Happy Sunday, Everyone!
2025 is officially in full swing (somehow - it feels like the holidays were just the other day), brands are already talking about Valentines’ Day, Spring & Memorial Day, summer camps (if you have kids, you know) have opened for registration, and Meta’s health data restrictions are officially rolling out into thousands of ad accounts.
One of the main predictions I made for 2025 was that the value and importance of exceptional, differentiated, business-relevant data would skyrocket. This trend has been building for quite some time – but with the advancements to LLMs/AI, the ongoing evolution of algorithms and a radically reshaped information environment, marketer’s actual ability to leverage data has finally caught up to platform’s claims about how data can be used.
But not all data is created equal – just as with anything else, data comes in several flavors. Most of us are familiar with the “standard” types of data, all of which are passively collected by companies/brands/organizations and used (with varying degrees of success) to inform and/or measure website performance, marketing campaigns and the like:
- First-Party (1P) - data observed by brands on owned properties. Think web analytics data, pageview data, ecommerce purchase data, etc. This is the data brands passively collect on each and every website visitor or app user.
- Second-Party (2P) - data obtained from a partner or affiliate (such as a content publisher, or a conference organizer, or a webinar host, or an ad platform audience).
- Third-Party (3P) - data aggregated from multiple, unaffiliated-to-the-brand/company sources and stitched together using one (or more) primary keys by a third party. Examples of this range from credit bureau data, to ZoomInfo data, to SparkToro data.
For each of these three types of data, the subject (read: the user) is unaware of what is being collected, what entities/organizations are involved & how (or if) that data is being used following its collection (yes, there are pop-ups and consents, but no-one reads them). As a result of this, the vast majority of conclusions drawn from these three data are inferred or assumed - a brand doesn’t know how or why a user visited a specific page (or subscribed to a specific mailing list, or was categorized as having a specific interest); the data provides some clues (for instance, where the user came from, what they searched, how they arrived, where they went afterwards) – but a clue is hardly a conclusion.
Then, there’s zero-party data (0P): information that an individual directly, knowingly, and intentionally shares with a company or brand. The best examples of 0P data could include lead form data, purchase data, quiz data, or subscriber preference data (when X.com asks you what topics you follow when you register for an account, as an example).
This highlights the fundamental difference between 0P and 1P data: with 0P data, the opportunity exists to understand not just the what, but the why. That makes 0P data an incredible point of leverage for brands – it avoids the challenges inherent in 1P, 2P & 3P data, while providing a persistent advantage (people’s preferences + traits don’t change *that* frequently).
Put simply: the right 0P/1P data strategy can be a game-changer for many brands – especially those operating in hyper-competitive spaces, growing niches and/or legacy industries.
To unlock that power, brands have to do three things brilliantly well: (1) develop and execute a strategy to collect, refine + leverage 0P/1P data; (2) layer that 0P/1P data with other data types (2P, 3P) to gain a more comprehensive understanding of their audience; and (3) deploy that data across the organization (and particularly in paid + owned channels, like email) in a way that creates incremental value for the business.
We’re not talking about data collection for the sake of data collection; we’re talking about collecting + deploying data to drive positive outcomes for the organization, such as:
- Enhance Audience Understanding: Let’s face it: most organizations don’t know their audience nearly as well as they claim, and the motivations of many audience segments are far more diverse than brands care to admit. 0P/1P data - when layered with 2P/3P data - is a fantastic source of consumer insights. The combination of these data allows brands to go beyond simple questions like challenges, pain points and value props to understand where the audience got their initial information (browsing habits, trusted sources), how they consume information and make decisions (channels, platforms), and what drives them (habits, challenges, pain points).
- Improve conversion volume, rates + customer/client value: Creating a more relevant, personalized experience is strongly correlated with higher conversion rates + higher LTV (which makes a lot of sense, since you can direct people to what they actually want, vs. what you hope they’ll want). This is doubly true when that same data can be used to enable better ad creatives + audience targeting with a higher probability of reaching those people (via LALs, exclusions and more relevant segmentation), along with better algorithmic performance (as you can bid on what you really want).
- Accelerate Product Improvement / Find Product-Market Fit: The easiest way to determine your optimal product development roadmap is to figure out what people are using your product for now, and where it’s falling short - whether that’s by survey, review, focus group or some combination. The best way to identify how to connect that product to the people you want to reach is by understanding their needs, motivations, information consumption habits, trusted sources and frequented sites/platforms. 0P + 1P data does all of that.
