3. data mining in crm

8/8/2019 3. Data Mining in Crm

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Presentataion by Mr.SureshShirhattikar 1

DATA MINING IN CRM

ANALYTICS-INTELLIGENT

MANAGEMENT OF PRODUCTLIFE CYCLE


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AN EFFECTIVE CRM

companies need to match products &campaigns to prospects & existing

customers.


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OPERATIONAL CRM Vs DATA

MINING (ANALYTIC CRM)

Operational CRM- Software that

creates a database which provides aconsistent picture of customersrelationship with the company,

through specific applications. e. g.

Sales Force Automation, CustomerService software etc.


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OPERATIONAL CRM Vs DATA

MINING (ANALYTIC CRM)

Whereas Data Mining is a process

that uses variety of Data Analysis &modeling techniques to discoverpatterns & Relations in a Databasethat may be used to make accurate

predictions.


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STEPS IN DATA MINING

Describe the data- summarize its

statistical attributes (e.g. means &standard deviations), visually review

it using charts & graphs & look atdistribution of values.


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STEPS IN DATA MINING Build a predictive model- Usingpatterns from a known set of results

& then test that model on youroriginal sample. E.g. in Classification-one predicts in what category an

element of data might fall. In

Regression one predicts a numbersuch as probability of that element of

data to respond to your action.


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STEPS IN DATA MINING

Determine propensity or create a

profile- a score to give a measure tothe propensity of customer behavior& create a set of characteristics that

could segment the customer into

groups with similar behavior.


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DATA MINING THROUGH

CUSTOMER LIFE CYCLES Data mining could improve yourprofitability in the following stages of

life cycle either by integration withOperational CRM or independently.

Acquiring customers.

Increasing the value to the customer.

Retaining good customers.


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AQUIRING NEW CUSTOMERS

THROUGH DATA MINING A company normally could win newcustomers either through change in the

method of solicitation by reaching

customers through different media oroffering attractive freebies.

Or through data mining techniques tobuild a predictive model of who is

likely to respond favorably or use themethod of scoring the existingcustomers.


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INCREASING THE VALUE TO THE

EXISTING CUSTOMERS

Technique of Cross Selling- The

Company may offer more features to itsproduct or facilitate better choosing &buying facilities by

The technique of Clustering to see

which products & add-ons groupedmore naturally


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Presentataion by Mr.SureshShirhattikar

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INCREASING THE VALUE TO THE

EXISTING CUSTOMERS

The technique of profiling thecustomers to see which group of clientsreadily tried new variants or which onesstayed loyal to old brands.

Use the personalization to proactivelyannounce new products to a select groupchosen by data mining.


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RETAINING LOYAL CUSTOMERS The cost of winning over a new

customer grossly exceeds that of

retaining a customer (Sometimes ashigh as 7 times).

The company at first will use datamining to identify repeat buyers.

Next it would study the attrition patternof once good customers (who left them& why?).


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RETAINING LOYAL CUSTOMERS

Next they would design programs toidentify signs of discontent in time &offer new products/feature to retain.

Eventually they would build data miningprograms that would predict which offerworks the best for customer retention.


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APPLYING DATA MINING TO CRM

Define business problem.

Build marketing database.

Explore data.

Prepare data for modeling.

Build model.

Evaluate model.

Deploy model & research on results.


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DEFINE BUSINESS PROBLEM

Each CRM application has an end goal orobjective. Depending on the end goal

appropriate models need to be built. Aclear statement of the objective wouldtherefore also include the way of

measuring effectiveness of your CRMproject.

The next four steps take the maximumtime as a lot of iterations of data building &model building are done as you go along &

learn from the model.


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BUILD MARKETING DATA BASE

Independent of operational &

corporate database. Multipledatabases from customer database,

product database & transactiondatabase needs to be standardized,

integrated & consolidated.


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EXPLORE THE DATA

To understand the data better,

various summaries such as averages,standard deviations & pivot tables for

multidimensional comparison aremade. Graphing & visualization like

histograms, scatter plots give multi-dimensional views.


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PREPARE DATA FOR MODELLING There are 4 steps -

Select the variables on which to build

the model. Construct new predictors derived from

new data.

Select a sub-set or sample of the entire

data on which to build models. Transform variables in accordance with

the requirements of the algorithm.


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MODEL BUILDING It is an iterative process where alternative

models are tried to get the one mostsuitable.

Most CRM applications are based on aprotocol called supervised learning. Onestarts with a customer information on

which desired outcome is already known.

This you try on a test basis on the newdata, first on a smaller group & then on

the whole of data.


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EVALUATING THE RESULTS Some methods are,

Lift- measures the improvement

achieved by the predictive model. Rise-Other measure is relative rise in

profits.

ROI-Third measure which is most

acceptable is ROI (Return onInvestment).


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INCORPORATING DATA MINING IN

CRM SOLUTION

The data mining is most of the times

combined with the knowledge ofdomain experts. The data mining isbuilt into the system depending onthe type of customer interaction

Outbound or Inbound.


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OUT-BOUND INTERACTIONS & IN-

BOUND TRANSACTIONS

Out-Bound-In this case the company

reaches out to the customer by adesired action using any one of

mode of contact.

In-Bound-In this case the customerscontact the company with a need for

action.


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PRODUCTIVITY IN DATA MINING

Intelligent transformations done in

the individual elements of data toinclude multi-elemental features,

decide the productivity of any datamining exercise.


