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CRM and Infovi

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  • CRM and Information VisualizationGrdal Ertek, Ph.D.Tue Gizem Martaan

  • Customer Relationship Management (CRM)

    Traditional MarketingCRMGoal: Expand customer base, increase market share by mass marketingGoal: Establish a profitable, long-term, one-to-one relationship with customers; understanding their needs, preferences, expectationsProduct oriented viewCustomer oriented viewMass marketing / mass productionMass customization, one-to-one marketingStandardization of customer needsCustomer-supplier relationshipTransactional relationshipRelational approach

  • The approach of identifying, establishing, maintaining, and enhancing lasting relationships with customers.

    The formation of bonds between a company and its customers.

    What is CRM?

  • Prospecting (of first-time consumers)LoyaltyCross-selling / Up-sellingWin back or SaveStrategies in CRM for Mass Customization

  • The Marketing Perspective

    CAMPAIGN MANAGEMENT

    RECENCY FREQUENCY MONETARY VALUE METHOD

    CUSTOMER VALUE METRICS

  • Campaign Management: The Marketing PerspectiveDeveloping effective campaignsEffectively predicting the futureRetaining existing customersAcquiring new customers

  • KNOW

    Understand market and consumers needs and preferences

    Exploit customer intelligence,

    Perform segmentationTARGET

    ( Offer is developed )

    Define market strategies

    Use channel integration

    SERVICE

    Retain customers by:

    Loyalty programsCommunicationService forcesSELL

    Acquire customers

    Use sales force effectively

    Develop marketing programsCampaign Management: The Cap Gemini Model

  • The marketing manager...

    Defines objectivesIdentifies customersDefines communication strategiesDesigns/improves products/offers/services/promotionsTests the impacts of her decisionsRevises her decisions for maximum effectivenessCampaign Management: The Marketing Perspective

  • Campaign ManagementStep 1: Define ObjectivesTargetingExisting Customers

    Retention StrategyCreating Loyalty?Increasing the satisfaction level?Cross-selling or Up-selling?TargetingNew Customers

    Acquisition StrategyTarget customers that show characterstics similar to existing groups of customers

  • Perform SEGMENTATION

    Define the right customersUse information of past transactions as key for making predicting future onesDefine the segments and their characteristicsDevelop customized marketing strategies for the different segmentsCampaign ManagementStep 2: Identify Customers

  • Campaign ManagementStep 3: Communication StrategiesWhich message should be transmitted?Which channel should be used?

  • Analyze the price, time period, risks, marketing costsDefine the product / offer / service / promotion and its general structureIdentify effective use of sales and communication channels Campaign ManagementStep 4: Design the Products, Offers, Services and Promotions

  • Campaign ManagementStep 5: Test the ImpactsImpacts of the decisions have to be tested and and assessed on a sample

  • Campaign ManagementStep 6: Revise the DecisionsMake revisions to the targeted offer / service / promotionsFinally apply the decisions to the whole segment or population

  • RFM Method(Recency, Frequency, Monetary Value ) RecencyWhen was the last customer interaction?FrequencyHow frequent was the customer in its interactions with the business?Monetary value of the interactions

  • RFM Method(Recency, Frequency, Monetary Value )

    Marketing Problem:

    A firm has sent e-mail to 30,000 of its existing customers, announcing a promotion of $100. 458 of them responded (1.52% of the customers)

    Is there any relation between the responding customers and their historical purchasing behaviours?

  • RFM Method:Recency Coding

    30,000 customers are sorted in descending order with respect to their most recent purchases

    Sorted data is divided into 5 equal groups, each of them containing 6,000 people

    Recency codes are assigned: Top group has code 5, bottom group has code 1

  • RFM Method:Recency CodingRecency Results

    According to analysis based on customer recency, the group having the highest recency group has also the highest response rate

    Remark: (3.10% + 2.00% + 1.50% + 0.62% + 0.38) / 5= 1,52% which is the response rate

    Strict Rule: Ones who have purchased recently are much more willing to buy new products than others purchasing in the past

  • RFM Method:Frequency CodingSort the 30,000 customers with respect to frequency metrics.Frequency metrics: Average number of purchases made by customer in a time period tSort customers in descending order with respect to their purchase frequency.Assign them to 5 groups, top %20 in the first frequency group. Assign frequency codes such that the top group has code 5 and the bottom group has code 1.

