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You’ve probably heard that first-party data is the most valuable to marketers. There are indeed a whole host of benefits to first-party data. It’s reliable, and, most of the time, it connects directly back to your particular audience. Still, first-party data has some downsides. Not every marketer can collect it easily, and even if you have plenty of it, it lacks scale and depth. Second- and third-party data, as well as tools to generate analytics, are essential to help you enrich and power up your first-party data.
Companies prize first-party data because consumers gave it to them directly. The information you collect yourself can be vast or limited, depending on your industry and the methods you use to gather it. The data you amass needs to translate directly into revenue. You need to understand it to inform your strategic decisions or marketing campaigns. Your first-party data needs a powerful analytics tool to identify trends and develop models you can use to your advantage.
Publishers need fresh insights and analytics tools to get a firm grasp of their audiences. They need to know exactly who their audience is and what content they read. The first-party data they’ve collected tells them about visitors’ behaviors within the publisher’s site or app. However, it cannot tell them what they do outside their websites, what other sites they visit, what they watch, what they purchase or what content they consume.
To package their readers’ data and allow their advertisers to target them individually, publishers need to see a complete picture of their audience across their connected digital lives. Since first-party data can only show a sliver of who a reader is, they need to supplement their information with second- and third-party data to get a panoramic picture of their customers and readers to grow inventory opportunities.
Marketers, on the other hand, can have different amounts of data. An e-commerce company usually has vast amounts of first-party data at their disposal. They have data on all the site visitors who engage with their content and browse their site. They know their customers’ names, shipping addresses, emails and sometimes even phone numbers. Consumer packaged goods marketers, however, work with customers who rarely interact with their brands online. Still, no matter how much data marketers collect, it cannot show you valuable data about who your consumers are outside of your controlled experience. To gain behavioral and attribute data from your consumers from across the web, mobile and elsewhere, you need additional data sources.
Purchased consumer data gives you a better understanding of your customers or audience. You can augment your first-party data with second- and third-party data. Then, analytics tools allow you to mine your data for trends that can inform your decisions.
When first-party data isn’t enough, marketers and publishers turn to second- and third-party data. Each type of data can complement the others, allowing you to enhance the quality, scale and scope of your data and insights.
Once you collect and buy data, you need to interpret it to discover the emerging trends. To achieve this, marketers and publishers turn to data insights and analytics. Data analytics is the process of examining datasets to conclude what information they contain. All your first-, second- and third-party data comes to you as raw data. Analysis uncovers patterns, letting you gain valuable insights.
Most analytics processes use software, machine learning and automation to visualize data, segment customers and find trends in user behavior. Companies can use data insights to evaluate the success of their marketing efforts, personalize content for groups of users or individuals and learn more about their audience.
To learn who your consumers are and what they are doing in the present moment, you can use real-time data to enrich your first-party data further. Lotame Data Stream is a firehose of data that flows continuously, giving you up-to-the-minute data as soon as it becomes available. Lotame gathers raw behavioral data from billions of consumer profiles from across our data network. You can access a data stream of your own first-party data or enrich your first-party data to identify your customers wherever they are across the web without a single cookie.
The only limit to the power of data streaming is what your data scientists can do. For marketing purposes, your data science team can slice and dice from a continuous stream of raw data to run models and tests. While predictive analytics often assumes trends stay the same, by supplementing historical data with a real-time data stream, you can achieve more accurate modeling. You can also adjust the scale of your data stream to focus on location data, sources and many other custom filters.
There are many applications for data streaming. Marketers and publishers can use data streaming to develop content or product recommendations and personalized digital experiences. This data can also inform CRMs and attribution modeling.
To understand how real-time data streams can enhance historical snapshot data, let’s look at some examples. The financial industry often uses data streaming to detect fraud. If statistically unlikely purchases occur on a customer’s debit card, for example, a program might automatically trigger an email alert to warn them about fraudulent purchases. Ride-sharing apps also use data streams. These apps can provide such an accurate estimated time of arrival because they combine historical and real-time traffic data with the driver’s and the passenger’s current location data.
There are also many use cases for data streaming for marketers and publishers.
