Data Analytics for Marketing: A Practical Guide to Analyzing Marketing Data Using Python
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
Birmingham
Packt Publishing, Limited
2024
|
| Ausgabe: | 1st ed |
| Schlagworte: | |
| Online-Zugang: | DE-2070s |
| Beschreibung: | Description based on publisher supplied metadata and other sources |
| Beschreibung: | 1 Online-Ressource (452 Seiten) |
| ISBN: | 9781801813839 |
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| 505 | 8 | |a Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Analytics -- Chapter 1: What is Marketing Analytics? -- What is analytics? -- An overview of marketing analytics -- Why should we bother with marketing analytics? -- Exploring different types of analytics -- Descriptive analytics -- Diagnostic analytics -- Predictive analytics -- Prescriptive analytics -- Walking through the maze of tools and techniques -- Beyond simple pivot tables -- Why Python? -- Modern challenges in the world of privacy-centric marketing -- The importance of data engineering and tracking -- Don't moonlight as a data engineer -- Web tracking is hard, and it is becoming harder -- Summary -- References -- Chapter 2: Extracting and Exploring Data with Singer and pandas -- Technical requirements -- What is ETL, and why should you care? -- Data pipelines -- What is Singer? -- Summarizing data and EDA -- Primer on descriptive statistics -- Percentiles, quantiles, and distributions -- Measures of central tendency -- Measures of variability -- Dealing with common data issues -- Bill Gates walks into a bar -- Missing values and data imputation -- Digging deeper into variable transformations -- Data standardization or scaling -- Power transformations -- Summary -- Further reading -- Chapter 3: Design Principles and Presenting Results with Streamlit -- Technical requirements -- Types of dashboards and their design -- Understanding the design concepts of a dashboard -- Thinking about how to best present data -- Thinking a bit about processing information -- Generating effective filters, dimensions, and metrics -- Filters -- Dimensions -- Metrics -- Getting your data into Streamlit and generating a basic dashboard -- Starting out with Streamlit -- Creating a marketing data dashboard with Streamlit -- Summary | |
| 505 | 8 | |a Further reading -- Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC -- Technical requirements -- What is a linear regression? -- What is a model? -- What are the assumptions of a linear regression? -- Exploring different types of regression models -- What we can do when the assumptions break down -- How to do a linear regression -- What is logistic regression? -- Objectives of logistic regression models -- Odds of an event -- What is causal inference? -- Correlation, causation, and key drivers -- A more practical application -- A small detour through the backdoor -- Watch out for colliders -- Summary -- Further reading -- Part 2: Planning Ahead -- Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast -- Technical requirements -- What is forecasting? -- Why forecasting is important -- Types of times series data -- Exploratory data analysis -- What to forecast -- Weekly, daily, and sub-daily data -- Time series of counts -- Prediction intervals for aggregates -- Long and short time series -- Transformations -- What types of patterns are present? -- Time series decomposition -- Time series features -- Basics of time series forecasting -- Simple methods -- Fitted values and residuals -- Correlation and forecasting -- Variable selection in time series regression models -- Advanced forecasting methods -- Extending regression models to time series -- ETS models -- ARIMA models -- The Prophet model -- Which model to use -- Summary -- Further reading -- Chapter 6: Anomaly Detection with StatsForecast and PyMC -- Technical requirements -- What is an anomaly? -- Techniques to detect anomalies -- Anomaly detection with STL decomposition -- Twitter's t-ESD algorithm for anomaly detection -- Isolation forests for anomaly detection -- Forecasting as an anomaly detection tool | |
