Getting Started with Machine Learning: 3 Things Marketers Need to Know

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

The buzz surrounding machine learning and artificial intelligence (AI) in the consumer world has rapidly bled over into the enterprise.

Much of this hype stems from the new consumer trends that hint at the possibilities of AI, such as self-driving cars and intelligent voice-first products like Amazon’s Alexa and Apple’s Siri.

At the same time, mainstream cloud adoption and ever-increasing computing power in the form of new solutions like Google Spanner are accelerating the development, accuracy and speed of AI’s underlying foundations, from data availability and spam detection, to machine learning, predictive analytics and natural language processing.

So it should come as no surprise that sales and marketing leaders are questioning what all this means for their departments and companies.

At a basic level, AI is about replacing human function with computers. Without using machine learning, people simply couldn’t sort through the plethora of data that’s out there without making costly errors.

Today’s machines have proven they can successfully process, understand, translate and interpret that data — parsing its meaning into visual and actionable outputs. That said, in AI’s current state, what we’re talking about is computers taking over very simple, mundane tasks as opposed to complicated, nuanced actions.

Machine Learning Already Exists in the Enterprise

Several machine learning and AI related breakthroughs are already having an impact on business outcomes, most notably predictive analytics, chatbots and natural language processing (NLP).

For example, predictive analytics is helping enterprises process their CRM and marketing automation system data, combine it with external signals from across the web, and determine where their highest revenue potential lies so they can double-down on the best bets.

Chatbots are more efficiently routing people to services and quickly answering customer questions 24/7, as well as creating new ways for business people to interact with their company’s data.

B2B enterprises and consumer companies alike are experimenting with these conversational user interfaces, which use NLP to recognize speech patterns, essentially mirror a human, and (hopefully) create better customer experiences. But using these technologies carries risks, and businesses in the US can learn plenty of lessons from mobile-first companies like WeChat out of China and India.

But in spite of the success stories, most companies are still wondering if they’re really ready for machine learning and AI. The truth is nearly any enterprise can benefit from infusing more data into its operations, especially if they watch out for common pitfalls.

3 Requirements for Successful AI

1. Start with a business problem

To start with, ask yourself questions like: How well is my business utilizing data to make decisions? Do we have a problem that better data and accurate insights can solve?

Rather than just inventing a problem to have, look towards the business practitioners who are succeeding with machine learning and AI today. Find out what solutions they’re using, and you’ll uncover communities of innovators that have similar market problems.

The real differentiation among today’s emerging vendors is in how well they address customers’ most important business needs, so choose a platform that’s built for your specific use case and will be able to scale and adapt as your business grows.

2. Optimize your technology stack

Few companies embarking on the AI journey recognize the importance of setting up their systems for success — both from a cost and performance perspective. Rather than thinking strategically about which tools they need in their technology stack, teams are too often distracted by shiny new objects and go on a spending spree only to find themselves with a bunch of fragmented tools (many of which end up as shelfware).

The truth is less can sometimes be more, especially when it comes to a scalable stack that’s architected to leverage AI for greater efficiency and effectiveness.

Another consideration is data storage. It can get very expensive, especially when you’re trying to pull in data to, say, a CRM system from a variety of different sources across and beyond your business. Some vendor’s data fees are in line with hardware storage costs from the ’90s, and can even cost more than your annual license.

Of course, you can’t succeed at AI without data, and even small companies have customer data spread across applications. Unfortunately, most of today’s automation systems weren’t architected to scale to the volumes of data that are now typical for midmarket, high growth companies. Get creative from the get-go and find strategic ways to more affordably connect all of the data you’ll need for your AI initiatives.

3. Make systems smarter through integrations

One of the best ways to address the stack efficiency challenge is to find an AI platform with an open architecture that can serve as your company’s system of intelligence.

When it comes to marketing technology in particular, recent studies suggest only 21 percent of companies use a single-vendor suite, while nearly half go with a best-of-breed approach. The integration of these multi-vendor stacks is key to their ability to deliver value.

Your system of intelligence should seamlessly connect with things like your system of record (CRM) and system of engagement (marketing automation), as well as any other business applications the company uses — and then leverage that data to make accurate and actionable predictions.

The result is you’ll be able to quickly and easily infuse predictive analytics into your business’ decision making without disrupting current workflows or adding more complexity to daily activities. Once you’ve gathered all of your data in one place, you can increase the intelligence of all your systems, thanks to the smart outputs you’ll get from your machine learning and predictive initiatives.

The Road Towards Self-Driving Business

Once enterprises address these considerations, they are well on their way to reaping the rewards of AI.

Yet these technologies will never be a silver bullet for every business woe. The industry is still at the early stages of its journey towards disruptive machine and data-guided business models.

We still has a long way to go when it comes to shifting routine enterprise work away from humans and towards machines. And we will always need the expertise and intuition of people to make AI successful, but I anticipate a bright future ahead.

In the next five to 10 years, we’ll see the business world’s version of self-driving cars (‘self-driving’ enterprise software) make headway in functions like sales, marketing, finance and human resources.

As computers successfully transform unstructured data from disparate systems into actionable intelligence, AI will upend the manual, rules-based workflows of traditional systems of record — causing vendors and early adopters alike to re-imagine what true automation should look like.


Introducing Similar Won Accounts for Infer Glance Sales Intelligence

Similar Won Accounts is the newest enhancement to the Infer Glance Sales Intelligence suite of products. With Glance Similar Won Accounts, sales and marketing users can gain immediately insights when researching prospects. This new feature analyzes closed won historical data from a customer’s CRM, and instantly identifies which current customers are most similar to the company being researched by the user. This information can then be used to deliver highly personalized and relevant messaging to prospects while also reducing the amount of time required for reps to craft their outreach. All of these benefits come together to ensure increased rep productivity, higher prospect engagement rates, and greater opportunity pipeline.

Infer Glance Sales Intelligence now includes Similar Won Account data to enable reps to make informed prospecting decisions

The Glance “Similar Won Accounts” feature bases its findings on the following criteria:

  • Competitors – Find current customers who are direct competitors to that prospect
  • Industry – Identify current customers in the same industry as the prospect
  • Revenue – Compare each prospect to current customers with similar annual revenue figures
  • Geography – Establish credibility with each prospect by referencing current customers headquartered in the same city
Similar Won Accounts provides analysis in four areas of similarity: Competitors, Industry, Revenue, and Geography


If you are interested in learning more about the new Glance Similar Won Account offering, please contact

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.


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:


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.


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.


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.


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: