Case Study Travel

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of integrating data across myriad sources, data quality became a challenge as targeting the wrong passenger or client wi
Travel Industry Case Study Our Client Achieves Better Customer Experience and Better Sales with Proper Data Quality and Governance in Place Industry Aviation/Travel

Profile Confidential Client “You changed the way our teams look at data quality. They already started brining your practices in their methodologies and our customer are more confident to close deals faster with clear data quality scorecards” K - Head of Customer Experience Department

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Our client’s biggest value proposition is its ability to deliver a high-quality customer experience. Our client’s global client base expected an even higher quality customer experience. At the same time, they were providing their clients with solutions to enhance the travel experience and deliver tailored marketing campaigns and personalization for the travel and aviation industry. However, as we are living in the era of big data and airlines along with other travel companies are facing a challenge of integrating data across myriad sources, data quality became a challenge as targeting the wrong passenger or client will result in a bad customer experience.

Business Need Our client was not able to provide full customer experience as data sources were scattered across myriad of sources such as PNR records, social media, web, etc… There was a number of inaccurate and duplicated profiles. Also, best practices and solutions to provide profile matching and deliver accurate marketing campaigns were missing. In addition, there was no structured data quality assessment and scorecard to conduct for clients before a solution implementation. Thus, they needed suitable solutions and algorithms for profile matching and relevant data quality assessment and KPIs to improve the sales process of customer experience solutions for their clients and get customer 360 view.


The Solution •

Recommendations, detailed steps and compositions of the layers needed to design a pre-sales data quality assessment for their customers.



Data quality KPIs and formulas. These KPIs were used as a standard to calculate scores that assess the messiness of datasets provided by their clients.

• • •

Visualizations of a data quality assessment scorecard Algorithms for scoring the quality of data with examples using their data. Algorithms and benchmarking for the best way to do profile matching and identity recognition.

Impact • •

Improved the effectives of marketing campaigns and targeting in the travel industry context. Improved customer satisfaction by improving their solution’s ability to deliver 360 view through the usage of advanced data science techniques.



Reduced customer complaints by managing expectations from their solution including project delivery timeline and insights boundaries.



Data Quality helped in maturing their solution into becoming a smart AI tool for the travel industry.

About Rebaie Analytics Group Rebaie Analytics Group researches and consults on the harmony of Anthropology and Data Science that govern changes in business, human affairs, and culture. It helps data natives, governments, and business executives to draw power from these universal patterns to build the next generation of AI experiences. We help Big Data and AI leaders in Fortune 500 companies and startups by an extensive network of data science anthropologists, consultants and industry analysts with extensive experience in the data science industry. We are the first firm that brings together both industry analysts, anthropologists and data scientists together to study the impact of data patterns that govern changes in business, human affairs, and culture. It helps data natives and business executives to draw power from these universal patterns to better understand its impact on people and the role each one of us play in the data era. For more information, visit www.rebaiegroup.com or call +49-30-58849500.

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