Customer Data & Analytics Blog

Why machine learning matters to B2B companies

Abhi Yadav | 2 minute read

Zylotech_Why Machine Learning Matters to B2B Companies_050219_headerWhile the number of B2C companies using machine learning is skyrocketing, the adoption of machine learning by B2B companies lags far behind in comparison. This post highlights two ways machine learning makes a difference for B2B companies.

Augments Account-Based Marketing (ABM)

Lead generation is fundamental to business growth, whether the business is B2C or B2B. 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 is 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 leads data is stored in customer relationship management (CRM) software. Marketing opportunities are missed when leads come into the CRM Zylotech_Why Machine Learning Matters to B2B Companies_050219_subbut 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.

Delivers the right B2B content at the right time

Traditionally, marketing to B2B customers involves creating content that captures information. For example, a qualified lead might fill out a form to download an e-book or request a product demo. Gated content is still a popular way to capture leads. But most website visitors are reluctant to fill in a form to see the content, and many won’t. Asking website visitors to fill out a form to request content or a demo is not the most efficient way to generate leads. If a B2B company website has thousands of visitors every month, how many of those visitors are willing to fill out a form? When it comes to content, B2B customers are increasingly expecting the same experience as B2C- easy access to personalized, relevant content from any device.

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.

Machine learning matters to every company

Machine learning matters to every company, whether they are B2C or B2B. And B2B companies can benefit from machine learning in the same ways as B2C companies- better understand customer behavior and buying habits, generate more leads, engage with customers at the right time on the right channel, and so much more.

If you liked this post, check out our recent blog post: IoT data and machine learning: A powerful combination for marketing.


Topics: machine learning