Getting past the AI hype: How predictive analytics fuels conversion optimisation

This article was originally published on MarketingTech by Sean Zinsmeister, Vice President of Product Marketing at Infer.
These days, marketers can’t read about their profession without getting bombarded with wild claims about how AI is going to disrupt everything they do. And with the sales and marketing functions evolving so rapidly in recent years, marketers in particular must embrace an entrepreneurial spirit and constantly explore new technologies in order to give their team a competitive edge. That mindset shift, along with new consumer trends—such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri—are bringing the possibilities of AI to the forefront of business-to-business marketing technology discussions.

But all of this begs the question, “Which AI claims are hype and which are reality?”

In order to know what a new technology like AI can bring to the table, it’s important to fully understand the problems you’re trying to solve. When it comes to the current state of AI solutions for marketing and sales, today’s reality is less futuristic robots or automating every single marketing workflow, and more about how data can answer one important baseline go-to-market question: who to sell and/or market to. There’s a wealth of intelligence that predictive analytics and machine learning bring to the task of answering these questions – and that’s the crux of where AI is delivering value today.

Sales performance management

Forward-thinking enterprise sales teams are saving tons of time by simply using predictive solutions to improve the way they filter and prioritise inbound leads. Companies with the “champagne” problem of an overwhelming volume of incoming prospects are using predictive analytics and AI to automatically research and qualify leads who looks like their company’s ideal customer. For example, Shoretel’s market development team found that predictive scores could tell them not just which prospects are the best fit, but also which ones are showing current buying behaviour. As a result, the telco leader’s sales reps can instantly understand who their best prospects are and determine where their time should be spent — insight which has resulted in 8X greater conversions. Now it takes just 12 calls to uncover one marketing-qualified lead (MQL) vs. the 100 calls it took before the company adopted predictive analytics.

Word on the Street: Predictive Advice from Infer Customers

Earlier this month, G2 Crowd sat down with Infer customers InsightSquared and Yesware to ask about how they use Infer’s predictive analytics and AI platform, what benefits they’ve seen, and their recommendations for other early adopters.

First up was Matthew Bellows, CEO of Yesware, a long-time customer of Infer. During the interview, he shared some helpful tidbits about how his sales team uses predictive to automatically qualify the best prospects from their high-volume lead flow, focus on the right accounts, and increase overall sales velocity and performance:

For more helpful advice from Yesware, check out this recent webinar with their director of demand gen, and learn how she was able to build a revenue-centric funnel with Infer. As a result, the company eliminated wasted sales efforts and won more deals.

G2 Crowd also chatted with Joe Chernov, VP of Marketing at InsightSquared, about how the company’s go-to-market teams use Infer to build alignment around the best accounts, and drive engagement as part of their account-based sales and marketing strategies.

Read our full snapshot to learn more about how InsightSquared is using Infer Predictive Scoring to make their marketing and sales machine much more efficient by identifying high-value leads and accounts, increasing conversions from top leads, and reducing overall cost-per-lead.

And for even more “word on the street” comments from other customers, browse Infer’s many reviews at G2 Crowd.

Interview with Infer on Predictive and ABM

Josh Hill’s interview with Infer’s Director of Product Marketing, Nikhil Balaraman, originally appeared on Marketing Rockstar Guides.

Josh: In the Martech Maturity Model I wrote about, I placed Predictive tools at Stage 6 – the very end of the 24-36 month implementation timeframe for firms to build out martech. Do you agree or disagree, and why?

Waiting until the end of a martech implementation is certainly one approach to adopting predictive tools, however, I’d argue that in most cases there’s no need to wait that long before getting a leg up on your competition. In fact, many of our customers start with predictive fit scoring prior to implementing marketing automation (MAP). Here are a few key use cases for predictive that we’ve seen at early stages of the Martech Maturity Model:

