5 Applications for AI in B2B Marketing
Posted March 1, 2021
5 Applications for AI in B2B Marketing
Posted March 1, 2021
AI in B2B marketing breaks new ground practically every day. How is Artificial Intelligence (AI) helping your B2B brand? Some marketers may not even realize the extent to which AI is already in play in their organizations. On April 14, the Anteriad Spring Summit takes a deep, virtual dive into “Accelerating Revenue with Artificial Intelligence.” Register for the event to learn how to bring the power of AI into your business. And in the meantime, read this article for ideas to get you started.
The Fourth Industrial Revolution refers to ongoing automation of traditional manufacturing using modern technology. One of the outcomes of the Fourth Industrial Revolution (aka Industry 4.0) has been greater AI adoption in B2B marketing.
What’s so exciting is that opportunities for implementing AI in B2B marketing have only scratched the surface. Already teams use AI to automate manual tasks such as lead scoring and routing.
Areas actively being explored include:
- Lead scoring
- Propensity modeling
- Predictive analysis
Let’s look closer at some of these. You may spot an idea you can use immediately.
Try These 5 Uses of AI in B2B Marketing
1 – AI for Lead Scoring
Many brands cite lead scoring as a primary driver for AI adoption. As a precision search tool, AI powers through raw big data to find the best possible leads. Machine-learning algorithms can pinpoint leads with high likelihood of conversion. This benefits teams doing account-based marketing, because of the accelerated ability to identify in-market prospects and prioritize promising accounts.
Although the technology for AI-driven lead generation and scoring already exists, the challenge for many has been integration with existing solutions. This has eased as more organizations adopt the marketing cloud model for activation and reporting.
2 – Using AI for B2B Personalization
“Big data isn’t all about gaining insight into the behavioral patterns of existing and potential customers; it also involves the use of these insights to personalize one’s marketing strategies,” according to one Forbes article. Customizing content and engagement is a powerful application of AI in B2B marketing.
In the past, marketers had to manually study and tailor their efforts to appeal to a specific sector or demographic, but with the rise of AI, they can target accounts more specifically and efficiently. With increased personalization comes better conversion rates. It also improves customer experience, because prospects receive content and messages relevant to their needs.
With AI driving accuracy in sales intelligence, there will be an increase in truly personalized marketing. Website personalization will help both content marketers and B2B buyers in-market as they try to connect for answers and solutions.
3 – Predictive Analytics
Predictive analytics uses present or past data (historical data) to anticipate future outcomes. It extracts patterns from big data to predict trends and behavior. This is probably a more familiar use of AI in B2B marketing.
When paired with propensity modeling, predictive analytics estimates the probability of achieving a particular outcome. This might be what is the price range where prospects are most likely to convert, or what kinds of customers will make repeat purchases, for example.
Brian Giese, CEO of Anteriad, has talked about the benefits of real time, fact-based data versus the “crystal ball” predictive analytics model. He takes on the widely held view that predictive analysis is the end-goal of predictive insight.
In an interview, Brian explained, “We enable information you can give to the executive suite about what’s actually happening. Not that we’re going to predict what’s going to happen, but what’s actually happening right now. CEOs want to know that.”
4 – Predictive Modeling
A widely used application of AI in B2B marketing is look-alike and predictive modeling. This is broader than propensity modeling, which deals with likelihood of a customer doing something specific. Predictive modeling uses regression and statistics to predict probability of an outcome, and it can be applied in many ways.
In advanced applications, AI creates models to foresee the probability of an outcome given certain data. In one modeling use case, brands acquire contacts by applying a first-party model to a broad third-party data set.
5 – Propensity Modeling
Propensity modeling is a type of predictive analytics that correlates buyer characteristics with anticipated behaviors. Think of a buyer being “prone” to do or be certain things. Machine-based learning algorithms process voluminous amounts of historical data, so marketers can build propensity models for things such as likelihood of opening an email, enrolling in a loyalty program, or survey participation.
AI Needs A1 Data
“The robust nature of AI allows for the combination of data from multiple sources, as well as the pooling of business intelligence from which actionable insights can be drawn,” according to a Forbes article.
Yet applications of artificial intelligence in B2B marketing (especially propensity modeling and predictive analysis) can only be effective if they’re fed reliable data. Otherwise algorithms may yield incorrect results:
- Dirty data
- Incomplete database entries
- Data with high degree of randomness
The challenge in these and other AI use cases is the quality of your data and how it’s segmented. It’s important to carefully evaluate the uniqueness and quality of a provider’s data and how they might use AI to drive data quality. Check for the database management best practices of any data partners you may want to work with.
AI Shines Light on Your In-Market Buyers
As Giese noted in his interview, “B2B marketers are in the dark,” but technology-driven data brings light to that darkness. Late-breaking, fact-based data allows businesses to reach out to potential customers when it counts most during their decision-making process. It’s a smarter way to guide your B2B marketing campaigns.
Anteriad’s managed services and platforms provide data about in-market buyers. “It involves factual, descriptive analytics with minimal ambiguity,” said Giese. The real-time capabilities of such services let organizations effectively separate the true intent signals from the proverbial noise.
Anteriad Spring Summit: Accelerating Revenue with AI
Learn more about the ways B2B marketers can tap into artificial intelligence at the Anteriad Spring Summit, April 14. Hear AI experts, interactive sessions, AND special guest, American comic and entertainer, Jay Leno! See the agenda.