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Emerging Trends in Product Bundling: Investigating Consumer Choice and Firm Behavior

Authors:
Vithala Rao at Cornell University
Vithala Rao
Gary J. Russell at University of Iowa
Gary J. Russell
Hemant K. Bhargava at University of California, Davis
Hemant K. Bhargava
Alan Cooke at University of Florida
Alan Cooke
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Emerging Trends in Product Bundling: Investigating Consumer Choice and Firm Behavior Bundling is the practice of selling two or more products together, often at a discounted price. In this article, we extend the concept of bundling to a wide variety of choice settings. We argue that bundle choice covers consumer decision scenarios which differ with respect to three key dimensions: the number of product categories in the bundle, the party in the distribution system who constructs the bundles, and the time frame of the bundle choice decision. These situational differences are important, from the standpoint of constructing an appropriate choice model and developing an appropriate framework for managerial decision making. After reviewing the historical evolution of bundle choice research, we provide detailed discussions of three key areas of current interest: psychological process, multiple category choice, and choice dynamics. We then discuss unresolved issues in bundling research: understanding the multiple rationales behind bundle strategies, specification and calibration of bundle choice models, and the challenges and opportunities of Big Data. We conclude that bundle choice research provides rich opportunities for collaboration among economists, psychologists and choice theory experts in marketing science. 3
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... whether previous bundle decisions impact future bundle picks. These aspects add to the complexity of bundle dynamics in consumer decision-making (Rao et al., 2018). ...
... The temporal dimension is also crucial, as it raises the question of whether past bundle selections influence future bundle choices (Rao et al., 2018). These factors collectively contribute to the complexity of bundle dynamics in consumer decision-making. ...
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Full-text available
  • Mar 2024
In the fiercely competitive retail landscape, success hinges on a retailer's ability to meet customer needs and exceed their expectations. One strategy that has gained traction in recent years is the customization of product bundles to align with individual customer preferences. By understanding and catering to what customers truly desire, retailers can unlock significant opportunities for profit maximization. In the following study undertaken, a total, of 2928 transactions were recorded as part of an exploratory longitudinal for four months on the retail sales of two provision stores in notable locations of the city of Ahmedabad. Half of the transactions were made during the lockdown period (Phase 1: days), with the remaining being made during the COVID-19 pandemic. It was seen that pre-pandemic the three-way lift between detergents, pulses, and cosmetics was the highest at 98% of the time as contradictory to the predicted combination sales of 19.38%. An intriguing association was also discovered between the sales of fruits, milk, and pulses which occurred 97.7% of the time contradictory to the predicted combination sales of 26.59%. It was discovered that during the pandemic lockdown, 99.87% of the time, vegetables, detergents, and cosmetics were purchased together, followed by detergents, fruits, and pulses 185 Northern Economic Review ISSN: 0262-0383 Vol. 15, No. 1 (2024) https://nerj.org/ https://doi.org/10.5281/zenodo.10720891 99.63% of the time. 422 Saturdays, 448 Fridays, 393 Tuesdays, 428 Wednesdays, 407 Sundays, 410 Mondays, and 420 Thursdays all saw transactions. Vegetables had the largest one-way lift overall during both periods, occurring 60.7% of the time pre-pandemic and 55.6% of the time during the pandemic. Product bundling can be an effective strategy for boosting revenue by offering customers a discounted package of complementary products. This approach not only increases the average order value but also helps to promote cross-selling and improve customer satisfaction. This paper highlights the potential of personalized bundle recommendations in retail. It combines market basket research, bundling methods, and creative choice models to optimize revenue and improve consumer shopping experiences. The study provides valuable insights into various research perspectives and practical applications for businesses looking to implement these techniques to increase revenue and customer satisfaction. Product bundling can be a way to prevent misleading demand estimates and counterfactuals. The focus is on endogenous pricing and bundle-level market shares. This research paper highlights the fact that data analytics plays a pivotal role in this approach. Retailers can harness the power of advanced analytics tools to mine valuable insights from customer behavior, purchase history, and demographic data. This allows them to identify patterns and preferences, helping create highly personalized product bundles that resonate with specific customer segments. Moreover, tailored bundles enhance the overall shopping experience. Customers appreciate the convenience and thoughtfulness of pre-assembled packages that cater to their unique interests or needs. This not only increases the likelihood of purchase but also fosters customer loyalty, leading to repeat business and word-of-mouth referrals. Furthermore, retailers can employ dynamic pricing strategies by bundling complementary products at a slightly discounted rate. This incentivizes customers to buy more, thereby increasing the average transaction value. Additionally, it helps clear slow-moving inventory and improve inventory turnover, ultimately boosting profitability.
