Customer success brief Snagajob scales to the rapid pace of hiring HP Vertica helps boost performance and key metrics Background

1. Hewlett-Packard Company
Software Customer Advocacy
hp.com
Page 1 of 1
Customer success brief
Snagajob scales to the rapid pace of hiring
HP Vertica helps boost performance and key metrics
Background
Snagajob, based in Richmond, Virginia, is the largest hourly employment networks for
employees and employers. Founded in 2000, Snagajob recently began using Big Data
techniques to improve their performance metrics and better understand how their systems
provide benefits to their end users in a fast-paced environment. A key aspect of the hourly-
market is high turnover, so managing data at scale is critical for Snagajob.
Recently, Snagajob delivered nearly half a million new jobs in a single month to their
systems. “We really have data at scale here,” says Robert Fehrmann, data architect at
Snagajob. “We frequently put up 20,000-25,000 postings per day, and we now have about
300 000-700,000 unique visitors depending of the day of the week. So data is coming in
very fast.
“A couple of years ago we started looking at our environment and realized that our
traditional technology was showing some signs of stress. But we also realized that we were
sitting on a gold mine. Though we were able to ingest data pretty well, we had problems
getting information and insight out.”
Top challenges
“Performance, performance, performance,” says Fehrmann. “There are good tools on the
market for data analytics but none provide the performance of Hadoop & Vertica.”
Top benefits for customers
• Much more granular insight into user behavior
• Ability to do micro-targeting for marketing campaigns
• Micro Personalization of user experience
The journey to Big Data
“We’re collecting about 600 million event based key-value pairs on a daily basis, 25 gigs on
a daily basis. That’s the data collection part. The second part was the ‘funnel.’ So what’s the
‘funnel’? People search for jobs by keyword, by zip code, etc. A subset of these people see a
posting that interests them and they click on it. And a smaller subset actually applies for the
job through an application, and yet a smaller subset of that is of interest to an employer,
and so on. We never had been able to analyze this funnel, so we turned to Vertica.”
Snagajob’s typical dataset is 300 to 400 million rows and 30 to 40 GB, “and we wanted to
make that dataset available not just internally, but to our customers, meaning employers,
Use case
Performance analytics
Industry
Employment network
Challenge
Existing solution not fast
enough, nor could it scale to
handle growing data volumes
Solution
• HP HAVEn engines: HP
Vertica Analytics Platform
and Hadoop
• Cloudera
Company overview
• www.snagajob.com
• Headquarters: Richmond,
VA
• Founded: 1999
• Employees: 215
“By implementing this
recommendation engine we
saw an immediate 11%
increase in job application
flow, which is one of our key
metrics. It indicates the
strength of the application
funnel for employers.”
– Robert Fehrmann,
data architect, Snagajob

2. Page 2 of 2
as well. Performance was critical. We set a limit of 5 seconds response time, meaning no
query on this dataset must run for more than 5 seconds. Vertica and Hadoop provided that
response time.”
Moving to metrics across the business
In addition to the performance improvements Fehrmann’s team initially focused on, they
soon realized they could obtain metrics from other key areas of the business. One measure
of success became clear when they implemented a “recommendation engine”—a system
for cross-referencing similar jobs and skillsets. “Jobseekers apply for a specific job, but they
can’t know all the other jobs that are similar that others have applied to. When we started
analyzing the search results to see if there was any, we saw an immediate 11% increase in
job application flow, which is one of our key metrics. It indicates the strength of the
application funnel for employers.”
“This really empowers our salespeople to provide great information during the prospecting
phase. We can tell a new prospect, for instance, ‘If you are in a specific industry and a
specific area, you can expect this application flow.’
“Search yield is another area where we’ve made improvements. Search yield is how many
applications we get for a specific search keyword. It’s a massive dataset, about 2.5 billion
rows, 100 gigabytes of data, and it's continuously growing. We are now able to run different
analytical scenarios for search yield in real-time and get result in a couple of seconds.”
Benefits for jobseekers
Fehrmann notes that some jobs are more popular than others. “Some jobs receive more
applications than others, which naturally creates a much more competitive application
process. Being able to provide that inside is a great benefit for a jobseeker who otherwise
doesn’t necessarily know how many other people have applied for that specific job.
“What we would like to do next is to close the feedback loop between our OLTP systems
and our analytical systems. That would benefit, in particular, first-time users, who don’t yet
have a good profile with us yet. If we are able to provide feedback between analytical and
OLTP systems in a much shorter period of time, this new user would be able to benefit from
the additional intelligence we can derive from behavior much quicker.”
“One of our trademarks is experimentation, and bringing benefits to all our customers in a
quick way. We want to try new things, and put them to the test. We have a 3.6 billion row
dataset—that’s about a terabyte—and we have given access this to a very small group of
data analysts’ so they can just explore the data.
“While it’s very difficult to attribute business success to one specific change, I can say that
our old solution wouldn’t be able to support the growth of the business. Now we are
actually in a position to scale ahead of the business without having to make any changes to
the underlying architecture.”
© 2014 Hewlett-Packard Development Company, L.P. The information contained herein is subject to change
without notice. The only warranties for HP products and services are set forth in the express warranty statements
accompanying such products and services. Nothing herein should be construed as constituting an additional
warranty. HP shall not be liable for technical or editorial errors or omissions contained herein.
4AA5-5364ENW, October 2014
See for yourself
The Vertica Community
Edition lets you experience
the blazingly fast analytics of
the Vertica Analytics Platform
and join the myVertica
community.
Try it now
Know your customers
100% better
Find out how HAVEn helps
enterprises like yours use
Big Data to predict customer
behavior, deliver high-quality
experiences, and drive better
business outcomes.
Learn more
, Rev. 1