| 505 | 8 | |a Practical implementation with StatsForecast -- Using rates of arrival to identify change points -- Pros and cons of using rates of arrival for change point detection -- Summary -- Further reading -- Part 3: Who and What to Target -- Chapter 8: Customer Insights - Segmentation and RFM -- Technical requirements -- Understanding the sources of customer dynamics -- Analyzing customer dynamics - unveiling segmentation and RFM -- Delving deeper into what segmentation is -- Clustering -- Classification -- Discriminant analysis and classification -- Exploring RFM -- Approaches and techniques - independent versus sequential sorting -- A practical example of RFM analysis -- Profitability evaluation -- ROMI after RFM -- Results of using RFM for targeting -- Summary -- Further reading -- Chapter 8: Customer Lifetime Value with PyMC Marketing -- Technical requirements -- Diving deeper into CLV -- CLV in practice -- Using CLV to calculate acquisition costs -- CLV and prospects -- CLV and incremental value -- What's wrong with the CLV formula? -- Issue 1 -- Issue 2 -- Issue 3 -- Issue 4 -- Issue 5 -- Beyond the CLV formula -- The BTYD model -- The Pareto/NBD model -- The BG/NBD model -- Implementing the BTYD model using PyMC Marketing -- Predicting the expected number of purchases for a new customer -- Estimating the CLV -- Summary -- Further reading -- Chapter 9: Customer Survey Analysis -- Technical requirements -- Steps in customer survey analysis -- Questionnaire construction -- Principles of questionnaire design -- Types of questions -- Asking questions -- Questionnaire design-layout -- Response formats -- Reliability and validity -- Reliability and classical measurement theory -- Standard error of measurement -- Using scales with high reliability -- How to do sampling -- Types of sampling -- Probability versus quota sampling | |
| 505 | 8 | |a Sample size for estimating population mean -- Response rate -- Control charts -- Customer loyalty and NPS methodology -- Issues with NPS -- Potential loss of revenue -- Advocacy, purchasing, and retention loyalty -- Factor analysis -- Summary -- Further reading -- Chapter 10: Conjoint Analysis with pandas and Statsmodels -- Technical requirements -- An introduction to conjoint analysis -- The fundamentals of conjoint analysis -- Setting up a conjoint study -- Step 1 - select the product attributes to be included -- Step 2 - select the product attribute levels -- Step 3 - create product profiles -- Step 4 - collect data from target customers -- Step 5 - estimate the utility of each product attribute and levels using regression analysis -- Conducting conjoint analysis in Python -- Determining the value of a product attribute -- Choice-based conjoint analysis -- Reporting findings -- Summary -- Further reading -- Part 4: Measuring Effectiveness -- Chapter 11: Multi-Touch Digital Attribution -- Technical requirements -- An introduction to attribution models -- Heuristic attribution models -- The implementation of different heuristic attribution models -- Algorithmic attribution models -- Shapley value attribution -- Fractribution -- Summary -- References -- Chapter 12: Media Mix Modeling with PyMC Marketing -- Technical requirements -- Understanding MMM -- MMM versus MTA versus lift analysis and A/B testing -- Steps toward implementing MMM -- Data collection -- How much data to collect -- Modeling -- How to measure the adstock effect -- Saturation and diminishing returns -- Which comes first? -- Selecting a model -- Experimenting and calibrating -- A synthetic data example of MMM -- Synthetic data generation -- Modeling -- Model results -- Summary -- References -- Chapter 13: Running Experiments with PyMC -- Technical requirements | |
| 505 | 8 | |a What makes a good experiment? -- A/A testing -- Type I and Type II errors -- p-values -- Common pitfalls -- Delving deeper into some pitfalls -- Conversion rate -- Uplift modeling -- Experimentation -- Observational studies -- Quasi-experiments -- Difference in differences -- Synthetic control and causal impact -- Summary -- Further reading -- Index -- About PACKT -- Other Books You May Enjoy | |
| 650 | 4 | |a Business-Data processing | |
| 776 | 0 | 8 | |i Erscheint auch als |n Druck-Ausgabe |a Diaz-Bérrio, Guilherme |t Data Analytics for Marketing |d Birmingham : Packt Publishing, Limited,c2024 |
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Datensatz im Suchindex
| _version_ | 1817704728635637760 |
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| author | Diaz-Bérrio, Guilherme |
