Sylvie Rouquette
SETTING THE SCENE
In this case study, we discover how a luxury retailer used Traveler Statistics data to gain a better understanding of the shopping tourism market. The retailer in question operates internationally and works with a multitude of major luxury brands, specializing in transforming shopping into a fun and memorable experience. It achieves this by creating places with something for everyone to enjoy. For example, the company offers a wide range of restaurants as well as a variety of activities that can be enjoyed by families and other target groups. It also stocks items from numerous luxury brands, often at a discounted price, to ensure no one leaves its stores unsatisfied.
THE CHALLENGE
The shopping tourism market is one of the fastest-growing segments of the tourism industry. Previously, it would only account for a small proportion of our client’s visitors. However, over time, shopping tourists have become a very valuable and reliable target group.
With the market growing at such a rate, the retailer required a better understanding of it. It had three key challenges in targeting shopping tourists.
- There are many types of shopping tourist – families, social groups, business travelers, etc. – and all these groups have their own needs and preferred brands, which made it hard for our client to know how and, more importantly, where to target these customers.
- The retailer struggled to predict when these shopping tourists would visit.
- It had difficulty establishing whether and how special holidays such as Chinese New Year and Ramadan influenced their sales.
THE SOLUTION:
What our client needed was a better understanding of who their shopping tourists were and, more importantly, where they came from. They also needed data that showed when these tourists were going to visit so that they could prepare for them – as well as the ability to predict the the impact of special holidays.
With the help of a correlation analysis, we sought to demonstrate the link between the findings of our data and shopping behavior. The analysis showed the value of our data, and we moved on to addressing our client’s requirements.
We were able to help the retailer gain a better understanding of shopping tourists through Traveler Statistics, a user-friendly, retail-oriented platform that provides in-depth flight data down to the airport terminal level of all international airports. Of particular interest to our client was the solution’s Forecasting module, which provides daily, monthly and even annual travel forecasts to allow users to anticipate future travelers to the location of their stores and better prepare for their arrival.
In addition, through custom built data sets, we enabled our client to inject our data into their own business processes.
RESULTS
With the information provided by the reports, the retailer was able to achieve several things. First, because they knew where their visitors came from, they were able to make strategic marketing decisions. For example, knowing where their customers resided, the client was able to start marketing campaigns in their home countries and at the airports they used to travel to their destination. This strategic marketing campaign had a positive impact on the number of shopping tourists visiting their stores.
Second, since the retailer was able to monitor and predict traveler flows, it could also anticipate demand. This meant it had the right amount of stock and the right staff, speaking the right languages, at the right time. The ability to anticipate demand also helped the client to decide what in-store promotions to offer and when. Finally, the reports provided many valuable KPIs that the client uses regularly in its reporting.
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