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.

Smart Signals: Account-Based Data Append & Enrichment for Salesforce


Account-Based Data Append & EnrichmentWe’re excited to announce our latest enhancement to the Infer Smart Signals account-based data append and
enrichment product. Instead of requiring a custom-built Salesforce package per customer, we are now offering fully self service Smart Signals to all Salesforce users. Through the Infer portal (app.infer.com), customers are able select the data they want to append to their records in Salesforce, as well as the specific fields to which they want to append.

This new Self Service release provides unlimited access to Infer’s data cloud with over 400 unique signals including many of Infer’s proprietary, web-crawled data. You can view the complete list of technographic and demographic signals available to append in this help center article.

By allowing customers the ability to append the individual signals to any field on any record, Infer is continuing to lead the way in providing transparency into its core data stack.


If you are interested in learning more about the new Self Service Smart Signals offering for Salesforce, please contact sales@infer.com

The Buyer’s Guide to Artificial Intelligence Software For Sales

This article was originally published on the Hubspot blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.


Salespeople have never had so much technology at their fingertips. Some of the latest — and possibly most promising — tools for sales teams use predictive analytics, a form of artificial intelligence technology that can optimize decision making around sales efforts. But with all the products promising to tell the future, it’s hard to discern which can actually deliver.

Top players like Salesforce and Microsoft have rolled out AI-driven tools over the last year. Investment in AI startups is at an all-time high. This type of software uses techniques that gathers customer and prospect data from multiple sources, runs it through machine learning models to predict which leads are most likely to convert, and present the findings to Sales in the form of top-scoring prospects and accounts.

So how do you know which tool, if any, is the best bet for meeting your sales goals?

What to Look For in a Sales AI Vendor

Predictive analytics for Sales truly can change the way you run your sales operation. AI-driven software can eliminate a great deal of manual work, helping you make decisions on how to approach prospects, personalize your conversations, and most importantly, focus on the leads that deserve extensive follow-up. For initiatives like moving up-market or adopting an account-based strategy, AI might be the only scalable way to do it.

There a few aspects which each vendor should be evaluated on to ensure you receive a strong return from your investment. Vet each vendor with the following five criteria before making a purchase.

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.

Leveraging AI for Sales & Marketing – Beyond the Hype

This article was originally published on the OpenView blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.

The hype around AI technology is at all-time high, with the market forecasted to reach $37 billion by 2025. In sales and marketing, the potential for 10x conversion rates and accelerated growth from AI-driven predictive analytics is enticing. But what’s really possible and what’s still in the distant future?

Sales and marketing are ranking in the top three markets most heavily affected by the boom in AI investment, according to sources like CB Insights and O’Reilly Media. Industry giants are announcing more AI plays—Salesforce acquiring Metamind and launching Einstein, or Microsoft rolling out Dynamics 365 with AI technologies Cortana, PowerBI, and Azure Machine Learning.

However, there’s a disconnect between what AI can do and what customers and investors expect. Buyers of AI-powered tools aren’t always familiar with the underlying technology, nor do they know which vendors can deliver on promises of accurate and useful predictions.

Predictive Analytics vs Other AI Technology

In sales and marketing, predictive analytics is a type of AI that has gained impressive momentum in recent years. Companies use this technology to predict which leads will become customers based on traits or behavior that indicates their likelihood to buy, then make decisions on how best to pursue those opportunities.

Predictive analytics isn’t the only type of AI used in sales and marketing applications, but it is where a majority of innovation is happening today. Although it’s easy to be enticed by the idea of fully-automated AI workflows or robot virtual assistants that are powered by deep learning and natural language processing (NLP), those technologies aren’t yet ready for implementation on a large scale. Predictive analytics offers tools that work now, making decision-making easier for startup sales and marketing teams who are increasingly inundated with data.

The Moneyball CEO: Navigating your Whitespace to Grow Revenue

This article was originally published on the Sales Hacker blog by Sean Zinsmeister, VP of Product Marketing at Infer.


Every CEO wants to squeeze more out of their investments in customer acquisition. And that’s particularly true in today’s market. Stock multiples have come down, especially for many business-to-business SaaS companies that are not yet profitable. In this environment it’s all about doing more with less, and hence we’re seeing the rise of the “moneyball” CEO. These smart leaders leverage predictive analytics to grow revenues with what’s already in their pipeline.

Before getting into how to identify this “whitespace” of untapped opportunities, let’s take a step back and think about what executives should be doing to drive revenue. If I were to encapsulate a go-to-market (GTM) manager’s key functions as an algorithm, it would look something like this (in pseudo-code):

Loop Until Fired():

  1. Forecast_The_Business()
  2. Identify_Gaps_In_Current_pipeline()
  3. Reassign_And_Enable()


There are fairly established tools for the forecasting step — like Birst, Cloud9, Domo and GoodData — and the reassigning and enabling step is generally covered by proven approaches to training, hiring / firing, territory management, reallocating spend based on customer acquisition costs (CAC), etc. However the step of identifying specific gaps in the pipeline is the toughest to nail (especially when it’s late in the quarter), and it’s the defining the step that separates mediocre from amazing strategic leaders.

It’s time for this second GTM step to have a framework that’s just as repeatable as the other two, and we finally have proven data science to make that possible. Predictive analytics is a great way for any B2B company to mine their business funnel for deals that can close quickly, and identify potential revenue they might be leaving on the table. In fact, I’ve benchmarked several companies and found that they’re consistently sitting on pipeline which could add 10% of overall revenue from leads and contacts they already paid to acquire.

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


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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