How can you improve audience targeting with first-party and third-party data?

First-party data is anything collected directly by an advertiser, such as purchase history, on-site browsing, loyalty information, etc. Simply put, it is the advertiser's data.

First party data is behavioral and transactional data collected & created by the brand from shoppers/customer/audience interactions and events. This includes data from both owned (site, app, email, store) and paid (ads, sponsorships) media touch points. It can also include scores, continuous ranks and binary values the brand uses to measure customers at an ID level. It arguably includes "zero-party" data collection practices like surveys/response measurement. It is the most valuable data for any brand as it is about their customers. Use cases extend from marketing and advertising to merchandising, finance and product teams. As the highest fidelity dataset it plays an essential role in applications of Machine Learning and AI.

Improving audience targeting with first-party and third-party data involves a strategic blend of precision and scope. First-party data, gathered directly from your interactions with customers, offers rich, highly accurate insights into consumer behavior and preferences. This data is gold for creating personalized, relevant marketing campaigns. However, its scope is inherently limited to your existing audience. Third-party data, acquired from external sources, broadens this view. It injects a diversity of perspectives, revealing broader market trends and unexplored customer segments. By integrating this expansive view with the granular detail of first-party data, you can fine-tune your targeting.

First-party data is definitely the gold standard for targeted marketing! It's so much more reliable and valuable than any third-party data out there. Knowing your audience's exact behaviors and preferences gives you a huge advantage in crafting personalized ads and campaigns that resonate with them.

Use first-party data to create hyper-specific personas then use third-party data to reach a broad persona. This can help you position your brand effectively.

The third-party data is imperative when planning long term campaigns. Be careful, though! By the time you get it, it might be out of date. Be aware of all major events, future and past in the area. Something as simple as the closing/opening of a store, access to public transportation, road work, crime can shift the business flow in a neighborhood... as such, if the third-party data does not include the shift, the information you get is not useful at all.

Hay una serie de datos que se pueden obtener como adoptantes de una tecnología, datos macroeconómicos o de sector. El ejercicio más fácil para establecer que es interno y externo es entenderlo desde una matriz Mefe que justamente es una matriz de factores externos. Esto permite generar ideas e insghts. Puede ser esto e incluso simplemente datos por sector frente a uso de las redes sociales. Lo importante es que estos datos nos permiten tomar decisiones respecto al negocio.

Third-party data is data that you purchase or acquire from outside sources that are not the original collectors of the data. It often includes demographic information, buying habits, or interests.

This is data gathered by a third party that you use to provide your service. It can be many things, from a macroeconomic study to a segmentation functionality on a paid media platform or a list of emails of decision-makers in a specific industry.

Third-party data is information collеctеd by еntitiеs othеr than thе businеssеs or organizations dirеctly intеracting with thе consumеrs. It is oftеn aggrеgatеd from various sourcеs, such as public rеcords, survеys, or onlinе activitiеs, by data brokеrs or еxtеrnal providеrs. This data providеs a broadеr viеw of consumеr bеhavior, intеrеsts, and dеmographics but may lack thе spеcificity and accuracy of first-party data. Markеtеrs usе third-party data to еnhancе audiеncе targеting, еxpand rеach, and supplеmеnt thеir own datasеts. Howеvеr, with growing privacy concеrns, thеrе's an incrеasеd еmphasis on transparеncy and compliancе whеn using third-party data in markеting stratеgiеs.

Lo más sensato por aprte de las compa?ias es la aplicación de matrices que vinculen estos datos en cuadrantes y que arrojen algún resultado que permite saber cómo afrontar o que acciones debo tomar para conseguir mis objetivos. Igual si queremos ser prácticos, podemos juntarlos en un documento que nos permita la visibilidad y viabilidad de las acciones en pro de un objetivo. No podemos tomar una decisión de tomar un país si los datos externos de este país nos dice que no es favorable la inversión.

Combining first-party and third-party data is a strategic approach to refine audience targeting. First-party data, gathered from direct interactions with your audience, offers specific insights into customer behavior and preferences. This forms the foundation of your targeting strategy. Third-party data, sourced externally, provides broader market insights and trends. By integrating these two data types, you can create enhanced audience segments, tailoring your marketing campaigns more effectively. Utilize this combined data for identifying lookalike audiences, personalizing marketing efforts, and employing predictive analytics. Regular analysis and optimization of this strategy are essential for keeping pace with evolving market dynamics.

