B2B businesses follow the lines of B2C – using ML for a wide range of use cases ranging from intelligent chatbots, personalized recommendations, and hyperlocal advertising
It’s time for B2B firms to jump into the competition. Here are multiple ways ML can make a massive difference for B2B companies:
Lead generation is central to all B2B business’ growth, and ABM strategies allow marketers to focus on creating specialized and better-targeted campaigns, addressing to specific needs of a fixed set of accounts. For many B2B businesses, much of their ABM and leads are stored in the CRM software. Marketing opportunities are usually missed when leads enter the CRM but are not well-tagged. ML, however, allows gaps in the customer information to be filled up automatically. It can also append missing customer fields and various tags such as account name, account ID, phone number, company, and email address.
Taking over repetitive tasks, ML is also helping professionals to focus on increasing the depth of personalization needed for ABM by unifying customer information at an account-record or individual customer ID level. Data unification involves matching and verifying data by leveraging AI, so that match rates are enhanced as more data is processed.
Delivering the Right B2B Content, at the Perfect Time
Considering the content, B2B customers expect the same experience as they enjoy from B2C brands — easy access to relevant and personalized content from any device.
ML has empowered marketers to generate leads from website content directly, without requiring visitors to complete lengthy registration forms. Website visitor data insights ensure content can be personalized and presented to potential buyers at the right time, as a process. B2B customers consume content based on their buying needs and the point they are at in their buying journey. Content gets presented at different buyer interaction points, and that is customized automatically to match the expectations of the customer in real-time.
Segmentation is crucial to marketing — grouping customers based on specific attributes like income level, behavioral patterns, and geographic location, is important. For B2B companies, the effectiveness of outreach depends on comprehending the needs of each customer at each point of their buying journey — including product research discovery and product purchase. The customers require 1:1 personalization that is contextual and adaptive — every piece of customer outreach should be tailored to a specific context and presented at the perfect time. ML helps to achieve such a granular level of personalization for hyper-segmentation — grouping and slicing each bit of available customer data.
Machine learning matters greatly to every business in the modern-day scenario. B2B companies can especially benefit from ML in numerous ways —gaining a better understanding of customers, generating more leads, establishing high-quality omnichannel relationships, and so much more. It’s time for CMOs to empower their marketers with ML to merely the entire marketing funnel and make campaigns more effective.