• Rex Chng

What On-demand Delivery Service Brands Need to Know About Consumer Perceptions of Their Brands

Introduction

On-demand delivery services, such as Uber or Instacart, are growing at an unprecedented speed and are expected to continue to grow larger in the years to come. These services allow consumers to purchase products or services by simply using the designated app. A report from PwC (n.d.) estimated that the on-demand economy will expand, reaching and surpassing a whopping $330 billion globally by the year 2025. Additionally, there are now more than 22 million consumers in the U.S. alone, spending over $57 billion on on-demand services annually (Mobile App Daily, 2020). Fueled by the needs expedited by the pandemic, as well as consumers’ willingness to pay for these services, many companies are flocking into this space. From prepared food delivery (e.g. Grubhub, Uber Eats) to grocery delivery (e.g. Instacart, Walmart) to two-day delivery goods (e.g. Amazon), there is no lack of options for consumers to choose from that fulfill their needs, in-the-moment.


Many on-demand delivery brands originally started their business in a specific category – for instance, food, grocery, or convenience store goods – with little overlap, staying in their own space. This, however, is no longer the case. In recent years, the popular on-demand delivery brands have been venturing outside of their primary offering category. For example, besides their ride-sharing service, Uber now offers their own on-demand delivery service for groceries through UberEats, in addition to takeout delivery services.


Expanding company deliverables, like UberEats going from restaurant-only meals to non-prepared food delivery, is a good way to soft launch ideas to gain consumer feedback. But how far can on-demand delivery service companies like Uber or Amazon stretch their on-demand services? How far outside of their lane can on-demand delivery service companies reach before they lose consumer trust and acceptance? And how can companies best strategize launching new services on their platform while maintaining true to their brand?

This study aims to investigate what types of on-demand delivery services consumers are interested in as well as their brand perceptions on current on-demand delivery companies. These learnings reveal potential areas for growth in this space, while addressing need-gaps that are authentic to growing the brand harmoniously.


The Study

Leveraging HCD Research’s MaxImplicit tool, we recruited a total of 200 participants for a survey. In late-March 2022, participants were asked to rank what features are most important when it comes to using on-demand delivery services via the MaxDiff (or Best-Worst) scaling. The MaxDiff method reveals participants’ relative preferences. In the second part of the study, we measured participants’ implicit reactions among five popular, on-demand delivery service brands (e.g. Amazon, Uber, Walmart, Lyft, and Instacart) with a list of 15 attributes (e.g. convenient, timesaving, caring, etc.) using the Go/No-go Association Task (GNAT). As a method that combines MaxDiff and IAT, MaxImplicit is excellent in revealing the gaps between consumers' needs and their perceptions of on-demand delivery service brands.


Types of Shoppers

Participants for this study were classified into three groups based on how frequently they use on-demand delivery services– Occasional shoppers, Frequent shoppers, and Heavy shoppers. Occasional shoppers are people who hardly ever use the service, Frequent shoppers are people who use it on a monthly basis, and Heavy shoppers are people who use it on a weekly to daily basis.


MaxDiff Results

Top-Ranked Needs


The MaxDiff results above revealed that the top five most important needs for consumers are Trustworthy, Good Value, Efficient, Convenience, and No Added Fees. These five items are considered the baseline needs for consumers when it comes to using on-demand delivery services and are perceived to have immediate benefits. This implies that consumers often come to on-demand delivery services with specific goals in mind, including spending the least amount of time and effort on getting what they need. Previous research argued that “recency effect” has influences on consumers and suggested that a positive experience is more powerful and can change the perceptions of a negative one (Ha and Perks, 2005). This suggests that a good experience is important in determining consumer’s satisfaction, thus making it easy for companies to build consumer relationships (Buchanan and Gillies, 1990). Consumers favor brands they have used, especially those they have had good experiences because they trust them to fulfill their needs. Lastly, the research suggests transparency in the pricing scheme also increases the chances consumers revisit the platform, as they are already aware of any additional fees in the final price.


Figure 1. Shopper Group Differences of Top-Ranked Needs from MaxDiff. Each graph includes the three groups ordered from left to right: heavy- (red), frequent- (blue), and occasional shopper (green). Kruskal Wallis test was run to compare the mean against each shopper group. The yellow arrow indicates the mean differences between each shopper group (Occasional shoppers consistently ranked the five top-ranked MaxDiff items higher than frequent shoppers and heavy shoppers).
Figure 1. Shopper Group Differences of Top-Ranked Needs from MaxDiff. Each graph includes the three groups ordered from left to right: heavy- (red), frequent- (blue), and occasional shopper (green). Kruskal Wallis test was run to compare the mean against each shopper group. The yellow arrow indicates the mean differences between each shopper group (Occasional shoppers consistently ranked the five top-ranked MaxDiff items higher than frequent shoppers and heavy shoppers).

