The History of How I Got Here (Continued)
From 2010 to 2014, I worked on various select projects related to lead generation. The projects included traffic media buying, CLP, CPA, and data deals. They also included offer and funnel system building, consulting contracts, and partnerships. I lived through the merchant account meltdown of 2010; it was brutal. CPA offer owners were losing their credit card processing accounts overnight. I felt the terror in the industry firsthand. When a six-figure client's weekly float cannot pay its bills, it is a serious test for partnerships. A real test of business relationships is not in good times. It's when the funds stop flowing. As a tech entrepreneur, I don’t operate as an independent island in this competitive industry. Almost always, I operate with partners in some shape or form. I’ve had many six and seven-figure projects fall apart. They failed due to some of the wildest outside constraints. Finding good partners is the key to long-term success.
In 2015, I saw a technological change in advertising and marketing with big data. I called it audience engineering. It occurs before you start advertising. It mixes behavioral data with many other data points. When configured, this mix allows for precise targeting and tracking. I started building identity graphs. It was complex. It involved collecting and organizing data about people and entities. Being a data-driven marketing organization at the time required focusing on People-based data.
People-based data refers to datasets that describe individuals. They are often collected from online and offline sources. We use them to create detailed and rich customer profiles. Often, it’s transactional (buyers) verified data. We use this data to understand and target real people. It's better than simply targeting devices or broad audiences. In the battle of fraudulent traffic, it is a severe problem. It's a cat-and-mouse game. Technology gets smarter. This is especially true now that AI seems more human. Bot farms are a massive problem. However, it's hard to fake buyer transactions at scale. This means that if you can identify buyers at scale, you can identify real people. I said my entire career in the lead industry, "Buyers buy more." They can also be identified at scale.
Have you ever considered where they get their audience data?
Think about when you go into an ad platform and set the target audience for your campaigns. You’re buying (renting) the audience you’re targeting from the ad platform. Then, you pay a premium to retarget the same audience within their walled garden. Have you ever considered where they get their audience data? The target audience. Of course, Google and social networks offer audiences within their walled gardens. They have their identity graphs. But have you considered how targeting occurs beyond those walled gardens?
I did. Have you ever noticed that when you buy something online, such as from a Facebook ad, you'll see the same ads targeting you for days, weeks, or even months later? Why? You already bought from that advertiser. Why are they targeting you with the same ad and the same messaging? Don’t they know you already purchased their product? Doesn’t Facebook know? Of course, they know. So why isn’t the technology smart enough to know? Why don’t they exclude you from those same old ads? They could send you newer ads based on an updated profile. The key to smart marketing is smarter data technology. Most companies live in a world of data silos. Their CRM isn’t connected to their ad targeting systems. The same can be said for almost all company data and companies. This was the tip of the iceberg-sized problem I wanted to solve. So, I started building data technology to provide solutions for these problems.
These are the systems I built from 2015-2022
From 2015 to 2018, People-based data solutions rose. This rise came with a flood of VC funding for customer data platforms (CDPs). A Customer Data Platform (CDP) is a data hub. It centralizes and unifies customer data from various sources. This creates a full, lasting, and accessible customer database. It enhances marketing. It personalizes customer experiences. It improves customer engagement across many channels. We chose not to build a CDP. Instead, we built specialized data systems. You can use them alone or with a CDP in a tech stack. Small businesses (SMBs) would find it easier to start with data-driven marketing. They could do it step by step. The reality was that it was hard to move an entire company over to become a data-driven organization. Our clients weren’t the Fortune 100-500. We wanted high-growth clients spending north of $20k-$30k per month on advertising.
HashTargetr: The first data system we built was HashTargetr. Customers placed a line of code, a tracking pixel, on their website. Our pixel would identify high-intent visitors. Visitors who are in the market for a specific product or service. Our pixel-created custom audiences that could be used inside all the major ad platforms. Advertisers could cut their customer acquisition cost (CAC) by 3x or more. They can do this by using our audiences as the seed audience.
We could identify people visiting a website at a rate of 35%-50% of the traffic. That percentage grew in the years to come. This was before the current privacy laws like GDPR or CCPA. We knew stricter privacy laws would come. We planned to be privacy-compliant. Our clients did not receive any personally identifiable information PII. This is why we called the platform HashTargetr. We matched a visitor to hashed email(s) and could build a profile on that person in real time. A hashed email address is a clear text email address that has been changed into a fixed-length string of characters. This uses a hashing algorithm like MD5, Sha1, or Sha256. This process ensures that the clear text email address becomes unrecognizable data. This enhances privacy and security. It still allows the email to be used for many purposes. These include marketing and identity resolution. We could upload these hashes into all the major ad platforms. Our campaign testing was nonstop, and we learned so much with HashTargetr. We learned from our seed data that a Look-a-Like (LALA) audience boosted performance. The better we could make the seed data, the better the LALA audience would perform.
One of the problems was that the whole process was manual, not automated, and not real-time. We felt that if we automated the process and fed the LALA algorithm in real-time, we’d see better results. Later, we’d see we were right. But, the problem was that we couldn’t get deeper integrated access to the ad platform. Facebook was dealing with the Cambridge Analytica data scandal. They didn’t want to give marketers more access to their system.
The 2016 Election
Trump vs Clinton
The 2016 election proved we were on the right track in data-driven marketing. I won't get political, but there was a scandal. Trump’s digital team got much cheaper Facebook traffic than Clinton's campaign.
Brad Parscale got lower acquisition costs. How? Facebook didn't like him or Trump, but we knew he used LALA strategies. They made Facebook's algorithm give him better results, and our strategies were doing the same. Here are a few articles on what they did.
How Trump Managed to Pay Much Less Than Clinton for Facebook Ads in the 2016 Election
Analysis Reveals Key Facebook Ad Strategies of Trump Campaign
Learning this lit a fire for us. We knew we were on the right track. But we weren't getting deeper access to Facebook's ad platform any time soon. So, we started building other data systems for omnichannel marketing.
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ABOUT GIL ORTEGA
For over 30 years, Gil has earned the esteemed moniker of "The Chief Rainmaker" due to his renowned expertise as a Customer Acquisition Specialist.
