Datalogix Using IBM Netezza data warehouse appliances to drive online sales with offline data Overview The need Infrastructure could not support the growing online data volumes and analysis required The solution IBM Netezza 1000 data warehouse appliance The benefit Speed: queries that once took 24 hours now take minutes Scale to support 50 percent data volume growth per year Increased campaign performance by 50 percent Improved ROI Most display ad networks serve ads based on the last click. Not Datalogix. Our targeting isn t about clicks, it s all about sales, says Joseph Benjamin, chief technology officer at Datalogix, an integrated database marketing and digital media firm that specializes in online targeting based on purchase data. Online marketers have had difficulty accessing multi-channel purchase data. With more than seven years of history on over 100 million homes housing 200 million consumers with over $1 trillion in spending power, Datalogix has found the solution. With Datalogix s solution, offline transaction data is overlaid with demographic and other data types from various third-party sources to add value to the customer. Datalogix uses this intelligence in two ways. First, it creates segments, such as foodies, greenies, golfers and active singles, for example, and syndicates them through its channel partners (ad networks, data management platforms (DMP s), demand side platforms (DSP s), Agency Trading Desks). In addition, the firm has built its own ad network to offer marketers integrated media programs, leveraging their own offline CRM databases. Why are offline transactions relevant online? Because they re a more predictive indicator of intent rather than banner ad clicks. Too often, marketers view click-throughs as response data. But a click-through is not a sale.
Our world before IBM Netezza was a bit painful. We built an infrastructure with traditional databases. But dealing with the volume and complexity consumed much of our time., Chief Technology Officer, Datalogix Look at the growing amount of time people are spending online, and then compare that with the amount of purchasing done offline maybe 90 percent, says Benjamin. We re bringing those two worlds together. We can tell the retailer when an online campaign drove purchases to the call center, the store or the website. In fact, in one representative campaign, the Datalogix platform s unique ability to leverage offline data for online ad targeting increased the campaign response rate we delivered by 50 percent over that delivered by our competitors. This is typical of the results we deliver for our customers. This process depends on good data and strong predictive analytics. If you have a poor target group, you will have a poor model, says Benjamin. This is classic direct marketing tried and true, says Benjamin. But it takes powerful computing to support this type of multi-channel marketing. Datalogix found this in the IBM Netezza data warehouse appliance. Challenge: handling the volume Founded in 2002, Datalogix has built a successful business helping companies drive better performance with their direct mail campaigns. But direct mail is a mature channel, and clients are increasingly extending their offline campaign objectives online. So Datalogix developed its online capability with a vision to leverage the power of offline data, targeting and accountability, and bring it to the digital world. Catalogs can cost $1,000 per thousand units, so there s no room for error in targeting. Datalogix had already proven that it could deliver a high return on investment with offline data, and it felt that the same targeting precision could be delivered online. There was one question: Could the existing platform handle growing online data volumes? It didn t seem likely. Our world before IBM Netezza was a bit painful, says Benjamin. We built an infrastructure with traditional databases. But dealing with the volume and complexity consumed much of our time. For one thing, the system required a lot of tuning. Every change broke what we d previously built, says Benjamin. With that very structured model, you have to guess correctly how to aggregate for a future query you ve backed yourself into a corner. 2
IBM Netezza showed up with a beautiful appliance. And it quickly proved its worth. These constraints led to rigidity and limited the business ability to be agile. Then there were the day-to-day issues like the queries that took 24 hours to complete. Datalogix started exploring other approaches to database design. Solution: the IBM Netezza data warehouse appliance Datalogix looked at EMC Greenplum and Vertica, but they required non-trivial effort to design and maintain the hardware infrastructure, says Benjamin. IBM Netezza showed up with a beautiful appliance. And it quickly proved its worth. The IBM Netezza data warehouse appliance was up and running queries in two days while the other vendors struggled to get the hardware they needed for the proof of concept. That was sort of a warning sign, says Benjamin. The IBM Netezza data warehouse appliance architecturally integrates database, server and storage into a single, easy to manage system that requires minimal set-up and maintenance. It delivers high-performance out of the box, with no indexing or tuning required. It also simplifies business analytics by consolidating all analytic activity in the appliance right where the data resides. As Datalogix saw it, IBM Netezza data warehouse appliance offered performance and simplicity. And it didn t require a rigid data model. We could work with complete data sets instead of having everything aggregated and summarized first, Benjamin says. Then there was the lower total cost of operations. Datalogix knew that the other solutions were going to be costly to deploy and administer because it takes a team of engineers to build and maintain online analytical processing (OLAP) cubes. When you put in the headcount and associated costs, there s more value in IBM Netezza, Benjamin continues. The benefit: new classes of analysis Having gone through this process, Datalogix decided to deploy the IBM Netezza data warehouse appliance. This appliance quickly became a critical piece of our infrastructure, says Benjamin. The overall migration effort took about two weeks and was much easier and faster than we had anticipated. 3
