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Marketing Analytics in Spreadsheets

Learn how to ensure clean data entry and build dynamic dashboards to display your marketing data.

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4 Hours15 Videos56 Exercises8,229 Learners4650 XPIntermediate Spreadsheets Track

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Course Description

Spreadsheets are an essential tool for any marketing professional, but how does one keep these spreadsheets clean and accurate - especially when multiple parties contribute data? Data validation and regular expressions are powerful tools for marketing analysts, but having clean data is only half the battle. After we learn how to clean the data, we will visualize it by building charts! Throughout the course, we will explore a dataset that includes the kind of information you will encounter in the world of digital marketing. We will spot errors in metrics using data validation, use regular expressions to aggregate campaign metrics, build charts to analyze campaign performance, and use everything we've learned to build a dynamic dashboard!

  1. 1

    Data Validation for Clean Data Entry


    In this chapter, you will explore the data validation options that Google Sheets offers to aid in clean data entry. You will also learn about the Bing and Google Ads paid advertising data you will explore throughout the course. After this chapter, you will be able to create spreadsheets that can be used by any number of people, without having to worry about disorganization.

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    The importance of clean data entry
    50 xp
    Test your knowledge
    50 xp
    Fix the errors
    100 xp
    Contribute to the data set
    100 xp
    Create dropdowns from lists
    50 xp
    Mitigating campaign name errors
    50 xp
    List from a range
    100 xp
    List of items
    100 xp
    Validations using cell criteria and checkboxes
    50 xp
    Text validation
    100 xp
    Check the checkboxes
    100 xp
    Putting it all together
    100 xp
  2. 2

    Regular Expressions

    In the digital marketing world, naming conventions may differ among paid advertising campaigns or ad groups, which poses a problem when the user wants to analyze campaign performance. Regular expressions can help match certain strings, replace parts of strings, or extract a portion of a string. In this chapter, you will learn to use regular expressions, along with Google Sheets' built-in functions REGEXMATCH(), REGEXREPLACE(), and REGEXEXTRACT(), to reorganize and aggregate data with ease.

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  3. 4

    Build a Paid Search Campaign Dashboard

    In the final chapter, you will be tasked with building a paid advertising dashboard that can be dynamically filtered by both source and campaign name. After completing the chapter, you should be able to tackle almost any data mitigation or dashboard creation project that you, or your boss, may think of!

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In the following tracks

Intermediate Spreadsheets


chesterChester Ismayamy-4121b590-cc52-442a-9779-03eb58089e08Amy Peterson
Luke Pajer Headshot

Luke Pajer

Digital Marketing Specialist

Luke is a part of the Digital Marketing Services team at Beacon - a digital marketing agency located in Greensboro, North Carolina. His passion for spreadsheets and data science with python developed during his studies as a Geology major at Baylor University, which helped him find his niche as a Google Analytics expert at Beacon. Beyond business-related projects, Luke enjoys working on projects using JupyterLab - such as developing his own Fantasy Football player rankings or generating March Madness brackets.
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What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA