A client recently told me "you're good at talking like customers talk".
It made me smile.
So I felt the need to document how I do this and why it matters.
I read what customers are saying, constantly.
On demos, live interviews, convos with the sales team, case studies, etc.
But there's one "watering hole" of customer language that's so slept on it pains me.
Amazon book reviews.
You heard that right, let me explain with an example.
SharpSpring has an email analytics offering.
So I looked up books related to the goals of email marketers, the people who'd be using and benefitting from the software.
Here’s what you type in:
To find books like:
Then you go read the reviews and pull out the sticky and emotionally-charged language:
The goal is to learn what prospects love, hate, are worried about, etc. Learn what their expectations and motivations are for solutions like SharpSpring.
You’re looking inside their heads to FIND sticky messages.
And voila, you end up with your copy:
Now SharpSpring’s copy speaks to what customers are actually saying they want, using their actual words to describe what job the software helps them get done.
Let’s look at another less marketing-y example.
Procore – construction management software.
Found this book that had a ton of reviews:
And read through the reviews and found ones like these:
Pasted them all into a spreadsheet:
And ended up with copy like this:
Summin’ it all up:
Review mining gives you data to write your copy. It’s one of those relatively low effort, high impact time investments.
No more "brainstorming" what phrases might actually land with prospects.
No more "blank pages".
We're now starting with the data.
Of course, you might not use every single quote you document, but you’re at least getting familiar with the way your prospects think and speak (not how you think and speak).
I should note, we supplemented this qualitative data set with competitor reviews from G2.com and Capterra.com. Here’s a post on how to use those to write headlines.