With ever evolving digital landscape and technological advancement, consumers like me and you are engaging with brands cross devices activity. It is about time for brands and agencies to have different and strategic conversion, as they need to look beyond which targeting method to use and decide on the best way to target and identify consumers for their personal data while maintaining its privacy. This is true as the issues of consumer trust has scaled up with increasing data breaches as the case with Cambridge Analytica and Facebook.
Utilizing deterministic methods is based on some form of specific metrics to identify data on a consumer (logins, registration data, physical addresses and in some cases offline consumer data, and these are just examples), a data company can determine who specific user really is. Deterministic methods has caused major issues to for first-party data companies such as Facebook. This is not just a limited data on their platform, but those data contain very specific details and information about a user, this rises lots of privacy concerns when such data is shared with third-party data companies.
On the other hand, probabilistic methods takes advantage of data science to collect variety of signals for cross channel data collection and build user profiles with completely anonymous data. The scale of profiles can increase by predicting behavior of users based on similar known users. With the huge concern of privacy in today’s data-driven world, probabilistic methods allow companies to create user profiles and target their desired segments without requiring the use of identifying information as the case with deterministic methods.
Along with specific, yet anonymous, customer data under their belts, companies now need to ask how to best leverage the data they have access to. Marketers need to be focused on scale and accuracy, and the key to this is the amount of data a company has access to and how it’s used.
Marketers need to be focused on scale and accuracy, and the key to this is the amount of data a company has access to and how it’s used.
Reach customers at scale
The issues of scale and trust are repeatedly a challenge in advertising technology. Probabilistic methods are able to overcome both these challenges and help companies reach consumers at scale with preserving their privacy.
To scale successfully, a company must be able to recognize, and tie across devices, virtually every digital consumer across a range of digital identifiers. This can be achieved through a sophisticated mix of technology and analytics that addresses all the consumer touch points and devices being used now. While this requires putting additional effort and assets into either an internal data analytics arm of the company or a probabilistic agency partner, both can help condense and analyze online and offline data to understand customer behavior and patterns and offer superior scale for campaigns. When done correctly, probabilistic targeting methods are highly effective in terms of scale, reaching more of the right people.
Make your marketing campaigns accurate
When it comes to customer-driven campaigns, we all know that the importance of accuracy cannot be emphasized enough, and the rate of accuracy goes hand-in-hand with the maturity of a company along it’s utilization of the probabilistic spectrum. The more data inputs a company has access to (in both quantity and diversity), the more sophisticated it can get with its targeting and accuracy. Both proprietary and third-party data providers will give companies a complete anonymous consumer view. But for accurate campaigns, a company cannot just splice together third-party audience segments bought on a marketplace. It needs to have a fully integrated Consumer Data Platform (CDP) so they have the ability to see a consumer over time.
Social media platforms, for instance, can offer tremendous customer data and insights into its users, but it’s more like native ads, it’s limited to its own universe. In addition to pulling data from multiple data sets, it’s important for companies to filter out bad data and avoid ad fraud. Skilled teams of data analysts should be able identify and filter out this bad data, they can use technologies to reach and understand users and tie online and offline actions.
As a company matures along the utilization of probabilistic spectrum and efficiently leverages an increased quantity of data sources, it will actually have more privacy advantages, since it won’t be using first-party identifiable information and can keep users anonymous.
Focusing on the customer and offering them a personalized and unique experience is the No. 1 goal that should be driving targeting and tracking methods. But privacy also needs to be factored into these campaign decisions. With the vast amounts of data available today, probabilistic methods allow companies to continue running highly targeted and personal marketing campaigns while ensuring the anonymity required by consumers today.