CRM Technology Behind

8/8/2019 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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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

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CRM Technology Behind