Let’s face it — the golden age of third-party data is over.
Gone are the days when mysterious parties hovered in the background, collecting customer data with impunity. Nowadays, strict legislation that reflects public distaste for tracking has changed the marketing landscape.
Not to worry, though, this new age isn’t data-less, and third-party data hasn’t disappeared. Instead, third-party data has been enhanced — and in some cases replaced — by other methods of data collection.
The new tools in your data Swiss Army Knife? First-party, zero-party, and multimodal data. Used together, these three data collection methods create a personalization flywheel effect. To future-proof your retail business, you’ll need to pivot to these more reliable and effective data resources. Let’s begin with first-party data.
What is first-party data?
First-party data, or 1P data, is unsolicited data collected directly from customers’ use of websites and apps. This behavior data may include:
- Purchase history
- Time spent browsing
- Transactional data (e.g., purchases, average order value, frequency, etc.)
- Email engagement (e.g., open rate, CTR, etc.)
Companies usually pull this data from sources like website activity, support tickets, and account histories of their consenting users. For example, when you log into your Google account while surfing the internet, Google collects your search history data. This is how they know which personalized ads to show you.
Like third-party data, first-party data is gathered passively as a user surfs the internet, engages with a brand’s website, or uses digital tools. However, 1P data is gathered through interactions between the organization and its users only once users have consented — i.e., when users are logged into that company’s site or have accepted cookies. This makes first-party data more secure and more reliable than 3P data, which is why it’s far more useful to marketers.
Why first-party data is more important than ever
When it comes to ad targeting, first-party data isn’t just on the rise; it’s arrived — 88% of marketers say it is more important to organizations than it was in 2020 — partly because 3P data faces increased public scrutiny and stringent government regulations. Meanwhile, 71% of customers expect personalization from your brand.
Thankfully, first-party data can reveal the direct customer insights you need to create personalized content — especially when combined with other powerful data sources like zero-party and multimodal data. Let’s look at how first-party data stacks up to its predecessor: third-party data.
Consent
The usefulness of 3P data has been limited by consent requirements in legislation: the California Consumer Privacy Act (CCPA) & California Privacy Rights Act (CPRA), the EU’s General Data Protection Regulation (GDPR), and the Personal Information Protection and Electronic Documents Act (PIPEDA) in Canada. Apple’s iOS 14.5 update further limited marketers’ ability to rely on 3P data when it turned off cross-app tracking by default.
In contrast, consent is at the core of first-party data — customers must opt in and can opt out at any time. This makes 1P data a sustainable option to enhance or even replace 3P data. Unlike third-party data, first-party data comes in through channels that your company owns. As long as you receive consent from your customers, you maintain long-lasting access to a compliant and accurate data stream.
While soliciting consent means you won’t always be allowed to collect data, there’s almost no better way to create customer loyalty. In an age of constant surveillance, people want to feel like their privacy matters. Allowing customers to control the level of data you can collect from them emphasizes your company’s focus on transparency. It also builds trust with your customer base, raises the chance that they’ll stick around for the long run, and increases the likelihood of recommending you to their friends.
Data security
Another benefit of shifting from 3P to 1P data is improved data security. Because customers consent to data collection on channels you own (your emails, website, etc), first-party data gives you direct control of how the data is collected, stored, and used. With third-party data, maintaining data security is extremely difficult because you can never fully trust the security measures of data vendors without auditing the data yourself. Doing so is costly and unsustainable as the number of data sources increases — which also increases the attack surface for a possible data breach.
First-party data sidesteps this issue altogether. All of your collection channels — from social media outreach to tracking on your website — are under the jurisdiction of your internal rules and privacy policies. When you receive customer data, there’s no intermediary because you’re the one doing the data collection. And doing that correctly is far less problematic or expensive than attempting to de-risk external data.
Additionally, maintaining tight data security is another way to build customer trust and underscore the value of their consent. Customer data is valuable to you, and keeping it secure is not only a good investment but also demonstrates to customers that you appreciate the gravity of their choice to share data with you.
Relevance
Since 1P data comes from your own website and marketing outreach, it’s inherently relevant to your business and products. On the other hand, using 3P data creates gaps in your customer profiles because it relies on guesswork. Piecing together information from disparate sources doesn’t automatically tell how your customers feel about your brand. This creates the opportunity for critical misunderstandings about customer behavior that decrease conversions.
