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What time should we be posting our content? What messages are resonating best with our audience? Is there a day of the week where our content performs best? How do we truly optimize our content on Facebook or Twitter? What kind of content generates the most clicks or interactions? How long does it take before we receive interactions after publishing a piece of content?
If you are managing your brand’s social media presence or working for an agency on behalf of a brand, you’ve no doubt heard these questions from your boss. All of those questions are things we’ve helped to solve in traditional marketing and PR for years, but the question of how we “scientifically” optimize our content in social is relatively new.
At Edelman, we talk a lot about social content optimization (or SCO) as being the new SEO. Essentially, the process is gathering all of your existing (and ideally future) content and performing an audit on it to ensure that it is not only visible, but also appropriate to drive engagement (or whatever metrics you use to gauge success) on social platforms.
One of the common ways we begin to analyze content performance for the purposes of optimization is conducting a dayparting analysis. Dayparting was originally a concept developed for the television and radio industries, but has since been applied to other marketing channels as a way to determine when (day/time) content performs the best. It can be a very useful exercise if done with the proper rigor and process. Unfortunately, that doesn’t’ always happen.
Many of you have seen the work conducted by HubSpot focusing on when the best time is to tweet, publish a blog post or post a Facebook status update. If you have not seen the research, go take a look for yourself. It is interesting work, however, only truly useful from a macro-trends perspective. I don’t know about you, but if I’m in the process of optimizing my own content, macro-trends aren’t particularly useful. I want to know how my content is performing. I’m not necessarily interested in how the Dell page is performing, for example. Competitive intelligence is important, but a different stage of the process all together.
So you might be asking yourself…this all sounds interesting, but how do I do a dayparting analysis myself? The answer is simple: *Microsoft Excel. No, really, that’s all you need! Ok, that’s a small lie. You need access to an insights platform for your social channels, which you already have access to for free. You also need to develop a plan for how you’re going to conduct the analysis. What does that look like?
- The amount of content you’ll analyze – Ideally, you will want to gather posts from the last six months. If you are in a pinch, it can work with three months but any less it becomes difficult to draw insights. Don’t rely on a month or less. It won’t work.
- What metrics do you want to examine – Surely you’ve taken a look at all of the data presented to you by Facebook Insights, correct? Is it feasible to be looking at all of those things? Probably not. However, you could be looking at impressions per post, shares, clicks, likes and comments. Again, completely dependent on what you feel comfortable with and how it ladders up to your goals.
- What’s the end-goal – Is the end-goal measuring how content is performing? Or are you doing it just to optimize future content? You should be doing both, but understanding which path you’re on will help feed the end product.
- Don’t forget about tracking messages – One of the problems with dayparting is that we’re often looking at post performance in absence of identifying core messages. If we have two or three core messages, a best-practice analysis would overlay those messages with the data we have in bullet point #2.
Dayparting is a useful exercise if done well. Macro-trend data isn’t enough to draw conclusions on future (and past) post performance. Combining the two leads to maximum social content optimization. Have you done this kind of analysis in the past? If so, what kinds of results have you seen?
*Disclosure: Microsoft is a client of Edelman
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