patterns.

Insurance companies may want to which claims should be investigated and which should be let through the system.

As set of claims that should be investigated are fed into the system.

For every new claim the system will look for a similar claim from memory.

CLUSTER DETECTION/MARKET BASKET ANALYSIS

The classic beer/diapers bought together analysis.

The technique finds relationships in products or customers.

DECISION TREE/RULE INDUCTION

A decision tree process will generate the rules followed in a process using data mining algorithms. Example

A lender at a bank goes through a set of rules when approving a loan.

Based on the loan data a bank has, the outcomes of the loans (default or paid), and limits of acceptable levels of default, the decision tree sets up the guidelines.

GENETIC ALGORITHMS

Used generally as an optimization technique.Example

Major airline looking to improving its customer experience needed a more efficient way to route customer comments. To determine if a comment (from phone,

comment card, or email inquiry) was a compliment or complaint.

GA based solution provided a 93% accuracy in predicting complaints.

ONLINE ANALYTICAL PROCESSING

OLAP allows users to browse data following logical questions about the data.

Ability to drill down into data, moving from highly summarized views of data into more detailed views.

SOME APPLICATIONS

ACRM technologies allow the organization to gain an insight into the behaviour of individual customers and, in turn to target and customize marketing communication and messages.

In addition, these tools generate data that support the calculation of customer lifetime value for individual customers.

IDENTIFYING STRATEGICALLY SIGNIFICANT CUSTOMERS

Some customers have higher value to an organization than others.

Organizations need to calculate and predict customer lifetime value.

High Life Value customers. Benchmarks : Early adopters of new products

and the “role model” that will set the trend. Complaining customers : Inspire changes in

the supplying company.

SEGMENTING CUSTOMERS TO PERSONALIZE SERVICES

Segmentation enables more personalized and, therefore, more attractive product and service offerings to individual customer groups.

PeopleSoft uses a customer scorecard to track key performance measurements and communicate progress against CRM-related goals.

Possible criteria to support customer segmentation are: Profitability by customer and distribution channel. Cost to support by product and customer. Average order value by customer. Customer acquisition rate, customer defection rate,

repeat customer rate, and customer satisfaction.

TRACKING AND MODELLING CUSTOMER BEHAVIOUR PATTERNS

Process that includes Segmenting target customer groups. Establishing criteria for measuring behaviour,

monitoring and tracking behaviour changes, Generating behaviour patterns, and predicting

possible future behaviour. Purchasing behaviour. Contact behaviour. Retention behaviour. Response behaviour. Migration and defect behaviour.

REQUIREMENTS

Customer knowledge acquisition should be treated as a dynamic and continuous process, to collect information about existing customers, defecting customers and new customers.

Knowledge about prospective customers and customers that are loyal to competitors should also be obtained.

GARTNER REPORT : DATA MINING

Leaders SPSS SAS ThinkAnalytics

Challengers Portrait Software Angoss Software Infor CRM Epiphany Viscover Unica

BUSINESS INTELLIGENCE : MAJOR PLAYERS Leaders

Oracle : DBMS is the strong point. IBM : Content analysis and packaged analytical

applications, application server, portal and data integration capabilities. Does not support its own ETL and data quality software.

Microsoft : SQL Server and nalytics, scorecards and dashboards built into SharePoint. Good corporate performance management and data integration

SAS. SAP. Information Builders. Microstrategy.

SAAS Open Source

CONCLUSION