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.”

WSJ on “The Data-Driven Rebirth of a Salesman”

Last week, Elizabeth Dwoskin and Shira Ovide of the Wall Street Journal wrote a great article on predictive sales technologies. Their comparison of Willy Loman from “Death of a Salesman” to modern data-driven salespeople really brings home how much has changed for reps in the world of big data. If you don’t have time to peruse the full story, here are some key excerpts:

WallStreetJournal_0

“Silicon Valley startups are automating sales departments for a shot at the more than $23 billion companies spend each year on sales software. Some of these startups mine sales staff emails, calendars, social-media feeds as well as news articles and customer databases for patterns that help them predict the likelihood of a sale or the behavior of potential buyers.

Infer’s Sean Zinsmeister on the Gold Rush for Predictive Data

Our very own Director of Product Marketing, Sean Zinsmeister, recently sat down with folks from a couple great interview series — MarTech Heads and TA Expert Interviews. During these insightful conversations, Sean spoke with the podcast hosts about predictive sales and marketing, marketing campaign successes and challenges, third-party intent data, the shift from predictive to prescriptive intelligence, his favorite martech tools and tips, and more.

Have a listen:

The Math Behind SaaS

Math Behind SaaSOur friend Tomasz Tunguz at Redpoint Ventures recently asked Vik Singh to write up his techniques for estimating Infer’s customer lifetime value (LTV) and customer acquisition cost (CAC) using a rolling sales and marketing period. Unlike the standard formulas that most investors use to determine LTV and CAC, Vik’s “expected” CAC/LTV approach is more forward-looking and actionable for SaaS entrepreneurs because it doesn’t require years of historical customer data or perfect attribution of sales and marketing spend.

Below are the formulas Vik uses to measure our business, building off of some basic metrics like cost per opportunity, win rate and average deal size:

Expected # of New Customers = Opportunity win rate X # of opportunities

Expected CAC = Fully-loaded growth spend / Expected new customers

Expected Payback Period = ECAC / Average annual contract value X 12 months

Expected First-year ROI = First-year ACV / ECAC

Congrats to CRM Market Elite Winner

It’s great to see the smart folks over at Concur be recognized for their impressive results with our predictive lead scoring. CRM’s 2015 Market Elite Customer Company winners all demonstrate how technology can impact operating costs and efficiency, and Concur is no exception. Here’s an excerpt from the company’s profile in this month’s issue of CRM Magazine:

Infer’s predictive lead scoring helps Concur close more deals more quickly

The Infer solution helps Concur identify and prioritize the marketing-qualified leads that are most likely to convert to closed deals. It pulls in thousands of external signals, going well beyond what most organizations track in their basic CRM and marketing automation tools.

Greg Forrest - Concur“We’re finding leads a lot quicker and getting them into the pipeline a lot faster,” Forrest states proudly, “and we’re closing at a much higher rate.”

Based on the results Concur has seen in the first six months, the company is expanding the Infer solution to its cross-selling and upselling activities, account scoring, and direct marketing.

REAL RESULTS

  • Five thousand marketing-qualified leads were uncovered in its database, leading to a dramatic run-rate increase.
  • The number of leads converted to closed deals tripled.
  • Conversion rates increased by 150 percent, from 0.8 percent to 2 percent.
  • Closed deals for new solutions were boosted by 76 percent.

For more details, check out the magazine’s full story on Concur.

Jerry Yang on His Investment in Infer

Jerry YangI’ve been very lucky during my career to work with some brilliant mentors, one of whom is the founder of Yahoo!, Jerry Yang. I worked with Jerry at Yahoo!, where he helped me push an ambitious product called BOSS (which stands for “Build your Own Search Service”). We’ve stayed in touch since then and he’s been incredibly supportive of Infer’s approach. Many Yahoo! products related to web search, content optimization, etc. live and die on data science, so he’s passionate about the opportunity to bring this rigor into enterprise software.

Following a recent Business Insider profile, we asked Jerry to answer a few specific questions about why he decided to invest in our company, and in the predictive analytics space in general. Here’s what he shared:

Why do you think there’s such a big opportunity in predictive analytics? Yahoo! through its development of Hadoop witnessed what predictive analytics could do with the massive scale of user data. Now it’s exciting to see companies like Infer bringing this technology to other vertical industries that can benefit from it. There’s definitely a huge opportunity for businesses to transform their operations and decision making by using data.

Why did you invest in Infer? For me, I place a very high value on the entrepreneur and founder. In the case of Vik Singh, we go way back to working closely together while we were at Yahoo. Vik was working on Yahoo! BOSS, a search product that quickly became strategically important to the company. We had a great relationship that continued beyond Yahoo!, and it’s a pleasure to support Vik in this venture.

Take a Listen to “Moneyball for Marketing”

This week our CRO, Jim Herbold, got a chance to sit down with B2B marketing thought leader, Glenn Gow, for his popular Moneyball for Marketing podcast.

Jim Herbold

Jim and Glenn talked about all things predictive – including marketing use cases for predictive intelligence, real world success stories, and four B2B barriers to predictive analytics adoption that are rapidly disappearing. Check out this excerpt from their lively discussion…

Glenn: Tell us an example of what companies are doing in the real world and how they’re taking advantage of predictive analytics.

Jim: Well, there’s an easy example I can speak to. I was the first customer of this company Infer when I worked at Box. When I was running sales at Box, I had the luxurious challenge of dealing with very large lead flows. We had a freemium aspect to the business. We also had a very vibrant free trial aspect to allowing people to get into our service pretty quickly. So, very large flows and leads, we’re talking tens of thousands and I could never afford to apply a lead qualifier to plow through all of those leads systematically over time. I needed a way to find the proverbial needle in a haystack and I started working with Infer.