  • RFM Method:Frequency CodingFrequency Results

    It is observed that highest response rate is from the customers having highest frequencyFrequent people respond better than less frequent ones but differences between groups are less than the ones in the recencyThe lowest frequency group always contains new customers That is why it is named RFM

  • RFM Method:Monetary Value CodingThe same process as recency and frequency codingSorting is done with respect to monetary value metricMonetary value metric is the average amount purchased in a time period tAt the end of the monetary value coding, assign monetary value codes M = 1,...,5 to groups according to their groups.

  • RFM Method:Monetary Value CodingFrequency Results

    It is observed that highest response rate is from the customers having highest monetary valueUnlike the recency case,there are not big differences between groups

  • RFM Method: Putting the Codes Together

    At the end of the monetary coding firm obtain R F M metrics for customers. Each customer belongs to one of 125 possible combinations of the RFM values: RFM123452122232425231232233234235Database

  • RFM Method:STEPSCreate 3 digits RFM codes cellsAll cells having the same number of customers in them RFM values are used to define group of customers that marketing campaign should target or should avoidUsed for identifying customers having high probability to respond to campaigns: 555s response rate > 552s > 543s >541....Increase the response rateIncrease profitability

  • Customer Value Metrics

    Critical measures used to define customer worth in knowledge-driven and customer-focused marketing

  • Customer Value Metrics:Size of Wallet

    Size of wallet =

    Assumption: Firms prefer customers with large size of wallet in order to retain large revenues and profitsSales to focal customer by firm j

  • Customer Value Metrics:Individual Share of Wallet (SW)A proportion expressed in terms of percentage, calculated among buyersMeasured at individual levelA measure of loyaltyCan be used in future predictionsDifferent from the market share, which also considers customers with no purchase

    Individual share of wallet % =

    Sales to focal customer by firm j

  • Customer Value MetricsShare of wallet and size of wallet should be analyzed together because...

  • Customer Value Metrics:Transition Matrix

    Shows expected share of wallet from multiple brands Depicts consumers willingness to buy over timeTransition probability from B to A, than from A to C: 10%*20% = 2%

  • The Engineering Perspective

    DATA MINING

  • Collection, storage, and analysis of typically huge amounts of- dataData readily resides in the companys data warehouseData cleaning is almost inevitableData Mining

  • Goals of Data Mining

    Developing deeper understanding of the dataDiscovering hidden patternsComing up with actionable insightsIdentifying relations between variables, inputs and outputsPredicting future patternsData Mining

  • Data selectionData cleaningSamplingDimensionality reductionData mining methods

    Data Mining: Steps

  • Exploratory Data AnalysisSegmentationCluster AnalysisDecision TreesMarket Basket AnalysisAssociation rulesInformation VisualizationPredictionRegressionNeural NetworkTime Series AnalysisData Mining:Methods

  • Information VisualizationData mining algorithms...Can only detect certain types of patterns and insightsAre too complex for end users to understand

  • Information VisualizationA field of Computer Science which has evolved since the 1990s.

    Before 1990s: Graphical methods for data analysis to pave the way for statistical methodsAfter 1990s:Computer hardware has advanced with respect to memory, computational power, graphics calculationsSoftware has advanced with respect to user interfacesData collection systems have advanced (barcodes, RFID, ERP)

  • Information VisualizationThe analyst does not have to understand complex algorithms.

    Almost no training required.

    There are no limits to the types of insights that can be discovered.

  • Case Studies

    Analysis of Supermarket Sales Data

  • The Data

    Field NameDesciptionTRANSACTION_IDTransaction IDPRODUCT_NOProduct Number

  • Frequent Itemsets

  • Frequent Itemsets

  • Association Rules

  • Case Studies

    Analysis of Spare Parts Sales Data

  • The DataAssumption: Each customer gives at most one order each day.

    Field NameDesciptionDEPOTDepot IDSKU_NOSKU (Stock Keeping Unit) NumberVENDORVendor (Customer) NumberDAYDay of the month (1,...,31)MONTHMonth of the year (1,...,12)YEARYear (ex: 2002)QUANTITYQuantity requiredUNIT_PRICEPrice of one unit of product in YTL*REVENUERevenue from the order line

  • Determining Top Products:Pivot Table for Determining REVENUE_SUM

  • Determining Top Products:Pivot Table for Determining COUNT (Frequency)

  • Determining Top Products:Scatter Plot

  • Seasonality of Top Products. . .

