How to Target B2B Buyers with Meta

Targeting B2B buyers on Facebook is challenging compared to B2C targeting. Unlike personal information, like LinkedIn has, users on Meta often don't provide detailed job-related data. However, leveraging Meta's algorithm can help overcome this hurdle.

Here's a breakdown of how to effectively approach B2B targeting on Meta.

The Problem

Meta offers some targeting options tailored for B2B, such as demographics including industries, job titles, employer targeting, and specific behaviors like Facebook Business Page Admins, Marketing API developers or mix like 'IT decision makers'.

However, these segments often have limited volume or outdated information. Luckily Meta prioritizes algorithmic improvements over relying on data collection. Many advertisers, particularly in B2C, use heavily broad targeting options leaving the Facebook's algorithms to find the right people (tactics like DABA - Dynamic Ads for Broad Audiences). And Advantage Detail Targeting and other features are going this direction.

Which is actually a good news for B2B marketers, given the scarcity of job-related user data. Your focus should be on two key components:

1. Accurate tracking and data flows to Meta, and
2. Creating relevant content tailored to specific category buyers

I. Conversion Data

I cluster tracking into 3 groups:

1. Traffic quality indicators, such as IP address data or exit-intent polls targeting potential buyers.
2. Interest indicators, including visits to key pages or interactions with specific calls-to-action.
3. Main conversions, such as website lead submissions or offline conversions tracking the sales process.

So I know early on how relevant is the targeting and it can be used as a conversion or at least source for lookalike audiences. Detailed insights how I use it and how to properly set conversion tracking will be in upcoming newsletters.

II. Targeting Setup

Submit the inputs to LinkedIn upfront by uploading customer lists, and then create lookalike audiences (LAA) from them to run the ads. The better the relevance, the better, but also the more inputs, the better. Try to follow this Lookalike Audience Source Pyramid.

If you don't have enough users (the minimum is 100 people but I recommend 1000) combine them. Use a customer value list. The higher up in the pyramid, the higher the score it receives. Customers are assigned the highest value, then 10 times lower opportunities, 10x lower to SQL, and 100x less to the MQLs.

Lookalike Stacking

I use the customer value list even with clients who have a very high volume of customers. And stack the lookalike audiences together. There is a common misconception that having a lookalike audience from your top customers is already in the lookalike from all customers. The audiences will be different, but they will have the same size. Here is the visualization.

Once you select the LAA audiences, narrow it down by broad detailed targeting options (usually interests). By broad, I mean at least a size of 150M - just to give an extra nudge to the algorithm but not limit it too much.

After this I try to improve the targeting by the content.

III. Creative is the Targeting

Tailor your ads to resonate with category buyers while remaining irrelevant to other audiences.

How? Focus on highlighting problems your product solves, particularly those relevant to your target audience.

But even without spending any money, Meta can adjust targeting from the creatives. Meta's algorithm analyzes ad copy, visuals, and landing pages to optimize targeting.

Ad Copy

Image that your ad claims "ideal for ecommerce and media companies". Meta will try to targete individuals working in the mentioned industries.

Visual Assets

They claim to be using AI and machine learning-fueled technology optimize your ad visuals for better audience interaction. Many experts, myself included, believe Meta can interpret images beyond text, recognizing the images if not what type of content is on the site then pictograms and symbols for sure. For instance, when Meta recoginze woman in her 40s the system won’t definitely miss this age group.

Landing Page is the Targeting

The same principle applies to landing pages. According to official documentation, Meta does this to calculate ad quality. However, it can glean much more information for targeting purposes. For example, the testimonial section on your landing page showcases the businesses you serve and the job titles of those recommending you, all of which can inform targeting strategies.

Longer ad copy with relevant content and visuals tailored to your audience can enhance targeting effectiveness.

Alternative Targeting Tools

Tools like MetaMatch by Metadata or integrations through tools like Liveramp enable you to target specific companies and even job titles on Meta.

However, be mindful of audience size limitations when exploring these alternatives. It seems that this techonology works on pushing emails. (Even industry leader Metadata does it and they have on average 30% match rate for Facebook - source)

When I tested it, the size was not what I expected so it is not suitable option if you are trying to reach all category buyers.

Summary

In conclusion, leveraging Meta's algorithm for B2B targeting can be a powerful tactic if executed effectively. If you have a robust client database that Meta can learn from, consider giving Meta’s algorithm a try.

While LinkedIn Ads remain a popular choice for B2B advertising, Meta's platform offers unique advantages, particularly when armed with comprehensive conversion data.

Stay tuned for further insights on conversion setup and ideal conversion volumes in upcoming newsletter.