Data Analytics for Marketing A Practical Guide to Analyzing Marketing Data Using Python
Most marketing professionals are familiar with various sources of customer data that promise insights for success. There are extensive sources of data, from customer surveys to digital marketing data. Moreover, there is an increasing variety of tools and techniques to shape data, from small to big d...
| Otros Autores: | |
|---|---|
| Formato: | Libro electrónico |
| Idioma: | Inglés |
| Publicado: |
Birmingham, England :
Packt Publishing
[2024]
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| Edición: | First edition |
| Materias: | |
| Ver en Biblioteca Universitat Ramon Llull: | https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009820418006719 |
Tabla de Contenidos:
- 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.
