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.

HotJar HubSpot

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.

Why Atlassian will be a $50+ billion company in 10 years

This article was originally published on VentureBeat by Vik Singh, Co-Founder and CEO at Infer.

Atlassian Valuation

 

Recently, Atlassian made a very smart move by acquiring Trello. While $425 million implies a high multiple (given Trello’s revenue run rate was around $10 million last year), I believe it positions Atlassian to become the next big enterprise software company. I project it will reach a $50 billion market cap in 10 years by taking over software for teams. Here are four reasons why:

1. Product-driven culture

I am a long-time user of both Atlassian and Trello’s solutions, and one of the first things I noticed about these companies was that each of them took an entirely product-focused path to expansion. In particular, Atlassian’s rise over the past 15 years came on the strength of products like JIRA and Confluence, which won over developers by being good enough to sell themselves. In fact, the company prides itself on not having traditional sales reps, even though pretty much every other business software company employs an army of them. That’s incredibly impressive given Atlassian’s revenues and customer count. This company lives and dies on building products that work and sell themselves.

Its leadership reflects this product-centric view and is doing a great job building a long-lasting engineering and product culture. I’ve used LinkedIn data to run some numbers about Atlassian’s engineering retention, and computed how long it would take for the company to churn through all of its current software engineers (a “wipeout” period*). It’s currently at an impressive 29 years, which makes Atlassian’s development team more sustainable than those at buzzier companies like LinkedIn, Facebook, Twilio, and Dropbox.

This is probably a big part of the reason the company’s flagship product has become the industry standard, with tens of thousands of customers. With JIRA, Atlassian built a very extensible framework not just for product development but for prioritizing any project task or ticket and for creating automation via triggers and workflows. So much so that companies now use this platform for all types of use cases – at my company, we even use it to support our human resources and recruiting processes. Atlassian repurposed the platform as the foundation for JIRA Service Desk, a newer product that specializes JIRA for customer support and IT teams and is now its fastest growing product line.

Many people don’t realize that Trello has demonstrated the same product acumen as Atlassian. At first glance, some might think of a Trello board as just a “to do” list, but it’s much bigger than that (I’ll expand on this in a moment). The company nailed the details while not bloating the product, delivering key features like checklists, dates, assignments, power-ups (where you can link cards to pull in information from other SaaS systems), progress meters, labels, attachments, and new feeds, etc. With these capabilities, Trello has delivered a near-perfect agile/kanban experience while managing to make its core collaboration tools incredibly simple and intuitive.

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.