When comparing the three types of shoppers, we can see that occasional shoppers ranked these five top-ranked needs higher than the other two groups of shoppers. This implies that occasional shoppers, though they might not use the service as much, note these five aspects are the utmost considerations when they need to use the service.


Bottom-Ranked Needs

In contrast to the top-ranked needs, the bottom-ranked needs are Contactless, Caring, Inclusive, For Me, and Exclusive. These five needs can be considered as additional features, which are features that could set on-demand delivery companies apart from their competitors after the baseline needs are fulfilled. As previously mentioned, consumers use on-demand delivery services when they need something in a short amount of time with the least amount of effort. They are less likely to care how it will be delivered, whether it is contactless or not, as long as their order will be delivered, ideally fast and reliably.


Consumers often come to the on-demand delivery service knowing what they want without the intention of shopping around. Therefore, knowing whether or not the product/service is from a minority-owned business or personalized seems to be secondary. Similarly, consumers tend to use these services for personal use, so gifting is not something focused on for on-demand delivery services. Thus, it is not hard to see that using these services to send gifts may lack a personal touch.


Lastly, unless there are compelling reasons for a membership (like Costco Gasoline or two-day delivery of Amazon Prime), it is understandable that consumers would avoid memberships, while still having access to deals and discounts.


Figure 2. Shopper Group Differences of Bottom-Ranked Needs from MaxDiff. Each graph includes the three groups ordered from left to right: heavy- (red), frequent- (blue), and occasional shopper (green). Kruskal Wallis test was run to compare the mean against each shopper group. The yellow arrow indicates the mean differences between each shopper group (Heavy shoppers consistently ranked the five bottom-ranked MaxDiff items higher than frequent shoppers and occasional shoppers).
Figure 2. Shopper Group Differences of Bottom-Ranked Needs from MaxDiff. Each graph includes the three groups ordered from left to right: heavy- (red), frequent- (blue), and occasional shopper (green). Kruskal Wallis test was run to compare the mean against each shopper group. The yellow arrow indicates the mean differences between each shopper group (Heavy shoppers consistently ranked the five bottom-ranked MaxDiff items higher than frequent shoppers and occasional shoppers).

When comparing the bottom-ranked needs across the shopper groups, we can see, as shown by the yellow arrow on the graph, that heavy shoppers on average ranked these items higher than the other two groups. This indicates that heavy shoppers are more likely to be interested in these additional features (i.e. supporting minority-owned businesses, personalized recommendations, etc.) after their top priorities are fulfilled.


IAT Results

Figure 3. Implicit Association Test Results
Figure 3. Implicit Association Test Results

The figure above shows the summary of the IAT results in relation to the MaxDiff findings. Each attribute is categorized into high, medium, or low association based on how fast participants respond when a word pops up under each brand. The words in green represent the top five needs from MaxDiff, whereas the words in red represent the bottom five needs. By mapping both IAT and MaxDiff findings together, we can see that participants have high associations with words that are ranked as top needs in MaxDiff. The opposite case is also true, in which participants have low associations with words that are ranked as low in priorities in MaxDiff. This shows how consumers value baseline needs of these services. Companies capable of fulfilling these needs are important to the consumer and, therefore, suggests companies should invest in these resources to ensure the services they provide are satisfactory.


From Figure 3, Amazon, Walmart, and Instacart have high and medium association with IAT words tested, with many of the words also being top-ranked needs from MaxDiff. In contrast, participants show low associations with many of the IAT words to Uber and Lyft. This indicates that consumers believe the two companies are not fulfilling their baseline needs. It would be beneficial for Uber and Lyft to further investigate the possible reasons behind this result to improve their brand image and interest in expansion in the market.


Consumer Clustering via Social Network Analysis

To see shoppers' relationships to one another, their responses were plotted (as seen in Figure 4) using social network analysis (SNA). This is a two-mode network, which connects each shopper to the types of services they use based on how frequently they use each service. The red color dots represent the types of services (grocery, ride-sharing, etc.), and the blue, green, and orange color dots are the different types of shoppers.


