I've been experimenting with Anyword's API to pull performance scores for our marketing copy into our data warehouse. The goal was to have a single source of truth for copy performance alongside our campaign metrics, which we could then join to our ad spend data in BigQuery.
The process was relatively straightforward. I wrote a Python script that hits the `list-predictions` endpoint, paginates through the results, and flattens the nested JSON structure for scores like `Overall Score`, `Engagement Score`, and `Conversion Score`. I load this daily into a staging table.
Some observations and questions for others who might have done this:
* The data model is simple, which is nice for ETL, but I wish there was a native timestamp for when the prediction was *run*, not just when the copy was *created*. I'm using the script's execution time as a proxy.
* Has anyone successfully mapped these scores to actual downstream performance (e.g., linking a predicted score for a specific ad copy to its eventual click-through rate in our analytics platform)? I'm thinking of joining on the copy text itself, which feels fragile.
* I'm currently storing the raw scores, but I'm considering building a dbt model to calculate week-over-week score trends by content category.
The main pitfall so far has been rate limiting – had to add some robust retry logic. Overall, it's a useful integration for trying to quantify the impact of copy variations. Keen to hear how others are operationalizing this data.