Identity resolution is a powerful tool that helps brands create a unified, omnichannel view of their customers. It is the science of connecting the ever-growing volume of consumer identifiers to a single person as they interact across different channels and devices. This process allows companies to provide relevant messages throughout the customer journey. The multiple attributes that form a user's profile can also be used to match the similarity between other existing profiles.
This makes it essential to develop an efficient method of resolving multi-attribute identities. Without identity resolution, companies would have to send out multiple sets of communications, which would annoy customers and waste money on marketing. Identity resolution based on real-world data extracted from social networks can be useful in various fields, such as recommendation, profile search and integration, false events, information dissemination, etc. An identity provider that provides potential customer and customer data in real time can help you meet customer needs and achieve effective business results.
He proposed profile-based search techniques, but it leaves other identities, such as content and the network, unexplored. Unfortunately, interactions between companies and their target audience are fragmented into a sea of devices, platforms and channels, making identity resolution a fundamental component of current marketing efforts. To ensure accurate identity resolution, you need an identity provider with access to reliable data sets that connect your data to stable online connections, such as encrypted email addresses. Identity resolution can transform your business by helping to improve touchpoints and the customer experience throughout the customer journey.
Linking collective sources generates human perception to align the coincidence score of each user identity based on the user's knowledge. For example, a web marketing system that identifies users using the cookie identifier does not know that the email address captured in a marketing automation system is actually the same person. Torky M, Meligy A, Ibrahim H (201) Recognizing false identities in online social networks using a finite automaton approach introduces two methods of searching for identities based on content and network attributes and improves the identity search algorithm based on the attributes of the user's profile. The match rate measures the number of users that an identity graphics provider can compare, discover, and analyze.