  • Seasonality of Top Customers:Pivot Table

  • Determining Top Customers:Pareto Curve (ABC Analysis)Revenue

    Chart1

    7.3584078239

    14.3121301746

    18.9054567877

    22.3182111646

    25.3219315133

    28.2404011269

    30.8951616963

    33.4452255158

    35.9948794209

    38.4860118159

    40.9338095586

    43.3652108574

    45.7561829142

    48.11508081

    50.4379665691

    52.2811979721

    54.00914728

    55.7058007716

    57.3155097719

    58.8903298071

    60.4651158926

    61.9526478072

    63.3323863062

    64.7022068292

    66.0457056955

    67.3010748141

    68.4696507065

    69.5446990271

    70.6092065134

    71.642765128

    72.6305084513

    73.605763702

    74.5683838626

    75.5305075931

    76.4309602988

    77.3237192571

    78.2141848456

    79.087086526

    79.8909544632

    80.6945795681

    81.4605273898

    82.2153657012

    82.8813004419

    83.5341707255

    84.1863654333

    84.8124997323

    85.4359886323

    86.0555995138

    86.6638406554

    87.2408259213

    87.7463573452

    88.2175435481

    88.68295657

    89.1469805976

    89.5875170884

    90.0228854871

    90.4358194013

    90.848110425

    91.2578427097

    91.6506175047

    92.0003429141

    92.3395875628

    92.6468700365

    92.952185837

    93.2531503984

    93.5448157507

    93.8220206708

    94.0945991511

    94.3530406781

    94.6078037652

    94.8601609967

    95.1078231282

    95.344956827

    95.5771909123

    95.8086082263

    96.038468072

    96.2667986718

    96.4833760501

    96.696248021

    96.9008727334

    97.098390195

    97.2751132939

    97.446684993

    97.5943782756

    97.7420568614

    97.8735634015

    98.0042589499

    98.1311415348

    98.2530104733

    98.3736054279

    98.4833625039

    98.5928175606

    98.6901203004

    98.7706770771

    98.8477408056

    98.9235982135

    98.9968240709

    99.0632407457

    99.1275922931

    99.1791216386

    99.2297562696

    99.2757873244

    99.3214324833

    99.3638242914

    99.4058509724

    99.4445002454

    99.4825412199

    99.5179840804

    99.5529952354

    99.5856664032

    99.6178175412

    99.6472768234

    99.676029844

    99.7035543265

    99.7289963933

    99.7509661791

    99.7728490563

    99.7938649005

    99.8111764333

    99.827346373

    99.8432491308

    99.8589147643

    99.8741010556

    99.8882332523

    99.9016443582

    99.9132229513

    99.9230631889

    99.9327973933

    99.9414531072

    99.950104863

    99.9584693781

    99.9661933911

    99.9725313048

    99.9784877827

    99.9843747547

    99.9892540552

    99.9934252341

    99.9969104487

    99.9985858623

    100

    Cumulative % Revenue

    Cumulative % Customers

    Cumulative % Revenue

    01_pivotVendorDepot

    Sum of REVENUEDEPOT

    VENDOR123456789Grand Total% VendorsCumulative RevenueCumulative % Revenue

    2847,5001,64419,7434,86782,986901465,24342,22610.714285714342226.4257177.3584078239

    2823,17131426,6691,500851103492926,95639,90421.428571428682130.55271414.3121301746

