Does your main street have touristic appeal?
David Clark / January 10, 2016
Small cities, towns, and villages often struggle to attract tourists. Many focus on the downtown core highlighting “historic” aesthetics as a key marketing message. The downtown is perceived to be an important draw for tourists, enhancing overall visitor experience, especially if it has an interesting mix of businesses. In some cases tourists will seek out a community’s downtown shopping district (DSD) because of its unique shopping and dining, interesting historical aspects, and overall aesthetic appeal. Municipal politicians promote the enhancement and preservation of an economically strong DSD, for both residents and tourists.
Municipal and private association tourism marketers are typically committed to promoting the existing businesses, and may not be tasked to assess whether the business mix is, in fact, a strong draw for tourists. Such analyses are often the purview of others, usually within city hall, or occasionally a community business entity (BIAs / DIAs, chambers of commerce, etc.). Often, though, such an analysis is not done, leaving marketers to promote a downtown that might lack touristic appeal.
Assessing a community’s downtown aesthetic appeal can be a challenge because it is personal to the viewer, hence subjective. Visitor surveys can be time-consuming and expensive requiring development, testing, polling, analysis, and interpretation. (These should not be left to those untrained in survey techniques – bad questions lead to bad data which leads to bad conclusions.)
Is there an objective technique that will help aid in assessing touristic appeal?
A review of the tourism literature turned up a paucity of assessment techniques. Certainly researchers have discussed, in general terms, what makes a community attractive to tourists, but appear not to have developed basic assessment tools. Some proprietary tools may exist but none were found in the literature search.
To fill this apparent gap, I undertook an analysis of the DSD of a small, mid-western Ontario town (population 22,000) to pilot a basic technique. Borrowing from geo-spatial analytic techniques I mapped the downtown according to what the literature and tourism studies indicate are typically “tourist” activities – basically where they spend their money!
The results include spatial and quantitative indicators, useful for developing strategies for change, as deemed necessary for enhancing a community’s touristic appeal. (Here I use the term “appeal” primarily in a financial sense – what types of shops do tourists like to spend their money.)
A four-step approach was used:
(1) create a draft map of the study area using Google Street View,
(2) verify accuracy through a field-check,
(3) transfer data to an Excel Spreadsheet, and
(4) colour-code the Excel map using pre-defined “tourist engagement” codes (TEC).
Four codes represent levels of likely tourist appeal based on approximate spending levels at specific types of retail establishments. One TEC indicates vacant storefronts and vacant lots, both of which are undeveloped, therefore not providing touristic appeal. In fact, the vacant stores are detractors.
The codes were “1” for high, “2” for mid-level, “3” for low, and “4” for none. Vacant buildings were coded “5”. Each was colour-coded on the Excel map.
The codes are based on tourism spending research. Several studies, which looked at product supply and demand from a tourism satellite account perspective, reported percentages for each tourist dollar spent on various products. These were used to rank product demand (Table 1). (Accommodation typically represents the highest tourism expense category.)
Figure 2 summarises the findings from the study for the subject community. The results suggest that the community’s DSD has a low touristic appeal, at least based on likely tourist demand as a percentage of all downtown businesses. About 1/3 of the all downtown businesses have high or mid-level touristic appeal. (Since the study was undertaken in 2014 there have been a number of changes to the business mix which likely improves the appeal. A follow-up study will assess the changes.)
The Excel spreadsheet map (Figure 1) shows the spatial distribution of businesses by likely touristic appeal. Looking at the map very quickly a sense of the shopping experience tourists might have and what areas of the downtown are more likely to attract – and hold – tourist attention.
The technique presented here provides an easy-to-use spatial analytic tool, as well allows for compiling some statistical (quantitative and qualitative) information. With reference to Table 2 we can develop ratios to further highlight the findings. For every touristic business, there are two non-touristic ones, including the vacant buildings.
As noted, a literature review to determine what an ideal touristic retail mix should be revealed a paucity of research. A number of studies for various communities referred to plans to develop tourist-appropriate mixes of retail businesses but did not offer any numerical or ratio indicators. One study, “Tourism as a retail development strategy”, found that tourism-oriented communities “had more than twice as many retail establishments than comparison communities of similar size” [emphasis added]. The study further states that as a percentage of stores, tourism communities have more “eating places, gift, apparel, sporting goods, and boat/RV/snowmobiling stores”.
It is possible for communities, using such tools as Google’s Street View, to assess the touristic nature of similar, competitive tourist destinations’ downtowns, thereby develop better strategies to enhance their own community’s downtown. More research and studies to better understand what makes a “pull-oriented” DSD will be beneficial in understanding what, if any, the ideal mix of retail stores, dining, and other touristic businesses is that attracts tourists. Any approach, though, must include balancing the needs of residents and visitors.
This evaluative tool can be used to establish baselines which then can be tracked over time, assess changes to the business mix, and the impact of those changes to the touristic appeal of a community’s downtown business district. The tool is simple to use, but not simplistic, especially useful when combined with other data, such as visitor surveys.
A full version of this brief research can be found at DMCmetrix.com, under the “By the Numbers” tab.