Monetizing Zero-Party Data for Training AI Models
Create a new revenue stream by licensing your users' data!
Quick summary: AI is booming. AI is expensive. Training data is especially expensive. It’s also unreliable and a major obstacle for businesses to overcome. Zero-party data makes great training data. Companies will pay more for high-quality data. We can help create data-driven reward and loyalty programs to trade perks and benefits for customer data. We can monetize your data for a brand new revenue stream. Oh yeah, and the global spend on AI is on pace to pass $300 billion in 2026. Nice!
The great user data sell-off of 2024 is here! First Reddit to Google, then Tumblr and WordPress to MidJourney and OpenAI. Will you be next?
Whether the public sale of user-generated content/data for the purposes of training AI models is a play at a new revenue stream or potential protection against lawsuits (there’s a bajillion of these, I just sorted by ‘most recent’.) is largely irrelevant.
The main takeaway from our perspective is simple: businesses will pay for data for training AI. And your business can make money while also rewarding users. And we can help you do that while keeping privacy, security, and compliance a top priority. Get paid. Pay your users. Don’t get sued. Nice.
In this example, we’ll look at the creation of zero-party datasets for the purpose of downstream re-licensing and monetization that AI companies can purchase to source training data for their AI/ML models. Quite the topical example, we think, but first, why does zero-party data matter so much?
The Growing Importance of Zero-Party Data
Zero-party data (ZPD) may be the equivalent to gold when it comes to understanding customers.
ZPD is data proactively and intentionally shared by a customer to a brand. This definition alone implies a few things.
First, the data is extremely reliable. It is sourced directly from a brand customers who willingly provide it to a brand. Second, it means the there is enough trust and loyalty in the B2C relationship for the consumer to want to provide the data in the first place.
For data buyers, a zero-party dataset is immensely valuable. For AI companies specifically, it could be their saving grace. Given the muddied waters when it comes to the legality of using scraped web data to train AI models, a solution that meets requirements of national and global privacy initiatives is imperative. In 2024, you wouldn’t build a product similar to Napster. You’d build a product similar to Spotify. Because Napster got their asses sued…a lot. And then they died. RIP Napster.
So if you’re an AI company, or a company looking to implement a new AI product, and you want to not get Napstered, you probably don’t want to have your entire product consisting of illegally sourced copyrighted material and intellectual property.
AI Reaches New Heights
The year is 2022. Rackspace Technology has just polled 1,420 IT professionals around the globe working in financial services, manufacturing, retail, hospitality, government, and healthcare. And what did they find?
69% of the surveyed ranked AI/ML as a high priority within their organizations, a 15% increase from 2020. Only 5% of the surveyed considered it a low priority, meaning in a two year span, the 15% increase was from considering AI/ML initiative somewhat of a priority to a high priority.
What’s more fascinating? Close to half (47%) of AI/ML users have begun their initiatives in the past two years and 78% of the surveyed had moved past the ideation stage into execution, with 40% actively using AI right now.
Safe to say, AI/ML is popping off, as the youths say. But most companies are in the early stages of execution. They’re looking to properly set up their AI systems and improve them over time, and getting access to proper training data is a huge part of that equation.
Challenges
It’s not always smooth sailing; in fact, often times it is not.
57% of the surveyed cited cost as an adoption barrier, a 31% increase from 2020. Additionally, 28% and 24% cite data security concerns and a lack of confidence in data quality as barriers in properly implementing AI.
Using our inference caps, we can infer there’s a market for quality data, but with costs of implementation already high, burning more resources on getting the right data can compound the issues businesses are facing.
“Help Us, We Need Better Training Data! We’ll Pay You!”
The above is absolutely and definitively a quote proclaimed by the majority of senior executives who are using AI to improve their business. Okay, maybe I can’t prove the quote is real, but…STATISTICS SPEED ROUND!
All these stats are from “315 senior decision-makers with verified relevant AI experience at US firms with annual revenue of over $100 million and a company size of more than 500 employees.” The survey was conducted by LXT in partnership with Reputation Leaders, a research firm.
24% have implemented a data strategy focused on data acquisition
66% expect needs for training data to increase
0% expect needs for training data to decrease
13% of investment budget in AI is dedicated to training data (tied for highest w/ product development)
33% cite data availability as the biggest challenge faced when it comes to training data for AI
30% cite lack of quality data available as the biggest challenge
87% are willing to spend more for higher quality training data
This is where zero-party data becomes a huge priority. It’s the best data around! It’s straight from the customer! They provide it purposefully and intentionally! It’s as accurate as a Greg Maddux fastball (which is very accurate, if you’re not a baseball fan!)!
“Oh! I have zero-party data! Wait…can I sell it to you?”
The above is absolutely and definitively a quote proclaimed by every business everywhere who just read those statistics.
Global spending on artificial intelligence is slated to surpass $300 billion in 2026 with a compound annual growth rate of 26.5% over the 2022-2026 forecast.
2022’s spend was already at $118 billion.
Taking a look at that previous set of data…would you look at that. 13% of the invested budget in AI is dedicated to training data. 13% of $118 billion is right around $15 billion. 13% of $300 billion is…$39 billion. Now I know this isn’t exactly how statistics work, so don’t grill me. But in short: global spend for training data for AI is a multi-billion dollar industry. Factor in that 66% of these execs expect the need for training data to increase and 87% are willing to spend more for higher quality data then…well, you get the point.
The key takeaways are these: more and more businesses are prioritizing AI initiative at their company. Most of them are very new to it, and most view cost as a major barrier to adoption. Because cost is an issue, incomplete, unreliable, unverified data is a huge obstacle to overcome. It screws up the product, and even bad data can be expensive. Training data is one of, it not the biggest, expenditures when it comes to AI budgets.
More than half of executives (63%) view issues related to data availability and data quality to be their biggest challenge when it comes to training data. So a heck of a lot of them (87%) are willing to spend more for higher quality data. They’re relying on it. ZERO percent predict training data need to decrease. 66% say it will increase.
How’s that for a summary?
Ok How Do I Do It Tho?
If you’re a business with data, mytiki is here to help get your customer data licensed and listed for sale quickly, safely, and programmatically. You can even use it to train your own AI, if that suits your fancy.
If you’re a data buyer at an AI company or company with an AI initiative, you might want to let everyone know…HEY IF YOU SET UP THIS LITTLE PINEAPPLEY SDK THING I WILL PAY YOU MONEY. LOTS OF IT! Or you know, a reasonable amount. Maybe you don’t want to start on the desperate end of the negotiation. But, I digress:
If you’re a business, you may be wondering, well…how does this work? How do I set up the pineappley SDK so I can start licensing and re-listing customer data for monetization/revenue stream creation?
Check out our Data Monetization summary here. From creating and enforcing data licenses, to creating reward programs, to data de-identification, pooling, cleaning & capture, we’ve got the tools to make monetizing your data as simple as possible.