| author_facet | Diaz-Bérrio, Guilherme |
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| author_sort | Diaz-Bérrio, Guilherme |
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| collection | ZDB-30-PQE |
| contents | Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Analytics -- Chapter 1: What is Marketing Analytics? -- What is analytics? -- An overview of marketing analytics -- Why should we bother with marketing analytics? -- Exploring different types of analytics -- Descriptive analytics -- Diagnostic analytics -- Predictive analytics -- Prescriptive analytics -- Walking through the maze of tools and techniques -- Beyond simple pivot tables -- Why Python? -- Modern challenges in the world of privacy-centric marketing -- The importance of data engineering and tracking -- Don't moonlight as a data engineer -- Web tracking is hard, and it is becoming harder -- Summary -- References -- Chapter 2: Extracting and Exploring Data with Singer and pandas -- Technical requirements -- What is ETL, and why should you care? -- Data pipelines -- What is Singer? -- Summarizing data and EDA -- Primer on descriptive statistics -- Percentiles, quantiles, and distributions -- Measures of central tendency -- Measures of variability -- Dealing with common data issues -- Bill Gates walks into a bar -- Missing values and data imputation -- Digging deeper into variable transformations -- Data standardization or scaling -- Power transformations -- Summary -- Further reading -- Chapter 3: Design Principles and Presenting Results with Streamlit -- Technical requirements -- Types of dashboards and their design -- Understanding the design concepts of a dashboard -- Thinking about how to best present data -- Thinking a bit about processing information -- Generating effective filters, dimensions, and metrics -- Filters -- Dimensions -- Metrics -- Getting your data into Streamlit and generating a basic dashboard -- Starting out with Streamlit -- Creating a marketing data dashboard with Streamlit -- Summary Further reading -- Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC -- Technical requirements -- What is a linear regression? -- What is a model? -- What are the assumptions of a linear regression? -- Exploring different types of regression models -- What we can do when the assumptions break down -- How to do a linear regression -- What is logistic regression? -- Objectives of logistic regression models -- Odds of an event -- What is causal inference? -- Correlation, causation, and key drivers -- A more practical application -- A small detour through the backdoor -- Watch out for colliders -- Summary -- Further reading -- Part 2: Planning Ahead -- Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast -- Technical requirements -- What is forecasting? -- Why forecasting is important -- Types of times series data -- Exploratory data analysis -- What to forecast -- Weekly, daily, and sub-daily data -- Time series of counts -- Prediction intervals for aggregates -- Long and short time series -- Transformations -- What types of patterns are present? -- Time series decomposition -- Time series features -- Basics of time series forecasting -- Simple methods -- Fitted values and residuals -- Correlation and forecasting -- Variable selection in time series regression models -- Advanced forecasting methods -- Extending regression models to time series -- ETS models -- ARIMA models -- The Prophet model -- Which model to use -- Summary -- Further reading -- Chapter 6: Anomaly Detection with StatsForecast and PyMC -- Technical requirements -- What is an anomaly? -- Techniques to detect anomalies -- Anomaly detection with STL decomposition -- Twitter's t-ESD algorithm for anomaly detection -- Isolation forests for anomaly detection -- Forecasting as an anomaly detection tool Practical implementation with StatsForecast -- Using rates of arrival to identify change points -- Pros and cons of using rates of arrival for change point detection -- Summary -- Further reading -- Part 3: Who and What to Target -- Chapter 8: Customer Insights - Segmentation and RFM -- Technical requirements -- Understanding the sources of customer dynamics -- Analyzing customer dynamics - unveiling segmentation and RFM -- Delving deeper into what segmentation