Verfügbarkeit: Marketing analytics

Marketing analytics: Data-driven techniques with Microsoft Excel

Gespeichert in:

Bibliographische Detailangaben
1. Verfasser:	Winston, Wayne L. 1950- (VerfasserIn)
Format:	Buch
Sprache:	English
Veröffentlicht:	Indianapolis, Ind. Wiley 2014
Schlagworte:	Datenanalyse Marketing EXCEL
Online-Zugang:	Inhaltsverzeichnis Klappentext
Beschreibung:	XXX, 690 S. Ill., graph. Darst.
ISBN:	9781118373439

Internformat

MARC


LEADER	00000nam a2200000 c 4500
001	BV041626138
003	DE-604
005	20190912
007	t
008	140204s2014 ad\|\| \|\|\|\| 00\|\|\| eng d
020			\|a 9781118373439 \|9 978-1-118-37343-9
035			\|a (OCoLC)871557260
035			\|a (DE-599)BVBBV041626138
040			\|a DE-604 \|b ger \|e rakwb
041	0		\|a eng
049			\|a DE-1050 \|a DE-473 \|a DE-573 \|a DE-739 \|a DE-355 \|a DE-945 \|a DE-N2 \|a DE-1028 \|a DE-20 \|a DE-1043 \|a DE-634
084			\|a QP 650 \|0 (DE-625)141922: \|2 rvk
084			\|a ST 610 \|0 (DE-625)143683: \|2 rvk
084			\|a WIR 800f \|2 stub
084			\|a DAT 426f \|2 stub
100	1		\|a Winston, Wayne L. \|d 1950- \|e Verfasser \|0 (DE-588)134241614 \|4 aut
245	1	0	\|a Marketing analytics \|b Data-driven techniques with Microsoft Excel \|c Wayne L. Winston
264		1	\|a Indianapolis, Ind. \|b Wiley \|c 2014
300			\|a XXX, 690 S. \|b Ill., graph. Darst.
336			\|b txt \|2 rdacontent
337			\|b n \|2 rdamedia
338			\|b nc \|2 rdacarrier
650	0	7	\|a Datenanalyse \|0 (DE-588)4123037-1 \|2 gnd \|9 rswk-swf
650	0	7	\|a Marketing \|0 (DE-588)4037589-4 \|2 gnd \|9 rswk-swf
650	0	7	\|a EXCEL \|0 (DE-588)4138932-3 \|2 gnd \|9 rswk-swf
689	0	0	\|a Marketing \|0 (DE-588)4037589-4 \|D s
689	0	1	\|a Datenanalyse \|0 (DE-588)4123037-1 \|D s
689	0	2	\|a EXCEL \|0 (DE-588)4138932-3 \|D s
689	0		\|5 DE-604
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe \|z 978-1-118-41730-0
776	0	8	\|i Erscheint auch als \|n Online-Ausgabe \|z 978-1-118-43935-7
856	4	2	\|m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment \|q application/pdf \|u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA \|3 Inhaltsverzeichnis
856	4	2	\|m Digitalisierung UB Passau - ADAM Catalogue Enrichment \|q application/pdf \|u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA \|3 Klappentext
999			\|a oai:aleph.bib-bvb.de:BVB01-027067136

