Facebook Status Updates: Exploring Optimal Timing

NOTE: This post is no longer current. An updated version of the post, including an updated spreadsheet, is posted here.

Although Facebook has unofficially admitted that there seems to be little rhyme or reason these days when it comes to the time of day or day of week when a brand posts content on their page, it’s still worth doing a quick analysis to see if this is, indeed, the case for your page.

The challenge, it turns out, is that there are multiple aspects of what sounds like a pretty straightforward assessment:

• What metric(s) actually make for a “successful” post?
• How do you effectively consider time of day and day of week?
• Have you actually posted on a sufficient variety of dates and times to have the data to do a meaningful analysis?

After scraping together some hasty cuts at this, I thought it would be worthwhile to try to knock out something that was easily shareable and reusable. The result is the downloadable spreadsheet at the end of this post.

What It Looks Like

The spreadsheet takes a simple export of post-level data from Facebook Insights (the .csv format) and generates three basic charts.

The first chart simply shows the number of posts in each time slot and each day of week — this answers the question of, “What spots have I not even really tried posting in?”

In this example, there have not been any posts from 9:00 PM to 6:00 AM, only one post between 6:00 AM and 9:00 AM, and only four posts on a Friday. Don’t worry if you don’t like the time windows — we’ll get to that in a bit.

The next two charts are crude heatmaps of a couple of metrics, but they both use the same grid as above, and they use a pretty simple green-to-red spectrum to show which spots performed best/worst relative to the other slots:

(I know, I know: red/green is not a colorblind-friendly palette selection. I’ll keep working on the visualization technique!)

The first of these charts looks at the average total reach of the updates that were posted in each time slot — the number of unique users of Facebook who were exposed to the post:

In the example above, Wednesdays looked to perform pretty well reach-wise, as did Saturday afternoon. If you have Facebook paid media running, these results may get skewed. It’s easy enough to update this chart to use Organic Reach rather than Total Reach, or, you can simply factor an awareness of what was running and when into your assessment of the results. Also, keep in mind that Facebook continues to fiddle with the EdgeRank/GraphRank algorithm, so there are aspects of a post’s reach that are beyond your control.

The next chart shows the average engagement rate of the posts, defined as the number of users who engaged with the post in some way (clicked on a link, posted a comment, liked the post, viewed a photo, etc.) divided by the total reach of the post. This is a pretty solid measure of the content quality — did the post drive the users who saw it to take some action to engage with the content? Now, arguably, the propensity for a user to engage is less impacted by the time of day and day of week, but, who knows?

In this example, Sundays and Thursdays were the days when posts appeared to get more engagement (although be leery of that Sunday, 6:00 PM to 9:00 PM, block — there was only a single post in the data set).

Timeframe Flexibility

Picking a set of timeframes is the most subjective aspect of this whole analysis, and it may be worth iterating through a few times to get to timeframes that are likely to be meaningful for the page given the target consumer. So, I’ve set up the worksheet to make it easy to customize these timeframes. For, instance, below is the same data set used above, but with only four windows of time:

The change look less than 60 seconds to implement (it’s all about VLOOKUPS, pivot tables, and conditional formatting!).

How to Use This for Your Own Page

If you want to try this out for your page(s), simply download the Excel file (this was created using Excel 2007, so it should work fine in both 2007 and 2010) and follow the instructions embedded in the worksheet. You will need to export post-level Facebook Insights data for your page, which may require several iterations (we’ve found that Facebook Insights is prone to hanging up if you try to export more than a couple of months of data at once):

Then, just follow the instructions in the spreadsheet and drop me a note if you run into any issues!

Some Notes on the Shortcomings

This approach isn’t perfect, and, if you have ideas for improving it, please leave a comment and I’ll be happy to iterate on the tool. Specifically:

• This approach measures all updates against the other posts for the same page — there is no external benchmarking. This doesn’t bother me, as I’m a proponent of focusing on driving continuous improvement in your performance by starting where you are. Certainly, this analysis should be complemented by performance measurement that tracks the actual values of these metrics over time.
• The overall visualization could be better. It’s not ideal that you need to jump back and forth between three different visualizations to draw conclusions about what days/times are really “good” or “bad”…including factoring in the sample size. I’ve toyed with making more of a weighted score and then doing the same color grid, but, then, you’d be looking at a true abstraction of the performance, so I didn’t go that route. Suggestions?
• A red–>yellow–>green scale just isn’t good when it comes to supporting: 1) black-and-white printouts, and 2) certain forms of color blindness. A more iconographic approach might make more sense.

Please do weigh in with how you would change this. I’m happy to rev it based on input!