For example, you may know that a customer was browsing for a certain product a month ago, but not know that they converted since then. That skews your marketing spend. Instead of pushing related products, you’re wasting cash trying to sell the original target.
That mistake would never happen if you’re using 1P data because it gives you access to the customer’s purchase history. Instead, you’d know exactly when they converted and have the opportunity to amend your marketing spend to where they are in the buyer’s journey at any time.
And with first-party data layered alongside other types of customer data (like zero-party and multimodal data), the personalization flywheel effect kicks off. As you get to know your customers better, your product recommendations become more accurate and helpful. Eventually, your brand becomes a valuable source of new and desirable products that customers depend on. When they want to buy something exciting or necessary, they think of you.
Why you can’t rely on first-party data alone
First-party data has the power to help you forge deep customer relationships and supercharge your ROAS. But to unlock 1P data’s full potential, you must combine it with other types of data. If you rely exclusively on first-party, you’ll likely encounter these common pitfalls.
Some users will opt out of first-party data collection
1P data is effective and superior partly because it’s consensual. However, giving people the choice to share their data means that some customers will opt out. That can leave you with data gaps, which make for less impactful marketing strategies and product offerings.
That’s why your opt-in data message needs to clarify why customers benefit from 1P data collection as well. Whether opting in means they can join a special loyalty program or receive personalized recommendations, you need to sell them on the opt-in.
When they do opt in, you can collect zero-party data alongside first-party to enable hyper-personalization. For example, if a user joins your loyalty program, you’ll have 0P data like their email address and name. Combining that with first-party data like their purchase history and additional zero-party data like style preferences, you can create custom marketing emails tailored to their preferences. At this point, you’re inferring what the customer might like, but with each browse or purchase, you learn even more about your customer and can revise your messaging. In this case, 0P data confirms which 1P inferences are true, and 1P data validates which 0P information is correct. This further hones your marketing efforts so that every cent of spend is as targeted and accurate as possible.
If your data is siloed, personalization can flop
Because of this flywheel effect, personalization works best when you utilize all of your data in context. That means your company needs a universal data approach. If the data is siloed, you risk an inconsistent customer experience and will miss valuable conversion opportunities.
For example, if your marketing team only has access to 1P data, they can target customers based on the products they’ve added to their carts. But if they don’t also have 0P data, they don’t actually know what the customer is looking for. So, they may send promotional emails for products that the customer doesn’t actually care about.
While that may not seem like a critical faux pas on the surface, that sort of mistake undermines your investment in personalization. Seemingly random marketing emails are just more spam that customers don’t want to receive. In contrast, receiving emails for offers they’re actually interested in doesn’t just drive customers back to your website, it makes them feel understood and cared for by your brand. In other words, effective personalization forges customer relationships that last for the long term.
⚡Panorama Powerplay: Fortunately, there’s an easy fix for siloed data: a centralized platform that’s a single source of truth. Panorama provides proactive, predictive intelligence across your organization so customers have consistent experiences with your brand. It also ensures that you’ll never miss an opportunity to upsell or cross-sell.
Using data learnings requires expertise
Even with a system for collecting all of that data, reconciling and analyzing it is far from simple. You need the right strategies, tools, and talent to go from reactive reporting to proactive and predictive intelligence that uses all available data.
You may not have the in-house knowledge to do this by yourself, and hiring for it is guaranteed to be extremely expensive. Data science and platform development talent is among the most expensive and difficult to recruit. These people need to know the ins and outs of complex machine learning, and they’re in high demand. Building in-house also introduces risk for companies that are primarily merchandisers rather than technologists.
On the other hand, bringing in experts ensures that you’re not introducing undue risk in a sphere that’s not your core competency. An external solution like Panorama AI removes the complexity and cost of producing predictive intelligence. It also streamlines the process of obtaining the insights you want. Rather than a years-long in-house build, you can have personalized experiences in less than 30 days.
5 benefits to enhancing your marketing strategy with first-party data
When used in conjunction with other types of data, 1P data helps you unlock superior marketing tactics that improve your revenue and customer value over time. Many marketers cite five core benefits.
