Learn about the role of assessing your marketing program from a performance perspective to ensure success and learn about the critical nature of creativity and finding the right balance.
- I've analyzed many marketing campaigns over the years.…Some were performing okay and others less so.…My job is to help these campaigns…and I find that using an approach…called marketing campaign testing…provides me with a nice set of tools…that guarantee campaign success.…Here's one way you might go about…doing a marketing campaign test.…I call it the MVC, or the minimum viable campaign.…Now, the minimum viable campaign…is a marketing campaign that invests…the minimum amount of resources necessary…to validate performance.…
So that a marketer can see the right opportunities…to scale marketing investments and drive growth.…Here's how it works.…First, you put an MVC road map in place,…which includes a hypothesis, stated objectives,…requirements for data, and a resource plan.…Second, you execute the campaign.…And then third, you analyze the data from that execution…to determine whether you can scale that program…or pivot the effort.…Now, you can take this approach…with any channel and any campaign.…You can do it with traditional media,…
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment
Skill Level Intermediate
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1. Software Installation
2. Data, Exploratory Analysis, and Performance Analysis
3. Inference and Regression Analysis
5. Cluster Analysis
6. Conjoint Analysis
7. Best Practices
Next steps1m 8s
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