Does have an AI answer to the demand generation challenge?

Profile picture for user barb.mosher By Barb Mosher Zinck April 26, 2018
Too often, ABM and demand generation are not used well together. But is determined to change that, with an AI-powered ABM tool designed to aid marketers by automating buyer profiling, lead generation and more. Here's a closer look.


Too many marketing tools, not enough people to work them and no one with the time or patience to connect all the dots, run all the variants and figure out what’s working for accounts, contacts or leads across the board. This challenge shouldn’t just sound familiar; you probably feel the pain on a daily basis. thinks it has the answer for mid-sized companies and it’s all about the AI.

Building from what you already have is the brainchild of Gil Allouche, an “A.I., and robotics software engineer turned B2B growth marketer.” Allouche spent seven years running demand generation and growth marketing working with some of the best marketing software. But he had to connect them himself and felt there needed to be a better answer for B2B marketers. is a closed looped account-based marketing solution. It connects to your existing marketing stack and provides three things: insights into current accounts and personas, campaign execution, and audience development.

It works by first connecting to your CRM, pulling in accounts, contacts, and opportunities. Next, it enriches contacts and accounts with historical data and ranks opportunities. It then creates buyer profiles using existing account and opportunity data and additional third-party data.

With profiles created, metadata then determines the best accounts and profiles to go after and generates “lookalike” audiences.

This is where the AI comes in, what Allouche called the AI Operator. Metadata initially focused on paid media, determining the optimal mix of ads across social networks like Twitter, LinkedIn and Facebook and DSPs (demand-side platforms), running hundreds of multivariate tests and automatically adjusting ad bids, placement and so on using the AI operator. Within 6-9 months they plan to add email.


The Metadata approach (used with express permission by

Allouche said that 90% of their customers use Salesforce, but they also work with clients using Marketo and Hubspot.

AI results aren’t automatically perfect, but they’re better than doing it all manually

He also said that while everything is automated using the AI Operator, clients can customize how it works if they want. They can see the experiences and change them. However, only about 10% actually go deeper. Most marketers aren’t technical, and they don’t want to know the finite details of how the solution works, they want reports and leads, Allouche noted. But he did acknowledge some marketers are more involved and want to see it all.

The important thing to understand, Allouche pointed out, is that deep learning on ads takes time and patience. The more data you have, the better your tests will be and the better the right ads on the right networks will be. I think some people think the results will be perfect from the beginning and get frustrated when they don’t see the results they expect. But imagine trying to run hundreds of tests to find the right mix manually - that requires true patience.

The relationship between demand gen and account-based marketing

One thing I like about Metadata is that it’s not a pure ABM solution. You do define personas and lookalikes from existing accounts and opportunities, but you can use this information to simply put out more targeted ads to personas in general (demand generation).

If you think about it, demand generation and ABM - go hand in hand. ABM is about targeting specific accounts through personalized email and targeted advertising, and so is demand generation (although demand gen uses more channels and tactics as well). Even with demand generation, you have to be much more targeted to your audience than ever before. Consumers are getting smarter, and they demand better experiences; if your marketing doesn’t consider context and information you can get on the visitor (demographics, location, firmographics and so on), then your messages aren’t likely to reach the right people.

I came across this really good description of the difference between demand generation and account-based marketing:

Think of each strategy as a bit like fishing. Demand Generation is like a large net that allows you the potential to catch many fish. Some fish are too small and irrelevant for the catch and will escape the net. Sometimes you might catch something unusual or large. But if you do not cast a wide net, you will never know what is out there.

ABM on the other hand is like spear fishing. You know what you want, even if it is usually larger than what you can catch in your net. To ensure that you have a better chance of success, you do your research and bring in the most relevant equipment available to help you succeed.

When used together, you will reduce the chance of coming back to shore empty handed.

My take

The two approaches may deliver somewhat in the channels or tools used, but we slowly see those come together as well, as seen with

Both also require a close alignment between marketing and sales - a "smarketing" team as Sangram Vajre, CMO of Terminus, calls it. The Sales team helps marketing understand what prospects and customers are looking for, so they can create the best content. Marketing works hard to find the right prospects and bring them in, nurturing them through great content until they are ready for Sales. It’s important the two teams do not operate in silos but work closely to ensure the best understanding of the market and customers and how to reach them.

Tools like and Albert and Action IQ go a long way to help with improving targeted advertising and email, supporting both demand generation and ABM. These are tools every marketer should be looking at for their martech stack.