Introducing Distribution Recommendations

At KIT we love giving recommendations. For instance recommendations on formats, on intent, on objectives and tonality – and now on distribution timing.

It's been a couple of months since we released the second app in the Story Engine suite – Distribute. With Distribute we've been tying the superior productivity in the Create app together with a highly efficient and smart calendar based distribution tool that lets users create, distribute and edit social media posts directly from Story Engine.

Even smarter recommendations

Now we've taken Distribute one significant step further, with the introduction of timing recommendations directly in the calendar view. And not your regular, generic timing recommendations – but instead recommendations based on each job's unique data profile. So instead of getting a "Thursday afternoon is the best time to post" we give you different recommendations if it's a recipe video than if it's a long article on personal finance. Since all content created in Story Engine and stored in KIT's Creative Works Store, have unique KITCORE data, we can drill deeper into what time slot works best for a particular job, with a specific taxonomy profile.

The recommendations show when you either click on a job in the bin to the left or when you start to drag it out onto the calendar – and just as with the KITCORE recommendations for taxonomies in Create, we give you four levels of recommendations: Recommended, Try it out, Needs data and Non-recommendation. We do these four levels on recommendations based on both performance and historical confidence level.

For a user to be able to get a better sense of when to distribute a job with a certain taxonomy profile gives both a better chance of success and an understanding of the patterns for different categories, job types and so on.

Since KIT commenced publishing two years ago, we've been tracking timing, and already from the beginning, we started to assign a time slot to posts, to make the data analysis a bit more productive. The time slots are designed to mean something, in kind of a mental modeling way, where "morning commute" – for example – is not just the between 7 and 9 in the morning. It is also a time when a lot of our audience is on their way to work, and their attention span most likely is a bit shorter than when leaned back at "prime time" between 8 and ten at night.

Different jobs – different distribution

So, do different kinds of jobs display different patterns of distribution? Well, they do. Take a look at how the recommendations for an agenda-setting, domestic news article differs from the recommendations for a questioning sports video (images below).

A great bonus in having the KITCORE data is not only the possibility to go into extreme detail on job data but also the opportunity to traverse up the data hierarchy, so even for data combinations where we lack in data we don't need to give just generic recommendations but can look at more limited number of taxonomies. And over time, as more and more data is available for Story Engine to use, we can give more and more granular recommendations.

The next step in recommendations for Distribute is to reverse the logic and give recommendations on what jobs in the bin are best suited for distribution in a particular time slot on a certain day. That will be a killer feature for a lot of social media managers that need to keep the activity on their social accounts up for example.

We hope you will enjoy your new recommendations in Story Engine; they are really something.

If you want to read more about Story Engine, you can always do it here!

Hey, want to know more about Story Engine?

Contact us and we'll tell you all about it!

By providing us with your contact information you give us permission to store your details. You can also choose to subscribe to our newsletter. All handling of personal data will be done in accordance with our privacy policy.
Thank you! Your submission has been received!
Oops! Something went wrong while submitting the form

recent posts