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Science of content social media publishing

Our plan to develop a Science of Content

This is a post for NewsWhip’s customers — and for anyone using data to guide their content strategy.

We believe publishing is entering a new era — one where success will be determined by how scientific you can be in your approach. That means interpreting multi-platform data, running experiments with different content types and distribution strategies, and bringing all of these results back into your work in a disciplined way.

That has consequences for your organisation and your workflows. It has consequences for us and the products we build for you. I want to set out the thinking about this that’s happening in NewsWhip, and kick it out for general consideration.

 

The Environment

 

As we all know, the digital content environment is fundamentally different from the era of print and broadcast distribution.

First off, there’s that seemingly infinite audience. With 4 billion Internet users (2 billion on Facebook), publishers and marketers all have access to an amazing, never-before-possible, number of minds. Of course, the audience is not waiting patiently for your next story to drop. They’re already deluged with a stream of infinite content — news alerts, Kardashian tweets, smashed avocado instagrams.

Much of this battle for attention is playing out on the Social Platforms. Here, content and stories are experienced as a stream of possibly interesting objects. How far each object will spread is determined by engagement and sharing on one hand, and platform algorithms on the other. Meanwhile, publisher-owned platforms — websites and publisher apps —  attract large, sometimes colossal, readerships. They are now the default place where publishers can develop direct relationships with audiences, serve advertising, and convert readers to subscribers.

social media monitoring science of content

In this environment, publishers and marketers have complex jobs. They must figure out the right mix of strategies for their owned properties — websites or apps — and other channels, including the social networks, and other third party platforms. They must determine the right channel for an audience (Facebook or LinkedIn?), the social influencers who can bring new audiences (foodies on Instagram?), and the the frequency and publishing methodology to maximize reach.

But there’s one more characteristic of the environment — and it’s the part that passes a massive chunk of power back into the hands of content creators: we now live in the era of big content data.

In the past, a print newsroom had no idea which of their stories were read each day. Today, newsrooms have instant, live audience data for every article, section, and topic they cover. If they use NewsWhip, they have predictive data for everyone else’s content, too.

Gathered and presented correctly, data can go much further, and act as a foundation for successful long-term audience building. It can precisely estimate interest in an event, surface engaging stories and guide optimal angles on stories. It is the core weapon for content creators wading into a sea where they must navigate third party platforms — with fast changing algorithms — in order to reach their audiences.

social media monitoring science of content surpriseMost importantly, data can support experimentation. It’s unlikely that any conventional wisdom on content strategy and distribution is yet optimal. So experimentation must become part of publishers’ DNA. By experimentation, we mean testing theories — adopting those that work, and rejecting those that don’t. This requires a particular kind of culture in an organisation — one of both play and discipline.

You might surprise your audience sometimes — but it’s how you’ll move toward getting things right.

 

 

 

Data for the Science of Content

 

Experimentation is far more likely to work if it is informed by hard, quality, relevant data. This is where we come in. We want to supply all the information you’ll ever need to pick the right topics for your audience, the right voice, the right social networks for distribution and community building, the right formats to publish. We want to give you data that shines light on the emotional angles that your audience will respond to, and the underreported stories they’ll likely care about today.

We’ve been thinking about how we deliver the data content creators will need in terms of four layers. Our entire team is engaged on at least one of these layers, working on deepening what we deliver on it.

 

science of content layer one social mediaLayer 1: The Data

NewsWhip must capture all data, globally, on content sharing and engagement. Already this includes video views on YouTube, sharing, comments and emotional reactions on Facebook and Twitter, images on Pinterest, LinkedIn shares, Instagram comments and likes. We gather exact timestamps for every individual share, so vectors and velocity can be calculated. Our engineering team keeps this data timely, reliable, and delivers it through an architecture that’s neat and clean to query. This data, effectively delivered, is your foundation for understanding the world today.

 

 

science of content layer two social media

Layer 2: Enrichment and Intelligence

It’s hard to extract meaning from raw data only, so we enrich our data by adding story metadata such as authorship, publication sources, entities, topic classification; social data including influencers on a story’s distribution, audience size, social velocity, and performance against peer stories. With this information, you can unpack each story and see how it is spreading. You can quickly spot events of significance in any topic, and analyze by format to find what works.

Meanwhile, our algorithms can already predict the eventual reach of newly published stories, and extract trending entities (recently, this power was used to pounce on and debunk “disinformation” spreading in the run up to the French Presidential election). Our metrics can be used to spot “white space” opportunities with an audience, by analyzing which angles on stories are over-performing with an audience but are underreported. We also plan to use algorithms to analyze whether a story is likely to be true, false, or biased based on source and other characteristics, and are working on making our recommendations much more powerful, such as: “Here’s what your audience will like today.”

 

science of content layer three social mediaLayer 3: Useful Outputs

We can’t get lost in the science lab. One of our big initiatives for NewsWhip is mapping onto our customers’ workflows with new, intuitive user interfaces and reporting. We are working on enhancements to our alerts, email reports, slack alerts, and monitoring dashboards. Speed of outputs is a key part of making it effective and easy to use. NewsWhip is already the fastest platform for identifying and predicting viral content, and we recently re-engineered it for greater speed, which is a core part of the output.

 

Layer 4: Putting it into Action

This is the human layer in NewsWhip. Our strategists in Customer Success set goals, create plans, train end users, and interpret results. Meanwhile, our own content team publishes best practices, insights, and interprets long-term data and trends. We’re beefing up our strategic engagement with customers this year, moving from merely training people on using data from our platform to helping them devise their plan, measurements, and workflows — helping whole content and communications organisations shift from flying blind to being data informed.

 

 

 

The Future

 

Long term, the better your map of reality, the better you can navigate. Today’s publishers have far more audience data, content data, and access to explanations of what works than ever before. If you ask the right questions, you can can “stand on the shoulders of giants”, and gain insight from what they’ve done. The right data can tame the out of control variables and other craziness of our new environment and enable you to navigate toward your goals — whether those goals are a stable and loyal audience or wildfire growth.

Of course, putting data to work is challenging. You have to learn to interpret and take the “right” lesson from a success (or failure). Culturally, embracing audience data is difficult for newsrooms, who have previously seen it as someone else’s job. Also, acknowledging the audiences of social platforms is difficult for publishers who are used to a fully controlled, direct relationship with readers. And navigation is hard: there is not yet any perfect playbook on how to use data to grow audience — everyone is figuring it out for themselves.

We believe the answer to these problems is to borrow the norms from another profession: that of science. Like journalism, science is committed to unveiling truths about the world, whatever they might be. The core method of science is to closely study reality, and explain it. For publishers who wish to grow audiences in this complex environment, that method will become the bedrock for success.

 

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