Welcome to the ENTITY RESOLUTION!

In today’s competitive business world, every Airline/Airport is seeking opportunity to serve their customers better, avoid risk and improved growth in revenue by identifying right customer. In the Era of data explosion, identifying the single customer record is a challenge because the data is stored and accessed from various sources (e.g. multiple booking channels, loyalty application, archives) and this makes it difficult to create and maintain a consistent, single view of customers across their enterprise with accurate and complete customer information.

Our Artificial Intelligence powered Entity resolution system "ERintell" can help you to fix this issue by giving single customer view. ERintell’s Identity Resolution framework can be expended to both real-time/off-line data matches in multiple view – resolving spelling variations (John/Jean/Jon or niam/Niamah/Niam) and address and/or deliberate attempts to deceive. The identity resolution and entity analytics techniques uses a library of Similarity identifying algorithms to compare data attributes such as name, gender, and email to determine likelihood of identity/entity matches as well as non-obvious relationships between people, places, and things.

You can look at the some of the industry use cases where Entity resolution can be leveraged for better performance :

PERSONLIZED EXPERIENCE TO FREQUENT FLYER : Identify and give a personalized experience during check-in and in-flight to frequent traveller (during check-in and in-flight) who has not signed-up for any privilege customer scheme but travels many times in a year.

CUSTOMER EXPERIENCE : Avoid sending any marketing / promotional mail to the people who have unsubscribed to the mailers. But due to duplicate entities, complete un-subscription of the mailers could not be achieved. Unwanted mailers could also lead to fines and also poor customer experience.

BOOST YOUR CAMPGAIN AUTOMATION : An Omni-channel campaign automation needs accurate data to reach right customer and avoid any duplicity of promotional mails. By providing single customer entity ERintell can gear-up your campaign effectiveness.

The ERintell's probabilistic entity resolution engine is able to execute algorithms quickly using advanced techniques such as heuristic matching and fuzzy logic matching. Current model is based on the data from (but not limited to) campaign email database, loyalty database and passenger transaction history. The transparent output complete with scores and confidence levels and contain 2 unique identifiers: operational (high match confidence) and marketing (lower match confidence). Want to know more technical details and how models works, Please click here