Motiva's Subject Line Analyzer (SLA) is an excellent tool for avoiding spam filters and for gaining clarity around the types of subject lines segments find engaging. The SLA automatically evaluates your email subject lines based on five categories grounded in best practices for B2B marketing.

Clarity: The number of difficult words within the word count.

  • Contacts should quickly grasp the subject of your email. Limit the number of difficult words in your subject lines unless you're using specialized keywords.

Spelling: The number of misspelled words and appropriate capitalization to avoid spam filters.

  • Build trust and confidence in your content by avoiding misspelled words and over-capitalization.

Length: The overall character count of the subject line.

  • Length and engagement often have a relationship. Long subject lines are too much to read and often result in lower engagement.

Concision: The use of acronyms and spam words within the word count.

  • Avoid obscure acronyms and the inauthentic use of FWD: and RE:. Avoid spammy words and phrases and be concise.

Engagement: The use of question words and mark, active verbs and merge fields.

  • Subject lines that are interrogative, use active verbs and incorporate merge fields for personalization often achieve higher engagement.

The SLA also provides an auxiliary analysis based on these categories to provide an evaluation of tone and readability.

Tone: A classification based on specific keywords: Friendly, Urgent, Curiosity Sparking, Neutral.

Readability: A range from "Easy" to "Difficult" based on the reading level of the vocabulary in the subject line.


You can find the SLA in two places:

  • Motiva steps for the Eloqua campaign canvas to analyze subject lines before a canvas is activated
  • The configuration tab of email reports on Motiva's web app, which allows you to retroactively analyze subject lines of completed emails.

Step 1: Open the configuration window of a Motiva step in the Eloqua canvas.

Step 2: Select email(s) from your asset library to use in the step. The SLA will appear next to each email you selected.

Step 3: Click "LEARN MORE" to view the analysis.

Step 4: Review the grade score for each characteristic and click "DETAILS" to get advice.

Key Considerations

  • Currently supports only the English language.
  • Supports tokens and merge fields for personalization.
  • The underlying models do not evaluate based on your own data, but rather broad B2B data across verticals. Treat this as basic guidance and a failsafe against truly bad SL practices. We'll catch issues like misspellings, ALLCAPS, bad grammar, etc.
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