How to export and visualize social listening data from Brandwatch

If you’re tasked with pulling insights from social media chatter—maybe for a report, a campaign, or just to see if your brand’s actually being talked about—this guide’s for you. We’ll walk through how to export data from Brandwatch and turn that raw firehose into clear, honest charts. No fluff, no jargon, just what you need to get the job done.

Let’s get straight to it.

Step 1: Figure Out What You Actually Need

Brandwatch is powerful, but it’s easy to drown in options. Before you even log in, ask yourself:

  • What question am I trying to answer? (e.g. “How did people react to our product launch last week?”)
  • What’s my timeframe? (A day, a month, an ongoing campaign?)
  • Do I care more about volume, sentiment, specific platforms, or something else?

If you don’t know what you want, you’ll end up with a spreadsheet that’s 10,000 rows of noise.

Pro tip: Start small. It’s better to export a focused batch and then refine your questions, rather than try to analyze everything all at once.

Step 2: Set Up and Refine Your Query in Brandwatch

Once you’re clear on your goals, it’s time to set up your search.

1. Log in and go to “Queries”

  • Find the Queries section. This is where you’ll build the search that pulls in relevant mentions.

2. Build your query carefully

  • Use Boolean operators (AND, OR, NOT) to zero in on what matters.
    • Example:
      ("YourBrand" OR "YourProduct") AND ("launch" OR "new") NOT "job"
  • Set your date range. Don’t leave it on “all time” unless you really want a massive export.

3. Filter ruthlessly

  • Use filters for language, country, platform, or even sentiment if Brandwatch’s auto-sentiment is good enough for your needs.
  • Exclude retweets, spam, or irrelevant sources when possible.

What works:
The query builder is pretty robust, especially if you know basic Boolean search.

What doesn’t:
Don’t trust Brandwatch’s auto-sentiment blindly—it’s machine learning, not magic. Always check a sample of results for accuracy.

Step 3: Export the Data

Here’s where people get tripped up. There are a few ways to get your data out, depending on what you want to do with it.

A. Exporting Directly from a Dashboard

If you just want aggregated numbers (charts, counts, etc.), you can usually export straight from the dashboard:

  1. Go to your Dashboard.
  2. Find the widget (chart/table) you want to export.
  3. Click the three-dot menu (...) and select Export.
  4. Choose your format—Excel (.xlsx) or CSV are most common.

Limitations:
- This gives you summary data, not raw mentions. - Good for quick reports, but not for deep dives.

B. Exporting Raw Mentions

If you want the actual posts/tweets/comments:

  1. In the Query results, look for the Mentions tab.
  2. Apply any extra filters (date, sentiment, author, etc.).
  3. Click Export. Pick CSV or Excel.

Heads up:
- There’s usually a cap on the number of mentions you can export at once (often 5,000–10,000 rows). - You may need to do multiple exports if your dataset is huge.

C. Automated Exports & Integrations

If you’re doing this regularly, consider:

  • Scheduled Exports: Set up in Brandwatch so data drops into your inbox or SFTP on a schedule.
  • APIs: If you’re comfortable coding or have a developer, Brandwatch’s API lets you automate data pulls. Just know the API quotas and limits—they’re real.

What works:
Manual exports are fast for one-off needs.

What doesn’t:
Automated exports can break if your queries change or Brandwatch updates its structure. Always sanity-check your data.

Step 4: Clean Up the Exported Data

Let’s be honest: Brandwatch exports are messy. You’ll probably get columns like mention_id, author, source, sentiment, date, snippet, and a bunch of stuff you don’t need.

Here’s how to get it into shape:

  1. Open in Excel or Google Sheets

    • Excel handles big files better, but Sheets is fine for small batches.

  2. Delete what you don’t need

    • Keep columns like date, text (or snippet), sentiment, platform, and maybe author.
    • Ditch tracking IDs and obscure metadata unless you know you need it.

  3. Check for weird formatting

    • Sometimes line breaks or special characters sneak in. Use “Find and Replace” to clean them up.

  4. Spot-check sentiment

    • Take a random sample of mentions. Is the sentiment (positive/negative/neutral) actually right? If not, be wary about using it for charts.

  5. Deduplicate

    • Sometimes you’ll get repeats, especially if you exported in batches. Use “Remove Duplicates” in Excel/Sheets.

Pro tip:
If you’re reporting numbers up the chain, always double-check that your counts match what you see in Brandwatch’s dashboard.

Step 5: Visualize Your Data

Now for the fun part—turning rows into something people can understand.

A. Use Brandwatch’s Built-In Charts (When You Can)

If your needs are simple (volume over time, sentiment breakdowns, top authors), the built-in charts are fine.

  • They’re quick, decent-looking, and exportable as images.
  • But they’re also limited—you can’t customize much.

B. Use Excel or Google Sheets for More Control

If you want custom charts or to combine Brandwatch data with other sources:

  1. Pivot tables are your best friend.

    • Example: Group mentions by date to get a timeline.
    • Or by sentiment to make a pie chart.

  2. Bar and line charts work well for:

    • Volume over time
    • Top sources or authors
    • Sentiment trends

  3. Pie charts are usually overrated, but fine for simple sentiment splits.

What works:
For most teams, Excel or Sheets are more than enough. You don’t need fancy BI tools unless you’re doing this at enterprise scale.

What doesn’t:
Don’t get hung up on making things look “slick.” Clarity beats aesthetics every time.

C. For Power Users: Try Tableau, Power BI, or Python

If you need dashboards or want to automate stuff:

  • Import the cleaned CSV into Tableau or Power BI for interactive dashboards.
  • If you code, pandas in Python makes quick work of grouping, charting, and even basic text analysis.

Warning:
These tools have a learning curve. Only go down this path if you really need advanced features or automation.

Step 6: Share and Iterate

  • Export charts as images or PDFs for slides and reports.
  • Keep your original data—someone will ask for “just one more cut” of the numbers.
  • Document your filters and methods. A month from now, you won’t remember why you excluded that spike from July 3rd.

What to Ignore (Most of the Time)

  • “AI-powered insights” in Brandwatch: Sometimes they’re helpful, but treat them as clues, not gospel.
  • Obscure metrics like “potential impressions” or “reach”: These are mostly guesses. Stick to hard counts when you can.
  • Overly complex dashboards: If your boss or client won’t understand it in 10 seconds, it’s too much.

Final Thoughts

Exporting and visualizing social listening data isn’t rocket science, but it can get messy fast. Don’t let perfect be the enemy of good—start simple, clean up your data, and make charts that tell a clear story. If you get lost in the weeds, step back and ask: What’s the one thing I want to show?

Iterate as you go. You’ll get faster every time. And remember: No one ever complained a chart was “too clear.”