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.

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.

UserVoice Increases MQLs by 37% Using Predictive Marketing

Many of our customers come to us with a common problem: they have no good way to differentiate best-fit prospects from the tire-kickers, and are often left to rely on “gut instinct” when it comes to prioritizing who they should target. This was a particular pain point for UserVoice, who needed a way to more efficiently prioritize lead flow so their reps could focus their effort on those prospects with the most revenue potential. Additionally, both the sales and marketing teams wanted more transparency into what attributes defined an ideal customer profile so they could personalize and prioritize high-value outreach to these buyers.

UserVoice deployed a fit-based Infer Predictive Scoring model, and is now able to identify and prioritize leads based on how likely they are to purchase the company’s product management software. Armed with new predictive insights, the company saw a 2x increase in conversion rates and a 37% increase in marketing-qualified leads.

Connor Fee, COO at UserVoice, recently joined us to share his company’s predictive intelligence story, and how Infer has become a core technology in their sales and marketing stack:


The Front Lines of Predictive Intelligence

This article was originally published on the Hubspot Blog by Infer customer, Nicholas Heim, Director of Inbound Marketing at Hotjar.

HotJar HubSpot

Hypergrowth SaaS businesses like my company, Hotjar, are often faced with the happy problem of too many free trial leads flooding into HubSpot. Thanks to lots of word of mouth buzz around our mission to democratize user analytics, and some clever advertising, we took off fast a couple years ago.

I wasn’t around for the early “fresh-out-of-beta days,” but as a newer team member, it’s quite nice to stand on the shoulders of an amazing product and founders who are true visionaries. When I joined around nine months ago, it had become challenging for our team to vet which leads (out of around 600+ new users each day) to target for premium business and agency plans.

While we get tons of value from using HubSpot for both our CRM and marketing automation needs, we couldn’t properly segment and personalize messages for our highest potential users out of the gate.

That is, until we added Infer Predictive Scoring to the mix. Now, we have a custom predictive model that works with HubSpot and our Intercom customer messaging platform to provide accurate data-based predictions of how well each lead matches Hotjar’s ideal customer.

Here’s what we’ve learned by using predictive intelligence to inform more advanced sales and marketing tactics.

4 Easy Tactics for Infusing AI and Predictive Analytics Into Sales Processes

This article was originally published on the Salesforce Blog by Sean Zinsmeister, Vice President of Product Marketing at Infer.

Unless you were hiding under a rock this year, you probably heard a thing or two about the rise of artificial intelligence (AI) for sales. As machine learning and predictive analytics technologies have rapidly matured, a whole community of forward-looking sales and marketing leaders are emerging as predictive innovators. Rather than relying on human intuition to inform their processes, these early adopters are leading the arms race for data by reinventing how their businesses operate based on intelligence that’s generated by AI and other related data science techniques.

In this environment, I’ve noticed four easy ways that smart sales leaders are hacking their team workflows to insert valuable data signals and key insights into day-to-day tasks—saving vast amounts of time and making sure all of their rep’s hard work is tightly aligned with the impact it delivers.

1. Use analytics to inform sales follow-up

There’s no doubt that confident and focused reps bring more opportunities into the pipeline. But it’s hard for them to feel confident when they’re given sparse lead records with little or no information about key buying signals – like a prospect’s fit for your product, or their likelihood to make a purchase soon based on marketing engagement. In order to avoid wasting hours every week researching leads, many teams are leveraging the latest predictive scoring and profiling technologies to create a habit of fast and efficient follow-up. When it’s easy for reps to prioritize the right prospects and plan their outreach, they follow-up more consistently, and as a result are more likely to hit their numbers each month.

For example, Shoretel is a company with a huge influx of leads, which market development reps individually call in order to qualify opportunity-ready MQLs. After adopting predictive analytics, the team started prioritizing their best-fit leads to qualify first, and MDRs went from having to call 100 leads to find 1 MQL, to just 12 calls per MQL – a huge productivity improvement.

With detailed information about each prospect, sales reps can also personalize every conversation for better engagement. By using advanced profiling techniques to create highly-segmented lists of prospects based on specific attributes and data signals (such as “VPs of Sales, in California, who use Salesforce, and have interacted with one of our marketing campaigns in the past 6 months”), reps can quickly sort out the best way to approach each group. For instance, that might send a particular piece of content or invite the prospects to a local meetup. Some tools even let you set up alerts for important events, auto-assign tasks to reps in Salesforce, and get recommendations powered by machine learning on which segments to invest more time into.

Carolyn Wellsfry Cheng of Shoretel Discusses Integration, Adoption, Closed Loop Reporting, and Combining Cloud and Location Based Solutions [Podcast]

We’re back with another episode of Stack & Flow! This week we’re joined by Top Predictive Innovator Carolyn Wellsfry Cheng from Shoretel to discuss the differences between account-based marketing and selling, which strategy she predicts is here to stay, and why buying flashy tools isn’t always the best choice.