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... The main takeaway from these studies is that there are various types of product interactions that determine the perceived utility of a bundle, whose structures are complicated and hard to be exploited for optimization purposes. For a more comprehensive literature review on how the bundle utility is modeled in the MVMNL model, refer to Rao et al. (2018), and Agarwal et al. (2015). The applications of the MVMNL model in the marketing literature include empirical analysis of market baskets (Russell and Petersen (2000)), ...
Assortment Optimization Under the Multivariate MNL Model
Preprint
  • Sep 2022
We study an assortment optimization problem under a multi-purchase choice model in which customers choose a bundle of up to one product from each of two product categories. Different bundles have different utilities and the bundle price is the summation of the prices of products in it. For the uncapacitated setting where any set of products can be offered, we prove that this problem is strongly NP-hard. We show that an adjusted-revenue-ordered assortment provides a 1/2-approximation. Furthermore, we develop an approximation framework based on a linear programming relaxation of the problem and obtain a 0.74-approximation algorithm. This approximation ratio almost matches the integrality gap of the linear program, which is proven to be at most 0.75. For the capacitated setting, we prove that there does not exist a constant-factor approximation algorithm assuming the Exponential Time Hypothesis. The same hardness result holds for settings with general bundle prices or more than two categories. Finally, we conduct numerical experiments on randomly generated problem instances. The average approximation ratios of our algorithms are over 99%.
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... The fundamental rules of today's consumer behavior are engagement, likeability, and scarcity [10]. How people behave and behave are cues and signals taken from the social interactions they have, mostly on social media, which adds to the melting pot of thought and action [11]. Through this paper, Ali attempts to understand the nuances of the psychological factors in the purchasing behavior of modern consumers [12]. ...
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The consumer psychology and consumption behavior of ethnic clothing is often a special category of modern clothing consumption, which has a significant impact on the composition of future consumer culture. The research on the consumption psychology of ethnic costumes will help us to better grasp the legal direction of the development of cultural trends. The purpose of this paper is to study the consumer psychology of ethnic wear based on artificial intelligence decision-making and the Internet of Things. After an overview and theoretical analysis of the existing results, the framework of the research and the direction to start are determined. In order to understand the basic situation of the current market, the main multichannels of ethnic wear are classified, and the preference information for these functions is explored in the minds of consumers. Use decision-making process research tools to quantify decision-making process data for all ethnic apparel consumption. The research obtains specific data through questionnaires and grasps the mass psychology of ethnic costume consumption. And then build a scientific system to promote the consumption of national costumes. Research has proved that the creation of cultural scope and the enrichment of commodity types have a very positive impact on the consumption of ethnic clothing and can become one of the important breakthroughs in promoting the consumption of ethnic clothing.
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... For example, a product bundle with variety (e.g., a package of yogurt with six different flavors) provides consumers with an opportunity to satiate their need for variety-seeking and thus would be more attractive, relative to a bundle with nonvariety (e.g., a package of yogurt with one flavor) in times of COVID-19. Additionally, marketing practitioners may utilize consumers' purchase data to identify favorite and nonfavorite products/services/attributes and incorporate the information when designing how to bundle them (Rao et al., 2018). ...
The impact of infectious disease threat on consumers' pattern‐seeking in sequential choices
Article
Full-text available
  • Feb 2022
  • PSYCHOL MARKET
The pandemic outbreak poses one of the most influential threats. When faced with such a threat, consumers engage in adaptive behaviors, and one way to do so may pertain to pattern-seeking in their choices. Across five studies, we show that consumers exhibit patterns in sequential choice under the threat of COVID-19. Specifically, consumers high (vs. low) in the perceived threat increase sequential patterns in repeated choice regardless of whether the levels of the perceived threat are measured or manipulated. The effect emerges even when a patterned choice option is objectively inferior to a nonpatterned option. The underlying mechanism of the effect is that consumers experience a lower sense of control, which motivates them to seek patterned choices to regain control threatened by the infectious disease. We further show that the effect on patterned choice is stronger for consumers with lower childhood socioeconomic status (SES), who are characterized by a lower sense of control, than their higher childhood SES counterparts. Noting that infectious disease threats are unavoidable, we offer theoretical contributions as well as novel insights into marketing practices under unpredictable and threatening situations.
