Correlatie tussen statistieken

In sommige gevallen kan er een correlatie bestaan tussen de engagementrate en het aantal bezoeken. Dit kan komen door ranking-algoritmen in zoekmachines. Zoekmachines rangschikken vaak alleen pagina’s met een hoge engagementrate; daardoor krijgen die pagina’s vaak ook hoge bezoekvolumes.

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Marketinganalyse in Tableau

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Marketinganalyse 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: Herhaling paid search Exercise 3: Bewerkingen met strings Exercise 4: Uitgaven per keyword Exercise 5: Mobiel vs. desktop Exercise 6: Click-through rate Exercise 7: Wat is organisch verkeer Exercise 8: Ranking van organische zoekresultaten Exercise 9: P-waarden, R-kwadraatwaarden en voortschrijdende gemiddelden Exercise 10: Engagement rate Exercise 11: URL-groep Exercise 12: Correlatie tussen statistieken

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Exercise 13: Kanaalcorrelatie

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: Introduction to LTV Exercise 2: LTV insights Exercise 3: DATEDIFF and DATEPARSE Exercise 4: Churn flag Exercise 5: Churn rate Exercise 6: Order size Exercise 7: Introduction to CAC Exercise 8: B2B vs. B2C CAC Exercise 9: Unioned data Exercise 10: Paid social CAC Exercise 11: Paid search CAC Exercise 12: Total CAC Exercise 13: LTV/CAC Exercise 14: Wrap-up video