Infer Grows Partner Ecosystem to Help Marketers Build Best-In-Class Programs

We believe that predictive intelligence serves as the brain of the sales and marketing stack. As such, we partner with technologies all across the stack to make sure it’s easy for our customers to fully power any workflow with predictive for maximum business impact. Today, we’re happy to share that Infer has officially joined the Oracle Marketing AppCloud and Terminus ABM Cloud ecosystems — extending upon our long-term collaboration with companies like Salesforce, Marketo and others.



The ABM Cloud for Salesforce partner program is helping marketers understand the MarTech software product landscape and select best-in-class tools across functional categories to implement account-based marketing programs at scale. Our integration with Terminus lets marketers measure the quality of their ABM programs in real-time, enabling them to quickly fine-tune messaging and channels for maximum effectiveness and efficiency.

Infer Named a 2016 CRM Market Rising Star

2016 CRM Market Rising Star

Infer is honored to be named a 2016 CRM Market Rising Star! The Market Awards aim to recognize leaders in the CRM industry, and are selected based on a composite score that includes revenue, company growth, market share, customer wins, reputation for customer satisfaction, depth of product functionality, and company direction. You can read the full write-up on Infer here, or below.

The Risks of Blending Customer Signals from Disparate Sources

Originally Posted on Data Informed

fit vs behavior

One of the more perilous steps in building a data model is determining the right signals to include. When it comes to business-to-business customer analytics, there’s a wide range of signals to choose from – a company’s business model, technology vendors, relevant job postings, public filings, social presence, website activities, marketing engagement, third-party intent data, and other attributes.

But some data scientists forget that all of these signals aren’t created equal, and they shouldn’t be treated the same.

Infer Releases New Predictive Behavior Scoring for Oracle Eloqua

Press Release: Deep Integration Between Marketing Automation and Predictive Solutions Helps Growing Companies Optimize Marketing and Sales

Infer Predictive Behavior Scoring for Oracle Eloqua


Infer Inc., a leading predictive sales and marketing platform that helps companies win more customers, today announced the most advanced predictive behavioral modeling solution for the Oracle Eloqua platform. Infer’s behavior scoring helps companies dramatically increase the impact of their sales and marketing programs by prioritizing business-to-business deals that are most likely to close quickly, and by continuously optimizing campaign effectiveness and pipeline velocity. After proven success with early customers, Infer is rolling out its updated behavior scoring solution for any company that uses the Oracle Eloqua marketing automation system.

Surfacing Gold from Nurture Programs with Predictive Behavior Scoring

Many companies that enjoy a healthy inbound lead flow push their top leads to sales reps, and send lower quality leads into a nurture database for marketing follow up. But as these archived leads accumulate, it becomes increasingly difficult for the marketing team to monitor all aging leads and find accounts that have re-engaged. One best practice is to add predictive behavior scoring that can model the full spectrum of activity data being collected by your marketing automation platform. With this insight, you can predict the likelihood of an outcome (e.g. conversion) within a set time period (e.g. next three weeks).

Over the years we’ve had the opportunity to work with amazing companies like Atlassian, Concur and Zendesk that are part of the movement shaping the future of predictive for sales and marketing. This playbook highlights how these companies are resurfacing gold out of nurture by identifying previously disqualified prospects that are worth another look.

predictive marketing behavior scoring

What’s the Difference Between Traditional and Predictive Behavior Scoring?

This is a question we get asked a lot. 

Instead of manually adding points for a given action, a predictive behavioral score mines the full spectrum of activity-data being collected by your marketing automation platform including every email click, website visit, and social engagement. Machine learning is used to weigh each signal appropriately in order to predict the likelihood of an outcome (e.g. conversion) within a set time period (e.g. next three weeks).

Learn the difference betweem tradition and predictive behavior lead scoring
To help explain these concepts in more detail we’ve put together a new eBook. Inside you’ll learn:

  • The six most common use cases for behavior scoring
  • Best practices for operationalizing your fit and behavior scoring
  • Why predictive behavioral models are more powerful than the scoring you’ll find in traditional marketing automation platforms

Download your copy here >


Congrats to CRM Market Elite Winner

It’s great to see the smart folks over at Concur be recognized for their impressive results with our predictive lead scoring. CRM’s 2015 Market Elite Customer Company winners all demonstrate how technology can impact operating costs and efficiency, and Concur is no exception. Here’s an excerpt from the company’s profile in this month’s issue of CRM Magazine:

Infer’s predictive lead scoring helps Concur close more deals more quickly

The Infer solution helps Concur identify and prioritize the marketing-qualified leads that are most likely to convert to closed deals. It pulls in thousands of external signals, going well beyond what most organizations track in their basic CRM and marketing automation tools.

Greg Forrest - Concur“We’re finding leads a lot quicker and getting them into the pipeline a lot faster,” Forrest states proudly, “and we’re closing at a much higher rate.”

Based on the results Concur has seen in the first six months, the company is expanding the Infer solution to its cross-selling and upselling activities, account scoring, and direct marketing.


  • Five thousand marketing-qualified leads were uncovered in its database, leading to a dramatic run-rate increase.
  • The number of leads converted to closed deals tripled.
  • Conversion rates increased by 150 percent, from 0.8 percent to 2 percent.
  • Closed deals for new solutions were boosted by 76 percent.

For more details, check out the magazine’s full story on Concur.

Q&A with Bombora Co-Founder Mike Burton

There’s a lot of buzz in the marketing community around “intent data” — and our friends over at Bombora are at the forefront of this exciting new movement. As described in our recent eBook, external intent data is collected by networks of B2B publishers that track the pages a contact or IP address visits, content they download, their site searches and potentially comments they left on an article or video.

We recently penned a guest article on intent data in VentureBeat to help spark a conversation, and are pleased to continue the discussion here. Read on for the first in our Q&A series with sales and marketing experts…

Mike Burton, Co-Founder and SVP of Data Sales, Bombora

It seems like the place that 3rd-party intent data has gotten the most early traction is with email personalization and targeted advertising. Can you explain that use case?

Mike BurtonEnabling programmatic targeting is a big part of Bombora’s business for sure, and compared to email it’s relatively simple. We build targetable sets of cookies that have very high consumption against specific B2B topics. We also create account-based segments, which plays very well together with predictive. Email is trickier, because Marketing Automation is built to market to contacts, and the monitoring of the B2B web mostly takes place on a company + location level. So, we created a product called “Frog DNA” (a Jurassic Park Reference), which looks at each contact’s company, location, and department and appends fresh intent data to every record every month. If we cannot use company level data we’ll fill in the gaps with overall B2B topics that are surging across broad topics like tech, sales/marketing, etc. Marketers then use that data to reactivate leads, alter nurture paths, etc. Probably my favorite use case is aggregating all of the most popular topics and using it to create relevant new content with a data-driven approach.

Can you share a customer success story that highlights how companies can measure the ROI of intent data?