Emerging CRM - Udayan Datta (42)-Presentation
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Transcript of 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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Data Mining Application
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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Data Mining Application
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 Application
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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Data Mining Application
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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Data Mining Application
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.