Emerging CRM - Udayan Datta (42)-Presentation

7/28/2019 Emerging CRM - Udayan Datta (42)-Presentation

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APPLIED ANALYTICS

WORKSHOP

NMIMS MPE 04 T4 COURSE: LECTURES 9-11


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Emerging CRM

Emerging CRM has high end value added components

This is highly demandable in current market


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Emerging CRM Data Mining

Data Mining a field at the intersection ofcomputer science and

statistics,is the process that attempts to discover patterns in large

data sets. It utilizes methods at the intersection ofartificial

intelligence, machine learning, statistics, and database systems. The

overall goal of the data mining process is to extract information from a

data set and transform it into an understandable structure for further

use. Aside from the raw analysis step, it involves database and data

management aspects, data preprocessing, model and inference

considerations, interestingness metrics, complexity considerations,

post-processing of discovered structures, visualization, and online

updating.
http://en.wikipedia.org/wiki/Computer_sciencehttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Data_sethttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Database_systemhttp://en.wikipedia.org/wiki/Data_managementhttp://en.wikipedia.org/wiki/Data_managementhttp://en.wikipedia.org/wiki/Data_Pre-processinghttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Statistical_inferencehttp://en.wikipedia.org/wiki/Computational_complexity_theoryhttp://en.wikipedia.org/wiki/Data_visualizationhttp://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Online_algorithmhttp://en.wikipedia.org/wiki/Data_visualizationhttp://en.wikipedia.org/wiki/Computational_complexity_theoryhttp://en.wikipedia.org/wiki/Statistical_inferencehttp://en.wikipedia.org/wiki/Statistical_modelhttp://en.wikipedia.org/wiki/Data_Pre-processinghttp://en.wikipedia.org/wiki/Data_managementhttp://en.wikipedia.org/wiki/Data_managementhttp://en.wikipedia.org/wiki/Database_systemhttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Machine_learninghttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Artificial_intelligencehttp://en.wikipedia.org/wiki/Data_sethttp://en.wikipedia.org/wiki/Statisticshttp://en.wikipedia.org/wiki/Computer_science


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Data Mining Common tasks

Data mining involves six common classes of tasks:

Anomaly detection (Outlier/change/deviation detection) The

identification of unusual data records, that might be interesting or data

errors and require further investigation.

Association rule learning (Dependency modeling)

Searches forrelationships between variables. For example a supermarket might

gather data on customer purchasing habits. Using association rule

learning, the supermarket can determine which products are

frequently bought together and use this information for marketing

purposes. This is sometimes referred to as market basket analysis.
http://en.wikipedia.org/wiki/Anomaly_detectionhttp://en.wikipedia.org/wiki/Association_rule_learninghttp://en.wikipedia.org/wiki/Association_rule_learninghttp://en.wikipedia.org/wiki/Anomaly_detection


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Data Mining Common tasks

Clustering is the task of discovering groups and structures in the

data that are in some way or another "similar", without using known

structures in the data.

Classification is the task of generalizing known structure to apply to

new data. For example, an e-mail program might attempt to classify

an e-mail as "legitimate" or as "spam".

Regression Attempts to find a function which models the data with

the least error.

Summarization providing a more compact representation of the data

set, including visualization and report generation.
http://en.wikipedia.org/wiki/Cluster_analysishttp://en.wikipedia.org/wiki/Statistical_classificationhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Automatic_summarizationhttp://en.wikipedia.org/wiki/Automatic_summarizationhttp://en.wikipedia.org/wiki/Regression_analysishttp://en.wikipedia.org/wiki/Statistical_classificationhttp://en.wikipedia.org/wiki/Cluster_analysis


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Data Mining Noteable uses

Games

Business

Science and Engineering

Human Rights

Medical and Spatial Data Mining

Sensor/Visual/Music Data Mining

Pattern Mining

Knowledge Grid


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Data Mining Application

Data mining tools take data and construct a representation of reality in

the form of a model. The resulting model describes patterns and

relationships present in the data. From a process orientation, data

mining activities fall into three general categories.

Discoverythe process of looking in a database to find hidden patternswithout a predetermined idea or hypothesis about what the patterns may

be.


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Forensic Analysisthe process of applying the extracted patterns tofind anomalous or unusual data elements.

Prediction - Classification

RFM

Churn Prediction

Cross/Up Selling Modeling Credit Risk Propensity Model

Acquisition modeling

Segmentation

Value based segmentation

Present value VS Potential value

Present value VS Risk

Behavioral segmentation Multi Attribute Segmentation


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Campaigning

Campaign testing using control groups

Next Best Activity

Logistic Regression Models

Predict like hood of purchase

Statistical knowledge for linear correlations

Association Detection

Groups of products typically held together

Very popular as Market basket Analysis

Sequence Detection

Actions and events which indicate next purchase

Web- mining taking into account the time order


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Campaigning

Campaign testing using control groups

Next Best Activity

Logistic Regression Models

Predict like hood of purchase

Statistical knowledge for linear correlations

Association Detection

Groups of products typically held together

Very popular as Market basket Analysis

Sequence Detection

Actions and events which indicate next purchase

Web- mining taking into account the time order


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Data Mining - SAP HANA PRODUCT

SAP is coming with the In-memory data computing engine which is purely

based on CRM

Data mining concepts.


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Data Mining SAP HANA PRODUCT


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RETAIL - Through the use of store-branded credit cards and point-of-

sale systems, retailers can keep detailed records of every shopping

transaction. This enables them to better understand their various

customer segments. Some retail applications include

Performing basket analysis

Sales forecasting

Database marketing Merchandise planning and allocation

BANKING - Banks can utilize knowledge discovery for various

applications, including

Card Marketing

Cardholder pricing and profitability

Fraud detection

Predictive life-cycle management


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TELECOMMUNICATIONS - Telecommunication companies around

the world face escalating competition which is forcing them to

aggressively market special pricing programs aimed at retaining

existing customers and attracting new ones. Knowledge discovery in

telecommunications include the following

Call detail record analysis

Customer loyalty

Other applications

Customer segmentation

Manufacturing

Warranties

Frequent flier incentives


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Data Mining Technologies Block DIAGRAM


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CRM Data Mining Techniques

Neural Networks -


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CRM Data Mining Techniques

CHAID VS Neural Networks

Both CHAID and neural networks can create predictive models. Such models

include attrition, churn, propensity to purchase, and customer lifetime value.

Yet in general, the application of neural networks is wider than CHAID. The

reason for this is that neural networks can be applied to both directed

(supervised) and undirected (unsupervised) data mining. Neural networks can

handle both categorical (e.g., marital status) and continuous (e.g., income)

independent variables, but these have to be transformed to 0/1 input variables. Neural networks can be used to solve estimation problems (with continuous

outcomes); whereas CHAID provides good solutions to classification

problems, can be used for exploratory analysis (perhaps prior to another

modeling technique), and can provide descriptive rules.

CHAID models are easier to build and implement than neural networks and

also are less costly. Theoretically, neural networks should provide models thatare better than CHAID in terms of power and accuracy. That means they

should be more powerful at discriminating between groups that fit the target

(for instance, churn) and that they should predict correctly more often.

Currently, this is not the case, perhaps because of the problems of over-fitting

and sub-optimal solutions.

Emerging CRM - Udayan Datta (42)-Presentation

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