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Effects of consumers' context‐dependent preferences on product bundling
Article
  • Dec 2022
Bundling can change consumers' choice set and affect their purchase decision. It is well‐documented that consumers' preferences depend on the bundling context. In this paper, we investigate the effects of consumers' context‐dependent preferences (CDPs) on the firm's optimal bundling strategy in various competitive situations. We consider a market where a monopolist firm in product category A also sells a product in product category B. We analyze three scenarios in category B: (1) an independent firm selling a higher‐quality product, (2) two independent firms selling the higher‐quality product, and (3) the monopolist in category A is also a monopolist in category B. We find that CDPs can indeed change the firm's optimal bundling strategy. When the firm has a competitive disadvantage in category B, CDPs encourage the firm to offer the bundle (by either pure bundling or mixed bundling). Furthermore, if the category B market is so competitive that the firm possesses negligible market power, mixed bundling can be strictly more effective at increasing the firm's profit than pure bundling since mixed bundling can better take advantage of CDPs. In contrast, if the firm has monopoly power in both categories, CDPs discourage the introduction of the bundle, and pure‐component selling is optimal. This article is protected by copyright. All rights reserved
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An Analysis of Drug Store Product Category Purchasing Using Block Clustering Methodブロッククラスタリングによるドラッグストアにおける商品カテゴリーの購買分析
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Advanced Consumer Psychology Based on Decision Tree Data Mining Algorithm
Chapter
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With the advancement of technology in this information age and the rapid development of e-commerce, payment methods have become more convenient and consumer behavior has also undergone tremendous changes. At the same time, the improvement of the quality of life also makes consumers pursue branding and gentrification of consumption. The phenomenon of pre-consumption is becoming more and more common, reflecting the status quo of irrational consumption. Based on the decision tree data mining algorithm, this paper analyzes the impact of monthly income on the number of advanced consumption. It is found that the higher the monthly income, the more the number of advanced consumption. At the same time, it also analyzes which type of advanced consumption is used by groups under 35 and over 35. The number of people is larger, and it is found that 32.86% are using Huabei, 27.21% are using credit cards, 26.50% are online borrowing, and 13.43% are borrowing from friends.KeywordsDecision treeData mining algorithmAdvanced consumptionConsumer psychology
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Determinants of willingness to pay for traditional bundles: a study from India
Article
Full-text available
  • Jul 2021
  • Int J Emerg Market
Purpose The present research aims to identify critical antecedents of willingness to pay (WTP) for traditional bundles (those comprising only goods or services) in an emerging market context. Further, it differentiates the relative importance of the determinants of customers' WTP according to the bundle type. Design/methodology/approach Data were collected from Indian customers. The paper uses conjoint analysis with an orthogonal design. The experimental conditions were manipulated using vignettes. Findings The results indicated that purchase autonomy was considered the most important driver for customer's WTP in the case of traditional bundles. Quality variability, overall bundle quality and complementarity followed autonomy in the order of importance. Moreover, the interaction effects of autonomy and complementarity with bundle type significantly influenced the customer's WTP. Customers had a higher WTP for services bundle in high autonomy and goods bundle in high complementarity situations. Practical implications Retailers should allow customers to buy either the entire bundle or its components separately, irrespective of the type of traditional bundle. They should try to make bundles whose perceived quality varies significantly in the target customers. Retailers should try to keep complementary components in the goods-only bundle. Originality/value The present study extends the relationship of the WTP with its antecedents to traditional bundles. Earlier studies have only studied these relationships for hybrid (combination of goods and services) bundles. With the current study results, retailers can bundle traditional bundles (goods only and services only).
