How to Analyze Predictive Models for B2B Sales & Marketing

As companies embark on the predictive journey, the first question most ask is “how can I tell if my model is really working as it should?” In order for your sales reps to trust predictive scores and invest the proper amount of time into the leads you send them, it’s critical to demonstrate the accuracy, efficacy and performance of your model.

Over the years we’ve had the opportunity to work with amazing companies like Tableau, Concur and Box that are part of the movement shaping the future of predictive for sales and marketing. This playbook highlights best practices members of the Infer community have used to evaluate their models.

beginners guide to predictive models for b2b

Inside you’ll learn how to:

  • Understand and compare your conversion rates across lead buckets
  • Calculate multipliers to see how much better buckets perform vs. average
  • Use this simple worksheet to analyze your company’s predictive model

Download your copy here >

This is a predictive playbook we often recommend when companies are just embarking on their predictive journey and want to easily understand if their model is performing as it should. We also have other playbooks for sales prioritization, filtering, net-new leads, nurture, executive dashboards, and campaigns. If you’d like to learn more, contact us and we’d be happy to connect.

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:

It’s Autumn, But Predictive Software is Blooming with SalesforceIQ — Welcome to the AI Spring!

SalesforceIQ-for-Sales-CloudThere’s a lot of conversation happening lately about predictive analytics, and there’s no question that it is starting to permeate the software industry. Yesterday’s SalesforceIQ announcement is just the latest example of an enterprise software company that’s sitting up and taking notice of the massive value data science can unlock. By leveraging relationship intelligence to help small businesses sell smarter, SalesforceIQ will help more and more companies begin the shift towards predictive-driven decision-making. This is one more way that machine learning and predictive modeling are increasing businesses’ appetite for actionable data.

The question is, what moves will the other big enterprise software players — like Microsoft, Oracle, Marketo and others — make during this ‘AI (artificial intelligence) spring’? If you look at how the predictive market is likely to play out, there are a few logical approaches the software giants will take as they look to bring predictive capabilities into their product portfolios.

#InferClock Campaign

ClockDreamforce has become a huge event and we’re always looking for new ways to stand out. We’ve had a lot of fun with our shoe campaign, but thought we’d try something different this year.

We came across this clocks and thought it’d be a great conversation starter on the show floor. What is it? I’ll give you a couple clues:

  • The clock calibrates itself
  • Its hand would be in the same position whether you are in Tokyo, Paris or New York
  • There aren’t very many of these clocks in the world today (and most of them are in the MOMA)
  • The concept is very new and very very old at the same time
  • Four year olds are often able to solve this riddle faster than adults

The answer… It’s a Seasonal Clock

At Dreamforce we’re going to give away 50 of them, one every hour.

Measuring Federer’s Prime – How Great was Great?

FedererRoger Federer is arguably one of the best tennis players of our time. His intense athleticism and ability to do the impossible on the court has been compared to a religious experience and even led to the coining of terms like “a Federer Moment.” His status among the greats can be quantified through a science of sorts, and illustrated with data and statistics. All eyes are on Federer during this year’s U.S. Open – can he win an eighteenth grand slam after a 3-year drought? Pete Sampras seems to think so.

Predictive Meets Inbound Marketing

This week, we’re celebrating all things inbound at the annual HubSpot Inbound conference in Boston, and it’s been great to see the enthusiasm around this exploding breed of marketing.



What’s even more exciting are the many discussions around the rise of predictive. HubSpot is the latest software player to join the AI Spring, announcing today that it is adding basic predictive features to its product. This is a great way to get started with predictive if you want to minimize your marketing automation configuration burden, but don’t yet have a significant customer base or aren’t ready to jump fully into advanced predictive lead scoring.

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

Dreamforce ’15

Arguably one of the hottest cloud events of the year is quickly approaching, and like many folks we’re gearing up for another exciting Dreamforce in San Francisco this month.

Want to book some time to connect?

Mike Cabot Mike Cabot – VP of Sales @ Infer – Mike heads up Sales at Infer where he joined over three years ago as the first employee (non-founder). Want to learn how Infer leverages predictive internally, hear some customer stories, or just talk shop? Book a time.
Jamie Grenney Jamie Grenney – VP of Marketing @ Infer – Jamie started as employee number 140 at Salesforce. After 11 years there he jumped to Infer and heads up the marketing team. Want to talk about our product roadmap or marketing strategy? Book a time.
SeanZ Sean Zinsmeister – Product Marketing @ Infer – former customer and now Director of Product Marketing @ Infer – Interested in learning how Infer’s customers using predictive intelligence? Get an inside look at predictive playbooks with Sean. Book a time.
Andrew Angus Andrew Angus – Demand Gen Marketing @ Infer – Hands on strategy to help generate more leads and tips on implementing predictive to get more out of our SDR team. Book a time.

Where to find us? 

Infer #df15

Predictive Playbook: Sales Prioritization

Many B2B companies are interested in predictive but need help building a business case. While there are many applications for scoring, sales prioritization is one of the most common. It is easy to set up the ROI story upfront and measure the impact over the first 60 days.

Over the years we’ve had the opportunity to work with amazing companies including Tableau, Optimizely, and Zendesk. This playbook highlights how they’ve used predictive to drive more pipeline with less effort.

Inside you’ll learn how to:

  • Articulate the business challenge
  • Quantify the amount of wasted energy
  • Project the revenue impact of predictive scoring
  • Document your success story

Access your free copy here > 

predictive playbook for sales and marketing - prioritization