Understanding analytics in PicPic Social

Analytics is the act at looking at data and telling a story about what it means. You look over data and present it in a way that makes sense and is meaningful to your client. PicPic Social has a lot of data and we present it to you in your analytics dashboard. You can access your analytics by logging into the dashboard and going to the analytics page and selecting your event.

Let’s go through some of the key terms so you know what it means.

Impressions – Are the the number of times your post is shown or displayed. If Jon Doe who has 300 followers posts their event photo to social media, this creates a total of 300 impressions.

Reach  – is the number of time John Does post was seen. Just because John Doe makes his post, it doesn’t mean that every single one of his 300 followers/friends will see the post. Only a percentage of his followers will see it, which is about 30%. Of his 300 followers/friends, 90 people will see it. This is the reach.

How are analytics calculated?

Calculating analytics can be tricky and not everyone calculates them the same way. We try to be very straight forward about our analytics and use simple math to come to our numbers.

  • Impressions = total # of followers per person who shares
  • Reach = total # of followers per person who shares * .30. We make it 30% because we know not every single one of a persons friends will see the post. However, we know at least 30% of them will see the post. If we wanted, we could go deeper and extrapolate another layer of reach based on people who comment and/or like the post. Every time someone likes or comments, it grows the reach exponentially. This ultimately comes down to speculation & can get complex, thats why keep it simple and don’t go so deep.

Each sharing method has a certain % of people who will view the post based on previously reported statistical analysis from the various social media channels. We use this statistical basis to create “assumptions.” If we’re honest, analytics all comes down to assumptions we make based on what we know to be historically true.

If you want a a deeper dive into analytics and what it all means, please see the following posts:


Leave a comment