  • Stage 0 (Marketing Transformation): Most companies don’t start building their sales and marketing stack by selecting a MAP vendor — their first step is usually to purchase a CRM system like Microsoft Dynamics or Salesforce to store sales data. At this juncture, the business challenge is to filter and prioritize leads so that sales knows which ones to work, which is a great use case for a predictive solution like Infer. As long as a company has captured sales data on at least 100 or so conversions in their CRM system, we can build and deploy a statistically accurate model for them that same day. Additionally, we can build Market Development Models for companies. These models are based solely on lists of their target companies, and helps them more efficiency enter new markets or roll new products out to market. In both scenarios, adoption is usually quite fast, since Infer Scores can be easily integrated into pre-existing CRM workflows, such as lead assignment and routing.
  • Stage 1 (Automation): Once a company has started the marketing transformation process and adopted a MAP as system of record, predictive behavior models can accelerate the impact and simplify the rest of the stages by providing a system of intelligence with insights and actionable intelligence for reps and marketers. These predictive models assign an immediate quantitative measure of value to each lead and account based on a machine learned model and trained on historical data; therefore, the score not susceptible to human bias in the same way as rules-based scores. This intelligence should be a considered part of every decision a company makes across their funnel.
  • Stage 2 (Lead Quality Management): At this stage, we’ve seen great results from predictive with customers like Nitro. The company had a “champagne problem” of so many leads that they were breaking their marketing automation system. Since their reps could only work a tiny percentage of their leads, Nitro needed to implement predictive scoring immediately so that they weren’t wasting time chasing low quality leads. Infer also helped the company determine which leads to keep in their marketing automation system.
  • Stage 3 (Nurturing in Sales Context): Here, companies should use predictive fit scoring to identify which prospects are not a good fit for their business, and won’t convert into revenue. These types of leads can be funneled into low touch nurture tracks. In addition, predictive behavior scoring can help monitor all prospects in these nurture tracks and push highly engaged prospects back into sales reps’ hands.

We don’t believe predictive is a single point solution to only be implemented at the end of a 3-year marketing transformation.

Josh: Interesting. While I agree that predictive can support Nurturing, I’ve found firms in these Stages aren’t ready to consider powerful tools because they are still learning how to use Marketing Automation, Nurturing, and sales-marketing alignment.

Account-Based Sales Development In A Predictive World

This interview was originally published on the Outreach blog by Jeremy Garbutt, Sales Development Manager at Infer.

Like any lean, fast-growing company, our sales team at Infer struggles with the age-old conundrum of quantity vs. quality. However, thanks to Outreach’s automation, we’ve been able to avoid spreading ourselves too thin. By using this great solution alongside our company’s own predictive and profiling platform, we’re tackling personalization at scale and really honing in on our highest quality leads.

Our sales development team lives in Outreach today. Whether we’re working inbound or outbound leads, Outreach makes it easy to automate what used to be manual workflows and sales sequences. We make this orchestration even smarter by adding a layer of predictive intelligence about both the fit of each account (i.e. how good a match are they for our product?) and the behavior of each lead (i.e. are they engaging with us enough to indicate that they’re likely to buy in the next three weeks?).

For lower quality inbound inquiries at the tail end of our lead universe, Outreach lets us fully automate all follow up and use email templates to save tons of time for our reps. As a result, we can now follow up with our very top quality inbound leads in less than 5 minutes. After our first contact, we use Outreach to cycle through different communications sequences (Email > Call > Social > phone > etc.) and make sure we’re staying in front of our best prospects with custom messages.

The difference between these two approaches is significant – our top leads get on average 10X more custom touches than our automated sequences for bottom leads. And this narrowed focus is paying off. We’ve boosted our conversion rate by 35% as a result of training our reps to prioritize quality accounts because they are accelerating follow-up, preparing targeted conversation points and sticking with these leads longer.

We also track the full spectrum of behavior we’re seeing from each individual lead in our Salesforce CRM and Pardot systems, so we can make sure that no missed opportunities fall through the cracks. Since we include predictive behavior scores as a distinct data point to inform our sales priorities (in addition to our less dynamic fit scores), we’re able to surface leads and accounts that are currently showing clear buying intent. Once a lower fit scoring lead responds to a sequence or crosses a behavioral threshold from our nurture programs, the account owner gets an alert that they’re in market to buy, and will start to pursue them more aggressively.

What is Account-Based Marketing And How Can You Leverage It? [Podcast]

Though account-based selling strategies are far from a new concept — sales teams have been using this approach for quite some time — the conversation around how to apply this to marketing has really gained steam for B2B marketers over the past year. Of course, it’s not hard to see why. ABM has the potential to open up new revenue channels, and when combined with predictive-driven tactics, this approach drives even higher conversion rates and larger average deal sizes.

In this episode of the Marketing School podcast, Neil Patel and Eric Siu talk about what account-based marketing (ABM) is, how to leverage it, and why Infer is one of their favorite solutions for finding their most valuable leads.

