Join, clean, and export your email data

One of the most common reasons that our customers use Parabola is to combine and clean multiple email datasets. It is not uncommon for companies to have multiple CRM’s, survey software, blog based email capture, and more — these different sources make it difficult to find a single source of truth.

By combining multiple pre-configured recipes, you can quickly and easily create a complex email list cleaning flow that is flexible for future data. Export the final list to an email marketing service with personalization information such as company name and recipient first name.

Each step of this tutorial is available as a standalone recipe on our recipe’s page. These can be instantly deployed into a new Parabola project with one click.

Parabola Recipes Directory

Recipes Used in This Tutorial

  1. Joining Data
  2. Separating Name Fields
  3. Scoring Leads
  4. Summarizing Data
  5. Automatic Filtering

Bringing together multiple data sources

Problem: Merging multiple data sets together is one of the most common but also frustrating data tasks. In Excel you may be used to using a series of VLOOKUP or INDEX(MATCH()) functions. If you know SQL, you can use a JOIN.

Parabola Solution: You can combine any number of data sources with a couple clicks using the Join Object or the Multiple Join Object. New data connected to the Object will automatically joined.

View The Joining Data Recipe

  1. Connect your data to Parabola via CSV, Google Sheets, a database connection, etc
  2. Use the Join Object to join your data based off of a common attribute, in this case email addresses
  3. Define the type of join (Left, Inner, Outer)
Joining two data sources with a common attribute

Cleaning names to isolate just the first and last name

Problem: Many forms ask for a contact’s full name (rather than first and last). This can help streamline data entry and improve form completion rates. However, it can cause problems when you want to use just a specific part of the name. For example, using merge fields to send custom emails. “Hi John Smith” doesn’t feel as personal as “Hi John”.

Parabola Solution: You can extract any part of the a name with with the Name Parser Object and split them into new columns. The Name Parser can even handle prefixes, suffixes, and multi-part names.

View The Separating Name Fields Recipe

  1. Select the Name Parser Object and connect data to it
  2. Choose the name parts to separate into new columns
Parsing first and last names out of a single column

Score leads to quickly assess their value

Problem: When sending emails to leads, you may have 10 or more contacts at a given company, but you don’t want to email all of them. To keep the sales process working effectively, you probably only want to email the top few contacts at each company based on lead scoring metrics.

Parabola Solution: You can automatically score leads against any criteria using the Scoring Object, such as the title that the contact holds. Quickly aggregate and export high value contacts with this data.

View The Scoring Leads Recipe

  1. Select the Scoring Object and connect data to it
  2. Create rules that govern the scoring, such as matching the contents of a column
Adding the Scoring Object
Creating sets of rules to score by
Applying the Scoring rules and viewing the results

Group by an attribute to summarize the data associated with it

Problem: Large tables of data can be difficult to understand at a glance. Rolling them up into summary tables makes them much more understandable. In Excel, you might use something like a pivot table, or in SQL you might use a GROUP BY.

Parabola Solution: You can group by any attribute and display useful information such as the average scores for each company by using the Group By Object. This is also useful for quickly aggregating transaction or sales data per region and sales rep.

View The Summarizing Data Recipe

  1. Select the Group By Object and connect the scoring data to it
  2. Select “Avg”, using the score as the data field and the company as the group by column
Grouping scoring data by the average per company

Filter columns to prepare an export to CSV for upload elsewhere

Problem: Filtering data in Excel is highly manual/time-consuming, and can lead to confusing errors when viewers are able to change their filter settings. Adding new data to the file may also require the filters to be reset.

Parabola Solution: You can create a separate branch of your data flow that has only the relevant columns or rows for export by using the Column Filter Object or Row Filter Object. As new data passes through the flow, it is automatically filtered using the existing criteria.

Automatic Filtering Recipe

  1. Select the Column Filter Object or Row Filter Object and connect your data to it
  2. Select the columns/rows to keep or those to remove

Exporting your data

Connect any data flow to the CSV Export Object to download the data. Alternatively, export the data to Google Sheets.

  1. Add the CSV Export Object and connect the filtered data to it
  2. Click Export to CSV
Adding Export Objects to download the transformed data

Become a data superstar

Parabola enables anyone with any level of data or technical knowledge fully transform, analyze, and visualize any dataset. Without using spreadsheets or code.

For more recipes and data solutions, check out our Recipe Page.