Strong Use Cases Emerging For AI-Powered Marketing Applications
- Written by Kim Zimmermann
- Published in Industry Insights
The addition of artificial intelligence (AI) to the traditional marketing ecosystem can help businesses leverage real-time customer interactions to automate routine tasks and personalize responses to convert more leads. Many tools in the marketing tech stack — including marketing automation, customer relationship management systems and content management platforms — incorporate AI to some degree already, and it is becoming more pervasive.
AI is already being applied by many B2B marketers with substantial success. According to the most recent Gartner Marketing Technology Survey, marketing leaders rated AI as their first choice of emerging technologies that will have the greatest positive impact on marketing over the next five years. The growing use of automated chatbots, text and other tailored marketing messages all require a strong AI foundation to provide a more human-like interaction, the report noted.
Of those using AI, most of the current applications are at the top of the sales/marketing funnel, according to The State of Artificial Intelligence in B2B Marketing report. Nearly three-quarters (72%) use AI to improve the reach or efficiency of digital advertising, and 72% deploy AI to identify the right accounts or individuals to target.
“AI is simultaneously overhyped and underappreciated,” said Scott Brinker, Editor of ChiefMartech.com. “We are nowhere near having machines do all of the marketing for us, but there are very real and practical use cases for AI that are delivering real value and improving interactions.”
This feature will explore how modern marketing teams are incorporating AI into their tech stacks to deliver more relevant and contextual communication with prospects and customers. In addition, learn how companies such as Panasonic, Iron Mountain and Sutter Shared Services are leveraging AI-powered applications to uncover buyer intent, personalize interactions and perform outreach to prospects.
How AI Supports More Precise Personalization
Among the benefits of AI-powered marketing tools is the ability to analyze data and predict behaviors to determine the most effective next steps and appropriate offers. Does the prospect prefer text-based content such as white papers, or do they tend to open more visual content such as a video or infographic? Are they still at the exploration stage where they are looking for more educational content, or are they at the decision point where they are comparing features and functions of various systems? Is there a time of day when they are more likely to open emails?
Many equate personalization with recommendations for content to read or view next. Previously, content recommendations were based on the paths of customers with similar roles at companies with comparable industries and sizes — think Amazon and Netflix suggestions on what to buy, watch or read based on the actions of others with similar interests. AI helps marketers present more relevant recommendations by leveraging data on an individual’s content preferences and past interactions.
Here are examples of companies using AI to boost personalization:
- Using the Folloze personalization platform, AutoDesk was able to deliver scalable, personalized content to named and owned accounts in a few clicks and accelerated completed sales by five months.
- Iron Mountain experienced a 78% lift in page views and a 36% lift in company engagement with Demandbase, according to a report.
- Nuxeo leveraged personalization tools to improve engagement with its blog, which resulted in an 11% lift in demo requests over 30 days.
With the ability to process more data in real-time, AI makes personalization more automated and accurate.
“The promise of AI is personalization at scale,” Brinker said. “The customer journey is not always linear, and it is not the same journey for everyone, even those with similar behaviors and personas.”
As the number of members on the buying committee increases, the need for a more personalized buyer’s journey goes up, Jon Russo, Founder and CMO of B2B Fusion Group, noted. “You need AI to support the level of personalization that is required when you have multiple people with different roles and areas of focus within the same organization. You can’t lump them all together.”
Without AI, personalization requires manual data wrangling and analysis. “Before AI, personalization involved a lot of manual work,” said Gartner Analyst Mike McGuire.
While companies are already using AI to present more effective content offerings based on individual interests, a next-generation use of AI is to analyze patterns to uncover new customer segments, McGuire said. “What marketers are banking on is discovering entirely new groups of customers, as AI tools can help identify new patterns of behavior that were not recognized previously because it was too difficult and time-consuming to sift through the data manually.”
AI can also help identify gaps in a company’s content database, experts noted. Data analytics can examine the content people are searching for and cannot find, pointing to areas where more content is needed.
Content performance is another area where AI can be beneficial. If prospects open a video and consistently view a segment, for example, that can be an indicator that the topic is of high interest and can be used to drive sales conversations. This data is difficult, if not impossible, to gather in a manual environment and it is often shared too late to have an impact on interactions with prospects and customers.
Achieving More Effective Account Targeting & Ad Spending With AI
Extending AI’s capabilities beyond personalization is key to moving the needle, experts noted. AI can enhance lead scoring, as well as audience and account selection, for more effective segmenting and targeting.
AI has helped Panasonic hone its ABM marketing strategy, according to Susan Campbell, Global Marketing Manager, in a case study. “Prior to AI-powered ABM, we were using the limited intent and insight information that we had, but we really didn’t have a tool that tied things together for us,” Campbell said in a statement.
AI is also automating some of the pre-sales work — such as reaching out to new prospects with content and scheduling appointments for demonstrations — that has typically been done by sales assistants.
Sutter Shared Services (S3), a provider of back-end administrative services to physician groups and health systems, uses Conversica’s Sales AI Assistant to proactively initiate contact with prospects who previously expressed interest but went untouched or unresponsive. According to a published case study, the B2B company has experienced a 25% increase in engagement with the tool designed to automate tasks surrounding outreach to prospects and customers through email or text. The system is intended to initiate contact, interpret replies and send a tailored response that replicates human interaction.
In addition to better personalization and automating some of the tasks around prospecting, AI also has the potential to boost the effectiveness of programmatic advertising. “A LinkedIn ad, for example, is an expensive proposition and companies with heavy digital spend can really save some serious money by applying AI to their programmatic advertising,” said Russo. “They can not only do A/B testing to determine the most effective ad for a campaign, but they can use data to get smarter with the next campaign.”
Best Practices For Applying AI
With AI-powered solutions throughout the marketing stack, the key is ensuring that your data is up to the task.
“AI is best when you have clean data and plenty of it available,” Brinker said. “It also has to be taken out of data silos in the organization, because if you only have data on part of a customer’s interaction with your organization, you lose the context of the entire customer journey.”
Gartner’s McGuire noted that there is a lot of prep work to getting data AI-ready, but the effort is worthwhile. “Getting data structured and tagged properly doesn’t happen overnight, but it is critical to the success of AI.”
Some additional tips on implementing AI from Gartner:
- Define clear use cases that leverage AI to personalize marketing engagements;
- Align AI efforts to business objectives, prioritizing short- and long-term goals of pilot programs; and
- Test AI or machine learning content tools on your established, dominant channels such as email and mobile messaging. Use tests to help marketing teams learn what’s possible with AI personalization.
AI is driving much of the innovation in marketing, from automating outreach to prospects to customizing the buyer’s journey. Marketers who are using AI-powered tools are seeing an uptick in engagement due to more relevant and personalized interactions based on a customer’s needs, preferences and point in the buyer’s journey.
Next-generation AI will continue to help marketers to target prospects more precisely with the right messaging at the right time and provide a human touch to automated interactions going forward.