IBM Netezza is the central point of our data stream. It absorbs data from 20 sources every day: purchase history data, campaign data, channel partner data, and more. And it seamlessly joins offline and online data. The first benefit it produced was speed. Queries that once took 24 hours now take 15 minutes, and that turnaround has improved Datalogix s overall analytic performance. Datalogix can solve problems for our customers in ways previously not possible, says Benjamin. Case in point: Are the wrong people being targeted? It now takes only minutes to figure that out. We can stop campaigns in mid-flight if partners for some reason aren t delivering the right audience, he says. Another benefit is scalability. With more customers and campaigns, Datalogix now pulls in 50 percent more data every year, and has the infrastructure in place to effortlessly support this growth. IBM Netezza is the central point of our data stream, says Benjamin. It absorbs data from 20 sources every day: purchase history data, campaign data, channel partner data, and more. (For example, Datalogix is the exclusive online provider of R.L. Polk s automotive data for online display targeting). And it seamlessly joins offline and online data. We still do some cleansing pre-netezza, but we get all the data into IBM Netezza first, using an ELT (extract, load, transform) approach, he says. Then we transform it to make it usable. The IBM Netezza data warehouse appliance even facilitates another classic direct mail practice online: testing. What else can Datalogix do that it couldn t do before? Ad-hoc querying when one answer leads to five more questions that can be answered right then and there has opened up new classes of analysis, says Benjamin. The firm can also now do spend-a-like modeling. This goes beyond traditional look-a-like modeling to identify prospects that spend in similar ways to current customers. We build very precise segments, but the segment sizes may be small, says Benjamin. Spend-a-like modeling in IBM Netezza enables us to extend the reach of our campaigns. Indeed, modeling is a core strength at Datalogix. The algorithms that determine the likelihood to respond have definitely been enabled through IBM Netezza. 4
The algorithms that determine the likelihood to respond have definitely been enabled through IBM Netezza. Furthermore, the IBM Netezza data warehouse appliance provides broader access to users. Around 20 people, mostly in the technical area, access the data warehouse every day. The IBM Netezza data warehouse appliance makes it easy for analytic people to run their own queries. Joining a 400 million row table to a 600 million row table in just a few minutes is still a thrill for our analysts, says Benjamin. Then there s measurement, including attribution analysis. The IBM Netezza data warehouse appliance plays a crucial role in tracking multi-channel performance. Finally, the IBM Netezza data warehouse appliance helps Datalogix optimize its bidding on the display ad-exchanges. That is a complex task: The company has to incorporate frequency capping and price volume curves into its bid optimization algorithms. Datalogix is happy with its IBM Netezza data warehouse appliance. The platform has been very stable, says Benjamin. He adds, We re able to develop data products more quickly and at less cost than we could without IBM Netezza. And that translates into more effective campaigns and more satisfied customers. The future: linking the channels What s next? Datalogix plans to ramp up its real-time bidding on display ad-exchanges. IBM Netezza will be front and center as it sharpens its predictive capability, says Benjamin. The firm has already integrated direct mail, email and display advertising, and will continue to broaden its channel integration across mobile, itv and other emerging digital channels. In all of these cases, Datalogix s ability to rapidly ingest, analyze and optimize the data it receives will translate into success across these new channels. In addition, faster delivery of conversion data will lead to other advantages. If we can remodel the audience on a more frequent basis, it should translate into better campaign performance, says Benjamin. There s a rush internally to run everything on IBM Netezza because we see improvements every time, Benjamin adds. His conclusion? IBM Netezza is an enabler and it increases campaign performance for all our customers. It is central to our ability to compete. 5
IBM Netezza is an enabler and it increases campaign performance for all our customers. It is central to our ability to compete. About IBM Data Warehousing and Analytics Solutions IBM provides the broadest and most comprehensive portfolio of data warehousing, business analytics and information management software, hardware and solutions to help customers maximize the value of their information assets and discover new insights to make better and faster decisions and optimize their business outcomes. All of IBM s data warehousing and analytics solutions are aimed at simplifying and accelerating the delivery of business analytics insights. IBM s portfolio includes data warehouse appliances that integrate database, server and storage into a single, easy to manage appliance that requires minimal set-up and ongoing administration, and provides faster and more consistent analytic performance. IBM also offers pre-built and pre-integrated workload-optimized data warehousing and analytics platforms and data warehouse software for operational intelligence. These offerings are enhanced with additional support for big data and new types of analytics workloads, including continuous and fast analysis of massive volumes of data-in-motion. For more information To learn more about the IBM Data Warehousing and Analytics Solutions, please contact your IBM sales representative or visit http://www-01.ibm.com/software/data/infosphere/data-warehousing/ 6
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