is -- Clustering -- Classification -- Discriminant analysis and classification -- Exploring RFM -- Approaches and techniques - independent versus sequential sorting -- A practical example of RFM analysis -- Profitability evaluation -- ROMI after RFM -- Results of using RFM for targeting -- Summary -- Further reading -- Chapter 8: Customer Lifetime Value with PyMC Marketing -- Technical requirements -- Diving deeper into CLV -- CLV in practice -- Using CLV to calculate acquisition costs -- CLV and prospects -- CLV and incremental value -- What's wrong with the CLV formula? -- Issue 1 -- Issue 2 -- Issue 3 -- Issue 4 -- Issue 5 -- Beyond the CLV formula -- The BTYD model -- The Pareto/NBD model -- The BG/NBD model -- Implementing the BTYD model using PyMC Marketing -- Predicting the expected number of purchases for a new customer -- Estimating the CLV -- Summary -- Further reading -- Chapter 9: Customer Survey Analysis -- Technical requirements -- Steps in customer survey analysis -- Questionnaire construction -- Principles of questionnaire design -- Types of questions -- Asking questions -- Questionnaire design-layout -- Response formats -- Reliability and validity -- Reliability and classical measurement theory -- Standard error of measurement -- Using scales with high reliability -- How to do sampling -- Types of sampling -- Probability versus quota sampling Sample size for estimating population mean -- Response rate -- Control charts -- Customer loyalty and NPS methodology -- Issues with NPS -- Potential loss of revenue -- Advocacy, purchasing, and retention loyalty -- Factor analysis -- Summary -- Further reading -- Chapter 10: Conjoint Analysis with pandas and Statsmodels -- Technical requirements -- An introduction to conjoint analysis -- The fundamentals of conjoint analysis -- Setting up a conjoint study -- Step 1 - select the product attributes to be included -- Step 2 - select the product attribute levels -- Step 3 - create product profiles -- Step 4 - collect data from target customers -- Step 5 - estimate the utility of each product attribute and levels using regression analysis -- Conducting conjoint analysis in Python -- Determining the value of a product attribute -- Choice-based conjoint analysis -- Reporting findings -- Summary -- Further reading -- Part 4: Measuring Effectiveness -- Chapter 11: Multi-Touch Digital Attribution -- Technical requirements -- An introduction to attribution models -- Heuristic attribution models -- The implementation of different heuristic attribution models -- Algorithmic attribution models -- Shapley value attribution -- Fractribution -- Summary -- References -- Chapter 12: Media Mix Modeling with PyMC Marketing -- Technical requirements -- Understanding MMM -- MMM versus MTA versus lift analysis and A/B testing -- Steps toward implementing MMM -- Data collection -- How much data to collect -- Modeling -- How to measure the adstock effect -- Saturation and diminishing returns -- Which comes first? -- Selecting a model -- Experimenting and calibrating -- A synthetic data example of MMM -- Synthetic data generation -- Modeling -- Model results -- Summary -- References -- Chapter 13: Running Experiments with PyMC -- Technical requirements What makes a good experiment? -- A/A testing -- Type I and Type II errors -- p-values -- Common pitfalls -- Delving deeper into some pitfalls -- Conversion rate -- Uplift modeling -- Experimentation -- Observational studies -- Quasi-experiments -- Difference in differences -- Synthetic control and causal impact -- Summary -- Further reading -- Index -- About PACKT -- Other Books You May Enjoy |
| ctrlnum | (ZDB-30-PQE)EBC31290476 (ZDB-30-PAD)EBC31290476 (ZDB-89-EBL)EBL31290476 (OCoLC)1434590816 (DE-599)BVBBV049876570 |
| dewey-full | 658.05 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 658 - General management |
| dewey-raw | 658.05 |
| dewey-search | 658.05 |
| dewey-sort | 3658.05 |
| dewey-tens | 650 - Management and auxiliary services |
| discipline | Wirtschaftswissenschaften |
| edition | 1st ed |
| format | Electronic eBook |
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| id | DE-604.BV049876570 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-06T15:18:34Z |
| institution | BVB |
| isbn | 9781801813839 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-035216020 |
