Act-On Improves Marketing Efficiencies by Embracing Predictive Marketing

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At Infer, we’re fortunate to work with forward-looking marketers around the world. As more companies join the predictive revolution, we love sharing their great success stories — and marketing automation innovator Act-On is no exception. To support its focus on the mid-market CMO, the company’s marketing team adopted Infer Predictive Scoring and gained a better understanding of how closely each lead fits Act-On’s ideal customer profile.

In a recent conversation with Act-On CMO, Kevin Bobowski, he discussed why his team adopted predictive, how it has changed their workflows, and several benefits that they’ve realized in just their first year of using Infer.

Sean Zinsmeister Discusses All Things Product, Predictive, and ABM

Flip the Switch podcast

Sean Zinsmeister recently joined Hana Abaza from Uberflip for “Flip the Switch,” which is a great weekly marketing podcast series that interviews growth-oriented marketing leaders about how they get results, approach problems and drive growth. Sean and Hana chatted about why product marketing should be the core of your content marketing strategy, the underlying truth about ABM that no one talks about, and how businesses are using predictive analytics help companies determine where to spend their marketing efforts and identify valuable untapped whitespace.

You can listen below, or on our podcast channel on SoundCloud.

Predictive Analytics: Why They Make Your Content More Impactful

This byline by Infer’s Sean Zinsmeister was originally published on the Salesforce blog.

Customers today have an insatiable appetite for information, making content a vital part of how sales and marketing teams communicate, educate and influence prospects throughout the buying cycle. But, there are some clear indicators that we need to get smarter about how we view content marketing and its relevancy to our customers and businesses. The stats are sobering: a recent study found that 50% of content gets eight shares or less; another uncovered that 60-70% of B2B content goes unused; and Forrester discovered that 90% of content is ignored by B2B sales teams. It’s clear that there’s misalignment between the content that’s being created and its intended audience.

3 Growth Hacks to Boost Revenue Upwards of 10%

This byline by Infer’s Sean Zinsmeister was originally published on the Salesforce blog.

Every sales leader, in every company, is searching for a killer growth angle. We all want to know how to squeeze more out of our pipeline. The best go-to-market managers differentiate themselves by pinpointing key segments where they can close deals quickly. However, this isn’t a code many people can easily crack. Whether you know it or not, your company is sitting on at least some “white space” of untapped opportunities that are already in your funnel. Few businesses realize how much potential revenue they’re actually leaving on the table, but we’re seeing more and more elite sales and marketing shops figure out how to turn pipeline gaps into revenue growth upwards of 10% – and I’ve had the privilege of learning from several of them.

One thing I’ve noticed in top-notch Sales VPs is that it all starts with how good they are at forecasting pipeline. But even when management is great at predicting how much revenue each rep and each channel will bring in, high-growth companies are never quite on target. That’s why identifying gaps is crucial to meeting sales goals. Once you determine where your team is missing the mark, you need to make adjustments quickly to address those gaps. This could mean reassigning territories, changing the makeup of the team, mandating training programs, etc. Good managers go through this cycle again and again to optimize and perfect sales motions until they hit or exceed their number.

The second stage in that sales management cycle – identifying gaps – is the toughest to nail, especially as a company evolves. But I’ve found that the gray area of discovering hidden gems already sitting in a CRM or Marketing Automation system is actually one of those variables that can have a large impact on the topline.

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.

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.

DIY Predictive Modeling – Pitfalls and Opportunities

Originally published on Business 2 Community

Self-Service predictive analytics for sales and marketing

As predictive analytics comes of age, we’re hearing a lot about data science methodologies like machine learning and data modeling. Until recently, these complex techniques were only employed by a relatively small group of data scientists. But new cloud services for machine learning from the likes of Amazon, Google and Microsoft claim to finally make it easy for any business to take advantage of the predictive revolution. As these types of solutions become common in the market, more do-it-yourself (DIY) tools will emerge for industry-specific flavors of predictive analytics in data-rich sectors like financial services, healthcare or retail, as well as in certain functional areas like predictive sales or marketing.