Seizing the Commuting Moment: Contextual Targeting Based on Mobile Transportation Apps

Abstract

Despite the average daily commuting time of commuters increasing by the day, the way marketers can benefit from our commuting behaviors has not yet been thoroughly examined.In collaboration with one of the largest global mobile service platform providers, this study investigates how contextual targeting with commuting is associated with responses to mobile coupons. The analysis is based on a field study in which 14,741 mobile coupons were sent to 9,928 public transit app users on commuting routes or routes other than commuting routes (i.e. commuters or non-commuters). The key findings indicate that commuters are about three times as likely to redeem their first mobile coupon compared to non-commuters. Further analyses allowing users to receive multiple coupons provide useful insight into an effective distribution strategy for mobile coupons. The findings suggest that enhanced receptiveness to mobile coupons is more perceivable when users obtain multiple coupons. Additionally, multiple-coupon distribution strategy increases response rates more effectively among non-commuters than commuters. Moreover, coupons with short expiration dates more successfully improve the response rates of commuters than do coupons with long expiration dates. For non-commuters, however, the reverse holds true; their response rates are higher for coupons with long expiration. Finally, commuters redeem coupons not only more frequently but also at a faster rate. Both dynamic and static matching methods are adopted to address possible selection biases. By carefully exploiting commuting, which is easily identifiable and occurs throughout the world, managers may improve their mobile marketing effectiveness.

Behaviorally targeted location-based mobile marketing

“…Andrews et al (2015) find that consumers traveling in crowded subway trains are considerably more likely to respond to mobile offers than consumers traveling in less crowded trains. In a similar vein, Ghose, Kwon, et al (2019a) find that consumers targeted via mobile transportation apps on their daily commute are more likely to redeem mobile coupons than non-commuters. Another stream of mobile marketing research is devoted to more targeted location-based advertisements.…”

“…First, in the out-store LBMM condition, participants were passengers in a (moving) car when receiving the ad. This movement might alter LBMM effects (Ghose et al, 2019a) and might therefore bias our results. Second, in this study as well as in the proof-of-concept study, we did not include a no-advertising baseline condition.…”

“…Second, consumers in the out-store condition were passengers in a car when receiving the mobile ad. The LBMM literature suggests that commuting behaviors can alter mobile ad efficacy (Ghose et al, 2019a), which therefore might confound our findings from the out-store condition. Moreover, the different modes of movement might affect how much attention consumers spent on the ads.…”

A Knowledge-based Model for Semantic Oriented Contextual Advertising

“…As stated by Kumar et al [11], contextual advertising employs automated techniques that display advertisers' ads that are relevant to the user's interests as well as publisher website's content. Several approaches have been proposed to realize contextual advertising in practical and real-world application domains [7][8][9][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27]. The authors of [1], [7] and [13] have exploited knowledge encoded in Wikipedia to interpret the textual content of ads.…”

“…To achieve the aforementioned goals, we carry on the research work on two important fields: 1) semantics-based content analysis, and 2) contextual ad matching. These two fields are strongly related in our proposed approach as they rely on the development and use of formal advertising concept standards and metrics [6][7][8]. To validate the effectiveness of the proposed system, we define an interface that facilitates human interaction and evaluate our precision-oriented context-based ad serving system using state-of-the-art indicators for advertising evaluation, namely precision.…”