    2045,02868617,669692,2391337522024026,35932.1428571429108489.48383918.9054567877

    1973,31774112,744312,416502048019,58442.8571428571128073.66871122.3182111646

    912,0905498,6351,4644,009511836817,23753.5714285714145310.6005625.3219315133

    21277013811,8762603,466716610016,74864.2857142857162058.31872128.2404011269

    2314,7362006,8082622,9154911115515,23475177292.73527730.8951616963

    313,1541,9178,814176646714,63485.7142857143191926.34665433.4452255158

    402,5643136,7854224,3632316114,63196.4285714286206557.60572635.9948794209

    28551541711,1834351,09372262314,295107.1428571429220853.03750238.4860118159

    151,6587754,6792,9143,948343914,047117.8571428571234899.79218440.9338095586

    699752967,4361,3233,6528091384413,953128.5714285714248852.45542243.3652108574

    18962481810,056122,0791911213,721139.2857142857262573.11434245.7561829142

    1081,5693966,1503592,44211154652,39113,5371410276109.71480748.11508081

    282,3001,0583,0286,0356653920513,3301510.7142857143289439.65863550.4379665691

    1034611117,4262232,238289010,5771611.4285714286300017.09274652.2811979721

    521,0421,5582,7321,2651,4791,7468879,9161712.1428571429309932.97738354.00914728

    2062,1441,2433,884254931603458749,7361812.8571428571319669.27011755.7058007716

    751,5884735,5671,0793994834509,2371913.5714285714328906.62949657.3155097719

    22731084,866983,6212416949,0372014.2857142857337943.77758958.8903298071

    2021,130845,8902051,693369,0372115346980.73086160.4651158926

    1863053196,392541,18411520151328,5362215.7142857143355516.97367361.9526478072

    375661034,0205542,3769202717,9182316.4285714286363434.64100463.3323863062

    1831,1813594,1482961,52524285427,8612417.1428571429371295.39375764.7022068292

    1361644,6087482,1897,7102517.8571428571379005.09895966.0457056955

    857702023,745252,35259517,2042618.5714285714386209.0691867.3010748141

    2092,8941463,05266224281041916,7062719.2857142857392914.97408468.4696507065

    9887664,217696081642316,1692820399084.16844569.5446990271

    2431235,5333471066,1092920.7142857143405192.87393870.6092065134

    541,308722,2612,25426105,9313021.4285714286411123.9784871.642765128

    2371573,4392291,79312385,6683122.1428571429416792.17071272.6305084513

    234408544,696370685,5973222.8571428571422388.69979673.605763702

    821,7538082,3651942782977195,5243323.5714285714427912.72206874.5683838626

    68369133,500361,552174325,5213424.2857142857433433.89556275.5305075931

    81452133,0961,463112325,1673525438601.16818476.4309602988

    2001161604,391135309125,1233625.7142857143443724.29002577.3237192571

    1515103,45261,06117655,1103726.4285714286448834.251378.2141848456

    49107153,09561,594111815,0093827.1428571429453843.42160579.087086526

    1389413381,9261491,16485114,6133927.8571428571458456.43987679.8909544632

    732643,299695744424,6124028.5714285714463068.06464780.6945795681

    229199822,79781,289194,3954129.2857142857467463.47729281.4605273898

    127342,6334261,2201084,3324230471795.13770782.2153657012

    1216471951,6261551,037211393,8214330.7142857143475616.62253582.8813004419

    1424479291,8314792593,7474431.4285714286479363.13661783.5341707255

    94392781,674721,51793,7434532.1428571429483105.77388984.1863654333

    2192,78967983,5934632.8571428571486698.86278884.8124997323

    2458725741,66710740242943,5784733.5714285714490276.77099285.4359886323

    35401,880711,5643,5564834.2857142857493832.4250886.0555995138

    1494911,0831,9163,4904935497322.83360386.6638406554

    723822475483911,731113,3115035.7142857143500633.87942387.2408259213

    224183241,96459332,9015136.4285714286503534.88540687.7463573452

    233228691,5985161912925462,7045237.1428571429506238.80039388.2175435481

    662821,0671,1701512,6715337.8571428571508909.58582288.68295657

    1456051281,2623852552072,6635438.5714285714511572.40046989.1469805976

    611991,10029884254362,5285539.2857142857514100.4312489.5875170884

    216161,72572124122,4985640516598.80477290.0228854871

    1602561,04044662072,3705740.7142857143518968.43739890.4358194013

    781774736674764501232,3665841.4285714286521334.38077990.848110425

    21158311,558215832,3515942.1428571429523685.64076691.2578427097

    12512881,850622513242,2546042.8571428571525939.58973291.6506175047

    1321236741,2102,0076143.5714285714527946.49861492.0003429141

    163473351,277161451,9476244.2857142857529893.26336392.3395875628

    123174131,0386480521,7636345531656.61229192.6468700365

    1051,710421,7526445.7142857143533408.67541192.952185837

    1414412749676041,7276546.4285714286535135.76882693.2531503984

