Applications Of Artificial Intelligence In B2B Marketing


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The onset of the fourth Industrial Revolution, with its technological innovations and advancement, has forced industries and businesses to review and reinvent their processes to avoid becoming obsolete. Although this has caused massive disruption, the resulting opportunities (especially those created by the software and tech industry) are well worth it.

One of the more recent developments is the increased use of AI in B2B marketing applications. Although digital marketers have already begun exploring the benefits of machine-learning algorithms, the opportunities for implementing AI in B2B marketing are yet to be exhausted. Some of the areas that still need to be explored include blockchain, predictive analysis, personalization, propensity modeling and lead scoring. Let’s take a look at how AI can be applied in these areas.


One of the more recent standards being developed for use in the B2B industry is the blockchain. Most supply chains involve the buyer, seller and a logistics provider; the information flow between these entities is usually point to point or one way through XML-based or EDI messages. Each party maintains its own view of the information flow, and through the use of mechanisms such as acknowledgment documents, they attempt to synchronize information flow within the supply chain. However, true synchronization does not exist since there are complex rules for all kinds of reconciliation and exception handling owing to the flow of information being one way or point to point.

Blockchain for B2B networks can be viewed as a form of distributed ledger that delivers an auditable and secure record of events for improved visibility of the information flow within supply chain networks. It allows multiple parties to view information, which is stored in a decentrally owned and immutable data store. Since individual parties aren’t able to doctor the data, multiple parties coordinating on a digital marketing campaign can reconcile third-party data obtained from the blockchain with the first-party data that is being tracked.

Blockchain simultaneously addresses data privacy compliance, data ownership, data security and attribution. Although the blockchain marketing industry is still in its infancy, it is poised to disrupt the status quo. When fully developed and integrated within existing systems, blockchain will enable marketers to directly reach out to clients without the need for agencies and go-betweens.



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. In the past, marketers had to tailor their marketing efforts to appeal to a specific sector or demographic, but with the advent of AI technology, they can now target business entities on a case-by-case basis. This enables increased personalization of the products and services being offered, precipitating an increase in conversion rates. It also improves customer experience because prospective clients are shown marketing messages that have been designed to provide viable solutions to their specific needs. With AI technologies driving better accuracy in sales intelligence, there will be an increase in personalized marketing for both B2B and B2C.

Lead Scoring

Aside from the cultivation of existing clients, businesses that want to retain and improve their position in the marketplace must constantly generate and score new leads. When integrated properly, machine-learning algorithms allow marketers to pinpoint leads with the greatest likelihood of conversion. In this area, AI can be likened to a precision search tool that goes through raw information (provided by big data) to find prospective leads that convert well. This is particularly beneficial for teams that are primarily concerned with account-based marketing because it helps them to identify hot prospects and prioritize the most promising accounts.

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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. Studies by the Aberdeen Group show that a majority of businesses (61% of research respondents) indicate lead scoring as the major reason for investing in artificial intelligence. Although the technology for AI-driven lead generation and scoring is already in place, its integration with existing business solutions remains a major point of concern.

Propensity Modeling

Propensity modeling refers to the use of machine-based learning algorithms to process copious amounts of historical data, enabling the creation of a propensity model that can make accurate predictions on things like contact information and lead elements. It also enables the automation of manual tasks such as lead scoring as well as app and web personalization.

Predictive Analytics

The application of propensity modeling in areas relating to the prediction of behavioral patterns of existing and potential customers is referred to as predictive analysis. When paired with propensity modeling, predictive analytics give accurate estimates on the probability of achieving a particular outcome. It gives an accurate prediction of the range of prices where customers are most likely to convert, the kind of customers that will make repeat purchases and more.

It must be noted that AI technologies (particularly in propensity modeling and predictive analysis) can only be effective if they are fed reliable and accurate data. Dirty data, incomplete entries on databases or data with a high degree of randomness may result in AI algorithms yielding incorrect results. However, the increased adoption of AI in the digital marketing industry will precipitate more emphasis on database management best practices in marketing organizations.


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