All posts by Michael Murphy

Consumer Clustering, COVID-19, Concerts & More

What Entertainment Venues Need to Know about Consumer Priorities

Entertainment venues—part of the industries hit hardest by the COVID-19 pandemic (Gössling, Scott, & Hall, 2020)—are yearning for consumers to return at pre-pandemic levels. Cancelled events left venues with massive losses, furloughs, and a recovery that could last into the next three years (Nhamo, Dube, & Chikodzi, 2020). But how can venues attract consumers given health concerns? Which illness mitigation measures should night clubs, movie theaters and the like continue utilizing as public health guidelines begin to relax? Right now, the United States is in somewhat of a gray area: increasing vaccination levels are encouraging, but there is still a risk of catching COVID-19, including its potential variants. Health concerns might be especially pressing for people who are ineligible for a COVID-19 vaccine or live in the same household as others who are ineligible.

However, consumers are likely eager to return to such venues. This comes as no surprise given the jarring alterations to daily life and devastation people have been forced to reckon with. But some consumers will prefer more safety measures (e.g., face masks and hand sanitizer) than others at public outings. In a survey of over 1,000 people from Croatia, Slovenia and Iran, measures like hand sanitizer availability and venue disinfection were perceived to be most important among respondents when attending sporting events (Perić et al., 2021). Among respondents in Croatia and Slovenia, who were less impacted by COVID-19 relative to those in Iran at the time of publication, limiting food and beverage availability at sporting venues was perceived to be relatively less important. If more venues were aware of consumer priorities, they could more selectively invest in COVID-19 mitigation strategies, which are sometimes costly.

The Study

Using HCD’s MaxImplicit methodology, we asked (n=250) people to rank COVID-19 mitigation measures at entertainment venues according to their perceived importance. This general population study was conducted in mid-July 2021. The first portion of the survey was conducted using the MaxDiff methodology, which illustrates strong predictors of what will influence respondents (Orme, 2009). Then, we measured the implicit associations respondents hold between venues (e.g., movie theaters and concerts) and their attributes, such as hygienic, crowded, and fun, using an Implicit Association Test (IAT). These complementary measures help to reveal gaps between consumer needs and venue perceptions.

MaxDiff Results

The MaxDiff revealed the top five consumer needs below. Interestingly, these needs highlight actions (e.g., deep cleaning and ventilation) that occur before arrival. In other words, they are largely not visible at the venue itself. This implies consumers appear to prioritize trust and reliability indirectly.

Top-Ranked Needs (MaxDiff)

In contrast with the top needs, the bottom five needs below largely involve specific and visible COVID-19 protection measures. These bottom needs are somewhat burdensome for consumers as well. Collectively, the MaxDiff findings suggest that consumers might be looking to place the onus of enacting safety measures onto the venues.

Bottom-Ranked Needs (MaxDiff)

The MaxDiff findings beg the question, which venues satisfy consumer needs? The IAT portion of the survey can help answer this question. We showed respondents multiple pairings of venues and descriptors. An example pairing is “movie theaters” and “organized.” Then, respondents revealed their association between the two by hitting the spacebar on their keyboard or touching the screen, depending on their device. Importantly, the IAT is a timed reaction test; a faster reaction implies a stronger association. Respondents could also indicate a lack of association by simply not hitting the spacebar or touching the screen. Nine venues and ten descriptors were tested in this study.

IAT Results

Below is a summary of the IAT findings in relation to the MaxDiff findings. The top needs can be considered related to the attributes Safe, Reliable, and Organized, which were tested in the IAT. The venues on the right—the “Top Venues”—were given their status because they had at least a minimum association with each of the words Safe, Reliable, and Organized. While these venues appear to satisfy consumer needs, the “Bottom Venues” (not listed in the graphic) do not. These include Amusement Parks, Indoor Bars and Nightclubs, Indoor Music Concerts, Indoor Sporting Events, and Outdoor Multi-Day Music Festivals. Therefore, we can recommend that these venues highlight their attributes of Safety, Reliability, and Organization within their messaging to better satisfy consumer needs.

Consumer Clustering

Another useful way to gather insights from these data is through consumer clustering. This technique allows for consumer segmentation according to similarity. Specifically, K-Means clustering was performed using the MaxDiff data (results shown below) using the software R, resulting in three consumer clusters. The Dimensions represent “collapsed” data. Instead of mapping consumers by the numerous individual variables that were collected, they were mapped according to Dimensions which help summarize the key drivers behind the clusters. The percentages next to the Dimensions indicate how much that Dimension is contributing to the overall clustering. Further, each has a unique profile. The top three variables contributing to Dimension 1 include 1) I feel I will belong at the venue, 2) The experience feels luxurious, and 3) The experience is fun. For Dimension 2, they are 1) The venue makes me feel safe, 2) The venue will require a quarantine period, and 3) The venue is hygienic.

What does each cluster look like? Even before summarizing the clusters by demographics, we can already see from the figure above that clusters 1 and 3 have some overlap. Cluster 2, however, is more of an “island” in that it has little overlap with the others. This observation is consistent in the cluster profiles shown below. From left to right, the consumer groupings were dubbed according to their most distinctive demographics: 1) Diverse Hesitant, 2) Conservative, and 3) Vaccinated. Among the unvaccinated segments of the Diverse Hesitant and Conservative clusters, 94% and 77% of them were unsure or unwilling to get vaccinated against COVID-19, respectively.

Aside from vaccination, the other demographics that distinguish the clusters include political leaning and the top needs indicated in the MaxDiff. While the Diverse Hesitant and Vaccinated Liberals clusters prioritize clear precautions at venues, the Conservative cluster desires fun and freedom of choice. With these findings in mind, venues can consider which demographics they cater to—or want to cater to—and create targeted communications.

Actionable Insights

Overall, there are several key findings produced by this study. It is important to recognize, however, that 1) the COVID-19 situation is very dynamic, and 2) the survey was conducted in July 2021. Over time, the COVID-19 situation—and consumer needs along with it—might change. Four key insights are shared below. Collectively, they prompt entertainment venues to consider their perceptions, investments in COVID-19 protection measures, and target audiences. Without careful consideration of these areas, venues run the risk of failing to resonate with consumers. And while a “wait it out” strategy might be appropriate for some contexts, COVID-19 does not appear to be one of them, meaning venues should proactively fine-tune their strategies.   


Gössling, S., Scott, D., & Hall, C. M. (2020). Pandemics, tourism and global change: a rapid assessment of COVID-19. Journal of Sustainable Tourism, 29(1), 1-20.

HCD Research. (n.d.). Implicit Association & Response [White paper].

HCD Research. (n.d.). Max Diff Scaling [White paper].

HCD Research. (n.d.). MaxImplicit [White paper].

Nhamo, G., Dube, K., & Chikodzi, D. (2020). Implications of COVID-19 on gaming, leisure and entertainment industry. In Counting the Cost of COVID-19 on the Global Tourism Industry (pp. 273-295). Springer, Cham.

Orme, B. (2009). Maxdiff analysis: Simple counting, individual-level logit, and hb. Sawtooth Software.

Perić, M., Wise, N., Heydari, R., Keshtidar, M., & Mekinc, J. (2021). Getting back to the event: COVID-19, attendance and perceived importance of protective measures. Kinesiology, 53(1), 12-19.