AI 101, Part I: What You Need to Know about Predictive Models

This article was originally published on MarTech Series by Sean Zinsmeister, Vice President of Product Marketing at Infer.

FOUR STEPS TO START BUILDING PREDICTIVE MODELS

While predictive analytics and AI are big topics in the sales and marketing profession these days, it can feel daunting when you’re trying to figure out how to get started with these data-dependent solutions. Although most marketers probably won’t actually be building any data models themselves, it’s vital that the next wave of go-to-market professionals develop a solid understanding of how to solve business problems using data. In this series of articles, I’ll break down key concepts surrounding these technologies piece-by-piece, and provide a helpful look under the hood of predictive modeling.

Business folks who are ready to get their feet wet with AI should first zoom out and learn the basics of predictive modeling – one of the underlying technologies that’s required in order to effectively replace human functions with machines. AI solutions use this advanced data science to process, understand, translate and interpret all the data that’s out there, and parse its meaning into visual and actionable outputs.

Let’s explore each of the four main phases of building predictive models:

1) DATA ACQUISITION

When it comes to building a predictive model, the first step is to gather all of your inputs or data sources. There’s no question that sales and marketing teams are acquiring a plethora of data. In many cases, however, marketers are collecting data that only they care about, and it might not be valuable, insightful or actionable for sales (and vice versa). Regardless, given the mass adoption of passive marketing channels, low-barrier free trials, etc., most businesses are gathering a lot of data about their prospects at scale – much of which can be used to inform smarter predictive models.

2) DATA PREPARATION

Before digging into all this data, it’s important to first step back and figure out what business problem you are trying to solve with AI, which will help you prioritize your data preparation tasks. The reality is that it’s very common for data to be incomplete and dirty (there’s no getting around the human error that comes with data entry), so data preparation is crucial to the future of AI. Your data should be properly cleaned up, if you will, so that you can normalize typical errors during the data acquisition phase, and ultimately produce a sound model. Only then, will predictive analytics answer your specific questions and drive the actions you want. Some common ways to prepare your data include enrichment (bringing in external signals to complement current records), spam analysis and title normalization – stay tuned for more on these techniques in my next post.

3) MODELING

Once you understand the machine-learning problem you want to solve for, the next step to building a model is to employ data science methodologies like classification or regression. Classification is also known as probability estimation, and it is used to predict which of a small set of classes each individual belongs to. For instance, you might ask “Among customers of Company X, who is most likely to respond to this offer?” There then would be two classes: “Will Not Respond” or Will Respond.”

On the other hand, regression (or value estimation) is used predict the numerical value of some variable for each individual. Looking at historical data, you might produce a model that estimates a particular variable specific to each individual, such as “How much will this customer use this service?” Both of these techniques, and many others, can deliver model outputs that drive powerful AI and predictive analytics use cases in sales and marketing.

4) OUTPUT

For example, sales teams can achieve major performance management improvements by using predictive models to improve the way they filter and prioritize both inbound leads and account-based outreach tactics. With score outputs that indicate which leads look most like the company’s ideal customer, sales can confidently focus their time on just those prospects that are likely to buy. In addition, teams can use AI to more thoughtfully route their leads – either to SDRs for outreach and development over time, to account executives for more aggressive follow-up, or to automated nurture programs – based on each lead’s potential value. Predictive behavior models can also alert sales when an old lead starts acting like a customer. By looking at engagement patterns in marketing automation and web analytics systems, you can determine when neglected leads are likely getting close to a conversion threshold. This helps reps find good qualified accounts and contacts that are “reawakening,” and then trigger data-driven workflows for more aggressive follow-up with just the right message at just the right time.

Another valuable use case for AI is to drive marketing efficiency. With the right customer intelligence, marketing teams can optimize conversions for the greatest possible funnel efficiency. And since predictive analytics outputs deliver immediate feedback on the quality of marketing campaigns, they can easily calculate key performance metrics in real-time rather than waiting for sales cycles to play out. Accurate predictions also add value when it comes to quantifying key marketing performance indicators like cost per good lead, average lead quality, pipeline-to-spend ratio, etc. By using these KPIs to look past traditional vanity metrics and identify top performing campaigns and content, marketers gain deeper insights into which programs attract the highest quality leads, drive larger deals, and accelerate deal velocity.

Each of these four steps to the predictive model build process is important to understand if you want your models to produce statistically accurate predictions, and these phases become increasingly mission-critical as AI takes over more and more everyday tasks from humans. No one wants to miss out on real revenue, just because AI made unnecessary mistakes when determining outbound prospecting lists or writing the content of sales outreach emails.

The AI Power Hour: Artificial Intelligence and Machine Learning, featuring Christopher Penn of Shift Communications [Podcast]

Guest Bio:

Christopher S. Penn is an authority on digital marketing and marketing technology. A recognized thought leader, author, and speaker, he has shaped three key fields in the marketing industry: Google Analytics adoption, data-driven marketing and PR, and email marketing. Known for his high-octane, here’s how to get it done approach, his expertise benefits companies such as Citrix Systems, McDonald’s, GoDaddy, McKesson, and many others. His latest work, Leading Innovation, teaches organizations how to implement and scale innovative practices to direct change.Leading Innovation, teaches organizations how to implement and scale innovative practices to direct change.

Christopher is a highly-sought keynote speaker thanks to his energetic, informative talks. In 2015, he delivered insightful, innovative talks on all aspects of marketing and analytics at over 30 events to critical acclaim.

He is a founding member of IBM’s Watson Analytics Predictioneers, co-founder of the groundbreaking PodCamp Conference, and co-host of the Marketing Over Coffee marketing podcast.

Christopher is a Google Analytics Certified Professional and a Google AdWords Certified Professional. He is the author of over two dozen marketing books including bestsellers such as Marketing White Belt: Basics for the Digital Marketer, Marketing Red Belt: Connecting With Your Creative Mind, and Marketing Blue Belt: From Data Zero to Marketing Hero. His new book, Leading Innovation, debuts in 2016.

Episode Breakdown:

In this special episode on artificial intelligence, Christopher Penn discusses machine learning and:

  • The Four Elements of Artificial Intelligence
  • Automation’s Political and Social Impact
  • The Fifth Element of Sapience
  • Data Science Rising

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We’re always looking for inspiration, so please drop us a line if there’s any topics you’d like us to cover or any guests you’d like to see interviewed — please feel free to comment below or fill out the suggestion form on the Stack & Flow website. If you love what you hear, please leave us a review on iTunes.

Hacking Content Marketing With Predictive Analytics

Content Summit is a 5-day virtual conference dedicated to sharing the most effective B2B content marketing strategies & tactics, which are delivered by a B2B marketing executive or thought leader. Our very own Sean Zinsmeister hosted a session on how content marketers can get the most out of their programs using predictive intelligence.

Watch the replay below:

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.

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.

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.

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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.