What does intent data mean for the data-driven marketer?

This byline by Infer’s Sean Zinsmeister was originally published on Marketing Land.

Columnist Sean Zinsmeister takes a deep dive into intent data, explaining how you can use it to increase your predictive power and revenue.

As big data increasingly becomes more accessible, marketers are looking for ways to make it more scalable and actionable in order to better target prospects in various stages of the buyer journey. Intent data is synonymous with this topic, but it understandably causes a great deal of perplexity for many marketers.

It can be difficult to sift through all the terminology: It’s variously referred to as activity, behavioral, internal intent data, or external intent data. Pairing intent data with other customer signals — like those housed within a company’s marketing automation system — provides an especially unique opportunity for businesses to understand and leverage customer insights.

Nonetheless, it’s a topic that will continue to gain steam as more companies look for new ways to identify and predict where customers are in their buying journey.

Defining intent data and its uses

To start, it’s helpful to define common terms for a clear understanding of the various types of intent data out there, and how they can be applied. Simply put, intent data is information collected about a person’s or company’s activity. For the most part, it falls in one of two main categories, each of which best serves a different purpose:

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Druva Chooses Infer to Predict Highest Potential Prospects

We’re always happy to welcome another customer to the Infer family. Like many of our customers, Druva has grown rapidly in recent years and needed a better way to evaluate and prioritize good prospects for personalized follow up. The company chose Infer Predictive Scoring to take the guesswork out of the equation and better predict its highest converting leads.

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In a recent conversation Melissa Davies — who is in the process of implementing Infer Predictive Scoring at Druva — shared her adoption tips learned from building successful predictive programs over the years, why she chose Infer, and her goals for predictive at Druva.

Predictive Analytics for B2B Sales and Marketing has Certainly “Crossed the Chasm”

This Q&A with Infer’s Sean Zinsmeister was originally published on MarTech Advisor.i1916_575e6510c2543

Predictive analytics for B2B sales and marketing has certainly “crossed the chasm”, but it’s still in the early adopters phase of the product development lifecycle, and will continue to mature. MarTech Advisor spoke to Infer’s Sean Zinsmeister about the predictive space and Infer’s product strategy.

Consolidation Versus Integration of Predictive Intelligence Platforms

I expect that we’ll see some consolidation, but even more integration as the industry evolves. The reality is that today’s marketing clouds are still fairly immature and cobbled together vs. providing one cohesive cloud. When you look at the predictive movement, there has been a lot of hype and a lot of vendors chasing shiny objects. Our strategy is not to build an all-in-one solution or a walled garden, but rather to deliver an open architecture that can share data, predictions, recommendations and action triggers across any marketing cloud, system of record or other specialized tool (i.e. AdRoll, Outreach, Pardot, Act-On, etc.).

Open architecture is especially important because martech is getting increasingly balkanized by salestech, and the go-to-market stack is expanding. Our approach is to build deeper hooks into engagement systems. This will in turn increase the predictive power of our models and allow us to drive more targeted segmentation, recommend appropriate next-best actions, and ultimately make all of a company’s systems run more efficiently.

How Marketing Helped Social Tables Increase Leads and Revenue 400+%

This article was originally published on MarTech Review by Infer customer Ray Miller, Senior Marketing Operations Manager at Social Tables.

Lessons from Using a Smorgasbord of Approaches including Content Marketing, Social Marketing, Lead Generation, and Predictive Analytics

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Social Tables is a high-growth company based in Washington, DC. It was founded in 2011 as a cloud-based event management software platform. Social Tables has helped venues and event planners work more collaboratively and efficiently together to plan over 1,000,000 events to date.

With our free mobile applications and 14-day free trial on our Website, we had figured out a way to consistently generate about 1400 leads per month. The Business Development team would pursue most of these leads, but they were often not great. They could be international leads (in markets we do not support), or leads from smaller event planners or venues where the transaction would be too small to justify an extensive sales effort.

In this blog, we will be sharing details about how we increased leads from 1400 to over 6000 per month, and then how we started managing those leads for maximum efficiency and impact. We used a number of techniques and technologies to achieve this, including:

  • Whitepapers and new mobile applications on the content side;
  • Facebook and Twitter advertising;
  • Deep analyses with Excel’s Stat Pack to assess the value of different types of leads;
  • Lead scoring and management with Infer (predictive analytics), Velocify (lead operations), Pardot, and Salesforce;
  • A full Marketing Technology stack for improved operations, including C3 Metrics; and
  • An online (aka, “low-touch”) sales model for smaller leads.

This blog is most relevant to B-to-B marketers, especially within high-growth companies.

Predictive Analytics: A Content Marketer’s Secret Weapon

We had a great time co-hosting a webinar with Uberflip last week about why predictive analytics matters for content marketers. Research shows that most B2B content just isn’t living up to its true potential. With a predictive-driven content marketing strategy, however, B2B marketers are able to leverage historical data from their content to produce more effective materials by relying less on educated guesswork and more on data.

Uberflip + Infer Webinar

 

In this webinar replay, you will learn:

  • Why content marketing and predictive analytics go hand-in-hand
  • How to drive more value from content using effective targeting and segmentation from predictive analytics
  • How top B2B organizations are leveraging predictive analytics to boost their B2B marketing results

We hope you enjoy the webinar, which you can watch here.

For more details on how predictive is helping businesses create more effective marketing organizations, request a free demo or start a free 14-day trial of Profile Management now.

New Relic Wins 2016 Demand Program of the Year

It’s always exciting when one of the outstanding predictive innovators in the Infer customer community is recognized for their forward-looking approach. We are thrilled to congratulate Baxter Denny, Isaac Wyatt and the New Relic team on winning a SiriusDecisions 2016 Demand Program of the Year Award. This impressive achievement celebrates their success using predictive analytics over the past four years to create demand for New Relic’s software analytics products.

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Infer Makes Headlines with Latest Product Innovations

This “In the News” roundup highlights Infer’s insights and coverage from around the web.

One topic that’s been trending lately is account-based marketing (ABM). The folks at Demand Gen Report featured our customer Booker’s best practices in this area in the premier issue of their new “ABM in Action” e-zine:

B2B Predictive Analytics for ABM Success

“At the onset of using predictive, Booker’s A- and B-Leads made up 17% of the business’ total raw lead volume, yet drove 74% of the company’s sales pipeline. With this knowledge, and coupled with two new attribution tools, Bizible and InsightSquared, Booker increased its A/B lead scoring by more than 25% in less than 60 days. D’Arcangelo noted that this resulted in “far more efficient ad spend, ad targeting by channel, segmentation, sales operations tactics and lead flow strategy.”

In a recent byline article, Infer’s senior director of product marketing, Sean Zinsmeister, explains the pitfalls and opportunities of DIY predictive modeling, and which three key questions teams should ask themselves before putting the task of model building into the hands of the everyday marketer or other business function.

“While self-service modeling solutions represent an exciting new frontier for these markets, businesses should be cautious to understand the tradeoffs before jumping in feet-first. Companies might initially save some time and money by shortcutting the heavy lifting of data science, but they will be remiss if they ignore the risks.”

“The Risks of Blending Customer Signals from Disparate Sources” by Infer engineer Joel Dodge details important considerations that can greatly impact the accuracy of your predictive model.