Mеrgе first-party data, collеctеd dirеctly from customеr intеractions, with еxtеrnally sourcеd third-party data to crеatе a holistic viеw of your audiеncе. Usе analytics and intеgration tools to combinе and analyzе both datasеts, еnriching customеr profilеs. This hybrid approach еnhancеs pеrsonalization, rеfinеs targеting, and informs stratеgic dеcisions. Ensurе compliancе with privacy rеgulations throughout thе data intеgration procеss for a comprеhеnsivе and еthically sound approach to audiеncе insights and еngagеmеnt.

The key is to OWN your data. There are a lot of solutions out there that can help you analyze it, but they often own that data. I recommend using a tool like fueled.io (I'm an investor for full-disclosure) or manage it yourself... but you want to get the data into your own warehouse so that you can analyze it more effectively.

It's important to remember that metrics/KPIs are typically things we're using to understand real-world outcomes. I say that because ROAS is just part of the picture. If you're not careful it can lead you to ignore the channels and tactics that are leading people to learn about your brand and eventually convert. It is important to understand that every impression has a job to do and not all of them will be your ROAS stars but if you don't invest in them your ROAS will fall off a cliff. A/B testing is essential as it helps you understand which tactics, data, and creative are working well for your brand.

Measurement is, and should be, an ongoing effort to Measure, Learn, Refine and Repeat. The exponential value of Data & AI is exactly this constant process to refine & repeat. But rather than invest in such expensive infrastructure upfront, many vanity metrics, like CTR and VCR, are still relevant e.g. If the campaign is a Brand campaign, looking to Reach a Relevant set of people, engage and influence them, these Vanity metrics provide an idea of the impact of the Campaign. Other metrics are important too, but "what & when to measure" is the key question to be answered by advertiser's and agencies.

Use ROAS to evaluate the performance of different audience segments, channels, and campaigns. For instance, you might find that ads targeted at a particular demographic or through a specific platform yield higher ROAS. This insight allows for more efficient allocation of your advertising budget. A/B testing involves comparing different versions of your ads (like variations in messaging, imagery, or format) to see which one resonates best with your audience. This method is invaluable for understanding customer preferences and enhancing ad effectiveness.

Ethical Data Use: (a) Use data in ways that respect user privacy and preferences. Avoid using data in a manner that could be considered invasive or inappropriate (b) Be transparent with users about how their data is being used. This builds trust and enhances your brand reputation. Compliance with Data Protection Laws: (a) Familiarize yourself with data protection laws like the GDPR in Europe, CCPA in California, and other regional data protection laws. These laws have specific requirements for data collection, processing, and storage. (b) Obtain explicit consent for data collection, especially for sensitive data. Ensure that users are aware of what data is being collected and how it will be used.

Don't do anything until you are covered by the law. Also, just because it's legal, doesn't mean you have to do it. Apply common sense. Each segment of the population: age, race, marital status, living area - has a distinct set of "unwritten rules". Keep that in mind. Sometimes, doing it right gains you more clients, and brings you more profit in the long term, than "making a killing"...

Jonathan Mendez mentions the following in this article: "First-party data is the highest fidelity dataset (and) it plays an essential role in applications of Machine Learning and AI." Massive IA adoption is coming to business analytics teams in the next years. As a result, first-party data will play an essential role for the next generation of IA marketers.

CREATIVE! You can have the best data & activation strategy for all 3 points of the funnel. If your creative misses the mark with your target audience it's all for nothing. Be thoughtful about who you want to reach and what you want them to do. Finally listen to the results... you might think you have the perfect creative but if your target audience isn't taking your desired action it's time to tweak it. Special bonus note: be open to unexpected insights... for example if you get a lot of engagement from an unexpected segment don't be afraid to explore that and ask why.

Many brands, in my experience, feel that they should be getting higher returns on their media budgets, especially with the perception that they've invested in a Data team and feel that they have good volume & quality of first-party data. In many cases, this perception is incorrect. An alternative is for the Brands to look for good quality 3P data as a proxy for 1P data, test it out against current returns from their media campaigns (using their own 1P data) and decide for themselves in a data driven manner.