Hands-on data science for marketing: improve your marketing strategies with machine learning using Python and R
Gespeichert in:
| Beteilige Person: | |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Birmingham, UK
Packt Publishing
2019
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| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031689432&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Abstract: | Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary |
| Umfang: | VI, 450 Seiten Illustrationen, Diagramme |
| ISBN: | 9781789346343 |
Internformat
MARC
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| 520 | 3 | |a Section 2: Descriptive Versus Explanatory Analysis; Chapter 2: Key Performance Indicators and Visualizations; KPIs to measure performances of different marketing efforts; Sales revenue; Cost per acquisition (CPA); Digital marketing KPIs; Computing and visualizing KPIs using Python; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Computing and visualizing KPIs using R; Aggregate conversion rate; Conversion rates by age; Conversions versus non-conversions; Conversions by age and marital status; Summary | |
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Datensatz im Suchindex
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| adam_text | Table of Contents Preface _____________________________________________________ 1 Section 1: Introduction and Environment Setup Chapter 1 : Data Science and Marketing Technical requirements Trends in marketing Applications of data science in marketing Descriptive versus explanatory versus predictive analyses Types of learning algorithms Data science workflow Setting up the Python environment Installing the Anaconda distribution A simple logistic regression model in Python Setting up the R environment Installing R and RStudio A simple logistic regression model in R 11 12 12 и 15 16 17 19 19 21 28 28 31 Summary Section 2: Descriptive Versus Explanatory Analysis Յ6 Chapter 2: Key Performance Indicators and Visualizations KPIs to measure performances of different marketing efforts 41 42 Sales revenue Cost per acquisition (CPA) Digital marketing KPIs Computing and visualizing KPIs using Python Aggregate conversion rate Conversion rates by age Conversions versus non-conversions Conversions by age and marital status Computing and visualizing KPIs using R Aggregate conversion rate Conversion rates by age Conversions versus non-conversions Conversions by age and marital status Summary Chapter 3: Drivers behind Marketing Engagement Using regression analysis for explanatory analysis 42 44 45 46 48 48 53 55 59 60 61 66 68 72 73 74
Table of Contents Explanatory analysis and regression analysis Logistic regression Regression analysis with Python Data analysis and visualizations Engagement rate Sales channels Total claim amounts Regression analysis Continuous variables Categorical variables Combining continuous and categorical variables Regression analysis with R Data analysis and visualization Engagement rate Sales channels Total claim amounts 77 78 78 79 81 83 84 87 90 92 эз 94 95 97 Regression analysis Continuous variables Categorical variables Combining continuous and 74 76 99 100 categorical variables Summary Chapter 4: From Engagement to Conversion Decision trees Logistic regression versus decision trees Growing decision trees Decision trees and interpretations with Python Data analysis and visualization Conversion rate Conversion rates by job Default rates by conversions Bank balances by conversions Conversion rates by number of contacts Encoding categorical variables Encoding months Encoding jobs Encoding marital Encoding the housing and loan variables Building decision trees Interpreting decision trees Decision trees and interpretations with R Data analysis and visualizations Conversion rate Conversion rates by job Default rates by conversions Bank balance by conversions Conversion rates by number of contacts Encoding categorical variables 104 107 юэ 111 112 112 11 з 114 115 116 117 119 120 123 125 125 126 128 129 129 130 134 135 136 136 138 140 142 144
Table of Contents Encoding the month Encoding the job, housing, and marital variables Building decision trees Interpreting decision trees Summary 144 146 147 148 iso Section 3: Product Visibility and Marketing Chapter 5: Product Analytics The importance of product analytics Product analytics using Python Time series trends Repeat customers Trending items over time Product analytics using R Time series trends Repeat customers Trending items over time Summary Chapter 6: Recommending the Right Products Collaborative filtering and product recommendation Product recommender system Collaborative filtering Building a product recommendation algorithm with Python Data preparation Handling NaNs in the CustomerlD field Building a customer-item matrix Collaborative filtering User-based collaborative filtering and recommendations Item-based collaborative filtering and recommendations Buüding a product recommendation algorithm with R Data preparation Handling NA values in the CustomerlD field Building a customer-item matrix Collaborative filtering User-based collaborative filtering and recommendations Item-based collaborative filtering and recommendations Summary 153 154 155 158 164 171 176 178 184 189 192 193 194 194 195 197 198 198 200 201 202 206 210 211 211 213 215 215 220 223 Section 4: Personalized Marketing Chapter 7: Exploratory Analysis for Customer Behavior Customer analytics - understanding customer behavior Customer analytics use cases Sales funnel analytics Customer segmentation [iii] 227 227 228 228 228