Datensatz im Suchindex

_version_	1804151841970192384
adam_text	Introduction .......................................xxiii I Using Excel to Summarize Marketing Data .........1 1 Slicing and Dicing Marketing Data with PivotTables ............3 2 Using Excel Charts to Summarize Marketing Data ............29 3 Using Excel Functions to Summarize Marketing Data ..........59 II Pricing ...................................83 4 Estimating Demand Curves and Using Solver to Optimize Price ...............................85 Price Bundling ......................................107 Nonlinear Pricing ...................................123 7 Price Skimming and Sales .............................135 Revenue Management ................................143 III Forecasting ..............................159 9 Simple Linear Regression and Correlation .................161 Using Multiple Regression to Forecast Sales ................177 Forecasting in the Presence of Special Events ...............213 Modeling Trend and Seasonally ........................225 13 Ratio to Moving Average Forecasting Method ..............235 Winter s Method ....................................241 Using Neural Networks to Forecast Sales ..................249 Marketing Analytics IV What do Customers Want? ..................261 16 Conjoint Analysis ....................................263 17 Logistic Regression ..................................285 Discrete Choice Analysis ..............................303 V Customer Value ...........................325 Calculating Lifetime Customer Value .....................327 Using Customer Value to Value a Business .................339 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making ...........................347 22 Allocating Marketing Resources between Customer Acquisition and Retention .....................365 VI Market Segmentation ......................375 Cluster Analysis ....................................377 Collaborative Filtering ................................393 2S Using Classification Trees for Segmentation ................403 VII Forecasting New Product Sales ..............413 Using S Curves to Forecast Sales of a New Product .......... 4Ί5 The Bass Diffusion Model .............................427 2E Using the Copernican Principle to Predict Duration of Future Sales ......................................439 VIII Retailing ...............................443 29 Market Basket Analysis and Lift .........................445 Marketing Analytics ι* 30 RFM Analysis and Optimizing Direct Mail Campaigns ........459 31 Using the SCANPRO Model and Its Variants ...............471 32 Allocating Retail Space and Sales Resources ................483 33 Forecasting Sales from Few Data Points ..................495 IX Advertising ..............................503 34 Measuring the Effectiveness of Advertising ................505 35 Media Selection Models ..............................517 36 Pay per Click (PPC) Online Advertising ...................529 X Marketing Research Tools ....................539 Principal Components Analysis (PCA) ....................541 Multidimensional Scaling (MDS) ........................559 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis ......................577 Analysis of Variance: One-way ANOVA ....................595 Analysis of Variance: Two-way ANOVA ....................607 XI Internet and Social Marketing ................619 Networks .........................................621 The Mathematics Behind The Tipping Point .................641 Viral Marketing .....................................653 Text Mining ........................................663 Index ............................................673 Introduction .......................................xxiii I Using Excel to Summarize Marketing Data ................1 1 Slicing and Dicing Marketing Data with PivotTables ........3 Analyzing Sales at True Colors Hardware ......................3 Analyzing Sales at La Petit Bakery ...........................14 Analyzing How Demographics Affect Sales ....................21 Pulling Data from a PivotTable with the GETPIVOTDATA Function .. 25 Summary .............................................27 Exercises ..............................................27 2 Using Excel Charts to Summarize Marketing Data ........29 Combination Charts .....................................29 Using a PivotChart to Summarize Market Research Surveys .................................36 Ensuring Charts Update Automatically When New Data is Added ................................39 Making Chart Labels Dynamic .............................40 Summarizing Monthly Sales-Force Rankings ...................43 Using Check Boxes to Control Data in a Chart .................45 Using Sparklines to Summarize Multiple Data Series .............48 Using GETPIVOTDATA to Create the End-of-Week Sales Report .................................52 Summary .............................................55 Exercises ..............................................55 3 Using Excel Functions to Summarize Marketing Data ......59 Summarizing Data with a Histogram ........................59 Using Statistical Functions to Summarize Marketing Data .........64 Summary .............................................79 Exercises ..............................................80 xii Marketing Analytics II Pricing ..........................................83 4 Estimating Demand Curves and Using Solver to Optimize Price ...........................85 Estimating Linear and Power Demand Curves .................85 Using the Excel Solver to Optimize Price ......................90 Pricing Using Subjectively Estimated Demand Curves ............96 Using SolverTable to Price Multiple Products ..................99 Summary ............................................103 Exercises .............................................104 5 Price Bundling ..................................107 Why Bundle? ......................................... 