Data modeling and data analytics are often interchangeable. Technically, modeling is only one piece of the analytics process. Data modeling is a method for visualizing or mapping out how sources of data come together within a company’s software and data storage applications. Most data marketers and publishers have many applications that require data input. You may have these programs to manage:
All this software needs a feed of data to optimize operations and enhance the customer experience. A data model plans out how to aggregate data, such as in a data management platform (DMP). It determines what events and scenarios to listen for in your data stream and what actions to trigger. Your data model will also lay out what data to send to each of your business software tools.
Data modeling also creates visuals that data analysts can use to understand trends and draw conclusions. Generally, businesses start their data modeling simple and then enhance it as they grow. Data modelers work alongside marketers to determine what data to track and how to organize it. In some cases, data tables work best. In other cases, such as mapping the customer journey, it’s essential to see a visual graph.
One way to enhance your data modeling is to identify gaps in your data and layer on more sources of information to generate more meaningful conclusions. To do this, you may purchase batches of second- and third-party data and combine with your first-party data in a stream. However you enhance your data modeling, it’s critical to carefully decide which data you add so that it will provide relevant, meaningful results.
Data streaming and data modeling go hand in hand to enhance your marketing efforts. A data stream offers real-time data and audience enrichment. Enrichment allows you to scale and optimize your first-party audience with third-party data. Enriched data enables you to access a complete picture of your customers, which your data modeling “paints.” Data modeling also sets up the processes so you can use this continuous stream in real time.
It’s not a one-size-fits-all approach. Every company must consider their first-party data and decide what kind of second- or third-party data are necessary to make it useful. You may need an up-to-the-millisecond data stream, or you might prefer purchasing data by the batch. The data modeling techniques that work for some marketers and publishers may not be informative for others. Every business needs a custom data approach for their unique audiences.
A customer data platform (CDP) explicitly handles first-party data. While some can also take in second- and third-party data, they need a significant level of first-party data to provide the functionality you need. Since DMPs are better able to handle anonymous second- and third-party data, they offer insights about your audience. DMPs can target similar audiences and retarget engaged prospects. Some advanced DMPs enable importing of first-party data in addition to first-party data collection.
One reason many marketers look for a CDP is that the technology enables identity resolution, allowing marketers to take advantage of people-based marketing strategies. A CDP can use your personally identifying customer data to market to customers using tailored campaigns.
First-party data collected through a CDP can be a powerful tool. However, since CDPs cannot easily manage second- and third-party data, they are limiting for the same reason first-party data is. They cannot reach new audiences, so many marketers have both a CDP and a DMP in their marketing stack.
While both CDPs and DMPs have limitations, Lotame is different. Our software houses and anonymizes your first-party data, while Lotame Cartographer provides the people-based identity resolution marketers look for in a CDP. Like a DMP, our technology can handle second- and third-party data, whether it comes from a data stream or our Lotame Panorama data enrichment tools. Our technology lets you work with all your data in a single platform.
U.S. third-party audience data spending isn’t showing signs of slowing down. Improving your audience data begins with data enrichment. A majority of users — 78.2% invested more and planned to invest more on data and related solutions in 2019, suggesting its increased importance for marketers.
Lotame Panorama is a suite of data enrichment solutions for digital advertising — no cookies required. We provide integrated tools for marketers and publishers to enhance data and turn personas into addressable audiences.
For Marketers, Lotame Panorama Insights enriches your first-party data with second- and third-party data from more than 250 online and offline data sources. Rather than dealing with data scattered from multiple providers and insights segregated in different data tools in your stack, access it all in one place. You can use Panorama Insights Self-Serve for curated, high-quality behavioral and attribute data on-demand. Or, try Panorama Insights Custom for your tailored analysis and modeling of all your data across many sources.
You can also enrich your first-party data and empower your data science teams with Lotame Panorama Data Stream. You can use a data stream with batched data or on its own, depending on the amount of first-party data you own.
Develop richer stories about your customers, activate everywhere — even in cookie-challenged environments, and uncover more moments to engage meaningfully and respectfully with customers everywhere they are. See what you’ve been missing with Lotame Panorama. Contact us to learn more about Panorama.
How are marketers and publishers tackling the identity challenge? We surveyed over 1,000 decision-makers to better understand how key industry players are planning to test and implement their cookieless strategies. Get access to our new global report, “Beyond the Cookie: Identity Testing & Adoption Among Marketers & Publishers”.