Table of Contents Predictive analytics _ Conducting customer analytics with Python Analytics on engaged customers Overall engagement rate Engagement rates by offer type Engagement rates by offer type and vehicle class Engagement rates by sales channel Engagement rates by sales channel and vehicle size Segmenting customer base Conducting customer analytics with R Analytics on engaged customers Overall engagement rate Engagement rates by offer type Engagement rates by offer type and vehicle class Engagement rates by sales channel Engagement rates by sales channel and vehicle size Segmenting customer base Summary Chapter 8: Predicting the Likeiihood of Marketing Engagement Predictive analytics in marketing Applications of predictive analytics in marketing Evaluating classification models Predicting the likelihood of marketing engagement with Python Variable encoding Response variable encoding Categorical variable encoding Building predictive models Random forest model Training a random forest model Evaluating a classification model Predicting the likelihood of marketing engagement with R Variable encoding Response variable encoding Categorical variable encoding Building predictive models Random forest model Training a random forest model Evaluating a classification model Summary Chapter 9: Customer Lifetime Value CLV Evaluating regression models Predicting the 3 month CLV with Python Data cleanup Data analysis --------------------------------------------------------- 229 229 231 231 233 235 239 240 242 248 250 250 251 253 256 257 259 264 265 266 267 268 270 271 271 272 274 275
277 281 286 287 287 288 290 291 292 296 299 301 301 302 зо4 305 308 [iv] ----------------------------------------------------------
Table of Contents Predicting the 3 month CLV Data preparation Linear regression Evaluating regression model performance Predicting the 3 month CLV with R Data cleanup Data analysis Predicting the 3 month CLV Data preparation Linear regression Evaluating regression model performance Summary Chapter 10: Data-Driven Customer Segmentation Customer segmentation Clustering algorithms Segmenting customers with Python Data cleanup к-means clustering Selecting the best number of clusters Interpreting customer segments 311 312 316 319 321 323 325 328 328 333 335 зз8 ззѳ 340 341 342 343 347 350 351 353 355 359 361 362 Segmenting customers with R Data cleanup к-means clustering Selecting the best number of clusters Interpreting customer segments Summary Chapter 11: Retaining Customers Customer churn and retention Artificial neural networks Predicting customer churn with Python Data analysis and preparation ANN with Keras Model evaluations Predicting customer churn with R Data analysis and preparation ANN with Keras Model evaluations Summary 365 367 З68 Յ6Ց 370 371 378 З80 384 385 391 394 зэб Section 5: Better Decision Making Chapter 12: A/В Testing for Better Marketing Strategy A/В testing for marketing Statistical hypothesis testing -------------------------------------------------------- [v] յցց 400 401 ---------------------------------------------------------
Table of Contents Evaluating A/В testing results with Python Data analysis Statistical hypothesis testing Evaluating A/В testing results with R Data analysis Statistical hypothesis testing Summary Chapter 13: What s Next? Recap of the topics covered in this book Trends in marketing Data science workflow Machine learning models Real-life data science challenges Challenges in data Challenges in infrastructure Challenges in choosing the right model 402 404 411 414 416 422 425 427 428 428 429 431 434 434 436 437 More machine learning models and packages 437 Summary 439 Other Books You May Enjoy____________________________________ 441 Index_______________ ________________________________________445 [vi]
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| id | DE-604.BV046312280 |
| illustrated | Illustrated |
| indexdate | 2024-12-20T18:49:03Z |
| institution | BVB |
| isbn | 9781789346343 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-031689432 |
| oclc_num | 1137846313 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG |
| owner_facet | DE-473 DE-BY-UBG |
| physical | VI, 450 Seiten Illustrationen, Diagramme |
| publishDate | 2019 |
| publishDateSearch | 2019 |
| publishDateSort | 2019 |
| publisher | Packt Publishing |
| record_format | marc |
| spellingShingle | Hwang, Yoon Hyup Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R Marketing (DE-588)4037589-4 gnd Data Science (DE-588)1140936166 gnd |
| subject_GND | (DE-588)4037589-4 (DE-588)1140936166 |
| title | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R |
| title_auth | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R |
| title_exact_search | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R |
| title_full | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R Yoon Hyup Hwang |
| title_fullStr | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R Yoon Hyup Hwang |
| title_full_unstemmed | Hands-on data science for marketing improve your marketing strategies with machine learning using Python and R Yoon Hyup Hwang |
| title_short | Hands-on data science for marketing |
| title_sort | hands on data science for marketing improve your marketing strategies with machine learning using python and r |
| title_sub | improve your marketing strategies with machine learning using Python and R |
| topic | Marketing (DE-588)4037589-4 gnd Data Science (DE-588)1140936166 gnd |
| topic_facet | Marketing Data Science |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=031689432&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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