- Future-Proof Your Data Infrastructure: We’ve all heard that cookies are going away (or being eaten) in the next few years, while new privacy laws are continually being rolled out. Properly-collected 0P + 1P data tend to have the lowest risk profile of any data type (disclaimer: I’m not an attorney or a regulatory compliance expert; if you have actual questions for your jurisdiction, find one).
- Forecast Demand + Consumer Trends: Understanding the types of users coming to your site (and their specific needs) is critical for both inventory + marketing performance forecasting – enabling you to allocate paid media more efficiently while avoiding sell-outs and overstocks.
- Content Strategy & Development: 0P data can help surface customer/client pain points, challenges, frustrations and needs, as well as shortcomings with competitors (consider, for instance, if you have an influx of sales leads, all of whom cite a competitor’s recent product changes, or lack of support, or price increases – that seems like fertile ground for ads, landing pages (or comparison pages), articles, social content + emails). The right 1P/0P data is more than just information for ad platforms & reporting; it’s a goldmine of strategic insight that can help you get (and keep) an edge.
While that all sounds well-and-good, the devil is (always) in the details. Here’s some specific examples for where the rubber meets the road:
Improve Paid Media Targeting
Virtually every high-performing paid media organization is using 0P + 1P data to improve the targeting of their paid media. This isn’t revolutionary, and in a year (or less), it will be table stakes. Here’s a few concrete examples that any brand, in any industry, can use:
#1: Improve Optimization Events:
Leverage 0P data to go beyond bidding for a class of actions (i.e. purchase, lead form submit, subscribe) to bidding only for a subset of those actions that truly deliver value to your organization. A simple example might be screening lead forms based on budget, location or need; a more advanced use-case might dynamically adjust the value of a conversion based on the responses a customer/potential customer provided (for instance, valuing a one-time or gift purchase less than an ongoing subscription).
#2: Build Better LALs:
Lookalike Audiences (LALs) are – for some reason - still chronically under-utilized across the paid media landscape. My hypothesis as to why is simple: most brands don’t know how to build them (hint: start with a tightly-related, homogenous seed audience, then exclude both the seed and your current customers). The best way to get to that ideal seed audience? Use your 0P + 1P data. Just as this data can be used to go beyond basic events for optimization, it can also be used to segment your converters or customers into more defined segments.
#3: Improve Prospecting:
Those same audiences that you’re using to improve ad targeting or build better LALs or leverage customer match (see below) can be mined for additional audience insights, via platforms like SparkToro, ZoomInfo and more. For Sparktoro (as an example), upload a customer list (or segment), and the platform will generate a profile of the audience using millions of 2P + 3P data points – giving you (the advertiser/brand) insight into who these people are (demographics + psychographics), their browsing habits, trusted sources, most used platforms, frequently used information sources (podcasts, YouTube channels, etc.), interests, behaviors and more. All of that data, then, can be fed back into ad platforms to generate more relevant (and therefore, higher value to you) interest stack audiences.
#4: Use Customer Match:
Upload first-party data, such as email addresses and phone numbers, to ad platforms like Google Ads to target existing customers with tailored ads.
Up Your Creative Game
Targeting + AdOps are nice; creative is essential. If you don’t have a robust feedback loop to connect your paid media management to your creative team, you’re not gonna make it (ngmi, as the kids say). The challenge most brands face isn’t acknowledging the import of creative to paid media performance; it’s in figuring out how to actually leverage the data they have to get the creative they need. If that challenge sounds familiar, here’s some ideas on solutions:
#5: Use Segmented Messaging/Creative:
One of the biggest mistakes in ad accounts is segmenting audiences, only to serve each segment the same creative slop. The entire point of segmenting your audiences is to enhance the relevance of the ads you’re serving to each one – such that an audience interested in sustainability and eco-friendliness is served creatives that highlight your brands commitment to the environment, while those individuals who are focused on efficiency and value are served ads that speak directly to how your brand is superior, and those who are interested in competitors are served ads that tastefully highlight why your brand/product should be considered alongside the competition’s (and so on and so forth).
#6: Find The Right Voices/Visuals:
The same tools (SparkToro) used to find relevant information on your audience can also be used to find the people your audience not just follows, but listens to. One of the most impactful levers a brand can pull from a creative standpoint isn’t the message, but the messenger – so if you find that a particular audience segment listens to a few niche creators/influencers, those people might be perfect targets for an influencer pitch.
#7: Assemble The Right DCO Assets:
Anyone who advertises on Google or Meta knows that DCO - done well - can be incredibly effective IF you have the right bucket of assets for a given audience/audience segment. Well, thanks to your well-mined 0P/1P data, you have that insight. You know what brands your audience loves. You know what platforms they use. You know what accounts they follow. You know what types of content they consume. And you probably know even more than that – you know how they responded to your quiz/survey, or what products they’ve purchased, or what points/features are most important to them. The problem most brands face is that they have never bothered to take the final step: translating this information into segment-specific creative production AND using those segment-specific creatives to power their DCO.