    2431105112987221011,6746647.1428571429536809.49847393.5448157507

    16131,5586241,5916747.8571428571538400.24653393.8220206708

    51151103395075921,5646848.5714285714539964.4456494.0945991511

    122451,08263511,4836949.2857142857541447.51945294.3530406781

    2419111981563451036411,4627050542909.48443694.6078037652

    5679793305612171821,4487150.7142857143544357.64335194.8601609967

    26361,341741,4217251.4285714286545778.85930595.1078231282

    162105712653071,3617352.1428571429547139.65755995.344956827

    266679110441971,3337452.8571428571548472.33924695.5771909123

    2131,1291991,3287553.5714285714549800.33386895.8086082263

    21491,1351751,3197654.2857142857551119.39091696.038468072

    96547561544601031,3107755552429.67234496.2667986718

    24671,098689611,2437855.7142857143553672.50758796.4833760501

    2511,0381005791,2227956.4285714286554894.07924896.696248021

    19020976162171,1748057.1428571429556068.32378996.9008727334

    11619264718971351,1338157.8571428571557201.78317697.098390195

    101194485471,0148258.5714285714558215.9135497.2751132939

    55365663839858359.2857142857559200.48245597.446684993

    220426637033138488460560048.0244297.5943782756

    488478478560.7142857143560895.48204797.7420568614

    1787557558661.4285714286561650.13594597.8735634015

    1757507508762.1428571429562400.13594598.0042589499

    1575218356293457288862.8571428571563128.25514998.1311415348

    107106166394336998963.5714285714563827.60340598.2530104733

    1473617306176929064.2857142857564519.64086998.3736054279

    2651275036309165565149.48486998.4833625039

    193179421286289265.7142857143565777.59572398.5928175606

    423935433525589366.4285714286566335.97017298.6901203004

    181593034629467.1428571429566798.24745198.7706770771

    6199217274429567.8571428571567240.47977798.8477408056

    179241626220244359668.5714285714567675.78959998.9235982135

    2224204209769.2857142857568095.99819698.9968240709

    2368202101713819870568477.13211199.0632407457

    1763693699970.7142857143568846.41523699.1275922931

    1812663029610071.4285714286569142.11780399.1791216386

    13013151612129110172.1428571429569432.68602699.2297562696

    21526426410272.8571428571569696.836599.2757873244

    19526226210373.5714285714569958.772599.3214324833

    3224324310474.2857142857570202.03905799.3638242914

    26724124110575570443.21032299.4058509724

    88221122210675.7142857143570665.00023899.4445002454

    2556138571721810776.4285714286570883.29941799.4825412199

    27920320310877.1428571429571086.68924799.5179840804

    1691247620110977.8571428571571287.60172399.5529952354

    25729134410218711078.5714285714571475.08611899.5856664032

    591355018511179.2857142857571659.58630899.6178175412

    3316916911280571828.63920999.6472768234

    23916516511380.7142857143571993.63920999.676029844

    415815811481.4285714286572151.58920999.7035543265

    27614614611582.1428571429572297.58920999.7289963933

    1312612611682.8571428571572423.66342999.7509661791

    248468012611783.5714285714572549.23892299.7728490563

    12012112111884.2857142857572669.83892299.7938649005

    268999911985572769.18162999.8111764333

    348499312085.7142857143572861.97327399.827346373

    2721909112186.4285714286572953.23168699.8432491308

    166909012287.1428571429573043.12935599.8589147643

    15451378712387.8571428571573130.27630499999.8741010556

    2818112488.5714285714573211.37430499999.8882332523

    5777712589.2857142857573288.33430499999.9016443582

    10666612690573354.77837999999.9132229513

    274565612790.7142857143573411.24685399999.9230631889

    119565612891.4285714286573467.10685399999.9327973933

    269253225012992.1428571429573516.77790599999.9414531072

    114505013092.8571428571573566.42624499999.950104863

    111484813193.5714285714573614.42624499999.9584693781

    45444413294.2857142857573658.75070599999.9661933911

    135363613395573695.12099799999.9725313048

    144343413495.7142857143573729.30240999999.9784877827

    2544303413596.4285714286573763.08496099999.9843747547

    8282813697.1428571429573791.08496099999.9892540552

    63242413797.8571428571573815.02138599999.9934252341

    172202013898.5714285714573835.02138599999.9969104487

    260101013999.2857142857573844.63579299999.9985858623

    27188140100573852.750860999100

    Grand Total70,29220,714318,40329,79296,83011,0711,1543,94821,649573,85300

    salesdata

    DEPOTSKU_NOVENDORDATEQUANTITYUNIT_PRICEREVENUE

    33008836941/1/02215.69431.388

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