CRM Technology Behind
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Transcript of CRM Technology Behind
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Technology behind CRM
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Highlights
What is CRM
CRM Phases
Integrated Architecture
Data Warehousing
OLAP
Data Mining
Neural NetworkConclusion
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Evolution of CRM
Initially, there were Corner stores and door-to-door sales forces to approach thecustomers.
Then, Mass marketing replaced the intimacyof a direct sales force.
Later, Targeted marketing evolved. Use ofdirect mail and telemarketing.
Latest is CRM, the next step in Evolution. Aconcept supported by latest technologies.
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What is CRM ?
A Customer- centric business strategy which
Focuses on Managing and optimizing entirecustomer life cycle .
Demand re-engineering of work processeswith customer in focus.
Layman Definition of CRMCollecting Customer data. Analyze this data
to take decisions which enable to make newcustomers and satisfy the existing ones.
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Phases of CRM
Planning phase Assessment Phase Execution Phase
CRM Phases
Plan to Approach theCustomersPlan for making new
CampaignsUses
Marketing toolsVarious Softwares
Select Customer basefor analysis.Analyze Customer
RequirementsUses Technologies
DatawarehousingData MiningOLAP
Customer InteractionExecutes CampaignsTrack Customer feedback
Uses Touchpoints likeInternetCall centersDirect mails etc.
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Integrated Architecture
DataMining
DataWarehouse
OLAP
Server
WarehousecontainingCustomer data.
Multidimensional Structuresto facilitate better and fastanalysis of data.
Integrates with Data Warehouse &OLAP to implement intelligentalgorithms to discover patterns.
User analyze thesepatterns to take decisionssuitable for his business.
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DATA WAREHOUSING
A data warehouse is a copy of transactional data. Data is specifically structured for querying and reporting. A data warehouse can be a relational database,multidimensional database, flat file, hierarchical database.
DISTINGUISHABLE FEATURES
Contains historical dataNo frequent updatesData stored is Subject Oriented
TERMINOLOGY
Data Mart- Contains Data about a specific subject.Metadata- Describes the data stored in Dataware house.Data Cleansing- Data Cleaning operation.ETL -Extraction, Transformation and Loading of Data.
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: sachin_kambhoj
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A Typical Data Warehouse
Data Warehouse
Detailed Data
Data
Mart
Data
Mart
Data
Mart
Summarized Data
MetaD
ata
Dataaboutdata.Facilitates in firingqueries ondetailed data.
Datamarts containdataspecific to a
subject. Eg. Officialdata, Customerdata, Campaigndataetc.
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OLAP
Online analytical processing is the name given to database anduser interface tools that allow to quickly navigate within data.
Provides a mechanism to store the data in multidimensionalcubes.
DISTINGUISHABLE FEATURES
Multidimensional Cubes- To store data which multidimensional innature.
Calculation Intensive- Allows complex calculations on database.
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Types of OLAPSystem
Multidimensional OLAP (MOLAP)Optimized for Multidimensional data queries. Appropriate forsmall to medium size data sets.
Relational OLAP (ROLAP)Keeps the data that feeds the cubes in original relational tables.Ideal for Large data bases which is infrequently queried.
Hybrid OLAP (HOLAP)
Combination of MOLAP and ROLAP.
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DATA MINING
Data mining predicts the future trends and behaviors, allowingbusinesses to make proactive, knowledge driven decisions.
Uses intelligent algorithms to discover patterns, clusters andmodels from data
Model is build using existing data resource. Then this modelis used to predict customer behavior. See figure below :-
PresentlyAvailableSales data
Model
PredictionFor future
Sales
PastSalesData
Model Building basedon past Sales data
Input Output
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DATA MINING-Major Models
DECISION TREESTree shape structures that represent sets of decisions.
GENETIC ALGORITHMSUses concepts of evolution as genetic combination, natural
selection.NEURAL NETWORKS
Non linear predictive models. Resemble biological neural
networks.
Now we will have a small introduction toNeural networks.
MODELINGIt is an act of building a model in one situation where you know answer
and application of that the model in a new situation to help prediction.Major data mining models are:-
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NEURALNETWORKS
A non linear, layered, predictive model basedon Human Brain.
Layers are made of nodes, just like biological
neurons.Input layer receives input from externalenvironment.
Output layer provide output to external
environment.In between these two, there can existmultiple hidden layers.
Network can be trained using various training
methods.
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50
60 cm0.60
0.31 Prone to Disease ?[0.50(0.5)+0.60(0.1)=0.31]
Weight
Weight
Age
Height
NEURALNETWORK-An Example
In above example Age & height of a patient are nodes of input layer.Weights are applied to each input node in hidden layer to performsome calculations based on model. Finally value of output node in
output layer helps to predict if person is prone to disease or not.
0.1
0.50.50
Input Layer Hidden Layer Output Layer
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Categories in Neural Networks
Prediction-Uses input values to predict some output. Eg:Predict people with high health risk.
Classification-Uses input values to determine classification.Eg: Is the input numerical 10.
Data Association-Like Classification but it also recognizesdata containing errors.
DataConceptualization- Analyze the inputs so thatgrouping relationships can be inferred. Eg: Identify group of peoplemost likely to go for a new scheme offered by company.
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Conclusion
CRM is a concept, implemented with thesupport of various technologies.
Supporting technologies include Data
warehousing, Data Mining, OLAPetc.Aproper Data warehouse should be in placefor any CRM initiative.
Customer needs should be in focus whileimplementing CRM.
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References
www.crmfans.com
www.crmguru.com
www.neuralmachines.comwww.siebal.com
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Thank
You