crm and infovi
DESCRIPTION
CRM and InfoviTRANSCRIPT
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CRM and Information VisualizationGrdal Ertek, Ph.D.Tue Gizem Martaan
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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
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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?
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Prospecting (of first-time consumers)LoyaltyCross-selling / Up-sellingWin back or SaveStrategies in CRM for Mass Customization
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The Marketing Perspective
CAMPAIGN MANAGEMENT
RECENCY FREQUENCY MONETARY VALUE METHOD
CUSTOMER VALUE METRICS
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Campaign Management: The Marketing PerspectiveDeveloping effective campaignsEffectively predicting the futureRetaining existing customersAcquiring new customers
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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
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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
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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
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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
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Campaign ManagementStep 3: Communication StrategiesWhich message should be transmitted?Which channel should be used?
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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
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Campaign ManagementStep 5: Test the ImpactsImpacts of the decisions have to be tested and and assessed on a sample
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Campaign ManagementStep 6: Revise the DecisionsMake revisions to the targeted offer / service / promotionsFinally apply the decisions to the whole segment or population
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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
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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?
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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
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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
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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.
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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
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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.
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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
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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
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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
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Customer Value Metrics
Critical measures used to define customer worth in knowledge-driven and customer-focused marketing
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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
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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
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Customer Value MetricsShare of wallet and size of wallet should be analyzed together because...
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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%
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The Engineering Perspective
DATA MINING
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Collection, storage, and analysis of typically huge amounts of- dataData readily resides in the companys data warehouseData cleaning is almost inevitableData Mining
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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
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Data selectionData cleaningSamplingDimensionality reductionData mining methods
Data Mining: Steps
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Exploratory Data AnalysisSegmentationCluster AnalysisDecision TreesMarket Basket AnalysisAssociation rulesInformation VisualizationPredictionRegressionNeural NetworkTime Series AnalysisData Mining:Methods
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Information VisualizationData mining algorithms...Can only detect certain types of patterns and insightsAre too complex for end users to understand
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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)
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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.
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Case Studies
Analysis of Supermarket Sales Data
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The Data
Field NameDesciptionTRANSACTION_IDTransaction IDPRODUCT_NOProduct Number
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Frequent Itemsets
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Frequent Itemsets
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Association Rules
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Case Studies
Analysis of Spare Parts Sales Data
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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
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Determining Top Products:Pivot Table for Determining REVENUE_SUM
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Determining Top Products:Pivot Table for Determining COUNT (Frequency)
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Determining Top Products:Scatter Plot
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Seasonality of Top Products. . .
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Seasonality of Top Customers:Pivot Table
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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
330113502841/1/0215.7203395.720339
330143842841/1/0213.1473.147
330187882841/1/0213.8498313.849831
330147472841/1/0214.854.85
330081332841/1/0220.0496360.099272
330084002841/1/0210.4788140.478814
330104982841/1/02210.89471221.789424
330145762841/1/0215.4386365.438636
11014774941/1/0226.63610713.272214
440020722841/1/02113.79237313.792373
770088172841/1/0215.5423735.542373
770088172841/1/0215.5423735.542373
910113292841/1/0223.1194926.238984
930186482841/1/02150.84745850.847458
33016542281/2/02130.86332830.863328
33016280751/2/02112.0233812.02338
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