| oclc_num | 1434590816 |
| open_access_boolean | |
| owner | DE-2070s |
| owner_facet | DE-2070s |
| physical | 1 Online-Ressource (452 Seiten) |
| psigel | ZDB-30-PQE ZDB-30-PQE HWR_PDA_PQE |
| publishDate | 2024 |
| publishDateSearch | 2024 |
| publishDateSort | 2024 |
| publisher | Packt Publishing, Limited |
| record_format | marc |
| spelling | Diaz-Bérrio, Guilherme Verfasser aut Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python 1st ed Birmingham Packt Publishing, Limited 2024 ©2024 1 Online-Ressource (452 Seiten) txt rdacontent c rdamedia cr rdacarrier Description based on publisher supplied metadata and other sources Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Analytics -- Chapter 1: What is Marketing Analytics? -- What is analytics? -- An overview of marketing analytics -- Why should we bother with marketing analytics? -- Exploring different types of analytics -- Descriptive analytics -- Diagnostic analytics -- Predictive analytics -- Prescriptive analytics -- Walking through the maze of tools and techniques -- Beyond simple pivot tables -- Why Python? -- Modern challenges in the world of privacy-centric marketing -- The importance of data engineering and tracking -- Don't moonlight as a data engineer -- Web tracking is hard, and it is becoming harder -- Summary -- References -- Chapter 2: Extracting and Exploring Data with Singer and pandas -- Technical requirements -- What is ETL, and why should you care? -- Data pipelines -- What is Singer? -- Summarizing data and EDA -- Primer on descriptive statistics -- Percentiles, quantiles, and distributions -- Measures of central tendency -- Measures of variability -- Dealing with common data issues -- Bill Gates walks into a bar -- Missing values and data imputation -- Digging deeper into variable transformations -- Data standardization or scaling -- Power transformations -- Summary -- Further reading -- Chapter 3: Design Principles and Presenting Results with Streamlit -- Technical requirements -- Types of dashboards and their design -- Understanding the design concepts of a dashboard -- Thinking about how to best present data -- Thinking a bit about processing information -- Generating effective filters, dimensions, and metrics -- Filters -- Dimensions -- Metrics -- Getting your data into Streamlit and generating a basic dashboard -- Starting out with Streamlit -- Creating a marketing data dashboard with Streamlit -- Summary Further reading -- Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC -- Technical requirements -- What is a linear regression? -- What is a model? -- What are the assumptions of a linear regression? -- Exploring different types of regression models -- What we can do when the assumptions break down -- How to do a linear regression -- What is logistic regression? -- Objectives of logistic regression models -- Odds of an event -- What is causal inference? -- Correlation, causation, and key drivers -- A more practical application -- A small detour through the backdoor -- Watch out for colliders -- Summary -- Further reading -- Part 2: Planning Ahead -- Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast -- Technical requirements -- What is forecasting? -- Why forecasting is important -- Types of times series data -- Exploratory data analysis -- What to forecast -- Weekly, daily, and sub-daily data -- Time series of counts -- Prediction intervals for aggregates -- Long and short time series -- Transformations -- What types of patterns are present? -- Time series decomposition -- Time series features -- Basics of time series forecasting -- Simple methods -- Fitted values and residuals -- Correlation and forecasting -- Variable selection in time series regression models -- Advanced forecasting methods -- Extending regression models to time series -- ETS models -- ARIMA models -- The Prophet model -- Which model to use -- Summary -- Further reading -- Chapter 6: Anomaly Detection with StatsForecast and PyMC -- Technical requirements -- What is an anomaly? -- Techniques to detect anomalies -- Anomaly detection with STL decomposition -- Twitter's t-ESD algorithm for anomaly detection -- Isolation forests for anomaly detection -- Forecasting as an anomaly detection tool Practical implementation with StatsForecast -- Using