The advantage of using SNA is that it focuses on ties, which is a form of relationship that we can define in any way we want – in this case, the tie is the type of on-demand services participants use. By using SNA, we can visualize the most popular types of on-demand delivery services and where each type of shopper is positioned in the graph in relation to one another. This output can help target different types of shoppers more easily.


The relational position of each shopper is determined by the combination of the amount of services they use, how popular the types of services they use, and the frequency of use of the specific service. Shoppers who use services that are popular (aka many others in the sample also use it) will be positioned relatively closer to the center of the graph. For example, shoppers who use on-demand delivery services for groceries or food/beverages will be positioned closer to the center in the graph.


Figure 5. Implicit Association Test Results (Left- Frequent Shoppers, Center- Occasional Shoppers, Right- Heavy Shoppers; Click image to expand.)


Figure 5 illustrates that heavy shoppers (5a) are concentrated in the center of the graph because they use different types of services regularly. They not only value the baseline needs of using on-demand delivery services, but they are also more open to trying new services than the other two groups of shoppers. Therefore, brands that are looking to bring new products or services to their platform should consider targeting this group of shoppers first.


In terms of frequent shoppers (5b), they are spread out across the graph, some using many services often, while others only stick to a few that they are familiar with. This corroborates with how they ranked most MaxDiff items in between the other two groups of shoppers. There is an opportunity within frequent shoppers to encourage exploration. Brands should allocate marketing resources to understand who in this group is interested in trying out new services, so the company can be better positioned to address their interests.


Lastly, occasional shoppers (5c) are located at the bottom of the graph, showing that they don’t use services as frequently and tend to stick to one type of service. Recalling that occasional shoppers value the baseline needs more than the other two groups of shoppers, we can tell that they use these services with a specific goal in mind. So, to keep the retention rate of occasional shoppers, brands could look into how well they are doing in fulfilling the baseline needs compared to their direct competitors.


Conclusion

In summary, we can see that occasional shoppers tend to know what they need when it comes to using on-demand delivery services. They are also less likely to try out new types of services without seeing how they would benefit them. In other words, they value the immediate benefits more than other features of on-demand delivery services. Frequent shoppers, on the other hand, tend to value the efficiency of on-demand delivery services. They also have a relatively higher trust in the five brands than the other two groups of shoppers. Frequent shoppers ranked most measures neither high nor low, so there may be an opportunity to persuade them to try out new services. Lastly, heavy shoppers are more adventurous and willing to try a variety of services. They are more receptive to additional features that give them a purpose – such as supporting minority-owned businesses. They also seek out a more personalized experience than occasional and frequent shoppers.


Overall, this study revealed that the trustworthiness of a brand matters to consumers when it comes to using on-demand delivery services. However, participants have no high associations with the five tested brands in terms of their trustworthiness. Therefore, companies should focus on increasing their trustworthiness to consumers. In addition, these brands should also investigate what their strengths are and explore those features. The survey results show that participants did not have high associations with any attributes towards Uber and Lyft, and this presents a challenge for the two companies in bringing new services into a new space. For example, consumers do not find Uber trustworthy when it comes to their ride-sharing service, and in turn also think UberEats is not trustworthy. Thus, Uber and Lyft should focus on researching what consumers associated them with before launching to market. The MaxImplicit methodology has provided an interesting insight into areas that would be beneficial for companies to dive deeper into to differentiate what types of shoppers they are targeting and guide decision-makers to allocate resources accordingly.


Reference


Aparna (2021). “How 'on-Demand Economy' Is Impacting Business World 2021?” MobileAppAaily, MobileAppDaily, 11 Nov. 2020, https://www.mobileappdaily.com/on-demand-changing-business.


Art Figures retrieved from Slidesgo. www.slidesgo.com


Consumer intelligence series: The sharing economy - PWC. (n.d.). Retrieved February 22, 2022, from https://www.pwc.se/sv/pdf-reports/consumer-intelligence-series-the-sharing-economy.pdf


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


Ha, & Perks, H. (2005). Effects of consumer perceptions of brand experience on the web: brand familiarity, satisfaction and brand trust. Journal of Consumer Behaviour., 4(6), 438–452. https://doi.org/10.1002/cb.29


Niedziela, M (2021). Consumer clustering, COVID-19, concerts & more. HCD Research Inc. Retrieved February 22, 2022, from https://www.hcdi.net/post/consumer-clustering-covid-19-concerts-more

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