1. Personalized marketing increases conversions
Personalized marketing leads to higher engagement and conversion rates. It’s human psychology — to the point that 56% of consumers become repeat buyers after a personalized experience. People feel close to your brand when you prove you know them. You prove this closeness through relevant content, offers, and recommendations. Even small shifts in your brand’s customer intimacy create a competitive advantage. What’s more, the effect compounds. The closer customers feel to your brand, the larger the gains.
For example, let’s say an online bookstore segments its audience based on favorite genres. They take that personalization a step further to tailor their email campaigns with the same preferences. Why? Customers are far more likely to shop at a bookstore that knows their tastes, stocks books accordingly, and serves up a personalized display of those books in their inbox.
2. Behavioral ad targeting improves ROAS
You can also deepen your customer relationships through specialized campaigns for different audiences. This is where intelligent, predictive ad targeting comes in. It goes beyond traditional segmentation by defining audiences based on their expected behaviors, like high conversion probability, rather than previously collected attributes like style preference.
With traditional behavioral targeting, a pet company could refine its target audiences on Meta to only advertise dog leashes to people who recently adopted a dog. That group is highly likely to convert, unlike those who have owned a dog for a year and probably already have several.
Predictive ad targeting takes it one step further. Our Panorama ID is attached to every user. So you can analyze signals for every shopper in real time. Instead of guessing at intent, we can tell you just when that customer needs to replace a worn leash. In other words, we use their past and present to predict the future.
3. Lookalike segmentation optimizes ad spend
In addition to predicting customer behavior, you can also create powerful lookalike audiences to keep you from wasting spend on broad advertising. Applying 1P data to your segmentation helps you direct the right resources to the right customers on the first try. For example, a fashion company could use 1P data to only advertise raincoats to users living and traveling in rainy regions. In this case, lookalike segmentation improves the utility of their ad spend and its ROI because they’re immediately reaching the people who are more likely to need raincoats.
Panorama AI’s predictive targeting uses your customers’ 1P data to send ads to the ones most likely to convert. Our platform collects and interprets photo, video, audio, text, and geospatial data to create powerful lookalike audiences for better ad targeting. Try this out by creating a custom Meta audience for your hero product and see how effective AI-powered lookalike audiences can be.
4. Better customer service drives acquisition and retention
With improved segmentation, you’re likely converting more customers, but that’s not the only way to use 1P data to increase new subscription acquisition and retention. First-party data also gives you the information you need to anticipate and proactively address customer needs. Over 80% of customers say added value during a service experience makes them more likely to repurchase.
For example, an online pet food store that offers monthly subscriptions can collect valuable first-party data from its customers. When used correctly, this data yields predictions and attributes like future lifetime value, churn likelihood, and pet demographics like size, weight, and age. This guides the store towards totally different customer service outcomes.
Alternatively, two different customers may call the store with complaints about overdue deliveries. The first customer has a large, two-year-old dog. They are likely to spend a lot of money on food each month for the next ten to twelve years, and they’re currently highly likely to churn. Meanwhile, the second customer has a two-year-old gerbil. Its life expectancy is lower than that of the large dog’s, and the customer will spend significantly less on food each month. Their churn likelihood is currently low. AI-powered operational intelligence could help agents fielding those calls provide the perfect incentive to retain the high-value customer with a high likelihood of churning. It could also prevent them from giving away incentives to the lower value customer who is less likely to churn.
5. Customer loyalty increases LTV
Using 1P data to prioritize personalization and forge meaningful customer relationships naturally leads to customer loyalty. Loyal fans are critical to your brand’s longevity because they have higher lifetime values (LTV). Because of that, they’re also a much better return on your ad spend. Acquiring new customers costs five to ten times more than selling to a current customer, and these loyal customers spend 67% more than new ones.
To target segments of highly loyal customers with retention programs:
- Use 1P data to identify customer retention patterns for easier retargeting and customization.
- Combine Panorama AI predictions with retention data to target high-LTV customers who are more likely to churn. Panorama helps you understand specific customer product affinities like style, color, function, and more for hyper-personalized offers.
