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Justin Norris of Perkuto on the State of B2B Advertising, How ABM is Making Outbound Cool Again, and Extending Marketo [Podcast]

This week, we’re joined by Justin Norris, Solutions Architect at Perkuto to talk about how his experience with hundreds of Marketo-based sales and marketing stacks has given him a unique perspective on sales automation, lead routing, and architecting solutions that string together many technologies and systems to do awesome things. Justin also chats with the hosts about his thoughts on B2B advertising and why ABM is making outbound cool again.

Infer’s Year in Review: Goodbye 2016, Hello 2017! (INFOGRAPHIC)

As we move into 2017, we can’t help but take a moment to reflect on everything that our team has accomplished this past year. We’ve released new products, hired new leadership team members, sponsored some incredible industry events, broken company records and so much more.

Here’s a hearty thank you to all of the folks who have not only helped us reach these milestones, but made the journey fun along the way. To our employees, customers and partners — thank you for making the Infer community a vibrant, educational and exceptional group to be a part of. We look forward to exciting new things to come in 2017!

Infer Year-in-review 2016

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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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Predictive Marketing: The Next Must-Have Technology for CMOs

This article was originally published on CMSWire by Sean Zinsmeister, Senior Director of Product Marketing at Infer.
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The more data-driven marketing becomes, the easier it is for CMOs to attribute closed deals directly to their marketing programs. But with so much information available they encounter a new challenge: knowing which tactics and strategies to prioritize when each bad decision can cost thousands in missed opportunities.

Predictive analytics is surging in popularity among marketing leaders. It combines several components of artificial intelligence (AI) to predict which prospects are most likely to become customers. This technology eliminates a great deal of manual and redundant work from marketing and sales analytics, letting reps spend more time on high-value outreach and lowering chances that a calculation error will cost the company important deals.

You can use predictive analytics to identify your most promising prospects, build hyper-targeted segments, and personalize outreach at scale—often resulting in significantly increased conversion rates on inbound and outbound campaigns.

But not all predictive technology is equal. As more companies adopt it for marketing, the competitive edge shifts from whether you’re using it to how. 

Adrian Chang, Director of Customer Programs at Oracle Marketing Cloud on Trends to Watch for 2017 [Podcast]

We are so excited to bring you a new episode of Stack & Flow with our latest guest, Adrian Chang. He is the Director of Customer Programs at Oracle Eloqua, where he has worked for nearly ten years, and is a marketing automation pro. Obviously no stranger to the ever-evolving world of martech, Adrian helps the hosts break down this year’s Gartner Hype Cycle for Digital Marketing and Advertising, including why companies are investing more in technologies that will help marketers deliver the best of the brand with customer data. He also shares how to get started with machine learning, and what trends will be big in 2017.

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.