Twitter’s downloadable analytics are far from being as robust as social media juggernaut Facebook’s, however, there are still a number of ways that you can slice and dice the data provided via .xls or .csv to glean deeper Twitter insights.
The focus of this article will be on how you can dissect the data that Twitter provides to measure and optimize social media performance. However, to extrapolate intelligence of greater value, data from outside sources would need to be overlaid. Such data could include, but is not limited to, sales data, website analytics, shopper surveys or market research. It’s important to remember that without this added context, engagement and other common social media metrics shouldn’t carry much weight.
With this said, following are 7 ways that you can break down and create correlations with the data that Twitter provides to glean deeper social insight about what seems to be working, what’s not, and potentially why:
Break Down Your Data
Separate Tweets and @-Replies
When you download your .xls or .csv from Twitter Analytics, it will contain all tweets, including @-replies. Because the two formats of tweets are objectively different, one of the first things I like to do is to create two spreadsheets from my initial download: the first with regular tweets, and the second with all @-replies. This will let you review performance of each type of tweet individually.
High-Level Tweet Performance
Sum Actions
As a high-level review of which tweets incited the most actions (favourites, retweets, replies), consider creating an additional column in which you sum all actions on each tweet. This shouldn’t be viewed as an ultimate indicator of success – actions have nothing to do with business results after all – but are a nice indicator of the social performance of your tweets.
Potential Influencer Identification
Sort Replies from Greatest to Fewest
Sorting your newly created spreadsheet that only contains @-replies by number of replies can help you to identify potential influencers. This will give you sight to who has been most actively interacting with you on Twitter, and if you take time to review those interactions and the people or organizations behind them, you’ll be able to form a subjective opinion as to whether you want to include them on a potential influencer list for future focused engagement.
Optimal Time of Day to Tweet to Maximize Social Actions
Separate the date and time of day of each tweet and graph the time against actions
Column B of your downloaded analytics contains the date and time each tweet was published. To make this information even more helpful, separate this data into two columns (in Excel: Data – Text to Columns – Delimit your data by Space). You’ll now be able to graph the social actions on your tweets against the time of day each tweet was published. If you see any spikes in social actions during specific times of day, and those actions have proven to be meaningful to affecting your objectives, then you may want to consider that range of time to be a sweet spot for publishing future tweets.
Optimal Weekday to Tweet to Maximize Social Actions
Convert dates of each tweet to day of the week and graph against actions
After separating the date and time of day of each tweet as described above, add another column beside what now just contains the date of each tweet. In your new column, extrapolate just the day of the week of each tweet (in Excel, use the formula: =TEXT(B2,“ddd”)). When complete, you will now be able to graph total social actions on tweets against each day of the week, which may provide some insight as to which days your audience is most likely to take action on your content. Again, if this has proven to be useful in the past, then you may want to consider increasing the number of tweets you publish on these days.
Optimal Tweet Character Count to Maximize Social Actions
Count characters of each tweet and graph against actions
Quickly count the characters of each of your tweets in a new column (in Excel, use the formula: =LEN(F2)). Now, you’ll be able to graph the character counts of each of your tweets against social actions, which may provide some insight about what length of tweet your audience has a propensity to respond to socially.
Optimal Number of Hashtags to Maximize Social Actions
Count the number of hashtags used in each tweet and graph against actions
Count the number of hashtags used in each of your tweets in a new column (in Excel, use the formula: =COUNTIF(F2,“*#*”)). Graph the total number of hashtags used against total social actions on your tweets to see if there is any correlation between the two. If there is a correlation, you might want to take this into consideration when crafting future tweets. If there is no correlation, this doesn’t mean one won’t emerge in the future, so consider continuing to track this and perhaps with a larger sample of tweets to review, you’ll find that one emerges.
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How do you break down Twitter analytics spreadsheets to glean deeper insight?
What data do you find useful to overlay on social media analytics?
What information do you wish that Twitter divulged as part of it’s analytics offering?
If you have any questions, would like some help with formulas in Excel, or would like to chat further about any of this, it would be great to hear from you in the comments.