107 Using Evolutionary Solver to Find Optimal Bundle Prices ........ 111 Summary ............................................ 119 Exercises ............................................. 119 6 Nonlinear Pricing ...............................123 Demand Curves and Willingness to Pay .....................124 Profit Maximizing with Nonlinear Pricing Strategies ............125 Summary ............................................131 Exercises .............................................132 7 Price Skimming and Sales .........................135 Dropping Prices Over Time ..............................135 Why Have Sales? .......................................138 Summary ............................................142 Exercises .............................................142 8 Revenue Management ............................ 143 Estimating Demand for the Bates Motel and Segmenting Customers .................................144 Handling Uncertainty ...................................150 Markdown Pricing .....................................153 Marketing Analytics · Summary ............................................156 Exercises .............................................156 III Forecasting .....................................159 9 Simple Linear Regression and Correlation .............161 Simple Linear Regression ................................161 Using Correlations to Summarize Linear Relationships ..........170 Summary ............................................174 Exercises .............................................175 10 Using Multiple Regression to Forecast Sales ............177 Introducing Multiple Linear Regression ......................178 Running a Regression with the Data Analysis Add-In ...........179 Interpreting the Regression Output ........................182 Using Qualitative Independent Variables in Regression ..........186 Modeling Interactions and Nonlinearities ....................192 Testing Validity of Regression Assumptions ...................195 Multicollinearity .......................................204 Validation of a Regression ................................207 Summary ............................................209 Exercises .............................................210 11 Forecasting in the Presence of Special Events ...........213 Building the Basic Model ................................213 Summary ............................................222 Exercises .............................................222 Modeling Trend and Seasonality ....................225 Using Moving Averages to Smooth Data and Eliminate Seasonality ...................................225 An Additive Model with Trends and Seasonality ...............228 A Multiplicative Model with Trend and Seasonality .............231 Summary ............................................234 Exercises .............................................234 Marketing Analytics 15 Ratio to Moving Average Forecasting Method ..........235 Using the Ratio to Moving Average Method ..................235 Applying the Ratio to Moving Average Method to Monthly Data .........................................238 Summary ............................................238 Exercises .............................................239 14 Winter s Method ................................241 Parameter Definitions for Winter s Method ...................241 Initializing Winter s Method ..............................243 Estimating the Smoothing Constants .......................244 Forecasting Future Months ...............................246 Mean Absolute Percentage Error (MAPE) ....................247 Summary ............................................248 Exercises .............................................248 15 Using Neural Networks to Forecast Sales ..............249 Regression and Neural Nets ..............................249 Using Neural Networks .................................250 Using NeuralTools to Predict Sales .........................253 Using NeuralTools to Forecast Airline Miles ...................258 Summary ............................................259 Exercises .............................................259 IV What do Customers Want? ........................261 Conjoint Analysis ................................263 Products, Attributes, and Levels ...........................263 Full Profile Conjoint Analysis ..............................265 Using Evolutionary Solver to Generate Product Profiles .......................................272 Developing a Conjoint Simulator ..........................277 Examining Other Forms of Conjoint Analysis .................279 Summary ............................................281 Exercises .............................................281 