|By Dana Gardner||
|January 31, 2015 05:00 AM EST||
The Power of Big Data Analysis in Ad Marketing
The next BriefingsDirect bid data trailblazer interview examines how New York-based adMarketplace, a search syndication advertising network, has met its daunting data-warehouse requirements.
Learn here how adMarketplace captures and analyzes massive data to allow for efficient real-time bidding for traffic sources for online advertising. And we'll hear how the data-analysis infrastructure also delivers rapid cost-per-click insights to advertisers.
For the inside story, BriefingsDirect sat down with Michael Yudin, the Chief Technology Officer at adMarketplace at the recent HP Discover 2014 Conference in Las Vegas. The discussion is moderated by me, Dana Gardner, Principal Analyst at Interarbor Solutions.
Here are some excerpts:
Gardner: Tell us first about what adMarketplace does. It sounds very interesting, but I'm not sure I fully understand it.
Yudin: Well, adMarketplace is the leading marketplace for search intent advertising, and let me explain what that means. Search advertising is the best form of advertising ever invented. For the first time, a consumer actually tells a computer what they're interested in. That’s why Google became so successful as a search engine.
Some things are changing in the marketplace these days. Consumer search intent is fracturing. You probably wonder what this means. It’s very simple. What this means is Google is no longer the only place you go to search for stuff.
I'll give you an example. Last night, I was looking for a Brazilian steakhouse here in Las Vegas. I didn't go on google.com. I opened my iPhone and I fired up a yellow pages (YP) app and I entered "Brazilian steakhouse" in the search box.
There are a variety of apps in my phone like that for travel, sports, news, and various other things I'm interested in. Anytime I search there, I don’t go to google.com. Consumer search has really fractured and adMarketplace has solved the monetization problem for that.
Gardner: So when people are searching in areas other than say Google or Yahoo, how does your organization intercept with that and how does that provide value to both the consumer that’s searching and advertisers that want to provide them information?
Yudin: It benefits both the consumer and the advertiser. In the search world, an ad is really nothing more than a search result in response to user’s query. That’s why it’s so great.
Our clients are the Internet's largest marketers and brands. They use adMarketplace to acquire additional customers in addition to the other marketing channels like Google, where they are pretty much already maxed out.
There are only so many searches that happen in Google and they're declining. So advertisers are looking for new ways to capture consumer intent and to convert this into sales and measurable return on investment (ROI), and that's what we do for them.
Gardner: Of course, a really important thing here is to match properly, and that requires data and analysis -- and it requires speed. Tell us a little about the requirements. How do you do this technically?
Yudin: You just nailed it. This is a very, very big data problem and it has to be solved at scale and fast. And it’s also a 24x7 problem. We can never take our system down. We have a global business, and anytime you go and you search for something as a consumer, you expect to see the result right away.
Our network handles about half a billion search queries per day and this results in about two terabytes of data per hour constantly generated by our platform, across multiple data centers. We needed a very scalable and robust analytical data warehouse solution that could handle this. Two years ago, we evaluated a number of vendors and settled on HP Vertica, which was best able to satisfy our tough requirements.
Gardner: And are these requirements primarily about the scale and volume, or are we talking also about a need for rapid query, or all the above? Give us a bit more insight into the actual requirements for your network?
Yudin: That's a great question, and I think this is what makes Vertica unique. There are products out there that can store a lot of data, but you can't get this data out of these solutions quickly and at high concurrency. We require a system that can ingest large amounts of data constantly. I am talking about terabytes and terabytes of data. This data has to be queryable right away, with very low latency requirements.
Some of our queries for Advertiser 3D and analytical dashboard are preplanned queries obviously, but they are very big data queries and the service-level agreement (SLA) on these queries is two seconds. Very few products can do that. Some queries are obviously more complex, but we're still talking about seconds and not hours.
On top of this, there's a concurrency requirement and that’s a very big weak spot of a lot of products. Vertica is actually able to provide sufficient concurrency, and it’s never enough.
I do know that there's an upcoming release of Vertica 7, where this is going to be improved even further, but it’s quite acceptable right now. And it has to be fault tolerant, which means that it should be able to sustain a hardware failure on any of its nodes -- and it can do that.
Gardner: Tell us a bit about where you've built Vertica in terms of data centers. Are they your own? Do you have managed service providers? How are you managing your infrastructure that supports Vertica and then therefore your data processes?
Yudin: We own our own infrastructure. So these are not managed services. We actually once used managed services, but we've outgrown them. And Vertica runs on dedicated hardware.
We also have several other Vertica clusters that run on virtualized hardware, and even though it’s dedicated infrastructure, it’s really dedicated at the cloud level now. So call it private cloud. It's a buzzword. It's a mix of dedicated and virtualized. It's elastic scaling.
Gardner: And the transition. You mentioned that two years ago, you were searching for a product. How were you able to bring this on board and what sort of growth have you had as a result -- in terms of data volume, but also in your business, in terms of customers and overall business metrics of growth?
Yudin: This was driven by business requirements. We didn’t just decide that we needed this. So we started to undertake a very, very ambitious project -- Advertiser 3D. If you go to our website, www.admarketplace.com, you can read more about it.
This is a very elegant, simple, and yet powerful, system to match and price traffic across a multitude of traffic sources. To deliver this product, we didn’t have a choice. We had to have a powerful analytical back-end data warehouse. That's when we started to evaluate products and chose Vertica.
Gardner: And have there been any other benefits of going to Vertica in terms of being able to increase the number of features, or have you been able to leverage the technology in new business opportunities in terms of what you can offer your customers, not just to have met the requirements, but perhaps whole new types of benefits?
Yudin: Definitely. Our customers don’t know and don’t even care that we use Vertica on the back end. That’s probably why we won an HP award, because we integrated it into our overall solution very elegantly and seamlessly, but it obviously does a lot of heavy lifting on the back end.
And the project was successful and transformed our business. Our growth rates have accelerated over 50 percent on our core revenue and performance. Data-savvy marketers, and our clients started to see significantly double-digit improvement in ROIs.
Gardner: As Chief Technology Officer there, you've gone through a fairly significant change in your infrastructure and adoption, as you've just described. Looking back, are there any lessons learned that you could offer to others who are also running into a wall with their data infrastructure or looking for alternatives? Any thoughts on how you would advise them to make the transition?
Yudin: Definitely. The number one advice I would give anybody is don’t believe anything until you do two things: Try it yourself and get references from people who actually use this and whom you trust. That's very important.
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