Albert is an AI-powered platform that automatically runs and optimizes paid advertising campaigns.
The solution works with advertising across Google, Facebook, Instagram, YouTube, and Bing. And it's used by brands and agencies to automatically allocate budget, improve campaign performance, and discover new opportunities.
We talked to Albert CEO and founder Or Shani to learn more about this solution for AI in advertising.
In a single sentence or statement, describe your company.
Albert is an artificial intelligence platform that plugs into a digital advertiser's existing tech stack and operates it across paid search, social, and programmatic.
How does your company use artificial intelligence in its products?
Albert is an autonomous AI that is the result of eight years of development by a team of 80 data scientists and engineers. It is a collection of over 200 intelligent skills working in concert that uses various machine capabilities learning to process and analyze audience and tactic data at scale to autonomously allocate, optimize and evolve paid digital campaigns.
What are the primary marketing use cases for your AI-powered solutions?
We work with brands as well as agencies. Both use Albert to accelerate their digital transformation.
One global client took control of their digital advertising moving from outsourced strategy and campaign management to bring it in-house with Albert. Across six geographies, their in-house teams are managing Albert on a self-service basis and have full clarity into how their budgets are being invested, greater insights into their customers and prospects, and improved confidence in their marketing strategy. With the machine taking on digital execution, their focus has shifted from rote mechanical tasks to thinking deeply about their customers and their brand.
Conversely, a major European media agency found that by complementing existing teams with Albert, they were able to quickly scale to take on new business, saving up to 50% of time spent on manual work, avoiding the common challenge of overburdening staff, and then dealing with increased attrition or productivity issues.
What makes your AI-powered solution smarter than traditional approaches and products?
Albert acts autonomously, taking an always-on approach to optimization across all available levers in each channel. The system tests various combinations to identify optimal conditions for performance, constantly iterating. Albert then informs the user of meaningful optimizations made via the "Insights" feed in the dashboard (UI). Albert will also make proactive recommendations or make requests for inputs that fall outside Albert's AI guardrails, like raising budgets, introducing new creative elements, or expanding channels.
Albert thrives on experimentation and expands activity in a controlled test and learn approach. The system's level of exploration or aggressiveness can be guided by Albert's human colleagues. For example, an increase of budget provided to Albert for a campaign with the same goal, or more cushion on a goal with the same budget, will allow Albert room to test and learn—adding new keywords to search, new topics/domains in programmatic, or audience exploration in social, as well as adding new targeting opportunities.
For example, early in the relationship with a leading telecom, Albert was able to beat their CPA benchmark by shifting budget allocation, even as the system continued to improve performance in individual channels. Allocation was shifted to 29% social, 21% display, 50% search from 16% social, 28% display, 55% search. The result? A drop in CPA from $72 to $65. This is why autonomous management of cross channel campaigns is so powerful. No human managed, manual process could have achieved this outcome in a matter of weeks.
The typical approach of setting channel budgets up front based on past performance and optimizing towards channel-specific metrics as proxies for business outcomes is an inexact science. Albert manages budgets flexibly in response to market conditions and optimizes against one business goal, resulting in better allocation of budget against channels, audiences, tactics. The post script to this example is that over time Albert matched the CPA in all channels as well, driving ultimate CPA even lower.
Are there any minimum requirements for marketers to get value out of your AI-powered technology? (e.g. data, list size, etc.)
Albert meets marketers where they are in their data journey. The initial strategy is based on the available historical campaign data, website analytics, and audience data. Albert utilizes historical campaign data to model an initial campaign structure, as historical data gives Albert a baseline of success to build from.
Once media is live, Albert will begin to evolve the structure and tactics of the campaign based on live data, optimizing the system's approach to set new benchmarks for performance. Albert can also take into consideration other sources of data like offline sales, which is critical for CPG brands, for instance.
The system also provides levers to adjust the "aggressiveness" of optimizations. Within marketers' provided parameters, Albert will decide which audiences and tactics at what thresholds are required to hit the KPI.
Who are your ideal customers in terms of company size and industries?
Albert supports B2C brands at Fortune 500 companies in CPG/FMCG, retail/ecommerce, telecom and financial services.
What do you see as the limitations of artificial intelligence as it exists today?
We believe the ideal applications of artificial intelligence today are those that tackle the challenges introduced by big data (across industries and applications) and make sense of it all, as well as those that automate recurring, manual tasks—but on a scale that can’t be achieved by humans.
Where we see AI as having limitations are in the obvious areas: strategy, emotions, creative thinking. Humans are unique in their ability to feel in a very complex way and translate those feelings into emotional connections. This is why the most powerful solutions are those that partner humans and machines to drive exponentially better outcomes.
What do you see as the future potential of AI in marketing?
We are on the cusp of a rapid adoption curve for autonomous AI tech. By 2025, we see a future where all paid digital campaigns are managed by marketers collaborating with autonomous AI that runs with campaign creation, execution and evolution at their direction.
This vision holds the promise of:
- Digital advertising teams shifting their conversation from today's basic "is my media working" to more robust, strategy-laden dialogue around "is my marketing working?"
- Once a marketing team can leverage an autonomous AI to test every idea they have quicker than they have ever had the ability to before, they will learn faster than ever possible, and make entirely new discoveries about customers, products and brand.
- What's more, with the ability to learn simultaneously from media and creative, this new pace of insights and learning hold the promise of informing not just marketing, but the entire enterprise.
Any other thoughts on AI in marketing, or advice for marketers who are just starting with AI?
Marketers should beware of "AI-washing." Ask vendors if the AI just provides recommendations and surfaces insights or does it autonomously take action on the marketer’s behalf?
When comparing answers, ask yourself if you want to do some or all of the execution manually. What level of automation is sufficient to solve your challenges? Helping with decisioning and actually doing decisioning are two very different use cases.
Paul Roetzer is founder and CEO of Marketing AI Institute. He is the author of Marketing Artificial Intelligence (Matt Holt Books, 2022) The Marketing Performance Blueprint (Wiley, 2014) and The Marketing Agency Blueprint (Wiley, 2012); and creator of the Marketing AI Conference (MAICON).