Travel and Hospitality Use Cases
As more and more data is collected in the travel industry, predictive analytics is vital to make sense of this data in order to boost customer experience and to deliver insights such as which marketing channels to prioritize.
- Price Optimization Modelling
- Recommendation Analysis
- Omni-channel Measurement
- Traveller Origination Analysis
- Traveller Affinity Analysis
To gain competitive edge and create improved and unique experiences for their customers, companies in the travel industry are constantly seeking to optimize and refine their customer segments. By identifying new strategies to innovate on customer experience and targeted at specific segments (e.g. domestic business travellers, short haul leisure travellers, sports enthusiasts) companies can drive revenue growth.
GDPR and other privacy laws mean that it is becoming more and more challenging to segment and analyse customer data. Without explicit and specific consent from the customer, significant portions of a company’s user base can be excluded from this analysis. As a company further refines and creates more targeted segments, this can lead to significant gaps in data, resulting in inaccurate or incomplete analysis.
Using the Trūata Anonymization Solution, clients can run analysis, segmentation and develop models across their entire customer base without the need to obtain consent. Under GDPR, Trūata can anonymize all your customer data based on the legal basis for which it was collected (e.g. purchasing a ticket, booking a room). Segments can be created inclusive of all your customers, unlocking more accurate trends, behaviour and insights from your data.
Loyalty programmes are a core customer value proposition. Discerning travellers are increasingly making purchasing decisions and long-term brand commitments based on the extra benefits they obtain from their travel providers through loyalty programs. Companies are under increasing pressure to identify optimal benefits and identify unique brand partnerships which will allow them to gain a competitive edge, match these to the right customers, and measure the progress of their programmes over time. Rich data analysis is required to accurately answer questions like “which portion of my non-loyalty customer base would be most receptive to joining the programme?” or “which loyalty benefit option would most appeal to a particular segment?”
Strategies for Identifying the optimal customers, optimal benefits and resulting performance of loyalty programs requires comprehensive analysis of customer data and customer behaviour over extended periods of time. The requirement for consent for analysis, and GDPR’s data minimization regulations, challenge organisations’ ability to perform comprehensive analysis over time.
Using the Trūata Anonymization Solution, clients can retain anonymized customer data for extensive periods of time – years if required – run comprehensive loyalty program performance analysis, analyse new loyalty opportunities, and identify new segments of customers to target for loyalty acquisition. Clients can benefit from these new insights without the need to obtain specific consent, either from existing or future customers and can include both loyalty programme and non-loyalty programme customers in their analysis and insights.
Follow the links below to learn more about the use cases for the Trūata Anonymization Solution.