The Front Lines of Predictive Intelligence

This article was originally published on the Hubspot Blog by Infer customer, Nicholas Heim, Director of Inbound Marketing at Hotjar.

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Hypergrowth SaaS businesses like my company, Hotjar, are often faced with the happy problem of too many free trial leads flooding into HubSpot. Thanks to lots of word of mouth buzz around our mission to democratize user analytics, and some clever advertising, we took off fast a couple years ago.

I wasn’t around for the early “fresh-out-of-beta days,” but as a newer team member, it’s quite nice to stand on the shoulders of an amazing product and founders who are true visionaries. When I joined around nine months ago, it had become challenging for our team to vet which leads (out of around 600+ new users each day) to target for premium business and agency plans.

While we get tons of value from using HubSpot for both our CRM and marketing automation needs, we couldn’t properly segment and personalize messages for our highest potential users out of the gate.

That is, until we added Infer Predictive Scoring to the mix. Now, we have a custom predictive model that works with HubSpot and our Intercom customer messaging platform to provide accurate data-based predictions of how well each lead matches Hotjar’s ideal customer.

Here’s what we’ve learned by using predictive intelligence to inform more advanced sales and marketing tactics.

Infer Launches New Predictive Behavior Scoring; Expands Sales Intelligence Capabilities

Press Release: Company Further Accelerates Product Innovation and Extends Open Ecosystem

Infer Inc., a leading predictive sales and marketing platform that helps companies win more customers, today announced several enhancements to its product portfolio. The latest improvements to Infer Glance, a sales and account intelligence application, as well as Infer’s new predictive behavior scoring for Salesforce Pardot, reflect the company’s strategy to continue deepening integrations with other enterprise systems. Infer’s open architecture makes it easy for sales reps and marketers to infuse predictive intelligence into their decision-making in order to close deals more quickly.

The significant product improvements we’ve made in 2016 reflect how we’re helping businesses reimagine automation through data science,” said Vik Singh, CEO and co-founder of Infer. “The market for AI solutions is rapidly expanding, and Infer fuels predictive adoption by serving as a guide that intelligently and continuously identifies revenue whitespace in a company’s funnel. In just a few years, I’m confident that every modern enterprise with a CRM system will also be leveraging predictive analytics and AI to supercharge its revenue growth.”

Infer Glance Sales Intelligence Powers More Productive Conversations

DNN Boosts Marketing ROI and Conversions with Predictive

It’s always a pleasure to uncover a new predictive innovator in our customer community, especially when they tell us that they were able to make an impact within just one week of adopting Infer. For example, content management software company DNN saw a 25% jump in its lead-to-opportunity conversions with Infer, along with a 75% jump in conversations to MQLs for its top group of leads. This success was the result of using Infer’s predictive models to find DNN’s highest revenue potential prospects, and gaining clear visibility into which sources and marketing channels generate the very best leads.

We recently had the pleasure of sitting down with Franck Ardourel, DNN’s director of marketing, who elaborated on the impact Infer’s predictive sales and marketing platform has had on his organization:

 

Additionally, you can download the full snapshot to learn more about how DNN is using Infer Predictive Scoring to:

  • Identify the leads most likely to convert to customers.
  • Improve sales prioritization, and increase new business opportunity conversion rates.
  • Optimize lead gen acquisition programs in order to consistently produce higher quality leads and increase ROI.
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When AI and Analytics Drive Business Disruption vs. Hype

This article was originally published on MarTech Today by Sean Zinsmeister, Senior Director of Product Marketing at Infer.

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The sales and marketing industry has been abuzz with talk of predictive analytics, machine learning and artificial intelligence (AI) this fall, especially on the heels of a flurry of AI updates from Microsoft and Oracle, Salesforce’s recent Einstein announcement at Dreamforce, and Google’s unveiling of its efforts in machine learning and AI yesterday.

In all of this hype, I’ve encountered several conflicting definitions and explanations of what AI really means.

Those of us close to the space know that AI, at its core, is actually foundational technology that’s been around in the consumer world for over a decade.

Think of the amazing intelligence behind Google Photos, which uses facial recognition technology to organize your images for you, as well as the highly accurate music recommendations that you get from Pandora based on your likes and dislikes. We see similar examples in major league baseball (remember Moneyball?) and, of course, the fast-evolving world of Uber, Waze and self-driving cars.

As AI enters the enterprise realm — in what Constellation Research predicts will be a $100 billion market by 2025 — it’s important to shift our focus away from science fiction perceptions, and instead look toward the specific business outcomes that AI can produce.

To help cut through the noise, I’ve outlined below four key roles that analytics plays in the sales and marketing landscape. Keep in mind that each of these approaches delivers insights based on sophisticated data processing, modeling and other scientific techniques — all of which are important aspects of AI.