Customer data is your most valuable retailing asset
Travel retailers generate huge volumes of customer data across multiple channels and devices. Itineraries, fare searches, PNR’s, reservations, contact information, booking history, social media, customer feedback, comments and complaints - all of this data has the power to transform the retailing capability of your business, if you could unlock its value. OpenJaw t-Data is the Customer Data Platform for travel that unlocks your customer data to supercharge your transformation to a customer-centric travel retailer.
Customer Data Acquisition
OpenJaw t-Data Acquire extracts customer data from any source system and then applies a sophisticated probabilistic Identity Resolution algorithm to create a unified profile for every customer. Transactional data such as PNR data is also loaded into the t-Data warehouse.
Customer Data Warehouse
OpenJaw t-Data Warehouse integrates all of the customer and transactional data by transforming it to align with OpenJaw’s enterprise data model. This model is fully compliant with GDPR and IATA’s Airline Information Data Model (AIDM) and New Distribution Capability (NDC). Data from the warehouse can be accessed programmatic via OpenJaw’s REST API.
Customer Data Visualisation
OpenJaw t-Data Visualise enables data access for business users through a rich set of interactive dashboards and visualisations that enables users to quickly navigate and understand the complex analytical data generated by t-Data.
Machine Learning for Travel Retailing
OpenJaw t-Data Predict provides the predictive Machine Learning insights that power your customer centric retailing strategies. OpenJaw has developed proprietary machine learning algorithms optimised for travel. Our algorithms encompass t-Data Life Score, t-Data Propensity, and t-Data Clustering.
Informing Better Travel Retailing
OpenJaw’s Identity Resolution algorithm creates a Single Customer View spanning multiple customer data sources. Identity Resolution is implemented as part of t-Data Acquire and leverages the power of cryptography, probabilistic record linkage and graph theory to deliver identity match rates up to 80% higher than traditional ‘exact matching’ methods.
Life Scores, Clustering and Propensity
OpenJaw’s Life Score algorithm estimates the monetary value of a customer by predicting their spend. OpenJaw’s Clustering algorithm segments customers into clusters, based on recency, frequency, as well as spend. OpenJaw’s Propensity algorithm predicts the products or categories of product that customers are most likely to purchase next.
OpenJaw t-Data is the only customer data platform built from the ground up for travel
Integrate, resolve unify all your customer, behavioural & transactional data from all sources, such as an airline PSS, website, CRM, social platforms or loyalty system.
- Create a single-customer-view even with disparate set of data sources
- Resolve the identities of your customers across any number of sources, even with incomplete data
- Ingest all customer data in its native format from every source – online, offline, historical , streaming and social into a common schema
- Integrate all transactional, contextual, and behavioural customer data to form a single customer view
- API-first enabled architecture enables you to integrate insights into your retailing ecosystem with ease
OpenJaw t-Data drives incredible customer insights with Machine Learning
Proprietary algorithms create predictive models that can segment customers, estimate their propensity to purchase specific products, and measure their lifetime value.
- Use advanced analytics and visual tools to have total 360 customer view
- Predict the propensity customers have to purchase specific travel products with t-Data Propensity
- Predict the lifetime value of every customer with t-Data Life Score
- Cluster customers into recency, frequency, monetary (RFM) segments with t-Data Clustering
- Predict, automate, and deliver the ideal next best action for every customer
OpenJaw t-Data can transform your revenues
All customer insights, user-interfaces and use cases are created and measured by KPI’s specific to travel - accelerating your path to ROI.
- Deliver personalised offers and experiences to reduce look to book ratios
- Increase conversion rates through more effective cross-sell and up-sell to increase revenue per trip
- Personalised, customer-centric retailing enables higher repurchase rates as customers return for more positive, personalised experiences
- Increase loyalty and satisfaction with personalised retailing, when executed with care and precision has been proven many times to increase the loyalty and satisfaction levels of customers.
- Dramatically improve the productivity of your teams by giving them the tools they need for every campaign