The Scenario
The engagement benchmarking project has been on the roadmap for two sprints. The ask is straightforward: run Apify's Instagram scraper against a list of 20 brand profiles, collect likes, comments, and timestamps for each returned post, and load the whole thing into an Excel workbook for analysis.
You have the usernames in column A. You know the Actor ID. You've opened the Apify console, connected your account, and pasted in the first five usernames. You triggered a test run. It returned 847 rows of JSON. You're now staring at a nested structure trying to figure out which field is "likes" and whether "timestamp" is Unix epoch or ISO 8601.
That was forty minutes ago. You still have 15 more usernames and no data in the workbook.
The bad version:
- Trigger the Apify Actor run manually via the console, wait for it to complete, download the dataset as JSON or CSV.
- Open the downloaded file, decipher the field schema, figure out which nested fields correspond to the columns you need.
- Import the cleaned CSV into the workbook, fix encoding issues, delete the 30 columns you don't need, and repeat for the next username batch.
The sprint demo is tomorrow morning. The workbook is still empty.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent inside your Excel workbook. It reads the username list in column A and, through its Apify MCP integration, triggers the Actor run, waits for it to finish, and writes the returned post metrics into the workbook — without you touching the Apify console or downloading a file.
Run the Apify Actor 'apify/instagram-scraper' using the usernames in column A as input. For each returned post, write the username, post URL, like count, comment count, and timestamp into a new worksheet called 'Instagram Posts', starting at row 2 with headers in row 1.
What You Get
- A new worksheet called 'Instagram Posts' created automatically if it doesn't exist.
- Headers in row 1: Username, Post URL, Likes, Comments, Timestamp.
- One row per post across all 20 brand profiles — however many posts the Actor returns.
- Timestamps in a consistent format, not raw epoch integers.
- No downloaded file. No field-mapping session. No 30-column cleanup.
What If the Data Is Not Quite Ready
The Actor returns more fields than you need and you want to limit columns
Run the Apify Actor 'apify/instagram-scraper' with the usernames in column A. Write only post URL, like count, comment count, and post date to columns B, C, D, and E. Ignore all other returned fields. Start writing at row 2.
You want to cap the number of posts per profile
Run 'apify/instagram-scraper' with usernames from column A, limited to the 10 most recent posts per profile. Write the results into a new worksheet called 'Instagram Posts' with one row per post. Include username, post URL, likes, and comments.
The Actor run returns duplicate posts across profile overlaps
Run 'apify/instagram-scraper' with the usernames in column A. Write all returned posts to 'Instagram Posts'. After writing, check for duplicate post URLs in column B and mark any duplicates "duplicate" in column F.
Full engagement benchmark in one pass
Run the Apify Actor 'apify/instagram-scraper' using all usernames in column A, limited to the 20 most recent posts per profile. Write to a new worksheet called 'Instagram Posts': username (column A), post URL (column B), like count (column C), comment count (column D), timestamp formatted as YYYY-MM-DD (column E). After writing all rows, add a summary section below the data showing average likes and average comments per username. Flag any post with over 10,000 likes as "high engagement" in column F.
Including the summary and flag logic in the same prompt means the benchmarking table arrives finished, not raw.
Try It
Get the 7-day free trial of SheetXAI and open any Excel workbook with a list of social profiles, competitor usernames, or Actor inputs you've been meaning to process, then ask it to run the Apify Actor and write the full returned dataset into the workbook. See the spoke on searching the Apify Store for the right Actor if you're still deciding which Actor to run, or check the Apify MCP overview for all available workflows.