rates of arrival to identify change points -- Pros and cons of using rates of arrival for change point detection -- Summary -- Further reading -- Part 3: Who and What to Target -- Chapter 8: Customer Insights - Segmentation and RFM -- Technical requirements -- Understanding the sources of customer dynamics -- Analyzing customer dynamics - unveiling segmentation and RFM -- Delving deeper into what segmentation is -- Clustering -- Classification -- Discriminant analysis and classification -- Exploring RFM -- Approaches and techniques - independent versus sequential sorting -- A practical example of RFM analysis -- Profitability evaluation -- ROMI after RFM -- Results of using RFM for targeting -- Summary -- Further reading -- Chapter 8: Customer Lifetime Value with PyMC Marketing -- Technical requirements -- Diving deeper into CLV -- CLV in practice -- Using CLV to calculate acquisition costs -- CLV and prospects -- CLV and incremental value -- What's wrong with the CLV formula? -- Issue 1 -- Issue 2 -- Issue 3 -- Issue 4 -- Issue 5 -- Beyond the CLV formula -- The BTYD model -- The Pareto/NBD model -- The BG/NBD model -- Implementing the BTYD model using PyMC Marketing -- Predicting the expected number of purchases for a new customer -- Estimating the CLV -- Summary -- Further reading -- Chapter 9: Customer Survey Analysis -- Technical requirements -- Steps in customer survey analysis -- Questionnaire construction -- Principles of questionnaire design -- Types of questions -- Asking questions -- Questionnaire design-layout -- Response formats -- Reliability and validity -- Reliability and classical measurement theory -- Standard error of measurement -- Using scales with high reliability -- How to do sampling -- Types of sampling -- Probability versus quota sampling Sample size for estimating population mean -- Response rate -- Control charts -- Customer loyalty and NPS methodology -- Issues with NPS -- Potential loss of revenue -- Advocacy, purchasing, and retention loyalty -- Factor analysis -- Summary -- Further reading -- Chapter 10: Conjoint Analysis with pandas and Statsmodels -- Technical requirements -- An introduction to conjoint analysis -- The fundamentals of conjoint analysis -- Setting up a conjoint study -- Step 1 - select the product attributes to be included -- Step 2 - select the product attribute levels -- Step 3 - create product profiles -- Step 4 - collect data from target customers -- Step 5 - estimate the utility of each product attribute and levels using regression analysis -- Conducting conjoint analysis in Python -- Determining the value of a product attribute -- Choice-based conjoint analysis -- Reporting findings -- Summary -- Further reading -- Part 4: Measuring Effectiveness -- Chapter 11: Multi-Touch Digital Attribution -- Technical requirements -- An introduction to attribution models -- Heuristic attribution models -- The implementation of different heuristic attribution models -- Algorithmic attribution models -- Shapley value attribution -- Fractribution -- Summary -- References -- Chapter 12: Media Mix Modeling with PyMC Marketing -- Technical requirements -- Understanding MMM -- MMM versus MTA versus lift analysis and A/B testing -- Steps toward implementing MMM -- Data collection -- How much data to collect -- Modeling -- How to measure the adstock effect -- Saturation and diminishing returns -- Which comes first? -- Selecting a model -- Experimenting and calibrating -- A synthetic data example of MMM -- Synthetic data generation -- Modeling -- Model results -- Summary -- References -- Chapter 13: Running Experiments with PyMC -- Technical requirements What makes a good experiment? -- A/A testing -- Type I and Type II errors -- p-values -- Common pitfalls -- Delving deeper into some pitfalls -- Conversion rate -- Uplift modeling -- Experimentation -- Observational studies -- Quasi-experiments -- Difference in differences -- Synthetic control and causal impact -- Summary -- Further reading -- Index -- About PACKT -- Other Books You May Enjoy Business-Data processing Erscheint auch als Druck-Ausgabe Diaz-Bérrio, Guilherme Data Analytics for Marketing Birmingham : Packt Publishing, Limited,c2024 |
