How to reach consumers while protecting their privacy
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The debate surrounding probabilistic versus deterministic cross-device tracking is nothing new. But with the rapidly evolving online landscape and technological capabilities, and with customers increasingly engaging across multiple devices, brands and agencies should be having a different conversation: They need to look beyond which targeting method to use and determine how they can best identify customers for more personal and relevant engagement while continuing to maintain consumers' privacy. This is especially true as the issues of trust and scale have recently been raised concerning first-party data companies, such as Facebook.
Utilizing deterministic methods, based on some form of specific identifying data on a consumer (commonly logins, registration data, physical addresses and sometimes offline customer data or IDs, for example), a company can determine who a specific user is. But this method has caused major issues for first-party data companies. Facebook faces this dilemma, since not only is its data limited to the information from only its platform, but those data also contain very specific user information, creating a privacy issue when used with third parties.
Probabilistic methods, on the other hand, use a data science approach to take a variety of signals across multiple channels to build user profiles with anonymous data, and they can increase scale by predicting behaviors of users based on similar known users. With the multitude of devices and touch points for companies to compile data from, probabilistic methods have evolved past the point of only tracking cookies. With privacy being a huge concern in today's data-driven world, probabilistic methods allow companies to create holistic customer profiles and target their desired customer segments without requiring the use of identifying information.
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.
Reach Consumers While Protecting Their Privacy The open deliberation encompassing probabilistic versus deterministic cross- gadget following is just the same old thing new. In any case, with the quickly developing on the web scene and mechanical capacities, and with clients progressively captivating over various gadgets, brands and offices ought to
have an alternate discussion: They have to look past which focusing on strategy to utilize and decide how they can best recognize clients for more individual and important commitment while proceeding to keep up purchasers’ protection. This is particularly valid as the issues of trust and scale have as of late been raised concerning first-party information organizations, for
Using deterministic strategies, in view of some type of particular distinguishing information on a shopper (ordinarily logins, enrollment information, physical locations and now and then disconnected the client information or IDs, and so on.), an organization can figure out who a particular client is. Yet, this technique hosts caused significant issues for first-gathering information organizations. Facebook faces this quandary, since not exclusively is its information constrained to the data from simply its stage, that information likewise contain quite certain client data, making a protection issue when utilized with outsiders.
then again, utilize an information science way to
deal with taking an assortment of signs over various stations to assemble client
profiles with unknown information, and can expand scale by foreseeing
practices of clients in light of comparable known clients. Probabilistic strategies With a large number of gadgets and contact indicates for organizations gather information
from, probabilistic techniques have advanced past the purpose of simply following treats. Achieving Customers at Scale With protection being a colossal worry in the present an information-driven world, probabilistic strategies enable organizations to
make all-encompassing client profiles and focus on their coveted client sections without requiring the utilization of distinguishing data.
Alongside particular, yet mysterious, client information under their belts,
organizations presently need to make the inquiry of how to best use the
information they approach. Reach Consumers While Protecting Their Privacy Advertisers should be centered around scale and exactness, and the way to this is the measure of information an organization approaches
and how it’s utilized.
Reaching customers at scaleThe issues of both trust and scale are repeatedly a challenge in advertising technology. Probabilistic methods are able to overcome both these challenges and help companies reach consumers at scale.
In order 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.
Making campaigns accurate
When it comes to customer-driven campaigns, the importance of accuracy cannot be emphasized enough, and the rate of accuracy goes hand-in-hand with the maturity of a company along the probabilistic spectrum. The more data inputs a company has access to (both in quantity and diversity), the more sophisticated it can get with its targeting and accuracy. Both proprietary and third-party data will give companies a complete 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 data platform so they have the ability to see a consumer over time.
For example, a social media platform can offer tremendous customer data and insights into its users, but it's limited to its own universe. In addition to pulling data from multiple data sets, it's equally important for companies to filter out bad data and avoid ad fraud. Skilled analytics teams can identify and filter out this bad data, use multiple technologies to reach and understand users and tie online and offline actions.
As a company matures along the probabilistic spectrum and 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 amount of data available today, probabilistic methods allow companies to continue running highly targeted and personal campaigns while ensuring the anonymity required by consumers today.