Social sharing

The amount your content is interacted with when shared across social is the best validation of its quality, or resonance with your chosen audience.

Until relatively recently though, the process of obtaining solid data on that had been convoluted at best and at worst expensive.

The agency I work for had relied on paid-for or third party SaaS options such as PinLeague (now Tailwind), RetweetRank, Pinalytics (which is no longer working), Twitalyzer and the like for any kind of insight from Pinterest and Twitter for everyday use but that may no longer be necessary.

Twitter Analytics

Twitter has had analytics options for several months as part of its ad platform, so it was only a matter of time before a more robust version rolled out across the entire platform.

The interface itself is very user friendly and while the data isn’t parsed as much as a data analyst may like it certainly provide enough insight for an individual or brand to positively affect how their content strategy is pieced together.

The main part of the interface gives you your latest tweets over the last 28 days and bases overall performance on a ‘month-on-month’ basis and as you can see from my personal profile I am underperforming based on last month’s engagement rate:

What is really interesting about this chart is that not only does it give you impression rates over time but, critically, gives you a view on days of the week also.

This can be really powerful for publication velocity decision making as looking for trends in publication rate V day-of-the-week can help you refine when you publish and what kind of content it may be.

Tools like SocialBro, Followerwonk, and Tweriod had always been the ‘go-to’ source for this kind of data for our agency until now but it now looks like Twitter may have many of the answers.

The issue, of course, is the depth of insight here and by complementing such data with the likes of Social Bro you can create a granular understanding of when to post.

Below you can see an example output from Socialbro showing when your followers are reading your tweets:

Knowing this ensures that you only publish your best content, and share reminders, at the most effective times of the day.

I have also written previously about how you can overlay different data sets to produce even greater understanding.

If we, however, look back at the example from Twitter Analytics the slight challenge is the size of the data set. At present it is not possible to affect the time period and so you must draw conclusions from the 28-day timeline.

This makes it difficult to create robust conclusions, but from what we see here, Monday and Tuesdays appear to be better days for me to tweet if I’m looking for eyeballs.

You can then overlay the data from the ‘Engagements’ column on the right hand side of the interface to really understand this is much greater detail. Let’s look at that next.


The most powerful aspect of the platform is, undoubtedly, the information contained within the ‘Tweets’ and ‘Tweet and Replies’ section.

This data is best viewed as a download and to do that look for the Export Data button in the top right of the window.

Once downloaded you can then play with this insight, sorting it by either Impressions, Engagement or by individual interactions such as Favourites or Retweets.

For those looking to inform content strategy we would want to look at either Impression data (to give us an idea of when to post) or Engagement column (what to content).

By sorting the data by either of these columns you are presented with a list of tweets, and therefore content, that you know resonates with your audience.

You can then splice this data fairly easily to give you a 24hr breakdown to show you when most engagements/interactions take place:

To do that you’ll need to separate the data in the ‘time’ column you can see below:

To do that use the Text to Column function in excel so you end up with something that looks like this:

By then sorting your time column you can chart impressions, or engagements by time of the day and work out when your potential audience is largest.

To make this data as user friendly as possible however we need to group time sand impressions into 30-minute slots, otherwise the data view is extremely complex.

To do that export the Time and Impressions and simply add the number of impressions that sit within every 30 minute slot.

For the 5.30am – 6am group we add the following together:

You can use the following excel formula to do that more easily: =ROUND(A1/(1/24),0)

What you end up with is a chart that looks something like this and as we can see for my personal Twitter account I have three ‘peak’ times of the day when my reach is best. It would make sense, therefore, to ensure I am sharing my best content during these times.

You can then use that insight within your content ideation process to ensure you create more content around those themes and build the analysis into your monthly routine to ensure you continually iterate.

Pinterest Analytics

Pinterest is an interesting proposition for most brands. The decision as to whether to spend time on it is predicated, in the main, on how visual your brand, or niche is.

For those where it does work, however, the rewards can be great as the quality of traffic and the propensity to spend is higher than the average ‘social visit.’

The great news is the ability to answer the question ‘should we invest in Pinterest’ is now much easier to answer thanks to the introduction of the Analytics platform.

What does it offer?

The new data platform has huge potential on first viewing but before you can access any data you must first ensure your account is a business account. Converting it is easy, as you’ll be asked to do so as part of the signing up process.

Once signed up you will have to wait 24hrs or more for the data to feed in but once in you can dive into your data in several different ways.

The two key areas of interest are Audience and Profile sections and both offer some great insight; and, critically, info that has until now been very difficult to get hold of.


This section offers the most potential insight in overall content strategy terms. Knowing who is sharing and following you on the platform will help you shape what you share.

You can use the data here to paint the perfect picture of your target consumer.

The demographics section is pretty standard with info on geographic breakdown and top cities but where it gets really interesting is when diving into Interests.

Within it you can see specific ‘other interests’ of those that follow your brand:

Knowing this makes it very easy to refine your pinning strategy.

Within the same section you can then also see Brands your audience follows and this can also help with content targeting and even potential strategic relationships you may want to build within your wider marketing plan.


All of this data is exportable also in the same way you can with Twitter data and it’s within the Impressions tab where this gets really interesting.

From here it is possible to look at the performance of specific pins and use the same principles discussed to splice the data and work out best times to pin, what to pin and why.

And you can even filter by device type to add extra detail to your content strategy, ensuring that it is fit for a multi-device world.

There are, of course, scores of other ways to play with this data and I’ll be sharing those in the coming weeks.