Data Prep 1, 2, 3: How to Use REV to Filter Your Email Contact List for Google Customer Match
Getting in front of your ideal audience (contextually) remains a big challenge for companies of all sizes.
As companies grow, it becomes harder and harder to manage customer data. Segmenting customers based on behavior, preferences, purchase history, and interactions becomes a dreaded, monstrous, time-consuming task in Excel.
Marketers dream about casting magic spells to cut through the noise. To create 1:1 enchanting experiences for their prospects and customers. To usher them gently through the sales funnel. And then keep them engaged and loyal as lifetime customers.
But how do use your data to upsell and cross-sell to your customers without cluttering their Inboxes with irrelevant offers or promotions?
How do you ensure you’re not showing customers redundant display ads?
(Hint: Relying on website cookies can sometimes work against you.)
Luckily, you don’t need to be an Excel whiz to manage your customer data. And you don’t need any magic to entice your audience with impossible-to-resist, how-did-they-know-I’d-love-this type of targeted offers and messages.
Enter Google Customer Match. Your free hall pass, of sorts, to target your customers while they’re online doing their thing.
Google Customer Match lets you break the generic cookie approach … so you can float your ads in front of your perfect audience. Subtly. Contextually.
When users log in to their Google accounts, Google will show them your ads on Google search results pages, Gmail, and YouTube. (Neat, right?)
And with over 900+ million Google account holders, the chances of finding your customers online look pretty darn good!
So, how do you get your customer email lists ready for Google Customer Match? All you need is a little data prep 1-2-3 to get you going…
How to use REV to filter email contact lists for Google Customer Match
For this example, I’m going to use a sample dataset that hasn’t been formatted yet, so we can fix common contact list issues together in REV. Then, I’ll show you how to filter customers according to their status: Gold, Silver, Bronze, and Unknown. To do this, I’ll walk you through three data prep tasks to get our lists ready for Google Customer Match:
- Split Column
- Hide Column
PS: I used the sample dataset included in the free REV trial so you can follow along. If you haven’t tried REV yet, head over to our website and set up your free REV account today.
Step 1: Log in to REV
Step 2: Open the “Sample Company Meeting” file in the My Projects panel.
Step 3: To create our targeted lists for Google Customer Match, we’re going to sort our customers by their status codes: Gold, Silver, Bronze, and Unknown. Each code represents a type of customer interaction or purchase, so we want to extract the email addresses from each group. However, there’s a slight issue with the way the data was entered in this column. Instead of having one value in the “status” column, there are two. Let’s fix that. REV automatically provides a suggestion to split the “status” column by the colon ‘:’ to create two columns.
Step 4: Now that you have two columns, you can start sorting the customer groups. To find out the number of customers in each category, click the “status2” column header. To see all your Gold customers, click “Gold” in the Value frequencies panel.
Step 5: Since our goal is to export only email addresses, we’re going to hide the other columns. To hide one column, click the column header and right click to select “Hide column” from the dropdown menu.
Pro Tip: To hide multiple columns at once, click the first column, hold the SHIFT key down, and click the last column you want to hide. Right click and select “Hide column.”
Step 6: Ta-da! At this point, your sheet should include one column with email addresses. Time to export to CSV! Click the Export icon (i.e., the up arrow icon) on the top toolbar and select CSV. Repeat this process to segment the rest of your customers.
Awesome! In just a few minutes, we created a new customer email list for Google Customer Match. (Without applying a single formula! How ‘bout that?)
But, don’t stop there. Let your imagination run wild! Use REV to double-check your datasets for mistakes. Reorganize columns and rows. Reformat and rename fields. Standardize weird values. Blend different datasets to reveal new patterns and relationships.
Don’t be shy. Explore and prep your data the way you’ve always wanted to: the fuss-free, I’m-too-busy-to-waste-time way!
Some Basics to keep in mind for Customer Match
- Format: Your list must be in .CSV format, no greater than 25MB.
- Time: Maximum list membership is 180 days
- Email addresses: Do not have to be Gmail domain only (Keep in mind: Employees often use business email addresses to access Adwords, Google Analytics, and Google Docs.)
Pro tip: Make sure your customer lists comply with the Google Adwords policy.
Bonus: Once you upload your first Customer Match list, you can set up similar audiences with related demographics, profiles, and preferences to reach new prospects. (Yummy secret sauce!)
I hope you found REV’s data prep shortcuts helpful and useful. By no means was this an exhaustive list of ways to segment your email lists for Customer Match. Rather, you can apply these steps to an array of datasets. Your data looks different than mine. Your customer status codes and categories are unique as well. But you can use these techniques in REV to prepare and clean up your datasets—fast. Without formulas. Just clicks.
And that makes data prep in REV as simple as 1-2-3…
Stay tuned for future blog posts, where we’ll share more of REV’s time-saving data prep tips and tricks with you!
Spending too much time prepping data? You’re not alone.
Analysts spend a disproportionate amount of their time preparing, reconciling, and manipulating data.
To better understand the distribution of time between data preparation and data analysis, Blue Hill Research surveyed 186 data analysts and published the results in this benchmark report, “Quantifying the Case for Enhanced Data Preparation.” Read the full report to learn:
- Macro trends influencing data preparation challenges, including the breakdown of data source types and corresponding adoption rates
- Time and cost savings associated with using dedicated data preparation solutions
- Specific suggestions for optimizing data analyst workflows
It’s Your Turn
What data prep tools to you use? What steps do you apply? What are your experiences with Customer Match? How do you segment your customers?
Questions? Comments? Please share your thoughts in the Comment section below!