If you’re an 80’s or 90’s kid, chances are you know the game Guess Who?
In Guess Who?, each player has a game board that holds 24 character cards, arranged in a grid. The characters have various distinguishing features such as hair color, eye color, facial hair, glasses, and hats.
The goal is to narrow down the possible choices as quickly as possible and correctly guess the character before the opposing player.
At Crosshatch, our team spent many hours playing Guess Who? as children, only to grow up and get jobs out of college working in the world’s most expensive and inefficient multivariate game of Guess Who?: Marketing Analytics and Data Brokerage.
Gone are the days of guessing who is wearing a funny hat or glasses. Gone are the days of guessing who has rosy cheeks but brown eyes.
Today, we spend billions of dollars figuring out who is a Spanish-speaking dog owner living within 30 miles of Philadelphia, Pennsylvania. Today, twenty person teams of $200,000-a-year salaried data scientists crunch first-party, second-party, and even zero-party data in Snowflake, Amazon Web Services, off Oracle, or even in layers and layers of Excel spreadsheets, in order to figure out whether Spanish-speaking dog owners living within 30 miles of Philadelphia, Pennsylvania have better return on ad spend on Facebook or Google.
Instead of trying to eliminate the Guess Who? characters who have mustaches but not black hair, we can look to data clean rooms. They offer publishers the ability to serve better ads for their brand partners who want to market to publisher audiences. However, if a publisher wants to use a data clean room, and they have many customers, doesn’t the ad buyer then merely help the publisher better tune their model, which would then help the ad buyer’s competitors if these competitors choose to also advertise with the same publisher?
This sounds like a problem for a small army of $200,000-a-year salaried data scientists could help solve. But wait, they are data scientists, not data engineers. To set up and manage a data clean room, you’ll need a data engineer. That will be another $250,000 a year on top of the small army of data scientists.
What about privacy? Data collection challenges? GDPR? Do you even know what GDPR stands for, or do you just know the acronym? You better hire some lawyers at $500 an hour to help guide the data engineer who is going to build the data clean room so that the data scientists can build audiences based on events like clicks and web activity tied to third-party enrichment. Then, and only then, can you effectively target all of the Spanish-speaking dog owners living within 30 miles of Philadelphia, Pennsylvania.
Oh, and you have five competitors all doing the same thing, trying to find all of the Spanish-speaking dog owners living within 30 miles of Philadelphia, Pennsylvania. That’s right.
Back when you were playing Guess Who? as a child it was a one-on-one game. You didn’t even know who your competitor was looking for. You would both be looking for different characters.
In the real world, in the world’s most expensive game of Guess Who? you and five other competitors are all trying to find the Spanish-speaking dog owners living within 30 miles of Philadelphia, Pennsylvania, and because you are all trying to find the same customers, the cost of reaching these customers rises as you and your five competitors all bid the prices up on ad platforms.
If you are unlucky, a brand new venture capital firm that just raised $1 billion in new funds just funded a new competitor. Now there are six competitors all fighting over finding the Spanish-speaking dog owners living within 30 miles of Philadelphia, Pennsylvania.
Yet it only gets worse.
It turns out that now that the small army of $200,000-a-year salaried data scientists has more data, you should actually be looking at all dog owners, not just the Spanish-speaking dog owners, and that you should be looking within 60 miles of Philadelphia, Pennsylvania, not 30 miles.
Now you’re targeting dog owners all the way out in Lancaster County, Pennsylvania. Do they even have the internet there? Isn’t Lancaster County one of the largest Amish communities in the world?
Yet it only gets worse.
It turns out your small army of data engineers and data scientists are not actually moving from Databricks to Snowflake. They are actually moving from Snowflake to Databricks. As a marketer, product manager, or executive you may have no idea what this means. It is entirely possible you will have to re-hire that $500 per hour lawyer again, as all her work presumed everything was going in Snowflake. Now, it’s all in Databricks.
Yet it only gets worse.
It will take 6 months to migrate everything from Snowflake to Databricks.
Welcome to the world’s most expensive game of Guess Who? You may just burn $10 million targeting the wrong people, only to make an incremental $2 million back in incremental revenue.
Crosshatch is out to solve the World’s Most Expensive Game of Guess Who?
At Crosshatch, we loved playing Guess Who? as children, but we hate playing Guess Who? all day long in our jobs as adults. We cut our teeth playing the world’s most expensive game of guess who as data scientists and engineers, and now we are out to end it altogether.
Instead of relying on clickstreams and events data patterns and making best educated guesses about who customers are and what they like, we are building a digital wallet that allows consumers to simply tell and inform the brands they trust what they want, what they care about, and who they are, with no guessing involved.
This two-way communication between trusted brands and consumers removes most of the Guess Who? games that we play today. With tighter feedback loops and data shared via consent, no longer do brands have to spend millions of dollars trying to guesstimate who their audience is and what audience members wish to buy. Consumers win by sharing data with brands they trust. Brands win by having their best audiences and highest value customers available without casting a wide, expensive net out into the market.
We are currently signing up initial customers, and we’d love to show you what we are building. If you’re interested in seeing a demo, click here to let us know and we’ll get something scheduled. Say hello, join us, and we’d love to hear from you and how you plan to avoid the world’s most expensive game of Guess Who? in 2024.