Introducing Infer Form Monitor: How a Simple Marketing Challenge Sparked Internal Innovation

As it’s often said, necessity is the mother of all invention. Recently, we decided to re-architect our Pardot Marketing Automation, and as part of this project, we began looking for a way to test all of the forms on our website in order to make sure they were working properly. Any Marketing Operations person will tell you that the last thing they want to worry about is whether they are forfeiting leads because of a broken form.

As we looked to the market for a solution to this problem, we were surprised that none existed — and, certainly not one that worked easily with WordPress and Pardot. So, we kicked off an internal project to find a better way to monitor the health of website forms. The result was our brand new Form Monitor, which is a simple WordPress plugin that comes fully integrated with Pardot and can be downloaded on the WordPress marketplace.

In the spirit of idea generation and innovation, we want to share our learnings with our friends in the broader marketing community. So, we sat down with our Senior Director of Product Marketing, Sean Zinsmeister, to learn more about the inspiration for the plugin, how it takes a big load off MOPS, and lessons learned along the way.

4 products Microsoft should build with LinkedIn

This byline by Infer’s Vik Singh was originally published on VentureBeat.

LinkedIN

Last week, Microsoft stunned the tech world with the largest ever software acquisition – the purchase of LinkedIn for $26.2 billion. While early news coverage has addressed plans to keep LinkedIn independent, there’s been little discussion about what exactly the two companies will do together. As someone who’s entrenched in the LinkedIn and Microsoft ecosystems, I thought I’d share four exciting products this acquisition makes possible:

1. Redefined business email

The quickest and broadest impact Microsoft can make with LinkedIn is to redesign its Outlook interface. The companies could easily bring LinkedIn insights, profile photos, etc. into the email experience (similar to whatRapportive offers today but with a seamless, actionable approach). Outlook could even show recent updates and thought leadership pieces from a particular profile as talking point suggestions to automatically populate in an email when selected.

Microsoft could also add automated email filtering and prioritization features with folder recommendations that improve email productivity. Imagine if you could get emails that meet certain criteria — say they come from a particular job title and are second-degree connections with at least 500 connections themselves — to stick in the top of your inbox until they receive your attention.

2. Universal identity

There’s no doubt that LinkedIn’s biggest asset is its social graph with data about virtually everyone in the business-to-business (B2B) world. Ask any salesperson — it’s the business data they trust the most. Social proofing goes a long way. More importantly, when someone moves from one company to another, their LinkedIn identity remains intact. By rethinking the sense of a “user” in theMicrosoft Graph (a focus at the company’s Build 2016 developer conference a few months ago) around their LinkedIn profile, Microsoft could capture more activities about that individual from before and after they joined their current company.

As a result, the Microsoft Graph would become richer, blending LinkedIn information and updates with corporate activities like emails, calendar events, etc. Companies could also leverage LinkedIn credentials for single sign on (albeit there are enterprise security challenges here). This would ultimately be a better experience, because rather than needing new credentials every time you start a new job, you could use the login you already remember (and won’t forget, as LinkedIn will always be a key part of your career). We might even see an evolution in messaging, and the approach of sending emails to corporate addresses that may no longer be valid will become old school. Instead, Outlook integration could make sending messages to each other’s LinkedIn accounts feel seamless.

LinkedIn’s social graph could also spruce up Microsoft’s bot framework, which got a lot of attention at the Build conference as well. More apps that enable personalized, compelling conversations on top of Microsoft platforms would help accelerate the company’s “conversation-as-a-platform” strategy and boost adoption and lock-in for Microsoft’s cloud platform. If the social graph was hosted in the Azure Marketplace and offered convenient API hooks that played nicer with Microsoft’s platforms, developers would want to host their apps on Azure.

For example, a developer could build a bot that analyzes email activity (via the Microsoft Graph), as well as which employees in the company are updating their LinkedIn profiles, and then models that data with Azure Machine Learning to notify managers which of their employees are likely to churn. Of course, this raises major privacy issues that Microsoft and LinkedIn would need to address. Protecting users’ privacy is paramount for a consumer service like LinkedIn that touts “members first,” and this will get more challenging as it moves into the enterprise space.

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

Growth-Chart

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.

Rapid7 Discusses Shift to Predictive-Driven Sales and Marketing

Our friends at Marketing Over Coffee recently interviewed one of our fantastic customers, Allison MacLeod from Rapid7. A provider of security data and analytics solutions, the publicly traded company has experienced major growth in recent years and needed a better way to qualify best-fit prospects. In this podcast, Allison talks account-based marketing, achieving sales and marketing alignment, and how Infer has helped Rapid7 shift to a quality over quantity approach.

Listen to the interview here, or check out some excerpts from Allison’s interview below.

The Risks of Blending Customer Signals from Disparate Sources

Originally Posted on Data Informed

fit vs behavior

One of the more perilous steps in building a data model is determining the right signals to include. When it comes to business-to-business customer analytics, there’s a wide range of signals to choose from – a company’s business model, technology vendors, relevant job postings, public filings, social presence, website activities, marketing engagement, third-party intent data, and other attributes.

But some data scientists forget that all of these signals aren’t created equal, and they shouldn’t be treated the same.

Infer’s Predictive Insights From Across The Web

It’s been an exciting couple of months at Infer — from product announcements and company updates to industry insights and best practices, we’ve had a lot to talk about. This “In The News” roundup highlights Infer’s insights around the web, including how our customers are seeing success with predictive and why hyper-segmentation is enabling B2B marketers to turn data into actionable intelligence that drives real revenue.

As the proliferation of sales apps increases, sales is taking marketing into its own hands, and marketers are being asked to give up some turf at the top of funnel. Infer CEO, Vik Singh, shares tips for how sales and marketing can stay in sync as companies’ sales stack inches towards marketing automation:

“If you work to solidify your goals, unify your stack, and establish which departments own which data and workflows, your GTM culture and targets will stay intact regardless of which new tools come and go.”

 

Infer’s Sean Zinsmeister Talks AI in B2B Marketing, Best Practices and More

If you’re in B2B marketing or sales, chances are you’ve heard a lot of talk about things like account-based marketing and predictive analytics lately. Our very own Sean Zinsmeister is making the rounds in the headlines recently too, offering up his thoughts on those hot topics and more. From 2016 predictions to best practices, here’s a roundup of Sean’s insights from around the web.

As artificial intelligence technology gives us more data than ever, B2B marketers are taking a cue from B2C marketers on how to transform large amounts of data into actionable intelligence. In this Technology Advice podcast, Infer’s Senior Director of Product Marketing, Sean Zinsmeister, chats with TA about how predictive analytics are transforming B2B marketing by empowering a more strategic and personalized customer experience.

“The more we get to know the types of people that are interacting with us online, the more we can really structure a great conversation. At the end of the day, the key [to using data] is to solve market problems. When you start focusing on solving market problems… you’re automatically using data to improve the customer experience.”