The Power of First Party Data

The digital marketing space is using many different data sources for website optimization, effective advertising, retargeting users who abandoned a cart, for onsite and offsite personalization efforts, and many more. Data integrations are key here. Every time you read about a “360° view”, “customer centricity”, “multi-channel attribution”, “media-modeling-mix” or “personalization” this respective approach requires multiple data sources to be connected in order to provide a value.

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From 1st to 3rd party data

Most paid traffic channels (e.g. social ads and retargeting) are based on 3^rd party data in order to provide a better, more relevant “right-message-at-the-right-time” towards a user in order to identify a target audience and to drive conversions.

At the same time your 1^st party data sources measure all activities and your business success on your own platforms and by your direct marketing efforts as per email, newsletter, apps, support desks, any customer communication (and possibly also by digital touchpoints in your stores).

3^rd party data sources are rather easy to add to a digital platform (the famous “single line of code”). Their power is coming from feeding cloud-based profiles with signals from multiple sites which allows the profile owner (the 3^rd party) to derive relevant information which again is used to optimize the different digital platforms, or the traffic being sent to it. However, 3^rd party data is creating another data silo and the data is usually granular or not customizable for the data controller (the website owner). In a nutshell: You pay for success, but you can’t look into the black box.

1^st party data mostly requires a more complex implementation in order to capture all relevant data points, segment the traffic, user types and events, and to configure all settings accordingly. It is more building a home rather than renting a fully equipped facility, so to speak. However, it’s all yours and the data is available in a raw unaggregated format for any further processing. Specifically, advanced analytics and data science require raw data in order to build models and derive insights.

Then, there is 2^nd party data which is any obtained data coming from another data owner. 2^nd party data is less discussed in terms of data ownership, privacy and data integration for marketing automation because the collection was done elsewhere and you are not feeding a stream into a 2^nd data profile.

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Overview over data parties

Here are examples for the three different data parties and where the data sources are coming from:

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In terms of sustainability we can differentiate between three perspectives for evaluating data types and sources needed for your business success:

• Ownership
  
• Data processing and data integration
  
• Long-term risk mitigation

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Data ownership

Having full data ownership is a big advantage as this allows you to use the data as you want, which has an impact on the perspective of data processing and data integration. Data ownership is also an important requirement from a legal compliance perspective in order to ensure certain standards and last but not least the ability to delete data, e.g. for “the right to be forgotten”.

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Data processing and integration

Data processing and data integration is very much depending on the data granularity and the ability to import and export the data across systems. Certain systems allow data processing and integration within its own ecosystem, but not to extract data into other custom systems. Anyway, you have to evaluate a data source also by how it can be integrated into your data ecosystem.

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Long-term risk mitigation

Long-term risk mitigation can be evaluated by different perspectives. One is costs, those can increase over time which has an impact on the ROI. If that comes in parallel with a dependency from this data source the exit options are limited. Another is the data quality and quantity which can decrease over time with regards to higher privacy standards for gaining user consent in tracking. A third perspective is data portability in case you want to keep the historical data but want to migrate into another system.

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Here you see an overview of different aspects across the three different data parties:

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Conclusion

While 3^rd party data has the benefit to be productive the fastest by simply adding a functioning system onto your platforms and channels, it has the downside of high costs, limited data processing, less user acceptance and creating an external dependency.

On the other hand the 1^st party data contains all the information about your users and customers, is cheaper to maintain and portable, but comes with a higher level of implementation and maintenance and it lacks external information which might be necessary for reaching out to a target audience which has not yet been in contact with you.

The solution is not an either-or decision. A sustainable data ecosystem has to include both worlds and has to support the digital marketing activities, but it has to identify where effective optimization can be done with a minimum of external dependency, long term risks and costs.

Often, the “quick and easy” approach of 3^rd party data wins over the “slower but cheaper and reliable” approach of using 1^st party data. It can also be a battle between marketing managers and data analysts / data scientists. Though, companies understood the risks and prepare for it. The increasing amount of highly specialized data warehouses and data lakes with an automated operationalization of the data in real-time is speaking for itself. So, there is a need for sophisticated technologies that help companies make the most out of their 1^st party data.

ODOSCOPE is fully relying on your own data, it is not creating another data silo but instead combining your different data sources in order to identify the most promising correlations and deriving data-driven personas. Our platform is made for integrating your raw data and for automating decisions based on your KPIs and business requirements. ODOSCOPE is a real-time decision engine optimized for personalization on the situation (“situationalization”) based on your data and with direct impact on the user experience – other data sources (e.g. from a CRM, ERP ETC.) can optionally be integrated as well. Behavioral segmentation, manual maintenance of rule-based systems and product sorting is automatically operationalized for optimal results based on your goals.

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For more insights and tips, read our whitepaper on data-driven customer experience optimization. You find it here.

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