Churn rate

Ora che abbiamo creato un flag per indicare se un cliente ha effettuato churn, possiamo calcolare il churn rate, cioè la percentuale a cui i clienti smettono di acquistare, usando il churn flag calcolato in precedenza.

Se hai perso i progressi, chiudi eventuali workbook aperti e carica 4_2_churn_rate.twbx dalla cartella Workbooks sul Desktop.

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Analisi di marketing in Tableau

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Analisi di marketing in Tableau

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In chapter one, you will gain a thorough understanding of the basics of marketing data and analytics, starting with how data is generated and stored. You will cover marketing channels, journeys, and understand how they are defined. By the end of this chapter, you will be able to restructure data to identify those journeys and assign credit to interactions via different attribution models.

Exercise 1: Marketing analytics in Tableau Exercise 2: Cookie functionality Exercise 3: Web data example Exercise 4: Identifiable traffic Exercise 5: Customer segmentation Exercise 6: User purchase flag Exercise 7: Touches, journeys, and attribution Exercise 8: Attribution models Exercise 9: Data offsetting Exercise 10: Prior touch Exercise 11: Next touch Exercise 12: Touch classification Exercise 13: First vs. last touch

In chapter two, you will dive deeper into two specific marketing channels: email and paid social marketing. You will cover the strategies behind each channel, typical use cases, and understand the components that make up each type of marketing. By the end of this chapter, you will be able to analyze performance across several metrics unique to each channel and understand how to use data to identify areas for improvement.

Exercise 1: Marketing funnels Exercise 2: Click-through rate change Exercise 3: Calculated field aggregation Exercise 4: Click-through rate Exercise 5: Open rate Exercise 6: Email diagnostic Exercise 7: Paid social and A/B testing Exercise 8: A/B testing Exercise 9: Analytics operations Exercise 10: Click-back rate Exercise 11: Engagement and purchase rates Exercise 12: Creative Exercise 13: Follower metrics

In chapter three, you will build on analytics learned in chapter two to better understand two other heavily-used channels; paid search and organic marketing. You’ll learn how each of these channels works, their similarities and differences, and the importance of web pages and digital strategy. Additionally, you’ll examine relationships between data variables and how to manipulate data (both qualitative and quantitative) to perform analyses. By the end of this chapter, you will be able to analyze performance across various metrics unique to each channel and understand how changes in one channel may impact another.

Exercise 1: Paid search Exercise 2: Paid search review Exercise 3: String operations Exercise 4: Keyword spend Exercise 5: Mobile vs. desktop Exercise 6: Click-through rate Exercise 7: What is organic traffic Exercise 8: Organic search ranking Exercise 9: P-values, R-squared values, and moving averages Exercise 10: Engagement rate Exercise 11: URL group Exercise 12: Metric correlation Exercise 13: Channel correlation

In chapter four, you will combine what you’ve learned in previous chapters to calculate and analyze LTV, CAC, and their ratio. You’ll learn how to build these metrics, such as average order size, age, and lifespan, from their component parts and examine changes in them. Returning to earlier work on paid marketing, you’ll analyze changes in CAC over time through a unioned data set, gaining exposure to connived data sets in Tableau. You’ll then wrap up the course by calculating LTV/CAC.

Exercise 1: Introduzione all'LTV Exercise 2: Approfondimenti su LTV Exercise 3: DATEDIFF e DATEPARSE Exercise 4: Churn flag Exercise 5: Churn rate

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Exercise 6: Dimensione dell'ordine Exercise 7: Introduzione al CAC Exercise 8: CAC B2B vs. B2C Exercise 9: Dati uniti tramite UNION Exercise 10: CAC paid social Exercise 11: CAC della paid search Exercise 12: CAC totale Exercise 13: LTV/CAC Exercise 14: Video di chiusura