Marketing Analytics 17 Logistic Regression ..............................285 Why Logistic Regression Is Necessary .......................286 Logistic Regression Model ...............................289 Maximum Likelihood Estimate of Logistic Regression Model .....290 Using StatTools to Estimate and Test Logistic Regression Hypotheses ..................................293 Performing a Logistic Regression with Count Data .............298 Summary ............................................300 Exercises .............................................300 18 Discrete Choice Analysis ..........................303 Random Utility Theory ..................................303 Discrete Choice Analysis of Chocolate Preferences .............305 Incorporating Price and Brand Equity into Discrete Choice Analysis .................................309 Dynamic Discrete Choice ................................315 Independence of Irrelevant Alternatives (IIA) Assumption ........316 Discrete Choice and Price Elasticity .........................317 Summary ............................................318 Exercises .............................................319 V Customer Value ..................................325 19 Calculating Lifetime Customer Value .................327 Basic Customer Value Template ...........................328 Measuring Sensitivity Analysis with Two-way Tables ...........330 An Explicit Formula for the Multiplier .......................331 Varying Margins .......................................331 DIRECTV, Customer Value, and Friday Night Lights (FNL) .........333 Estimating the Chance a Customer Is Still Active ..............334 Going Beyond the Basic Customer Lifetime Value Model ........335 Summary ............................................336 Exercises .............................................336 xv Marketing Analytics Using Customer Value to Value a Business .............339 A Primer on Valuation ...................................339 Using Customer Value to Value a Business ...................340 Measuring Sensitivity Analysis with a One-way Table ...........343 Using Customer Value to Estimate a Firm s Market Value ........344 Summary ............................................344 Exercises .............................................345 21 Customer Value, Monte Carlo Simulation, and Marketing Decision Making ........................347 A Markov Chain Model of Customer Value ...................347 Using Monte Carlo Simulation to Predict Success of a Marketing Initiative ...................................353 Summary ............................................359 Exercises .............................................360 22 Allocating Marketing Resources between Customer Acquisition and Retention ..................347 Modeling the Relationship between Spending and Customer Acquisition and Retention .......................365 Basic Model for Optimizing Retention and Acquisition Spending .. 368 An Improvement in the Basic Model ........................371 Summary ............................................373 Exercises .............................................374 VI Market Segmentation .............................375 23 Cluster Analysis ................................377 Clustering U.S. Cities ...................................378 Using Conjoint Analysis to Segment a Market ................386 Summary ............................................391 Exercises .............................................391 24 Collaborative Filtering ............................393 User-Based Collaborative Filtering ..........................393 Item-Based Filtering ....................................398 Marketing Analytics «vii Comparing Item- and User-Based Collaborative Filtering ........400 The Netflix Competition .................................401 Summary ............................................401 Exercises .............................................402 25 Using Classification Trees for Segmentation ............403 Introducing Decision Trees ...............................403 Constructing a Decision Tree .............................404 Pruning Trees and CART .................................409 Summary ............................................410 Exercises .............................................410 VII Forecasting New Product Sales ......................413 26 Using S Curves to Forecast Sales of a New Product ......415 Examining S Curves ....................................415 Fitting the Pearl or Logistic Curve ..........................418 Fitting an S Curve with Seasonally .........................420 Fitting the Gompertz Curve ..............................422 Pearl Curve versus Gompertz Curve .......................425 Summary ............................................425 Exercises .............................................425 27 The Bass Diffusion Model .........................427 Introducing the Bass Model ..............................427 Estimating the Bass Model ...............................428 Using the Bass Model to Forecast New Product Sales ...........431 Deflating Intentions Data ................................434 Using the Bass Model to Simulate Safes of a New Product .......435 Modifications of the Bass Model ...........................437 Summary ............................................438 Exercises .............................................438 Marketing Analytics 28 Using the Copernican Principle to Predict Duration of Future Sales ...................................439 Using the Copernican Principle ............................439 Simulating Remaining Life of Product .......................440 Summary ............................................441 Exercises .............................................441 VIII Retailing .....................................443 29 Market Basket Analysis and Lift .....................445 Computing Lift for Two Products ..........................445 Computing Three-Way Lifts ..............................449 A Data Mining Legend Debunked! .........................453 Using Lift to Optimize Store Layout ........................454 Summary ............................................456 Exercises .............................................456 SO RFM Analysis and Optimizing Direct Mail Campaigns — 459 RFM Analysis .........................................459 An RFM Success Story ...................................465 Using the Evolutionary Solver to Optimize a Direct Mail Campaign .................................465 Summary ............................................468 Exercises .............................................468 31 Using the SCANPRO Model and Its Variants ...........471 Introducing the SCANPRO Model .........................471 Modeling Sales of Snickers Bars ...........................472 Forecasting Software Sales ...............................475 Summary ............................................480 Exercises .............................................480 32 Allocating Retail Space and Sales Resources ............483 Identifying the Sales to Marketing Effort Relationship ...........483 Modeling the Marketing Response to Sales Force Effort .........484 Marketing Analytics Optimizing Allocation of Sales Effort .......................489 Using the Gompertz Curve to Allocate Supermarket Shelf Space ................................492 Summary ............................................492 Exercises .............................................493 33 Forecasting Sales from Few Data Points ..............495 Predicting Movie Revenues ..............................495 Modifying the Model to Improve Forecast Accuracy ............498 Using 3 Weeks of Revenue to Forecast Movie Revenues .........499 Summary ............................................501 Exercises .............................................501 IX Advertising .....................................503 34 Measuring the Effectiveness of Advertising ............505 The Adstock Model ....................................505 Another Model for Estimating Ad Effectiveness ...............509 Optimizing Advertising: Pulsing versus Continuous Spending .....511 Summary ............................................514 Exercises .............................................515 35 Media Selection Models ...........................517 A Linear Media Allocation Model ..........................517 Quantity Discounts .....................................520 A Monte Carlo Media Allocation Simulation ..................522 Summary ............................................527 Exercises .............................................527 36 Pay per Click (PPC) Online Advertising ...............529 Defining Pay per Click Advertising .........................529 Profitability Model for PPC Advertising ......................531 Google AdWords Auction ...............................533 Using Bid Simulator to Optimize Your Bid ....................536 Summary ............................................537 Exercises .............................................537 χ* Marketing Analytics X Marketing Research Tools ...........................539 3/ Principal Components Analysis (PCA) ................541 Defining PCA .........................................541 Linear Combinations, Variances, and Covariances ..............542 Diving into Principal Components Analysis ...................548 Other Applications of PCA ...............................556 Summary ............................................557 Exercises .............................................558 ІШ Multidimensional Scaling (MDS) ....................559 Similarity Data ........................................559 MDS Analysis of U.S. City Distances ........................560 MDS Analysis of Breakfast Foods ...........................566 Finding a Consumer s Ideal Point ..........................570 Summary ............................................574 Exercises .............................................574 39 Classification Algorithms: Naive Bayes Classifier and Discriminant Analysis ..................577 Conditional Probability ..................................578 Bayes Theorem .......................................579 Naive Bayes Classifier ...................................581 Linear Discriminant Analysis ..............................586 Model Validation ......................................591 The Surprising Virtues of Naive Bayes .......................592 Summary ............................................592 Exercises .............................................593 40 Analysis of Variance: One-way ANOVA ................595 Testing Whether Group Means Are Different .................595 Example of One-way ANOVA .............................596 The Role of Variance in ANOVA ...........................598 Forecasting with One-way ANOVA .........................599 Marketing Analytics Contrasts ............................................601 Summary ............................................603 Exercises .............................................604 41 Analysis of Variance: Two-way ANOVA ................607 Introducing Two-way ANOVA .............................607 Two-way ANOVA without Replication .......................608 Two-way ANOVA with Replication .........................611 Summary ............................................616 Exercises .............................................617 XI Internet and Social Marketing .......................619 42 Networks .....................................621 Measuring the Importance of a Node .......................621 Measuring the Importance of a Link ........................626 Summarizing Network Structure ..........................628 Random and Regular Networks ...........................631 The Rich Get Richer ....................................634 Klout Score ...........................................636 Summary ............................................637 Exercises .............................................638 The Mathematics Behind The Tipping Point .............641 Network Contagion ....................................641 A Bass Version of the Tipping Point ........................646 Summary ............................................650 Exercises .............................................650 Viral Marketing .................................653 Watts Model .........................................654 A More Complex Viral Marketing Model ....................655 Summary ............................................660 Exercises .............................................661 χ* Marketing Analytics 45 Text Mining ....................................663 Text Mining Definitions .................................664 Giving Structure to Unstructured Text ......................664 Applying Text Mining in Real Life Scenarios ..................668 Summary ............................................671 Exercises .............................................671 Index ........................................673 Powerful techniques for analyzing business data with Excel Most businesses are awash in data. To make that data work for your business, you need a simple, cost-effective tool — ideally, one you already know something about. Excel is that tool. Every example in this book features step-by-step instructions, a downloadable Excel file containing data and solutions, and plenty of Screenshots. To sharpen your marketing analytics, you just need this guide and Excel. This book will help you master many important marketing analytic concepts, including: • Using Excel charts and functions to summarize marketing data • Estimating demand curves and using Solver to determine profit-maximizing pricing strategies • Using cluster analysis for market segmentation • Developing customized forecasting models that show you how your marketing mix impacts sales • Measuring the effectiveness of your advertising program • Understanding the analytics underlying social networks and viral marketing Companion website At the companion website, www.wiley.com/go/marketinganalytics, you can download all the Excel files used in this book, find answers to all the exercises at the ends of the chapters, and be advised of any errors discovered.
any_adam_object	1
author	Winston, Wayne L. 1950-
author_GND	(DE-588)134241614
author_facet	Winston, Wayne L. 1950-
author_role	aut
author_sort	Winston, Wayne L. 1950-
author_variant	w l w wl wlw
building	Verbundindex
bvnumber	BV041626138
classification_rvk	QP 650 ST 610
classification_tum	WIR 800f DAT 426f
ctrlnum	(OCoLC)871557260 (DE-599)BVBBV041626138
discipline	Informatik Wirtschaftswissenschaften
format	Book
fullrecord	<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>02052nam a2200433 c 4500</leader><controlfield tag="001">BV041626138</controlfield><controlfield tag="003">DE-604</controlfield><controlfield tag="005">20190912 </controlfield><controlfield tag="007">t</controlfield><controlfield tag="008">140204s2014 ad\|\| \|\|\|\| 00\|\|\| eng d</controlfield><datafield tag="020" ind1=" " ind2=" "><subfield code="a">9781118373439</subfield><subfield code="9">978-1-118-37343-9</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(OCoLC)871557260</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-599)BVBBV041626138</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-604</subfield><subfield code="b">ger</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1="0" ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="049" ind1=" " ind2=" "><subfield code="a">DE-1050</subfield><subfield code="a">DE-473</subfield><subfield code="a">DE-573</subfield><subfield code="a">DE-739</subfield><subfield code="a">DE-355</subfield><subfield code="a">DE-945</subfield><subfield code="a">DE-N2</subfield><subfield code="a">DE-1028</subfield><subfield code="a">DE-20</subfield><subfield code="a">DE-1043</subfield><subfield code="a">DE-634</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">QP 650</subfield><subfield code="0">(DE-625)141922:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">ST 610</subfield><subfield code="0">(DE-625)143683:</subfield><subfield code="2">rvk</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">WIR 800f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">DAT 426f</subfield><subfield code="2">stub</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Winston, Wayne L.