AdRoll Uses Infer to Increase Sales Performance Management and Marketing Effectiveness

AdRoll is a leading performance marketing platform with over 25,000 clients worldwide, and receives hundreds of thousands of inbound leads every year. To maintain its amazing growth trajectory and stay one step ahead of the competition, AdRoll has instilled a culture of data-driven decision making.

AdRoll uses Infer’s Predictive Platform to qualify and prioritize its best fit leads so that sales reps can focus their energy on the “fireballs” that are most likely to convert. The company also uses Infer to measure marketing effectiveness and efficiency by identifying which marketing channels and campaigns are driving the highest quality prospects. Not only has predictive intelligence helped AdRoll to fortify sales and marketing alignment, the company has also increased sales performance management with a 15% increase in deals per seller over their flourishing global org.

AdRoll’s Jessica Cross, Head of Customer Lifecycle Marketing, and Chris Turley, Global Head of Revenue Operations, sat down to share how they’ve incorporated Infer inside the organization.

Infer Extends Profile Management to the Oracle Marketing Cloud

Customers can now use engagement data from Eloqua to build lead, contact and account-based profiles in Infer Profile Management

We’re excited to announce that Infer Profile Management now supports Eloqua engagement data, allowing customers to take their segmentation and profiling one step further and ensure that they’re sending the right message to the right person at the right time. While customers like Tableau and Avalara have been using Infer Predictive Scoring with Eloqua for a while, we’re happy to now extend that seamless connection to users of the Infer Profile Management platform as well.

For those who aren’t yet familiar with Infer Profile Management, it’s a self-service solution that gives marketers and sales reps unprecedented control in targeting their ideal customer profiles for more personalized outreach. The platform joins your company’s internal records with signals from Infer’s data cloud, which contains hundreds of individual attributes and useful data points for use in segmenting your database. With this full spectrum of information, Infer Profile Management lets you easily build profiles over lead, contact, account and opportunity data directly from your CRM system. To bring this to life, here’s a real-world use case of Infer Profile Management from our customer Looker:

In addition to engagement data from sources like Google Analytics and Marketo, Eloqua data can now be pulled directly into the Profile Management interface so you can build dynamic segments at the lead, contact and account levels. Once you decide to publish these profiles, Infer will index your entire CRM system to identify any leads, contact and/or accounts that meet your specific definitions of engagement (such as “Infer A-Accounts, that attended a webinar in the last month and use Amazon Web Services”), and then tag those records across your systems for follow up.

Infer Profile Management’s addition of Eloqua engagement data allows you to:

  • Enhance your account-based sales and marketing tactics by immediately tagging engaged accounts in your CRM for more aggressive follow up at the right times
  • Gain visibility into your most highly-engaged accounts and contacts
  • Easily hyper-target your database in order to deliver personalized follow up based on specific behaviors and engagement
  • Bridge the sales and marketing divide by making Eloqua’s marketing engagement data more useful and accessible for sales

Contact us today for a demo of how Profile Management can help you connect, define and engage your prospect database.


Can predictive bring sales and marketing together?

Barb Mosher Zinck’s interview with Infer’s VP of Product Marketing, Sean Zinsmeister, originally appeared on Diginomica.


Knowing which prospects and customers to focus time and effort on is critical for marketing and sales success. You can’t hit everyone; you have to hit the right ones. Predictive and AI can help.

How is the technology adapting to support sales intelligence? I spoke with one predictive sales and marketing platform vendor to get a feel for how the market is evolving.

In my discussion with Sean Zinsmeister, VP of Product Marketing at Infer, he talked about three main issues sales and marketing face.

The Inbound problem

Lead generation is the implementation of strategies to capture the attention of prospective customers. The goal is to get contact information to pass on to Sales for follow up and hopefully conversion. Successful lead generation can yield a lot of contacts, but not necessarily a lot of qualified leads.

So what happens when you are getting way too many leads coming in from Marketing? How do you know which ones to focus on? Which ones are the right ones?

Zinsmeister gave the example of one company that had too many leads pouring in, and it was taking Marketing 100 calls to generate one marketing qualified lead (MQL – a lead that’s most likely to buy). This company adopted predictive scoring and profiling to help it narrow down the best-fit prospects to follow up with and reduced the number of calls to 12 per MQL.

How does predictive scoring help? Not only does Infer look at a contact in terms of their interactions with your company (by looking at your CRM and marketing automation), it also mines the Web and other third party data looking at potentially thousands of data points, each weighted specific to the company’s requirements. Put all that profile information together, and score it and you have a better idea of which prospects are engaging more with your company at the time when they are ready to take the next step.