#8: Use Sequential Messaging:
Use 1P data to create a sequence of ads that tell a story or guide users through a specific journey. For example, a SaaS/tech company could use sequential messaging to first show ads that introduce their solution/product, then follow up with ads that highlight specific benefits, and close with testimonials from well-known/well-regarded brands/influencers.
#9: Tailored Landers + Experiences:
The same principles that I’ve highlighted above for ad creative can also be used to build better (and more relevant) landers – with the added advantage of 1P data (web analytics data, heatmap data) to provide further insight into what resonates with each audience/audience segment.
Optimize Paid Media Campaigns
0P + 1P data aren’t just for campaign configuration and ad creative; these data can (and should) also be used to optimize your in-market campaigns. The rise (and ubiquity) of AI tools makes this remarkably easy and surprisingly accessible; the days of needing a data scientist to (for instance) build a proclivity model or assess LTV are gone.
#10: Identify Conquesting Opportunities:
One of my biggest frustrations with many sales-led organizations is a lack of a robust feedback loop from sales → marketing. Case in point: I recently spoke with a sales leader from a lead generation client. She made an off-hand comment about more people leaving Y competitor – something no-one had mentioned at any point during our campaign. I asked for more information. She shared that she’d seen some sales transcripts where prospects were mentioning that a particular competitor had raised prices and cut services, which was causing some major issues with their current customer base). I took that information, pulled all the sales transcripts, fed them into Gemini (it’s good now), and - lo and behold - the same thing emerged. We then took this information and used it to update our targets, bids, creative + lander for that competitor ad group on Google Ads. Result? +10 more conversions at a CPL that was 23% below target.
#11: Preference Cascading:
Another major issue I see (particularly in eCommerce, but everywhere else) is that brands simply don’t use the 0P + 1P data they collect. A recent example: a customer takes a skincare quiz that identifies her as having dry, sensitive skin. The brand doesn’t use that data to refine the audience(s) she is a member of, so this individual with dry, sensitive skin is still served up ads for products intended for people with oily skin. Is that the end of the world? No. But if the customer/client has gone out of their way to give you information on their characteristics/preferences/needs/challenges, why not use it to make your future ads, emails and interactions more profitable? From a paid media standpoint, when someone is tagged (for example) “dry” and “sensitive”, exclude them from all of the SKUs intended for people with oily skin (and vice-versa). In Google Ads, add a modifier (adjusts priority) to your “dry skin” audience for those SKUs that are appropriate for the dry-skinned crowd.
#12: Keyword Discovery:
Any 0P data is a goldmine for any brand/company using Google Ads. Load up whatever 0P data you have - quizzes, sales calls/transcripts, inquiry forms, interests, preferences, whatever – into whatever analysis engine you want to use (Claude, Gemini, GPT), surface relevant pain points, challenges, features, modifiers, etc. Then translate those insights into keywords or modifiers – for instance, if you uncover that customers prioritize “sustainably sourced” or “ethically sourced” products, you can include those modifiers in your search terms (or build out a separate ad group/campaign for those products). The same is true for pain points or challenges – simply turn whatever problem they’re facing into a query that a similar member of the audience might search on Google or Bing.
#13: Value Prop A/B Testing Matrix:
This is something I wish more brands would do. Create a testing framework based on explicitly stated value drivers. If customers rank their top 3 purchase considerations through a preference mechanism (survey, sales feedback, etc.) (e.g., price, functionality, customer service, brand), develop parallel campaign structures + creatives testing these different value propositions against each other. This allows you to validate if stated preferences align with actual response rates and optimize your messaging hierarchy accordingly. What is particularly interesting about this is that it often reveals that stated preferences are not completely aligned with actions – which is the foundation for a “wedge” strategy going forward.
#14: Life Event Trigger Campaigns:
These are particularly fun for the right brands (travel, tourism, home goods, consumer products). The data required to power this (time to event) is quite different from some of the other use cases I’ve talked about, but it’s still stupidly cool. The jist of what you want to do is simple: leverage declared life events or upcoming milestones (wedding dates, home purchase timelines, college admissions timelines, move-in timelines, business expansion plans) to create time-sensitive campaign sequences. For instance, if someone indicates they're planning to buy a home in 6 months, build a Google Ads campaign structure that progressively introduces relevant products (home insurance, moving services, furniture) aligned with their timeline. On Meta, use dynamic countdown creatives that reference their specific timeline to create urgency. From a technical standpoint, this may sound complicated, but it’s not that bad – you’ll start with a timeline-based audience matrix (0-30 days from event, 31-90 days from event, 90-180 days from event, 180+ days from event), then simply adjust your tCPA/tROAS for each audience group (either at the ad group level OR via Value Rules), along with tailoring your creative to each group. The end result is a progressively-more-aggressive campaign structure that leverages an audience’s self-reported timeline to drive action, cross-sell or up-sell.
