Mobile Coupon Optimization and Consumer Behavior

Mobile coupon optimisation merges digital voucher distribution with advanced analytics to enhance consumer engagement and drive sales. Mobile coupons take the form of discount codes, electronic vouchers and loyalty rewards delivered via smartphones. Their effectiveness hinges on precise targeting—leveraging data on location, purchase history and individual preferences—to present timely and relevant offers. Consumer behaviour in this context is shaped by psychological factors such as reactance, perceived value and convenience. Advances in data analytics and machine learning enable hyper-personalisation, allowing firms to segment customers and tailor offers that maximise redemption while controlling cost. Location-based marketing, using geofencing and real-time signals, refines outreach by prompting coupons when consumers are in proximity to retail outlets. Research examines how timing, offer type (price versus non-price) and context (in-store versus out-store) influence uptake. The field unites marketing practice, behavioural science and computational methods to optimise campaign performance, understand heterogeneity in consumer response and ensure equitable benefits across customer groups.

Research from Nature Portfolio

No recent Nature Portfolio content available.

Research from all publishers

Recent studies in the wider literature highlight methodological innovation and strategic insights. A causal machine learning framework has been deployed to quantify the impact of diverse coupon categories on retailer sales and uncover treatment-effect heterogeneity across customer segments. By evaluating group average effects and employing optimal policy learning, this work identifies which offer types and recipient groups maximise campaign profitability.

Behavioural targeting within location-based mobile marketing has been shown to interact with product category involvement to modulate consumer reactance. Field and experimental evidence reveals that in-store promotions are more persuasive for low-involvement shoppers, whereas out-store campaigns require matching of non-price offers to high-involvement audiences and price incentives to low-involvement consumers to minimise resistance and boost engagement.

Theoretical models of competing retailers using location-based mobile coupons analyse equilibrium strategies under varying market intensity and targeting costs. Results demonstrate that symmetric adoption or non-adoption emerge at low costs, while product differentiation yields mixed promotional tactics. These findings guide managers in selecting promotional intensities and segment-specific formats to enhance market share and profitability.

Mobile Coupon Optimization and Consumer Behavior publication trend

The graph below shows the total number of articles in mobile coupon optimization and consumer behavior across all publications each year (not limited to Nature Index journals).

Technical terms

Mobile coupon: A digital discount code or voucher delivered to a consumer’s smartphone to incentivise purchases and foster loyalty.

Location-based mobile marketing: A strategy that uses consumers’ geographic data to send targeted promotional messages when they are near retail venues.

Consumer reactance: A psychological response in which individuals resist persuasive messages perceived as threatening their autonomy.

Causal machine learning: A set of algorithms designed to estimate the causal impact of interventions by accounting for confounding variables and heterogeneity.

Optimal policy learning: A data-driven approach to determine the most effective targeting strategy by maximising expected outcomes across different customer segments.

References

1. Behaviorally targeted location-based mobile marketing. Journal of the Academy of Marketing Science (2021).
2. How causal machine learning can leverage marketing strategies: Assessing and improving the performance of a coupon campaign. PLOS ONE (2023).
3. Competition Strategies for Location-Based Mobile Coupon Promotion. Journal of Theoretical and Applied Electronic Commerce Research (2021).