- Offer exclusive loyalty programs to these target customers. Over time, use the loyalty programs to supply your most valuable customers with retention-driving personalized incentives.
3 simple ways to collect first-party data
Of course, you can only reap the benefits of first-party data if you know how to gather fully relevant data securely and consensually. The good news is it’s incredibly quick and easy to do. Get started with these three simple collection methods.
1. Leverage login and registration systems
When a user logs into their profile on your website, you know exactly whose behavior you’re tracking, which means you can accurately capture that customer’s demographic information and product preferences as they traverse your site.
To achieve this helpful and essential level of granularity, encourage visitors to create an account on your website or app. During that process, collect their contact information and ask them to opt-in to future data collection. If you do this, you can track 1P data like their purchase history, time spent browsing your site, and how they interact with your email outreach. This helps you personalize ads to products they look at frequently or cross-sell based on their purchase history.
There are several ways to encourage account creation:
- Promote the benefits of making an account – Use homepage banners, pop-ups, and overlays with enticing offers like personalized product recommendations, easy checkout, and exclusive offers.
- Simplify the sign-up process – Ask for minimal information upfront and allow single sign-on (SSO) through Google and social media accounts — anything that might remove friction. Then, wait to request additional information as the customer relationship develops.
- Ensure privacy and security – Make your privacy policies easily accessible to customers on your website. Give them control by adding custom privacy settings to their accounts. Back this up by implementing strong data transmission and storage security practices.
2. Add in-store / POS interactions
Many brands fail to collect in-store customer data, so your attribution and ROI analysis doesn’t include people who convert IRL. This can lead to poor investments in the wrong channels.
To avoid this common oversight, ensure your brick-and-mortar data collection is working in tandem with online collection methods. Train associates to gather:
- Basic contact information such as email and phone number
- Reason for the visit, e.g., just browsing, looking for a specific item, etc.
- Credit card or loyalty program registration
Once you have this information, you can connect people’s online shopping habits with their in-person purchases. This paints a more complete picture of their purchase history and allows you to send marketing online that upsells or cross-sells based on their IRL purchases.
For example, West Elm allows customers to browse color and style variations on in-store computers. To use the machines, customers have to ID themselves by logging into their profiles. West Elm then uses this data to personalize future emails and web experiences for the same customers.
In a similar case, IKEA allows customers to scan products as they shop in-store. When they checkout, the discrepancy between the products they scan and what they actually buy provides interesting remarketing information. IKEA has the info they need to send them personalized ads for items they didn’t convert on during that visit.
3. Implement a data analytics tool
Data analytics tools track various behavioral and engagement data across the customer journey, such as page views, clicks, time on site, and click-through rates. These tools give you the deep insights you need to understand which parts of their experience they found most engaging and where their purchasing intent may lie — so you can tailor your ad spend accordingly.
Keep in mind that most analytics tools only report on what has happened. They’re excellent for spotting trends but fall short of proactive intelligence. While they’re giving you critical knowledge, you need smart projections to make better decisions and drive revenue. In other words, tracking isn’t enough. Instead, put your 1P data to use with predictive models designed to anticipate customer behavior rather than react to it. If you do this, you can promote products that customers are likely to want in the near future to prompt conversions sooner.
In addition to all of the reporting that analytics tools typically offer, Panorama AI combines our Identity Resolution and Tracking and AI-powered Insights Engine to help retailers predict customer behavior, understand shopper preferences, and personalize their experiences.
What’s more, tracking solutions like the Panorama ID and JavaScript Tag improve upon cookie-based tracking in several ways, such as:
- Collecting behavioral and engagement data across sessions for longer periods
- Making unique predictions with data collected by Panorama’s Insight Engine
- Ensuring data security and compliance
Unlock the value of your first-party data with Panorama AI
- A unified, privacy-first data platform – Panorama is the only privacy-first personalization platform that helps retailers holistically understand customers and tailor every interaction to enhance revenue and gain a competitive advantage. Focus on driving business value while our team safely collects and synthesizes your data.
- Cutting-edge personalization — Industry-leading AI models that predict customer behavior and enable tailored engagement to make every part of your organization operate more intelligently.
- Identity resolution and tracking – Panorama ID provides cross-session, cross-device tracking that builds end-to-end customer understanding.
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