Getting The Data You Need
While all of the above ideas are cool (or, at least I think so), the challenge most brands face is that each one relies on a brand obtaining certain pieces of information. This might be as simple as a vacation destination (for travel) or a gender (for clothing) or skin type (for makeup/cosmetics) – or far more complex (for IT consulting or B2B or home buying).
But, at the end of the day, there are a few pieces of information that can enable a remarkably better experience for the user AND provide outsized value to the brand. You don’t need all of the data (contrary to what some gurus claim); 80% of the impact comes from 20% of the data (and for many brands, it might well be 90% of impact from just 10% of data).
That makes your challenge this: find the 10% or 20% of data necessary to unlock 80%-90% of the value. Easier said than done, to be sure.
Once you know what to collect, make it remarkably easy for the user to share. This quiz from Winc is one of my absolute favorites - it’s delightfully simple (6 questions, each with visual aids) but collects a staggering amount of data on palate profile + wine preferences. To Winc’s credit, they’re quite clear on how that data is used (to match me with wine) and the “results” page follows through on that, showing me how the wine in my box was curated based on my responses to the quiz. As a bonus, this type of structure also triggers the IKEA effect (to a degree) – causing the prospective customer to place a higher value on that box simply because s/he thinks they helped create it.
If that’s a bit daunting, consider this pop-up experience from Vuori - the only thing they want to know (to start) is my gender - and they use it to send a personalized welcome email with some of their best-selling products based on that. The actual workflow behind this is as simple as it gets - but the result is dramatically higher conversion rates + a delightful customer experience. Jones Road Beauty does the same thing - only they ask about skin type (oily vs. dry) - which they immediately action via “personalized” recommendations.
We did something similar for Echelon Fitness in their emails, where each week’s “classes” recommendations were powered by overlaying the product (bike, rower, weights) and gender associated with each subscriber profile, then showing that machine in use by that gender at the top of the email, followed by a curated set of classes based on the same combination (Gender + Product). The end result was massively higher email engagement, more high-intent clicks, and a boatload more revenue via class sign-ups/subscriptions.
The thing most brands miss is that integrating a cohesive 0P collection strategy ends up being a win-squared for the brand, plus a win for the customer:
- Win #1: the brand collects insanely valuable data on their audience that they can’t get anywhere else and which enables them to raise the expected value of each visitor
- Win #2: the 0P data collection often *replaces* the typical discount offered for newsletter/email capture - increasing margin and customer lifetime value
- Win #3: the data enables a better, more personalized experience for the customer
Finally - and if you *really* want to up your game, check out personality test sites. They have this down to a literal science. Answer a bunch of questions (in <5 minutes) and sit back while they generate a personality assessment preview, upsell that 0P data into a $19.90 downloadable guide (which is automatically generated from existing content) AND use your personality profile to sell you on future stuff, using a communication style and language they are pretty confident will resonate with you (since they now have a pretty good idea of what makes you tick).
The bottom line: in every case, a successful 0P/1P strategy starts with understanding exactly what you need to know in order to dramatically improve your odds of converting that user into a customer (or a lead/prospect/whatever), then making it remarkably simple for the user to share that information with your brand.
And once you have the data, obsess about how you can continue to leverage it across the entire customer journey, from campaign setup and platform selection to ad creative, post-click experiences, landers, the works. Just when you think “We’re using this way too much!” – you’re probably just starting to use that data enough.
0P/1P data is the currency of the future – so invest in building it now. I hope some of the ideas above are helpful, or at least get your wheels spinning about how you might be able to start doing some very cool things with the 0P/1P data you already have.
Until next week,
Sam
PS: If you have sales or customer service/success teams, make sure they’re using some kind of call recorder on EVERY call, then have those transcripts added to the customer/prospect record. You will never stop being amazed at how much value any brand can get by simply loading up a group of those transcripts for a particular segment (i.e. SQLs, Non-Qualified Leads, Low LTV customers, High LTV customers, etc.). Most brands are sitting on literal goldmines of data, and they’ve never even bothered to mine it for insights that could help them grow their business, improve their products and close more deals.
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