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The History of Parse.ly’s Analytics Dashboard
In 2010, Parse.ly graduated from the then Philadelphia-based (now in NYC and Israel) accelerator, DreamIt Ventures. At DreamIt we planted the seed of an idea that grew into the Parse.ly Reader, an intelligent news reading application that got better as you used it. Parse.ly Reader was successful – it grew in size to several thousands of users in a matter of weeks and had great reviews (ReadWriteWeb, ZDNet, Louis Gray, Thrillist, to name a few).
However, we knew what we built had the potential to not just change the way people consumed content, but how content was created and delivered.
New Yorkers at heart, we came back to the city after DreamIt, itching to contribute to one of New York’s biggest industries – media. Some of the biggest and best publishers on the web call NYC their home, and virtually all of them are looking to leverage new technologies that push the boundaries of traditional content sites.
Several months and meetings later, it was clear that publishers were entering the age of big data – billions of pageviews, millions of readers, thousands of active pages, hundreds of writers and editors. Despite all these signals created by the web, there was a big gap in the tools available to leverage them.
The Early Days
Initially, we thought the problem at hand was content delivery – which is why we built the Parse.ly Reader. The Reader was built to understand a user’s interests, and evolve with the user as his or her interests change. On the web, there were clear examples of other technology companies leveraging personalization technology to fine-tune a user’s experience (Amazon/products, Netflix/movies, Pandora/music). Yet, when it came to content, most online publishers were treating each user the same.
This was our initial Aha! moment and with confirmation from the publishers we were talking to, we were off to the races. We built P3, the Parse.ly Publisher Platform, to deliver personalized recommendations of content to users based on a slew of inputs, ranging from the context of the current article to what other, similar users were interested in. We launched on a few publishers including a top 100 news site and were increasing engagement and readership across the board.
Then, a curious thing happened…
It’s The Data, Stupid!
Some of our publishers started to ask us how we were recommending content. Editors, in particular, were really interested in how we decided what article to show to one user versus another. So, being the tech geeks that we are, we began explaining the whole stack:
- We analyze all content on your site to understand exactly what each post is about from a topic perspective
- We measure reader interest across these topics and start to build interests graphs between users
- We look at topic, post, and author velocity combined with referral information to give us cues on what might pop
- We mash up the treasure trove of data that’s on your site to come up with recommendations that your users will love
Specifically, editors at separate organizations asked us the same question: Can you share some of that data with us? You know, the topic data and the data on authors?
Begrudgingly, we agreed, and started to send out reports on a monthly basis.
Editors: “Hmm, this is great! Can we get this quicker?”
Parse.ly: “Uh, sure. We can give it to you weekly.”
Editors: “Awesome! Actually, it’d be great if we could get this daily.”
Parse.ly: “OK, what’s up here? Why do you care more about the data than the recommendations?”
Well, as it turns out, nobody had really showed them this data before, and the data was simply eye-opening for the editorial team. They were using it to go beyond monitoring individual articles to understanding what was resonating with their audience.
Queue the second Aha! moment in early 2011. We took a step back and did some research on analytics tools for online publishers. What we found was astounding. Almost no innovation had happened on the analytics side for online publishers. Most tools were one-size-fits-all systems that treated an e-commerce site the same as a content site, and obviously, that’s not the way to do it.
Content-based sites are dramatically different than an e-commerce property from both a data and business perspective.
It’s no wonder these publishers were clamoring for data that provided fresh insights on their property. Publishers need to know how their content breaks out by topic, what causes a post to go viral, why one author does better with search traffic than another, and a bevy of other key insights that are specific to their needs. We knew this was a big opportunity, and decided to dive head-first into the analytics space.
Meanwhile, In The Workshop…
2011 was the year Parse.ly Dash (now just Parse.ly Analytics) was born. We quickly built a bare bones tool to surface some of the data that we were collecting for publishers in the early months of 2011, and released it into private beta shortly thereafter. The response after showing a few major publishers the first version of Dash was both invigorating and a bit unexpected.
Not only did they understand what we were building, but they were extremely vocal with feedback that helped shape and evolve Dash throughout the year. This feedback can be summarized through three key areas that represent he biggest opportunities for improvement:
- Tracking. Publishers had tools that tracked data, but unfortunately they were not tracking the data that these publishers really cared about. Key metrics around topics, authors, sections, referrers were just not available. Luckily, our backend technology was built to pick up on exactly these areas.
- Planning. Tracking wasn’t enough to really be competitive in the media industry. Publishers needed to be proactive around topics that were trending on their site and across the web. Further, they needed tools that look at what would happen in the next several minutes, hours and days. We spent many engineering resources developing technology that would measure trends local to a property against trends that are happening across the entire web. This allowed publishers to not just identify patterns, but actually understand what was causing them. This has become invaluable for many of our customers.
- Promoting. As social media became a major distribution channel for content, so has the need to understand exactly how content goes viral and who on the social web has the most influence. Marketing to the right audience and in the most effective way is incredibly important to publishers moving forward, and Dash gives them this capability by actually plugging directly into the biggest social APIs.
We are also proud to say that Dash has offered a humane interface for analytics. We built the product with the user in mind. Most analytics tools are clunky, have a steep learning curve, or don’t go far enough with their analysis. Dash is different. It’s beautiful to look at, simple to use, and almost unassumingly powerful. The data, and as a consequence the insights, are what shine here.
I’d like to thank our early pilot customers. You’ve been incredible to work with, and have provided us with invaluable feedback. We’ll continue to work tirelessly to give you the best analytics tools on the market.
I want to thank our investors and advisers for giving us the resources, experience and insight to capture this opportunity and many more in the future.
And of course – a big shout out to the Parse.ly team. I’ve lost count of the tireless hours and late nights that have gone into Parse.ly Dash — from the earliest days in 2009 to present. The team here is inspiring to work with, and I can’t wait to keep pushing online media forward.
There’s much more work ahead of us, and we’ve already started on the next phase of Dash, so stay tuned for latest updates and more from the team, right here on the blog, or you can follow us on Twitter.
Editor’s Note: We’ve come a long way since Parse.ly introduced Dash in 2012. Want the latest update? Read on to learn about the latest version of the Parse.ly dashboard, including multiple metrics like attention time, loyalty indicators, benchmarks, real-time and historical tracking, an API, reporting suite and more!