Infer Introduces New Connector for Microsoft Dynamics 365 to Help Amplify Sales Effectiveness

Press Release: New Integration Marks Significant Expansion of Infer’s Open Ecosystem, with Over a Dozen Pre-Built Connectors

Infer Inc., a leading predictive sales and marketing platform that helps companies win more customers, further expanded its open ecosystem today with a new connector for Microsoft Dynamics 365 for Sales. With this integration, Infer now supports all of the major customer relationship management (CRM) and marketing automation (MA) platforms, and offers more connectors than any vendor in its industry. By bringing predictive analytics and artificial intelligence (AI) into any application a company uses, Infer helps businesses fuel more intelligent, successful customer growth strategies.

Infer mines comprehensive customer information and buying signals from CRM, MA and web analytics systems, and returns accurate predictions and insights through connectors for Dynamics 365 for Sales, Salesforce CRM, Marketo, Oracle AppCloud, Pardot, HubSpot and Google Analytics. It also pushes out data signals, predictions, profile segments and other useful insights to sales management and engagement applications – including AdRoll, LeanData and Velocify – in order to orchestrate a variety of go-to-market workflows across automation platforms. Finally, Infer delivers its predictive intelligence to analytics systems like Microsoft Power BI, InsightSquared and Tableau to drive transparency through next-generation sales and marketing management reporting. Infer’s predictive platform can also seamlessly connect with any other system via open REST application programming interfaces (APIs).

Stack & Flow: The Inspiration Behind Our New Sales & Marketing Technology Podcast


Today, we are excited to announce that we are launching our very own podcast series called Stack & Flow. Hosted by Infer’s Sean Zinsmeister and EventHero’s John Wall, the podcast is designed to be a platform for up and coming thought leaders to share how they’re solving technology and data challenges in modern businesses. With the sales and marketing automation landscape being reimagined everyday, we aim to provide listeners with tangible insights that will enable them to implement innovative programs, tactics and best practices to fuel go-to-market success in real-world scenarios.

Each episode will discuss key news stories and events in the sales and marketing technology space, and feature interviews with the top practitioners, strategists and influencers who are helping to shape a vision for the sales and marketing technology stack of tomorrow.

Infer’s Top 25 Predictive Sales & Marketing Innovators (INFOGRAPHIC)

One of the best parts of Dreamforce is getting to meet so many pros who are changing how their companies do business and engage customers. This year’s event was a great reminder of all the passion for sales and marketing innovation that exists within the Infer community of folks who we have the pleasure of growing with and working alongside everyday. These innovators are helping to build and align their businesses using predictive intelligence, and many of them joined us at our booth to share how they leverage Infer. They talked about how they’ve reinvented the way their companies identify and target their best prospects to increase conversion rates, boost average deal size, optimize marketing spend, and more.

When we decided to celebrate the Top Predictive Sales and Marketing Innovators, it was hard to narrow it down to just 25. However, the following group of Infer customers are truly leading the pack when it comes to predictive sales and marketing. Each of these predictive practitioners has their own unique story of how they’re driving real and tangible value across their businesses, and they share a spirit of innovation and dedication to building data-driven sales and marketing organizations.

Tips for Using Retargeting to Drive ABM Success (Part 2)

This article was originally published on the AdRoll Blog by Sean Zinsmeister, Senior Director of Product Marketing at Infer.

AdRoll Customer Blog - ABM tips, part 2

When it comes to our account-based marketing (ABM) strategy specifically, retargeting is a valuable channel for getting the right messages in front of our top prospects. However, in the past, I have struggled to find the right approach for optimizing and focusing media spend. It’s important for us marketers to buck our instincts and remember that ABM is not about quantity, it’s about delivering quality messages to a smaller number of quality prospects.

Making the Case for Predictive Marketing & Sales

Moderated by Kerry Cunningham of SiriusDecisions, Infer and Lattice Engines joined forces last month to discuss the key strategies and use cases to get your business started with predictive marketing and sales. We had a great time discussing best practices for how to deploy a predictive marketing solution, and why real customers are achieving success by infusing predictive analytics into their go-to-market machines.

Lattice Engines webinar for blog


In this webinar replay, you will learn:

  • Several different approaches to launching a predictive solution.
  • The top use cases for getting started with predictive analytics.
  • How to measure success.

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

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

How Host Analytics Operationalizes Intelligent Workflows With Predictive Analytics

Host Analytics Blog

Over the years we’ve had the tremendous opportunity to work with some of the best companies in the world that are helping to shape the future of predictive for sales and marketing. One such business is veteran customer Host Analytics, who has achieved amazing business results over the years by leveraging predictive intelligence in their sales and marketing stack.

With the entire company focused on MQLs as a leading indicator of whether it will hit its revenue targets, the Host Analytics marketing team needed a better way to evaluate and prioritize good leads. This included reducing spend on marketing channels that deliver bad leads and gaining real-time insight into how marketing programs are performing. On the sales side, the team couldn’t risk spreading itself too thin by calling every lead that exhibited any type of interest.

CEO Insights: The AI Overhype


Yesterday, I wrote about the last mile of AI and building sticky business applications through predictive technology. My final post in this series explores the tremendous overhype we’re seeing around AI, especially as larger players like Salesforce enter the game, and touches on when we should expect mass adoption.

Vendors in our space often over-promise and under-deliver, resulting in many churn cases, which, in turn, hurts the reputation of the predictive category overall. At first, this was just a problem with the startups in our space, but now we’re seeing it from the big companies as well. That’s even more dangerous, as they have bigger voice boxes and reach. It makes sense that the incumbents want to sprinkle AI-powered features into their existing products in order to quickly impact thousands of their customers. But with predictive, trust is paramount.

Historically, in the enterprise, the market has been accustomed to overhyped products that don’t ship for years from their initial marketing debuts. However, in this space, I’d argue that overhyping is the last thing you should do. You need to build trust and success first. You need to under-promise and over-deliver.

CEO Insights: AI’s Last Mile


Yesterday, I wrote about the timely topic of artificial intelligence (AI), and what it means to be a technology that’s built to be AI-First, as opposed to AI-Later. My next post in this series digs into the “last mile” problems of AI that too many companies ignore, and which are critical to making your solutions sticky.

How do you get regular business users to depend on your predictions, even though they won’t understand all of the science that went into calculating them? You want them to trust the predictions, to understand how to best leverage them to drive value, and to change their workflows to depend on them.

This is the last mile problem. It is a very hard problem and it’s a product problem, not a data scientist problem. Having an army of data scientists isn’t going to make this problem better. In fact, it may make it worse, as data scientists typically want to focus on modeling, which may lead to over-investing in that aspect versus thinking about the end-to-end user experience.

To solve last mile problems, vendors need to successfully tackle three critical components: