DIY Predictive Modeling – Pitfalls and Opportunities

Originally published on Business 2 Community

Self-Service predictive analytics for sales and marketing

As predictive analytics comes of age, we’re hearing a lot about data science methodologies like machine learning and data modeling. Until recently, these complex techniques were only employed by a relatively small group of data scientists. But new cloud services for machine learning from the likes of Amazon, Google and Microsoft claim to finally make it easy for any business to take advantage of the predictive revolution. As these types of solutions become common in the market, more do-it-yourself (DIY) tools will emerge for industry-specific flavors of predictive analytics in data-rich sectors like financial services, healthcare or retail, as well as in certain functional areas like predictive sales or marketing.

Mapping the Future of Predictive

There’s no doubt about it, the predictive space is heating up. And with all the noise out there, it can be difficult to understand what separates one predictive vendor from another. To help navigate the space and see where the technology is headed, we thought it’d be helpful to draw an analogy to the evolution of maps. Today we we’re excited to unveil an interactive page to tell the story. Check it out!

Mapping the Future of Predictive

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.

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.

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.

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

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.