| spellingShingle | Diaz-Bérrio, Guilherme Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python Cover -- Title Page -- Copyright -- Dedication -- Contributors -- Table of Contents -- Preface -- Part 1: Fundamentals of Analytics -- Chapter 1: What is Marketing Analytics? -- What is analytics? -- An overview of marketing analytics -- Why should we bother with marketing analytics? -- Exploring different types of analytics -- Descriptive analytics -- Diagnostic analytics -- Predictive analytics -- Prescriptive analytics -- Walking through the maze of tools and techniques -- Beyond simple pivot tables -- Why Python? -- Modern challenges in the world of privacy-centric marketing -- The importance of data engineering and tracking -- Don't moonlight as a data engineer -- Web tracking is hard, and it is becoming harder -- Summary -- References -- Chapter 2: Extracting and Exploring Data with Singer and pandas -- Technical requirements -- What is ETL, and why should you care? -- Data pipelines -- What is Singer? -- Summarizing data and EDA -- Primer on descriptive statistics -- Percentiles, quantiles, and distributions -- Measures of central tendency -- Measures of variability -- Dealing with common data issues -- Bill Gates walks into a bar -- Missing values and data imputation -- Digging deeper into variable transformations -- Data standardization or scaling -- Power transformations -- Summary -- Further reading -- Chapter 3: Design Principles and Presenting Results with Streamlit -- Technical requirements -- Types of dashboards and their design -- Understanding the design concepts of a dashboard -- Thinking about how to best present data -- Thinking a bit about processing information -- Generating effective filters, dimensions, and metrics -- Filters -- Dimensions -- Metrics -- Getting your data into Streamlit and generating a basic dashboard -- Starting out with Streamlit -- Creating a marketing data dashboard with Streamlit -- Summary Further reading -- Chapter 4: Econometrics and Causal Inference with Statsmodels and PyMC -- Technical requirements -- What is a linear regression? -- What is a model? -- What are the assumptions of a linear regression? -- Exploring different types of regression models -- What we can do when the assumptions break down -- How to do a linear regression -- What is logistic regression? -- Objectives of logistic regression models -- Odds of an event -- What is causal inference? -- Correlation, causation, and key drivers -- A more practical application -- A small detour through the backdoor -- Watch out for colliders -- Summary -- Further reading -- Part 2: Planning Ahead -- Chapter 5: Forecasting with Prophet, ARIMA, and Other Models Using StatsForecast -- Technical requirements -- What is forecasting? -- Why forecasting is important -- Types of times series data -- Exploratory data analysis -- What to forecast -- Weekly, daily, and sub-daily data -- Time series of counts -- Prediction intervals for aggregates -- Long and short time series -- Transformations -- What types of patterns are present? -- Time series decomposition -- Time series features -- Basics of time series forecasting -- Simple methods -- Fitted values and residuals -- Correlation and forecasting -- Variable selection in time series regression models -- Advanced forecasting methods -- Extending regression models to time series -- ETS models -- ARIMA models -- The Prophet model -- Which model to use -- Summary -- Further reading -- Chapter 6: Anomaly Detection with StatsForecast and PyMC -- Technical requirements -- What is an anomaly? -- Techniques to detect anomalies -- Anomaly detection with STL decomposition -- Twitter's t-ESD algorithm for anomaly detection -- Isolation forests for anomaly detection -- Forecasting as an anomaly detection tool Practical implementation with StatsForecast -- Using rates of arrival to identify change points -- Pros and cons of using rates of arrival for change point detection -- Summary -- Further reading -- Part 3: Who and What to Target -- Chapter 8: Customer Insights - Segmentation and RFM -- Technical requirements -- Understanding the sources of customer dynamics -- Analyzing customer dynamics - unveiling segmentation and RFM -- Delving deeper into what segmentation is -- Clustering -- Classification -- Discriminant analysis and classification -- Exploring RFM -- Approaches and techniques - independent versus sequential sorting -- A practical example of RFM analysis -- Profitability evaluation -- ROMI after RFM -- Results of using RFM for targeting -- Summary -- Further reading -- Chapter 8: Customer Lifetime Value with PyMC Marketing -- Technical requirements -- Diving deeper into CLV -- CLV in practice -- Using CLV to calculate acquisition costs -- CLV and prospects -- CLV and incremental value -- What's wrong with the CLV formula? -- Issue 1 -- Issue 2 -- Issue 3 -- Issue 4 -- Issue 5 -- Beyond the CLV formula -- The BTYD model -- The Pareto/NBD model -- The BG/NBD model -- Implementing the BTYD model using PyMC Marketing -- Predicting the expected number of purchases for a new customer -- Estimating the CLV -- Summary -- Further reading -- Chapter 9: Customer Survey Analysis -- Technical requirements -- Steps in customer survey analysis -- Questionnaire construction -- Principles of questionnaire design -- Types of questions -- Asking questions -- Questionnaire design-layout -- Response formats -- Reliability and validity -- Reliability and classical measurement theory -- Standard error of measurement -- Using scales with high reliability -- How to do sampling -- Types of sampling -- Probability versus quota sampling Sample size for estimating population mean -- Response rate -- Control charts -- Customer loyalty and NPS methodology -- Issues with NPS -- Potential loss of revenue -- Advocacy, purchasing, and retention loyalty -- Factor analysis -- Summary -- Further reading -- Chapter 10: Conjoint Analysis with pandas and Statsmodels -- Technical requirements -- An introduction to conjoint analysis -- The fundamentals of conjoint analysis -- Setting up a conjoint study -- Step 1 - select the product attributes to be included -- Step 2 - select the product attribute levels -- Step 3 - create product profiles -- Step 4 - collect data from target customers -- Step 5 - estimate the utility of each product attribute and levels using regression analysis -- Conducting conjoint analysis in Python -- Determining the value of a product attribute -- Choice-based conjoint analysis -- Reporting findings -- Summary -- Further reading -- Part 4: Measuring Effectiveness -- Chapter 11: Multi-Touch Digital Attribution -- Technical requirements -- An introduction to attribution models -- Heuristic attribution models -- The implementation of different heuristic attribution models -- Algorithmic attribution models -- Shapley value attribution -- Fractribution -- Summary -- References -- Chapter 12: Media Mix Modeling with PyMC Marketing -- Technical requirements -- Understanding MMM -- MMM versus MTA versus lift analysis and A/B testing -- Steps toward implementing MMM -- Data collection -- How much data to collect -- Modeling -- How to measure the adstock effect -- Saturation and diminishing returns -- Which comes first? -- Selecting a model -- Experimenting and calibrating -- A synthetic data example of MMM -- Synthetic data generation -- Modeling -- Model results -- Summary -- References -- Chapter 13: Running Experiments with PyMC -- Technical requirements What makes a good experiment? -- A/A testing -- Type I and Type II errors -- p-values -- Common pitfalls -- Delving deeper into some pitfalls -- Conversion rate -- Uplift modeling -- Experimentation -- Observational studies -- Quasi-experiments -- Difference in differences -- Synthetic control and causal impact -- Summary -- Further reading -- Index -- About PACKT -- Other Books You May Enjoy Business-Data processing |
| title | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_auth | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_exact_search | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_full | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_fullStr | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_full_unstemmed | Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python |
| title_short | Data Analytics for Marketing |
| title_sort | data analytics for marketing a practical guide to analyzing marketing data using python |
| title_sub | A Practical Guide to Analyzing Marketing Data Using Python |
| topic | Business-Data processing |
| topic_facet | Business-Data processing |
| work_keys_str_mv | AT diazberrioguilherme dataanalyticsformarketingapracticalguidetoanalyzingmarketingdatausingpython |