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:


“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:

Welcome to the AI Spring: How Predictive Will Permeate the Software Industry

AI-Spring-1LinkedIn’s entry into predictive analytics has sparked an important conversation, both regarding the state of the emerging predictive industry and LinkedIn’s place in the enterprise software world. Given many of the company’s moves – most notably its Bizo and Fliptop acquisitions – it is becoming increasingly clear that LinkedIn intends to be much more than just an online “professional network.” There’s little doubt that it wants its place in the B2B sales and marketing software stack.

The question is, what does this mean for the other big players? LinkedIn’s latest announcement was very likely the first of many moves that we’ll see in the predictive market from folks like Salesforce, Microsoft, Oracle, Marketo, HubSpot and others. In fact, Marc Benioff recently spoke with Fortune Magazine about the ‘AI (artificial intelligence) spring’ saying, “When I look at the next set of technologies that we have to build in Salesforce, it’s all data-science-based technology. We don’t need more cloud. We don’t need more mobile. We don’t need more social. We need more data science.”

If you look at how the AI spring is likely to play out, there are a few logical approaches the big software players will take as they look to bring predictive capabilities into their product portfolios.

Adding Predictive Features
The first of these is to extend their existing apps with basic predictive functions – essentially playing it safe. These predictive features will probably be based on data the vendor already controls and should work with minimal customization. For example, rather than requiring manually assigned point values to arrive at basic lead scores, marketing automation vendors might enhance their lead scoring capabilities by using a handful of variables that are consistent across their customers to start calculating predictive scores.

How to Calculate the ROI of Predictive Lead Scoring

With content marketing, freemium products, and list-buys, marketers are generating more leads than ever before — but only a fraction of them are good prospects. Predictive scoring solutions like Infer help filter out the noise by programmatically researching every lead and identifying high-potential MQLs. That not only saves sales reps time, but just as important, it gives you an objective way to measure lead quality.

We’re continuously documenting best practices in order to provide a framework for measuring the ROI of a predictive scoring initiative. One approach that lends a lot of clarity to this process is to look at three simple metrics — your number of sales reps, your average cost per rep, and your percentage of bad leads. With this information, you can quantify the cost of wasting effort on bad leads.

ROI_01However, cost savings is only one way to measure the impact of predictive scoring. Most companies want to quantify the top-line impact as well. By looking at your average leads per month, conversation rate and revenue per opportunity, you can understand the potential revenue increase you’re likely to see from predictive scoring.

The Game-Changing Potential of Predictive Analytics

Jeremy is Director of Marketing at Ambition, friend of Infer and guest blogger.

For sales professionals, time and money are one in the same. Ask any successful sales organization, and they’ll tell you that efficiency is the ultimate ally of a good sales process.

Matt Heinz preaches it. Lori Richardson preaches it. Dionne Mischler preaches it. Just yesterday, I spent a solid hour speaking with a Sr. Account Executive for an Enterprise SaaS company. The topic: Sales process adjustments his company made to increase efficiency, which cut his average sales cycle length in half.

Bottom line: If you’re not aggressively pursuing greater efficiency in your sales organization, here in 2015, you’re falling behind the curve and placing yourself at a disadvantage.

Infer Named a “Cool Vendor” by Gartner

Predictive Leader Powers Smarter Sales and Marketing by Pinpointing Buying Signals


Infer Inc., a leading provider of predictive applications that help companies win more customers, today announced it has been included in Gartner Inc.’s list of “Cool Vendors” in the “Cool Vendors in Tech Go-to-Market, 2015[i]” report. Vendors selected for this report are considered innovative, impactful and intriguing in providing applications that optimize technology marketing, sales or channel management.

Infer’s New Behavior Scoring Predicts Imminent Buyers with Unprecedented Accuracy

Press Release: New Relic Boosts Win Rates by Using Infer to Model Nearly 10 Million Monthly Marketing Automation Signals and Uncover Prospects Who Will Convert within Three Weeks

Infer Inc., a leading provider of predictive applications that help companies win more customers, today announced the general availability of its behavior scoring solution, which helps sales and marketing teams predict which prospects will convert in the next three weeks. Infer is the first company to extensively mine the full spectrum of detailed activity data that is summarized in marketing automation platforms, and use advanced machine learning and predictive analytics to produce highly accurate behavior scores. Already delivering results for several rapidly growing businesses like AdRoll, Chef and New Relic, Infer has generated over 30 million behavior scores for New Relic alone by modeling 47 million activity records.

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.

Infer Helps Leading B2B Companies Accelerate Expansion into New Markets Using Predictive Analytics

Press Release: Companies Like Brightcove, Atlassian and Optimizely Leverage Infer Models to Predict Winning Customers across Multiple Regions, Products and Industries

Moon progression- lens update

Infer Inc., a leading provider of predictive applications that help companies win more customers, today announced that businesses such as Brightcove, Atlassian and Optimizely have used Infer’s “Lenses” solution to successfully accelerate expansion into new geographies, industries and products. Infer Lenses extrapolate market-specific predictive scores from existing Infer models, providing a faster and more accurate way for a company to identify top prospects in new markets.

“We’ve worked with countless companies from early stage to IPO and beyond, and have seen how they’ve expanded into new markets to grow their business,” said Vik Singh, co-founder and CEO at Infer. “With Infer Lenses, we leverage all of a company’s great learnings from other markets and tune their predictive scores so they are useful and actionable for a new region or industry, even if there’s no customer data there yet. This unique service is a huge breakthrough in helping companies scale, and it’s something Infer uniquely provides.”