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Mashable (www.mashable.com) is a

global, multi-platform media and

entertainment company. Powered by

its own proprietary technology, Mashable is the

go-to source for tech, digital culture and

entertainment content for its dedicated and

influential audience around the globe.

Mashable’s use of Parse.ly’s Data Pipeline

About Mashable

As a digital-only, startup media company, Mashable has always

relied on data to help inform its editorial choices.

The latest step in its evolution brings this strategy even more into

focus as the organization has brought together previously separate

teams, audience development, video, social and more, into one

content team. Their mission is to focus on the best way to tell stories.

At the heart of this storytelling effort? Using data science to inform

their decisions. But Chief Data Scientist, Haile Owusu, found out

getting access to the company’s own data was half the battle, until

Parse.ly provided Mashable with a better solution.

2005FOUNDED

330

75m 30m 7.5m

Events per second sent through the Data Pipeline

*Across website and distributed platforms

Monthly uniquevisitors*

Social media followers

Shares per month

San Francisco London

Los AngelesNYC

THE STR ATEGY

Use data to tell the best stories

`We need to think about the audience at every step.

The day of publishing stories and handing it off to the social team to promote are over.

In my mind, they’ve been over for a long time.

GREGORY GITTRICHChief Content Officer, Mashable

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Mashable had used another third-party data provider to store

view log data. To access that data however, the team had to

call the data provider and have them manually set up a FTP

server. The process also required them to buffer and monitor

the server so that the files wouldn’t overwhelm the limited

storage provided.

The result? It would often take multiple days to pull simple

information. And if the team forgot to request a field? The

process started all over again. Owusu and his team found this

extremely frustrating: “It was OUR data.”

THE SOLUTION

Parse.ly’s Data PipelineTHE CH ALLENGE

Access the data

CASE STUDY

In 2016, Mashable integrated Parse.ly’s raw data pipeline with

the goal of obtaining more granular information about its

data. Upon integration, the team received its own secure S3

Bucket and Kinesis Stream, hosted in Amazon Web Services.

This provided them with full access to all of their historical

raw data (batch/bulk) and to new analytics data as it arrives

(real-time/streaming).

The Mashable team was able to push this data to BigQuery, its

data warehouse, and scale its infrastructure to help uncover

specific and actionable information to benefit its editorial team.

Parse.ly provided us with the actual ability to pursue analyses beyond dashboard aggregations.

HAILE OWUSUChief Data ScientistMashable

Parse.ly’s Data Pipeline unlocks all

the data behind Parse.ly’s analytics,

and analyzes it for an organization’s

own needs. It is specifically optimized to make it trivial to bulk

load Parse.ly’s data into existing data warehouse tools —

allowing organizations like Mashable to fully own their

analytics data and ask any question they want of it.

This provides data analysts with an endless supply of good,

clean raw data about their company’s website interactions.

ABOUT PARSE .LY ’S

Data Pipeline

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CASE STUDY

Using the Data Pipeline also means that Mashable can use

content data in combination with custom events and other

data sets, such as demographic data. Collecting and

processing is as fluid as a simple query — no “faffing about”

for days just to complete a join.

Using data from Parse.ly’s Data Pipeline has made audience

profiles and article recommendations more tractable and

adjustable in ways that were not possible with its previous

data provider. And further, according to Owusu, building

recommendation systems and identifying viewer clusters was

impossible before.

“Of course there are a slew of third party vendors happy to

offer an out-of-the-box recommender, but building one out

forces an acquaintance with one’s own viewers that one simply

doesn’t get without access to granular data,” said Owusu.

Said Chief Data Scientist Haile Owusu: “Parse.ly provided us

with the actual ability to pursue analyses beyond dashboard

aggregations.”

ENRICHED DATA

Combining data sets FTW

With Parse.ly’s Data Pipeline bringing content

data to Mashable’s team, they were able to build a

user-item similarity matrix quickly.

Without needing an elaborate architecture of

third-party software, the data science team

quickly found patterns in

their audience that helped

answer questions the

editorial and business

teams had asked of them.

CONTENT AN ALY TIC S

Taking content into account

This is no small thing: we can experiment with an idea and not worry that that could constitute vast amounts of time wasted.

HAILE OWUSUChief Data Scientist, Mashable

Without content-level data

With content-level data from Parse.ly’s Data Pipeline

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TBD

CASE STUDY

Get access to your content and website data today.

Visit Parse.lywww.parsely.com

Prior to Parse.ly’s Data Pipeline, Mashable didn’t even broach certain

analyses because the time cost of retrieving data was prohibitive. Now,

the organization has a whole range of accessible approaches to strategic

questions most recently related to general business intelligence and

personalization of its site experience.

For publishers building data science teams, this type of data is the only

way to get to the heart of the questions endemic to modern publishing:

Who is our audience? What do they enjoy? Under what circumstances do

they return to further enjoy our offerings?

THINKING AHE AD

Improving data strategy at Mashable