Where do you go, my ‘local’?
Opportunities waiting to be unlocked in Domestic Tourism.
International tourist arrivals world-wide grew by a remarkable 7% in 2017 to reach a total of 1.3 billion, according to the latest UNWTO World Tourism Barometer.
While taking cognizance of this statistic and building strategies to optimize revenues from the same, many brands have missed out on the omnipresent opportunity — Domestic Tourists. By assuming that their on-going messaging in domestic markets will also cater to the local tourists, brands have left a big chunk of the pie on the table.
To understand this in context, we looked at the sector in New Zealand — where tourism generated a direct contribution of 5.9% to GDP for the year ending March’17.
The chart below shows the spend across various categories by tourists.
Retail expenditure growth over the last 3 years, split by contribution from Domestic and International tourists shows the consistency in domestic demand, even in a bad year.
The volatility in the international tourist retail spend is amplified further if we look at the international arrivals over the same period. The chart below further showcases the robustness of domestic demand.
Although intuitive, the chart below (figure 4), highlighting the category-wise domestic retail expenditure, gives us insights into how the expenses are seen at a macro level.
One of the ways to monetize this opportunity is for brands to utilize data from macro trends in conjunction with location data to build an effective revenue strategy.
Brands can utilize location intelligence to get highly specific information such as:
- Competitive analysis of the area in consideration — to gauge possible business opportunities.
- Travel patterns of their target audience and this split by loyal customers vs customer lost to competitors.
- Understand the online and offline behavior of their target audience using a data platform along with qualitative data from commissioned consumer studies.
To illustrate, in the maps below, we’ve looked at the Marlborough Sounds region, a famous tourist attraction in the South Island of New Zealand.
Given the considerable share of tourist spend on F&B (figure 4), we looked at prominent QSRs to build a competitive POI chart (Table 1) of the locations they serve in our consideration polygon (Figure 5).
Using spatial intelligence, we built a heat map of domestic tourists traveling to Marlborough Sands (Figure 5).
The first map defines the area under study as a blue polygon and the second highlights the home locations of these domestic tourists to this polygon area during the summer season.
The domestic tourists can be analyzed in the following categories:
- % of tourists arriving from a particular location
- % of population of a particular location arriving at the destination
The chart below (Figure 6) shows how this impacts decision making, allowing us to fine tune the audiences we want to understand further — for example, while 10% of Porirua City visited Marlborough Sounds last summer, it comprises only 1% of the arrivals. Spending time and resources building plans for this audience may not be as effective compared to that spent on understanding audiences in the other 4 cities in the chart.
The final step in the analysis is for brands to understand customer behavior. Data platforms, such as Near, can help you analyze your audience of interest in the real-world. Here’s an example illustrating characteristics of the audience at Amazon Go.
A data led approach like the one above can benefit brands in multiple ways:
- Marketers can plan messaging and communication schedules to drive sales during the tourist season.
- Businesses can improve resource planning for the increased demand driven by marketing activities — both human and product.
- They can also factor in strategic trade promotion spends and tactical inputs for particular locations to further drive sales.
- Retailers can estimate stock requirement both at departure and arrival locations by understanding the time series movement of market basket data.
Contact Us to use data for superior decision-making.
Also published in Medium.