DATABERG
Technology has been transforming travel for decades. From the invention of GDS to the arrival of the Internet, technological advancement is constantly producing new opportunities and disruptors that impact business decision making.
Today, computing plays an increasingly crucial role in the hospitality sector. Self-service travel booking and the sharing economy are creating an industry where every customer encounters numerous digital platforms throughout the customer journey. At every touchpoint, customers generate an enormous amount of data – which makes travel one of the most data-intensive industries in the world. As such, the sector can learn a huge amount about their clientele through this rich resource. Through analysis, companies can monitor purchase patterns, personal information, carrier and hotel group preferences, and even entertainment choices.
The majority of travel executives know full well the power of big data in decision making. Big data, automation, and artificial intelligence can help businesses to improve customer service, boost revenue and streamline operations. However, many senior staff still struggle to exploit these resources to their fullest potential. Here, find out how with a defined strategy, integrated departments and an incisive approach, your business can use big data and AI to maximum effect.
1. Define goals to add value
Many businesses do not have a targeted approach to data analytics. Companies tend to apply analytics sporadically and inconsistently, which represents limited value. Efforts become fragmented and unfocused, which often creates a negative perspective on data analytics. Instead of implementing analytics without a clear strategy, companies should identify distinct goals.
2. Filling your data reservoir
EyeforTravel conducted a recent survey that found many travel professionals were struggling with data quality and hygiene. Due to the rapid change in the sector, many hospitality companies operate across poorly integrated systems. For example, whilst many businesses use up-to-date websites, apps and booking systems, their data storage solution may be an incompatible, fragmented infrastructure. As a result, companies can end up with ‘dirty’ data – that is, data that is incomplete, incorrect, poorly formatted or duplicated.
The beginnings of an effective data cleansing project start by creating a data lake. Essentially, to mine the most relevant, valuable data, it is necessary to pool all data into a single, accessible data storage system. From here, companies can explore efficient, hygienic analytics solutions without undertaking a more extensive IT systems project. This approach will allow the company to extract meaningful, actionable insights to drive decision making.
3. Collaborate to create prototypes
Once all of the company’s data has been pooled into the data lake, the next step to accessing quality data is to collaborate with the right analytics partner. In order to gain the most meaningful insights from the data pool, it is necessary to identify business experts who can extract information through advanced analytics. These partners could be data scientists, data translators, specialist tech start-ups, or a combination of all three. Once a business identifies the correct partner, these experts and your organization can work together to examine relevant case studies to generate streamlined business prototypes. Once these prototypes are formulated, they can be applied to numerous scenarios to aid decision making.
4. Integrate artificial intelligence
Collaboration and partnerships need not stop with a business’s data lake – in fact, they should be just the beginning. Considering the scarcity of data experts, businesses should form multiple partnerships in order to make the most of the possibilities technology presents. For instance, external partners can prototype digital assistants, chatbots, automated-learning systems and cloud storage networks. Furthermore, external partners can save money. More often than not, outsourcing innovation is more economically efficient than investing in internal research and development.
However, this is not to say that quantity represents quality. When selecting a data analytics or development partner, businesses should be sure to choose a reputable organization. After all, data are an extremely valuable business resource – and when working with partners, it is crucial to protect the company’s IP and customer data. This is an increasingly pressing issue as legislation around data handling and privacy tightens up, particularly in Europe.
5. Make advanced analytics and AI integral to decision making
The results of a data project should not only impact individual case studies but a company’s entire culture. The insights gained from big data analytics and artificial intelligence platforms should be built into operations processes, staff workflows, and business decision making. As such, these successful pilot projects can cultivate broader organizational benefits and business momentum. Once a small-scale project shows promise, the team should consider how to upscale their findings. Once this research begins to filter through a company’s entire infrastructure, the benefits will be rapidly reflected in the return on investment.
6. Harness the benefits of data today
Technology is continuing to change the face of travel. The industry leaders are already harnessing the power of advanced analytics and AI to influence their decision making. Through data analysis, companies can not only increase revenue and streamline operations but also improve the customer experience. After all, in a sector where customer service is quickly becoming the major product differentiator, it is essential that companies learn as much about their clientele as possible.
In summary, it is vital that businesses of every scale invest in data analytics to remain relevant. Advanced data analytics represent an enormous opportunity – and in an industry that is constantly undergoing rapid change, businesses should prioritize exploring the possibilities. Through aligning departments, pooling data and creating partnerships, companies can extract valuable business insights to boost revenue.
