Which technology will have the biggest impact on marketing over the next five years? In OnBrand’s 2017 State of Branding Report, 23 percent of CMOs and global brand managers answered with two words: machine learning.
Yet only 43 percent said their marketing department is an early adopter of new technologies like machine learning. A big part of this hesitation comes from not knowing what these technologies can offer or how to take advantage of the opportunities they present.
Here are a few key aspects of machine learning technologies you need to know and four specific ways to leverage them.
Machine learning algorithms have been around for a while, but their application to big data in modern marketing is the reason for the recent commotion.
The SAS Institute defines machine learning as:
Any industry working with substantial amounts of data, from healthcare to financial institutions, is starting to appreciate the value of machine learning technology and they are investing in it. Marketing teams are catching on too. Gleaning insights in real time is an attractive perk when it comes to understanding behaviors, which in turn enhances experiences — which might result in a conversion or sale.
Remember when personalization was such a buzzword? Now machine learning empowers marketers with insights that help them gain a deeper connection with their target audience. The technology confirms what customers want, then continues learning, becoming smarter over time about those expectations.
We’ve heard about delivering the right content to the right person at the right time — well, this is it. Machine learning serves high-definition information on a platter for a more strategic decision-making process.
Instead of making an educated guess about what kind of content your audience is interested in, insights will steer your entire strategy. This isn’t some futuristic concept or wishful thinking; machine learning is personalization in action. And, it’s being used by CMOs who are willing to leap into this new territory — and it’s giving them the upper hand.
Machine learning algorithms gather customer profiles and behaviors across multiple channels so you can decide the best course of action. Think about how that can elevate your marketing efforts. Opportunities include:
1. Personalized customer journeys. First there were “related posts” recommendations — plugins that enabled marketers to offer users relevant, personalized content based on content they consumed on your site. Now, the entire customer journey can be personalized, as algorithms learn the behavior of users and suggest content based on their interests. Blueshift and Oracle Responsys are two services that help you create targeted, engaging customer experiences at every stage of your funnel.
2. Predictive lead scoring. Predictive lead scoring uses a machine learning algorithm to predict how qualified your leads are. It analyzes the data around closed/won leads, identifies patterns and relationships and then drops them in different buckets for your marketing and sales teams. There are numerous predictive lead scoring services out there including HubSpot, Marketo, Pardot and Infer.
3. Geofencing. Proximity marketing is not new for those in the B2C space — but machine learning is taking it up a level. More advanced applications take a user’s location data and apply it to other important data such as shopping history and demographic and geographic details, enabling marketers to zero in on highly targeted people. This Acquisio post gives a more detailed explanation about geofencing.
4. Bots for customer service. If you’re not taking advantage of chatbots and automated self-service technologies, you should start. Let them handle the simple questions, resolve customer service inquiries before they become a full-blown issue and free up your customer success team. Machine learning chatbots are able to to discern context and carry on sophisticated conversations. You can build them in common messaging platforms like Facebook Messenger and Slack or on your own site.
From the moment a person learns about a company to the point when they decide to make a purchase, personalization at every stage is critical. Using machine learning you’ll have a more immediate, precise and intelligent method for engaging with your customer compared to a traditional approach that casts too wide a net — even with the best intentions by a marketing team.
The rise of machines doesn’t mean the demise of the CMO. Quite the opposite is true for marketers who take advantage of this technology. Digital Summit events have workshops in machine learning — and a host of other important digital marketing topics, including content, search, social, email, UX and design. Don’t miss these premier events that bring together the top marketing experts and influencers with amazing keynotes, workshops and networking opportunities. Visit Digital Summit to learn more and register for an upcoming conference.
Yet only 43 percent said their marketing department is an early adopter of new technologies like machine learning. A big part of this hesitation comes from not knowing what these technologies can offer or how to take advantage of the opportunities they present.
Here are a few key aspects of machine learning technologies you need to know and four specific ways to leverage them.
What is machine learning?
Machine learning algorithms have been around for a while, but their application to big data in modern marketing is the reason for the recent commotion.
The SAS Institute defines machine learning as:
“A method of data analysis that automates analytical model building. It is a branch of artificial intelligence based on the idea that machines should be able to learn and adapt through experience.”
Any industry working with substantial amounts of data, from healthcare to financial institutions, is starting to appreciate the value of machine learning technology and they are investing in it. Marketing teams are catching on too. Gleaning insights in real time is an attractive perk when it comes to understanding behaviors, which in turn enhances experiences — which might result in a conversion or sale.
Remember when personalization was such a buzzword? Now machine learning empowers marketers with insights that help them gain a deeper connection with their target audience. The technology confirms what customers want, then continues learning, becoming smarter over time about those expectations.
We’ve heard about delivering the right content to the right person at the right time — well, this is it. Machine learning serves high-definition information on a platter for a more strategic decision-making process.
Instead of making an educated guess about what kind of content your audience is interested in, insights will steer your entire strategy. This isn’t some futuristic concept or wishful thinking; machine learning is personalization in action. And, it’s being used by CMOs who are willing to leap into this new territory — and it’s giving them the upper hand.
4 machine learning applications you can use right now
Machine learning algorithms gather customer profiles and behaviors across multiple channels so you can decide the best course of action. Think about how that can elevate your marketing efforts. Opportunities include:
1. Personalized customer journeys. First there were “related posts” recommendations — plugins that enabled marketers to offer users relevant, personalized content based on content they consumed on your site. Now, the entire customer journey can be personalized, as algorithms learn the behavior of users and suggest content based on their interests. Blueshift and Oracle Responsys are two services that help you create targeted, engaging customer experiences at every stage of your funnel.
2. Predictive lead scoring. Predictive lead scoring uses a machine learning algorithm to predict how qualified your leads are. It analyzes the data around closed/won leads, identifies patterns and relationships and then drops them in different buckets for your marketing and sales teams. There are numerous predictive lead scoring services out there including HubSpot, Marketo, Pardot and Infer.
3. Geofencing. Proximity marketing is not new for those in the B2C space — but machine learning is taking it up a level. More advanced applications take a user’s location data and apply it to other important data such as shopping history and demographic and geographic details, enabling marketers to zero in on highly targeted people. This Acquisio post gives a more detailed explanation about geofencing.
4. Bots for customer service. If you’re not taking advantage of chatbots and automated self-service technologies, you should start. Let them handle the simple questions, resolve customer service inquiries before they become a full-blown issue and free up your customer success team. Machine learning chatbots are able to to discern context and carry on sophisticated conversations. You can build them in common messaging platforms like Facebook Messenger and Slack or on your own site.
From the moment a person learns about a company to the point when they decide to make a purchase, personalization at every stage is critical. Using machine learning you’ll have a more immediate, precise and intelligent method for engaging with your customer compared to a traditional approach that casts too wide a net — even with the best intentions by a marketing team.
Learn more about machine learning at Digital Summit
The rise of machines doesn’t mean the demise of the CMO. Quite the opposite is true for marketers who take advantage of this technology. Digital Summit events have workshops in machine learning — and a host of other important digital marketing topics, including content, search, social, email, UX and design. Don’t miss these premier events that bring together the top marketing experts and influencers with amazing keynotes, workshops and networking opportunities. Visit Digital Summit to learn more and register for an upcoming conference.