Success Story: How LinkedIn and Bombora Provided a More Intelligent Targeting Option for IBM’s Watson Analytics

July 30, 2017

IBM Bombora LinkedIn

Editor's Note: The text of this post orginally appeared on LinkedIn's Marketing Solutions Success Stories page. 

LinkedIn, is the world’s largest professional network on the Internet, with more than 450 million members worldwide, including executives from every Fortune 500 company. These attributes make it a prime channel for B2B marketers looking to reach very specific audiences. But LinkedIn can be even more powerful when external data sources are brought to bear.

One such data source is B2B intent data from Bombora. Bombora monitors the normal level of content consumption on specific topics from within a company or business location. When content consumption increases — or "surges" — it indicates that the business is more likely to be in active research mode prior to a purchase cycle. By using Bombora’s company-level surge data, B2B advertisers are able to focus their advertising and sales outreach only on those companies that have demonstrated some level of intent.

Enter Watson Analytics — a new tool from IBM that visualizes data for its users. IBM’s Brian Stoller explains the value of Watson Analytics for businesses: “If you are attempting to gain insight from your data, such as an Excel file, you could pop that file into Watson Analytics, and the tool would automatically create tables and charts based on what it finds as strong data proven information. Watson Analytics is more for line of business users and gives the user starting points. With those automatic starting points, the user has ability to ‘ask questions’ of the data to find a quick analysis.”

As IBM’s Director of Performance Media, Stoller is responsible for generating leads for the Watson Analytics sales team. He was looking to generate more leads at lower cost and combination of LinkedIn and Bombora’ seemed like a natural fit.

In the past, Stoller has relied on fairly sophisticated data-driven targeting to drive registrations for a free trial download of Watson Analytics, targeting professionals in the marketing, information technology, and analytics field. He also used data on job title, seniority, and professional group membership to further hone this targeting.

While this approach performed well, Stoller and IBM’s agency, Neo@Ogilvy, saw an opportunity to use an account-based marketing (ABM) approach to boost Watson Analytics registrations and to reduce cost per registration by working with LinkedIn and Bombora.

Bombora identified about 12,000 companies around the globe that were surging on more than four of IBM’s selected topics, such as big data analytics, data visualization, social analytics, and more. Bombora’s data also identified a longer list of about 40,000 companies where content consumption suggested an interest in one to four of IBM’s topics. Again, content consumption indicates that a specific company is actively researching on a particular topic and is likely to be in the evaluation process leading up to a purchase.

Armed with this data on surging companies, Neo@Ogilvy used LinkedIn Sponsored Content and LinkedIn Account Targeting to serve in-feed advertising to the right people at companies demonstrating an interest in tools like Watson Analytics. “We also layered in functional targeting with LinkedIn to reach technology and marketing professionals who were most likely to engage around Watson messaging,” said Veronica Luik, Programmatic Group Planning Director at Neo@Ogilvy.

Using this Account Based Marketing approach, BomboraLinkedIn combination drove powerful results. In the fourth quarter of 2016, Bombora-LinkedIn helped drive a 41 percent lower cost-per-registration than the business as usual tactics used in the previous quarter, which featured targeting via job title, seniority, and professional group membership. Additionally, the Bombora-Linkedin ABM approach delivered a 19 percent lower cost-per-registration than another Q4 tactic that targeted social media professionals.

Future plans include expanding this approach to include geographic data to support field marketing and customize website content based on intent data.

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