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Predicting Bundle Preference Using Configuration Data
Article
Full-text available
  • Apr 2017
Understanding customer preference for bundles is key to the success of this widely-used segmented pricing strategy. In addition to conjoint analysis, researchers and practitioners often use a " build-your-own-bundle " or configuration approach to understand customer preferences for bundles. In a configuration study, subjects are asked to create an ideal bundle from a given menu of various products (or product attributes). The resulting choice data poses two challenges for researchers. First, as the size of the menu grows, the number of possible bundles grows geometrically. This implies a large number of alternatives in the choice set. Model estimation from a large choice set can be computationally expensive or even impossible. Second, because each customer only configures one ideal product bundle, researchers have only one choice observation per customer. Thus, researchers are limited to aggregate level analysis. In this study we show analytically that the aggregate choice model for bundles has a closed form representation which takes the form of a multivariate logistic (MVL) model. The parameters of the MVL model reflect the overall preference for bundles and preference heterogeneity. This model deals with the challenges of configuration data and provides clear insights into customer preference segmentation. This research highlights both the challenges and the benefits of using configuration data to analyze bundle preferences. 2
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Modeling Consumers’ Choices of Multiple Items
Article
  • Nov 1995
Contemporary choice models focus on choice opportunities in which consumers purchase a quantity of a single item in a product category. However, failing to recognize the possibility of assortments of multiple-Item purchases can lead to incorrect conclusions about the impact of past purchase behavior on current choices. The authors propose a model that allows for multiple-item shopping trips and apply it to scanner data for powdered soft drinks. The model provides descriptions about the influence of (1) consumer's prior behavior across previous shopping trips, (2) behavior within the same shopping trip, (3) the in-store shelf assortment available at the time of purchase, and (4) marketing mix variables on multiple-item shopping trip choices. The authors’ model provides better choice predictions in a holdout sample at the aggregate and assortment composition levels than a traditional, single-purchase choice model. Using the model, they present simulation and naturally occurring experiment results in the powdered soft drinks category. Finally, they discuss the value of their results for understanding the consequences of consumers’ choices and their implications for manufacturers.
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The Price Bundling of Services: A Normative Framework
Article
  • Apr 1987
  • J MARKETING
As product lines have broadened in many industries (particularly service industries), the use of mixed price bundling has increased. In mixed price bundling, a firm offers its customers the choice of buying one or more products/services individually or of buying a “bundle” of two or more products or services at a special discount. The author presents a normative framework for selecting appropriate types of services for different mixed-bundling discount forms. The framework extends the economic theory of bundling (which historically has been applied to tie-in sales) to permit explicit consideration of different types of complementarity relationships and strategic marketing objectives.
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Divide and Prosper: Consumers’ Reactions to Partitioned Prices
Article
  • Nov 1998
Many firms divide a product's price into two mandatory parts, such as the base price of a mail-order shirt and the surcharge for shipping and handling, rather than charging a combined, all-inclusive price. The authors call this strategy partitioned pricing. Although firms presumably use partitioned pricing to increase demand and profits, there is little clear empirical support that these prices increase demand or any theoretical explanation for why this should occur. The authors test hypotheses of how consumers process partitioned prices and how partitioned pricing affects consumers’ processing and recall of total costs and their purchase intentions and certain types of demand. The results suggest that partitioned prices decrease consumers’ recalled total costs and increase their demand. The manner in which the surcharge is presented and consumers’ affect for the brand name also influence how they react to partitioned prices.
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Preference Measurement of Item Collections
Article
  • Nov 1972
In many cases of practical interest the marketing researcher may wish to analyze preferences for collections of items. This article shows how these collections can be analyzed by conjoint measurement techniques. An application of conjoint measurement to measuring menu preferences is illustrated.
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The Dynamic Effects of Bundling as a Product Strategy
Article
  • Jan 2012
Several key questions in bundling have not been empirically examined in marketing: Is mixed bundling more effective than pure bundling or pure components? Does correlation in consumer valuations make bundling more or less effective? Does bundling serve as a complement or substitute to network effects? To address these questions, we develop a consumer-choice model from microfoundations to capture the essentials of our setting, the handheld video game market. We provide a framework to understand the dynamic, long-term effects of bundling on demand. The primary explanation for the profitability of bundling relies on homogenization of consumer valuations for the bundle, allowing the firm to extract more surplus. We find that bundling can be effective through a novel and previously unexamined mechanism of dynamic consumer segmentation, which operates independent of the homogenization effect, and can in fact be stronger when the homogenization effect is weaker. We also find that bundles are treated as separate products (distinct from component products) by consumers. Sales of both hardware and software components decrease in the absence of bundling, and consumers who had previously purchased bundles might delay purchases, resulting in lower revenues. We also find that mixed bundling dominates pure bundling and pure components in terms of both hardware and software revenues. Investigating the link between bundling and indirect network effects, we find that they act as substitute strategies, with a lower relative effectiveness for bundling when network effects are stronger.
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