</subfield><subfield code="d">1950-</subfield><subfield code="e">Verfasser</subfield><subfield code="0">(DE-588)134241614</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Marketing analytics</subfield><subfield code="b">Data-driven techniques with Microsoft Excel</subfield><subfield code="c">Wayne L. Winston</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="a">Indianapolis, Ind.</subfield><subfield code="b">Wiley</subfield><subfield code="c">2014</subfield></datafield><datafield tag="300" ind1=" " ind2=" "><subfield code="a">XXX, 690 S.</subfield><subfield code="b">Ill., graph. Darst.</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">Marketing</subfield><subfield code="0">(DE-588)4037589-4</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="650" ind1="0" ind2="7"><subfield code="a">EXCEL</subfield><subfield code="0">(DE-588)4138932-3</subfield><subfield code="2">gnd</subfield><subfield code="9">rswk-swf</subfield></datafield><datafield tag="689" ind1="0" ind2="0"><subfield code="a">Marketing</subfield><subfield code="0">(DE-588)4037589-4</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="1"><subfield code="a">Datenanalyse</subfield><subfield code="0">(DE-588)4123037-1</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2="2"><subfield code="a">EXCEL</subfield><subfield code="0">(DE-588)4138932-3</subfield><subfield code="D">s</subfield></datafield><datafield tag="689" ind1="0" ind2=" "><subfield code="5">DE-604</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-118-41730-0</subfield></datafield><datafield tag="776" ind1="0" ind2="8"><subfield code="i">Erscheint auch als</subfield><subfield code="n">Online-Ausgabe</subfield><subfield code="z">978-1-118-43935-7</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Bamberg - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Inhaltsverzeichnis</subfield></datafield><datafield tag="856" ind1="4" ind2="2"><subfield code="m">Digitalisierung UB Passau - ADAM Catalogue Enrichment</subfield><subfield code="q">application/pdf</subfield><subfield code="u">http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA</subfield><subfield code="3">Klappentext</subfield></datafield><datafield tag="999" ind1=" " ind2=" "><subfield code="a">oai:aleph.bib-bvb.de:BVB01-027067136</subfield></datafield></record></collection>
id	DE-604.BV041626138
illustrated	Illustrated
indexdate	2024-07-10T01:01:14Z
institution	BVB
isbn	9781118373439
language	English
oai_aleph_id	oai:aleph.bib-bvb.de:BVB01-027067136
oclc_num	871557260
open_access_boolean
owner	DE-1050 DE-473 DE-BY-UBG DE-573 DE-739 DE-355 DE-BY-UBR DE-945 DE-N2 DE-1028 DE-20 DE-1043 DE-634
owner_facet	DE-1050 DE-473 DE-BY-UBG DE-573 DE-739 DE-355 DE-BY-UBR DE-945 DE-N2 DE-1028 DE-20 DE-1043 DE-634
physical	XXX, 690 S. Ill., graph. Darst.
publishDate	2014
publishDateSearch	2014
publishDateSort	2014
publisher	Wiley
record_format	marc
spelling	Winston, Wayne L. 1950- Verfasser (DE-588)134241614 aut Marketing analytics Data-driven techniques with Microsoft Excel Wayne L. Winston Indianapolis, Ind. Wiley 2014 XXX, 690 S. Ill., graph. Darst. txt rdacontent n rdamedia nc rdacarrier Datenanalyse (DE-588)4123037-1 gnd rswk-swf Marketing (DE-588)4037589-4 gnd rswk-swf EXCEL (DE-588)4138932-3 gnd rswk-swf Marketing (DE-588)4037589-4 s Datenanalyse (DE-588)4123037-1 s EXCEL (DE-588)4138932-3 s DE-604 Erscheint auch als Online-Ausgabe 978-1-118-41730-0 Erscheint auch als Online-Ausgabe 978-1-118-43935-7 Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA Klappentext
spellingShingle	Winston, Wayne L. 1950- Marketing analytics Data-driven techniques with Microsoft Excel Datenanalyse (DE-588)4123037-1 gnd Marketing (DE-588)4037589-4 gnd EXCEL (DE-588)4138932-3 gnd
subject_GND	(DE-588)4123037-1 (DE-588)4037589-4 (DE-588)4138932-3
title	Marketing analytics Data-driven techniques with Microsoft Excel
title_auth	Marketing analytics Data-driven techniques with Microsoft Excel
title_exact_search	Marketing analytics Data-driven techniques with Microsoft Excel
title_full	Marketing analytics Data-driven techniques with Microsoft Excel Wayne L. Winston
title_fullStr	Marketing analytics Data-driven techniques with Microsoft Excel Wayne L. Winston
title_full_unstemmed	Marketing analytics Data-driven techniques with Microsoft Excel Wayne L. Winston
title_short	Marketing analytics
title_sort	marketing analytics data driven techniques with microsoft excel
title_sub	Data-driven techniques with Microsoft Excel
topic	Datenanalyse (DE-588)4123037-1 gnd Marketing (DE-588)4037589-4 gnd EXCEL (DE-588)4138932-3 gnd
topic_facet	Datenanalyse Marketing EXCEL
url	http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027067136&sequence=000004&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA
work_keys_str_mv	AT winstonwaynel marketinganalyticsdatadriventechniqueswithmicrosoftexcel

Verfügbarkeit

Es ist kein Print-Exemplar vorhanden.

Fernleihe Bestellen Achtung: Nicht im THWS-Bestand! Inhaltsverzeichnis

MARC

Datensatz im Suchindex

Es ist kein Print-Exemplar vorhanden.

Ähnliche Einträge