Project Description

Retail tourism case study

“A forecast is like a compass; it guides your steps, and you need to recalibrate it or check it often to get a grasp of the constantly changing situation. For companies that rely on retail tourism, frequently refreshed forecasts are essential.”

Vicent Botella Soler
Innovation & Machine Learning Manager, ForwardKeys

Retail tourism is becoming a more important market year by year. The big question is how to create an effective marketing campaign to attract retail tourists.

This case study focuses on a large luxury retail store with multiple branches that works with many well-known luxury brands worldwide. The retailer is not just an expert in selling products but also in offering meaningful experiences – by changing in-store themes according to the season, for example. Furthermore, the company is constantly innovating and improving to stay ahead of the competition and maintain its position as a market leader.

The right data for the job

The retailer came to ForwardKeys with the following objectives:

• To gain greater insight on international travellers to help with establishing a customer profile.
• To understand changes in sales to these travellers.
• To forecast sales made to potential retail tourists.

Before we could address these goals, the client had to determine whether a correlation existed between our data and its own. This was incredibly important as it would demonstrate the ability of our data to fill the retailer’s information gaps and support it with any further requiremements regarding retail tourists.

The value of a correlation analysis

To demonstrate that our data was relevant to this retailer, we offered to carry out a correlation analysis.

To perform this analysis, we took some of our client’s sales data and compared it with our data on traveller flows. If the results indicated that a correlation, it would mean that we could now use our platform to explain past and future sales based on the travellers at the location.

For example, if there was a sudden increase in sales, we could see where visitors had come from during that period, and our analysts could help to explain the reasons behind these sales trends.

Finally, and most importantly, we could provide the retailer with forecasts that were incredibly useful for its retail tourism marketing campaigns and for forecasting future sales.

The power of ForwardKeys data in promoting retail tourism

The results of the analysis showed a near-perfect correlation, giving us the green light to use our database to create profiles for the retailer. After conducting the correlation analysis, we were able to add some characteristics to the target group.

In this case, we saw that their customers mainly travelled in a group of at least two people and spent at least one night at the location of interest. This information allowed the retailer to adjust their marketing strategy to target people with these characteristics.

Because of the close correlation between our data and theirs, we have been able to inform them regularly about the evolution of these particular traveller profiles. This has helped them significantly with their marketing, enabling them to use a more targeted marketing mix that appeals to the interests of actual retail tourists.

To help them create an effective retail tourism marketing campaign, we also provided them with access to Traveller 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 the client was the solution’s Forecasting module, which provides daily, monthly and even annual travel forecasts to allow users to anticipate future travellers to the location of their stores and better prepare for their arrival. Meanwhile, through Licensed Datasets, we enabled our client to inject our data into their own business processes.

In conclusion, through the analysis of their historical sales performance alongside our correlation analysis and products, the client gained an understanding of their expected sales performance over the three to four months ahead. With this information, they were able to plan in-store staffing, promotions and product offerings based on the nationalities that would be visiting their stores at various times, using their marketing budgets more effectively for a higher return on investment.


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