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THE ROUTE TO A SUCCESSFUL

BUSINESS

Depends on how effectively you can

use information about customerneeds to transmit into more profitsfor you. For this it is necessary that

The Operational CRM is aptly

supported by Analytical CRM, withaccurate predictive capabilities.


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TECHNIQUES USED IN DATA

MINING

DECISION TREES NEURAL NETWORKS

CLUSTERING


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DATA ON COOKING EQUIPMENT SALESCooking-

Total 16,12617,95

4 19,542 20,846 21,548 21,917 23,315 25,390 23,809 24,641 24,961

Electric

Range

s -Total 4,240 4,639 4,983 5,026 5,066 5,338 5,622 6,145 6,194 6,317 6,410

Freestanding 3,177 3,481 3,785 3,826 3,842 4,030 4,238 4,612 4,677 4,775 4,858

Built-In 617 652 705 706 726 780 841 963 975 993 997

Surfac

eCooking Units 446 506 493 494 498 528 543 570 542 549 555

GasRange

s -Total 2,744 2,950 3,137 3,176 3,036 3,268 3,419 3,719 3,755 3,809 3,836

Freesta

nding 2,391 2,543 2,698 2,729 2,580 2,781 2,897 3,124 3,131 3,170 3,193

Built-In 73 71 72 70 72 71 67 67 64 63 61

Surface

Cooking Units 280 336 367 377 384 416 455 528 560 576 582

MicrowaveOvens 9,142

10,365

11,422 12,644 13,446

13,311

14,274 15,526

13,860

14,515 14,715


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OBJECTIVE OF THE STUDY There is a considerable variation inthe sales of a set of local dealers in a

Metro city.

The company expects that by & largethe sales of all the dealers in the

town should be comparable.

The study wants to find out whatmeasures need to be taken to boost

the laggards.


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DECISION TREES

Decision Trees are a way of representing a seriesof rules that lead to a class or value.

The basic components of a decision tree are, The Root node- This is the top decision node

by which a common test is applied.

The branch node- There could be severalbranch nodes, starting at root node & fanning

out in various branches. The leaf node- Through this, one finally

reaches the end of the tree.


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Presentataion by Mr.Suresh

Shirhattikar

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DECISION TREES The root node- Segregate the dealers

sales wise into groups of incremental

sale of 2000 units. Branch node- Segregates intodomestic/ commercial units wise

sales.

Leaf node-Segregate into repeatbuyers/First time buyers.


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Shirhattikar

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NEURAL NETWORKS Neural Networks offer a means of

effectively modeling large andcomplex problems involving several

hundred predictor variables. Neural nets are commonly used for

Regressions.

Neural nets start with Input layer,where each node corresponds to a

predictor variable.


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Shirhattikar

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NEURAL NETWORKS Input layer is connected to a number of

nodes in hidden layer. Each node in thislayer takes a set of inputs & multiplies

them by a connection weight age, addsthem together, applies a function (called

activation or squashing) & passes theoutput to next layer.

Finally on to Output layer which consists ofone or more response variables.


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Shirhattikar

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NEURAL NETS Input Layer- e.g. take sales in the

sets of 1000.

Hidden Layer-applies connectionweight age of seasonal variation toeach reading , adds them & appliesfunction of change in GDP, sends to

Final layer.

Final layer- applies various variableslike a) competitive presence,

ineffectiveness of the dealer etc.


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Shirhattikar

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NEURAL NETWORKS A typical example of neural network

is the Back propagation in training,

where parameters like speed ofconversion are applied.

Successful implementation of NeuralNetworks involves very careful data

cleansing, selection, preparation &pre-processing.


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Shirhattikar

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CLUSTERING

Classification is a way to segregate datainto already known groups &

Segmentation refers to identifying groups

that have common characteristics,Clustering is a way to segment data into

groups that are not precisely defined.

Clustering divides a database into

different groups. Clustering helps toidentify different groups, whose members

have common features.


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Shirhattikar

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CLASSIFICATION, SEGMENTATION& CLUSTERING

CLASSIFICATION- Segregate into knowngroups, e.g. type of ovens sold i.e.

Microwave +convection+ grill.

SEGMENTATION-Identify sales made togroups that have common characteristics

e.g. households, hotels, industrial canteensetc.

CLUSTERING-Identify groups whosemembers have common features, e.g.

locality wise, income group wise.


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Shirhattikar

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A CASE STUDY

ICICI Prudential a pioneering private sectorInsurance company was launched in Indiain 2000 & today it is a major challenger to

LIC Corporation. Today it stands at 1900 branches,210,000

advisors & 7 bancassurance partners with10 million policies issued since inception .

Its USP is to offer customer a wide choiceof products.


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Shirhattikar

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A CASE STUDY

Their capital infusion stands at

Rs.4780 crores. Products developed after a thorough

research on customer needs.

Easy accessibility to the customer.

Smooth & hassle free claimsettlement procedure.


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Shirhattikar

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A CASE STUDY

The entire staff is regularly trained &equipped with the latest sales

material . Robust risk management &

underwriting practices result in strongcore values of Integrity, Customer

First, Boundary less, Ownership andPassion.


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Shirhattikar

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A CASE STUDY

The entire staff is regularly trained &equipped with the latest sales

material . Robust risk management &

underwriting practices result in strongcore values of Integrity, Customer

First, Boundary less, Ownership andPassion.


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Shirhattikar

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HOW THEY MANAGE CRM DRIVE?

It operates on a multi-channeldistribution strategy including

advisors, banks, direct marketingstaff & corporate agents.

It has made rapid strides inRetirement & education solutions.

Works on human emotions of trust,togetherness & protection.

3. data mining in crm

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