Source – cmswire.com
Many marketers have taken the AI plunge. In fact, according to a survey by Salesforce, marketers expect AI will have a substantial impact on their business in the next five years in the areas of hyper-personalization of content (61 percent), dynamic landing pages and websites (61 percent) and delivering the right message, on the right channel at the right time (61 percent). They also feel it will help increase marketers’ productivity (59 percent), campaign analytics (59 percent) and digital asset management (59 percent).However, analysts and experts warn, to crawl before you walk. Or in this case, know your data before you implement AI.
Data is a Must
Marketers’ ability to successfully inject Artificial Intelligence (AI) into their marketing programs depends heavily on their organization’s data and analytics maturity, according to Martha Mathers, practice leader for marketing and marketing technology at Gartner. “It’s pretty critical that you look at the data that you have,” Mathers told CMSWire in an interview about AI use cases for marketing. “And do you know enough about your customers to actually be able to use the tool? We can get so distracted by that next shiny object that we don’t think about the fundamentals. A lot of the marketers we talk to today are still early in their data and analytics maturity.”
So just how are marketers leveraging AI into their programs? Experts shared with CMSWire the latest trends for AI in marketing in the areas such as content, lead scoring and customer profiling.
AI Boosts Content Searchability
Mark Gross, president of Data Conversion Laboratory, said organizations are leveraging AI to better search for relevant content. For instance, if a marketer wants to discover material in a scientific journal without the manual headache, they can turn to AI engines that help surface the relevant metadata and content. “I think you have to be very specific and you have to know what problem you’re trying to solve,” Gross said of leveraging AI for these types of searches. “And you have to have enough examples that you can teach the machine literally what to do.”
The Data Conversion Laboratory released a survey last month that backed the struggle with content management. It found that most organizations (64.6 percent) cited their search capabilities needing improvement as their biggest shortcoming for content management. Another 48.6 percent cited another content shortcoming, “We have so much content that customers cannot find the correct information to help them be successful.”
AI for Personalization and Volume Gains
Gartner’s Mathers sees content production and personalization as big growth areas for the use of AI in marketing — companies pursuing personalization and trying to target and tailor messaging. He cited a company in the medical space that essentially built a capability to personalize content using AI. “They’ll take a core asset and use AI to actually accomplish some of the tailoring. AI can boost your volumes pretty quickly. Our clients have gotten excited about the ability to produce content at much higher clips than they would be if they were just relying on their content marketing team,” Mathers said.
Marketers Leveraging AI for Offer Design
Brands always want to be ahead of the game on next best offers, and AI is helping, according to Mathers, who called it “offer design.” This area, Mathers shared, has a lot of people in the experimentation phase. “We see companies in the hospitality space or telecommunication space doing some pretty cool things here. They’re suggesting different offers or next best offers.”
AI’s helping marketers create these best offers for a certain segment of their customers. That, according to Mathers, is another area where he is seeing ongoing focus and innovation, as people are able to tie real-time results and a more predictive ability to send that follow-up message.
AI’s Impact on B2B Buying Habits, Predictive Lead Scoring
Chandar Pattabhiram, chief marketing officer of Coupa, said his team leverages machine learning technologies for everything from predictive lead scoring and customer pulse across social channels to determine propensity to buy based on buyer behavior to helping orchestrate multi-channel campaigns. They also use it for predictive content that automatically offers the right asset to the right buyer at the right stage of the buying cycle. “The impact of machine learning in our marketing strategy is substantial,” Pattabhiram said. “Because we now have the ability to better understand our buyer based on real data, we are able to develop a stronger relationship and open dialogue with sales. We can focus on being more creative in our work while leveraging knowledge gathered by our technology to point us in the right direction, so we don’t waste time on projects that are destined to fail before they begin. It enables all of us to be true data-driven marketers.”
Building an ‘Ideal Customer Profile’
One of Pattabhiram’s focus today is building and refining his team’s “Ideal Customer Profile.” They leverage predictive modeling for this to enable sales and marketing to collectively target the same accounts in their Account-Based Marketing (ABM) strategies. “We call this the ‘Allbound’ approach vs. the traditional inbound and outbound approaches to demand generation,” Pattabhiram said. “We also are focused on automating the delivery of the right content to the right buyer at the right time.”
Through the “Ideal Customer project,” Coupa marketers identify the accounts with the highest propensity to buy across. For example, 5 percent of Coupa accounts may have a 6X propensity to buy based on multivariate factors that machine learning algorithms use including location, industry, average sales price, win rates, sales cycle length, buyer personas and more. “This project has also enabled us to identify sub-verticals within target industries,” Pattabhiram said.
“We have moved to an ‘Allbound’ model where sales and marketing collectively target these ICP accounts. We will also use AI-based technologies to determine customer pulse and the best time to upsell and cross sell products at the right stage of the customer journey.”
Adding the Human Touch to AI
Marketers like Pattabhiram that are fusing AI into their marketing processes are also ensuring it comes with its share of human, emotion-based logic. Without that human touch, it can be dangerous for marketers, Pattabhiram said, because you could drown in too much data. “Sometimes the efficiencies created or data identified doesn’t tell the whole story,” Pattabhiram said. “You still need to tell technology what to do and determine how to optimize based on results. Some of this can be automated, but not all of it.”
Marketing, he added, is still a storytelling function. Although machines can automate processes and analyze large data sets, they cannot create great stories and true “Authentic Interactions.” Pattabhiram called this “the other AI.” “Whether you are a B2C or B2B marketer, your buyer is still human, not a machine, and humans crave authentic understanding of their pain points and aspirations,” he said. “As modern marketers, we need to leverage machine learning as superhuman extensions of ourselves, but never forget to think and analyze what the data is telling us both from a quantitative and qualitative perspective, and then determine the right stories to tell to capture the emotional side of our buyers.”