B2C companies use machine learning for a wide range of use cases ranging from personalized recommendations, intelligent chatbots and hyperlocal advertising, just to name a few. While the number of B2C companies using machine learning is skyrocketing, adoption by B2B companies has yet to take off.
It’s time for B2B companies to get their game on. Here are some of the ways machine learning can make a significant difference for B2B companies.
1.) Augments Account-Based Marketing
Lead generation is fundamental to all business growth. For B2B companies, ABM tactics allow marketing teams to focus on creating highly targeted campaigns that address the specific needs of each set of accounts. Because ABM focuses on the best-fitting accounts, it’s an ideal approach for both new and existing customers. ABM allows B2B companies to fill the pipeline with qualified prospects while also engaging and retaining current customers.
For many B2B companies, much of their ABM and lead data is stored in CRM software. Marketing opportunities are missed when leads come into the CRM but are not tagged to the accounts they belong to. However, machine learning allows gaps in customer information to be filled automatically. Machine learning can append missing customer fields and tags such as account ID, account name, phone number, email and company address.
Machine learning also increases the depth of personalization required for ABM by unifying customer information so that it is on an individual customer ID or account-record level. Data unification involves matching data by leveraging AI, so that match rates improve as more data is processed. Improving match rates along with automatic data enrichment enables companies to build a complete view of each customer based on all available data sources.
2.) Delivers The Right B2B Content, At The Right Time
Machine learning allows B2B companies to generate leads from website content without requiring visitors to complete registration forms. For example, a B2B company could use machine learning to categorize every piece of website content, including product pages, E-books, instructional videos and blog pages. Website visitor data could then be analyzed so that content would be personalized and presented to potential buyers at the right time automatically. B2B customers consume content based on not only their buying needs but also the point they are at in the buying journey. Content could be presented at specific buyer interaction points, and that content could be customized automatically to match the needs of the customer in real-time.
3.) Enables Hyper-Segmentation To Better Understand B2B Customers
One of the essential tasks in marketing is segmentation — grouping customers based on specific qualities, such as behavioral patterns, income level and geographic location. Segmentation is crucial to understanding customers and allows marketers to personalize customer outreach effectively. For B2B companies, effective outreach depends on understanding the needs of each customer at all points of their buying journey — product research, product discovery and product purchase. B2B customers require 1:1 personalization that is adaptive and contextual — every piece of customer outreach must be tailored to a specific customer context and presented at the right time. To achieve such a granular level of personalization requires hyper-segmentation — grouping and slicing every bit of available customer data until you reach a “segment of one.”
Marketers are not capable of researching and analyzing the massive amounts of customer information required for hyper-segmentation. However, with machine learning, B2B companies can automate much of the segmentation process. Hyper-segmentation driven by machine learning allows B2B companies to quickly identify and segment key individuals who are most likely to convert based on a narrow set of attributes. And machine learning can be used to personalize many types of customer outreach automatically to match the attributes of segmented groups.
4.) Enables High-Quality Omnichannel B2B Relationships
The concept of omnichannel marketing is relatively new to B2B companies because they traditionally have mostly offline-only customers. However, offline customers are often influenced by online information and as many B2B customers are also B2C customers. Most are accustomed to personalized, seamless marketing and buying experiences across devices. Today, an omnichannel marketing strategy is crucial for both B2C and B2B companies.
B2B companies can leverage the same marketing channels as B2C, including corporate websites, digital advertising, mobile apps, email and live salespeople. The best channels to reach B2B customers depend on whether those customers are making a purchase for the first time or repeating a purchase. A customer who is making a repeat purchase of the same product might want to make that purchase online using a laptop or a smartphone. A customer who is researching a new product may want to speak with a salesperson or ask questions about the product via an intelligent chatbot.
B2B companies can use machine learning to leverage historical and real-time customer data to gain a comprehensive picture of each customer and determine precisely where they are in their buying journey. This analysis can be used to create personalized marketing campaigns that engage with customers at the right time on their preferred channels. Machine learning allows B2B companies to reach out to customers wherever they are and when they need that personalized interaction the most.
Machine Learning Matters To Every Company
B2B companies can benefit from machine learning in numerous ways — generate more leads, gain a better understanding of customers, establish high-quality omnichannel relationships, and so much more.
Abhi Yadav is CEO of Zylotech, a self-learning customer analytics and data platform that he co-founded and spun out of MIT. Renowned customer analytics and data technologist, as well as industry entrepreneur, Abhi Co-Founded Prognosys e-Services and N-CARE.