9.8 e-Marketplace
(Dholakia and Rego 1998) Analyzing Marketing Information and Hit-Rates on Commercial Home-Pages
Objectives:
To comprehensively and rigorously document the types and nature of marketing information presented on commercial home-pages, aiming to uncover the primary objectives of prevalent commercial websites.
To empirically identify key factors of commercial home-pages that contribute to higher visits or hit-rates.
Framework & Methodology:
The study draws inspiration from Resnik and Stern’s “information content” paradigm to assess the informativeness of commercial home pages.
This approach aims to evaluate how much relevant and valuable information is being provided to visitors of these home pages.
Potential Findings and Implications:
Nature of Information: The research may unveil specific types of marketing information that are predominant on commercial websites, which can shed light on the marketing priorities and strategies of companies on the web.
Informativeness Evaluation: By using the “information content” paradigm, it’s possible to determine how beneficial and valuable commercial home-pages are from a user’s perspective.
Key Determinants of Hit-Rates: By understanding which factors lead to increased visits, companies can optimize their websites to attract more visitors. This could include insights on website design, content prioritization, user experience, interactive elements, and more.
Value to Stakeholders: The findings are particularly beneficial for:
Web Page Designers: Insights can inform best practices in design and content placement to drive user engagement and hit-rates.
Businesses: Especially for those looking to enhance their online presence, understanding what resonates with online audiences can be pivotal in shaping their online strategies.
(R. R. Burke 2002) Consumer Value of Technology in Shopping
Objective:
- To comprehend the value consumers attribute to innovations in customer interface technology during the shopping process.
Methodology:
A large-scale national survey involving 2,120 online consumers.
Exploration of consumer preferences in both online and brick-and-mortar shopping contexts.
Examination of 128 varied facets of the shopping journey, ranging from traditional elements to novel technological innovations.
Key Findings:
General Satisfaction:
High Satisfaction: Consumers generally find current retail offerings satisfactory in terms of convenience, product quality, selection, and perceived value for money.
Areas of Dissatisfaction:
Service Levels: Consumers are not entirely pleased with the quality of service they receive.
Product Information: There’s a felt need for better, more detailed product information.
Shopping Speed: Consumers desire a more swift and seamless shopping experience.
Potential of New Technologies:
Enhancing Experience: Emerging technologies have the capability to augment the overall shopping experience.
Customization is Crucial: However, the deployment of these technologies should be strategically tailored to:
Cater to distinct consumer segments.
Address the specific needs and dynamics of various product categories.
Synergy of Media: The study hints at the combined strength of both interactive and conventional media in steering consumers through their buying journey.
based on shopping motives (e.g., convenience, physical store orientation, information use in planning and shopping, variety seeking), there are 4 types of shoppers
convenience shoppers
variety seekers
balanced buyers
store-orientated shoppers
(Montgomery et al. 2004) Modeling Online Browsing and Path Analysis Using Clickstream Data
Background:
- Clickstream data offers insights into users’ navigation patterns on websites by capturing the sequence or path of pages they view.
Research Objective:
This study aims to classify and model path information using a dynamic multinomial probit model of Web browsing.
The goal is to ascertain if and how path data can enhance predictions about users’ future moves on a website.
Methodology:
The research utilized data from a prominent online bookseller.
The dynamic multinomial probit model was compared against traditional multinomial probit models and first-order Markov models to gauge its predictive accuracy.
Key Findings:
The memory component in the dynamic model is vital for accurately predicting users’ paths.
Traditional models (multinomial probit and first-order Markov) fall short in predicting paths effectively.
The results hint that users’ paths may be indicative of their goals on the website, which could aid in forecasting their subsequent movements.
When applied to anticipate purchase conversions, the dynamic model demonstrates that purchasers can be predicted with over 40% accuracy after just six page viewings. This is notably superior to the baseline 7% purchase conversion prediction rate achieved without leveraging path information.
Relevance:
The findings underscore the potential of clickstream data in personalizing web designs and product suggestions based on users’ navigation paths.
Businesses can benefit from this approach by offering a more tailored online experience, potentially increasing conversion rates.
(S. Li, Sun, and Wilcox 2005) Cross-Selling Sequentially Ordered Products: in Consumer Banking Services
Premise:
- Customers’ buying behaviors follow predictable life cycles. Recognizing these life cycles, firms observe that some products or services are typically bought before others.
Research Objective:
This study aims to understand how customer demand for multiple products changes over time and the resulting patterns of sequential product acquisitions when these products have a natural order.
The objective is to utilize these insights to enhance cross-selling strategies for firms.
Methodology:
A structural multivariate probit model was employed to study customer purchasing behaviors.
The research used data from a major bank in the Midwest, focusing on customer purchase patterns of its marketed products.
Key Findings:
There exists a discernible pattern in which customers buy certain bank products before moving on to others.
Gender and age play roles in influencing purchase decisions:
Women and older customers are more influenced by their overall satisfaction with the bank when deciding on acquiring additional services than younger customers and men.
Households led by more educated individuals or males tend to progress more rapidly along the financial maturity continuum compared to those led by less educated individuals or females.
Implications:
By understanding these buying patterns, firms can tailor their cross-selling strategies more effectively.
Recognizing the significance of satisfaction for certain demographic groups (like women and older customers) can help in customizing service delivery to retain and upsell to these segments.
Addressing the diverse financial maturity progression rates across demographics allows for better timing and targeting of product offerings.
Overall Importance:
- Recognizing and acting upon the sequential purchasing behaviors of customers, based on their life cycles, can enhance a firm’s ability to successfully cross-sell, ultimately boosting revenue and customer loyalty.
(S. Li, Srinivasan, and Sun 2009) Internet Auction Features as Quality Signals
Background:
- Due to the inherent risks and uncertainty of online auctions, companies have introduced tools to help sellers showcase their credibility and the quality of their products to tackle the “lemons” problem (where buyers risk purchasing low-quality or misrepresented goods).
Research Objective:
The authors aim to:
Develop a categorization for Internet auction quality and credibility markers based on signaling and auction theories.
Modify and employ Park and Bradlow’s 2005 model to study the effectiveness of these indicators on eBay.
Analyze how these indicators influence consumer participation and bidding decisions.
Empirical Findings:
The research investigates how certain auction features affect user engagement and choices.
It delves into factors that can influence the perceived reliability of these quality markers and also studies the varied responses of different buyer segments to these indicators.
Key Contributions:
The research confirms the importance of signaling in influencing consumers’ bidding behavior in online auctions.
It is the first empirical study of its kind to assess the role of extensive Internet auction institutional tools in tackling the adverse selection issue, enhancing the understanding of the economic rationale behind novel online auction designs.
The findings underline the significance of credible signaling in online auctions. By understanding and leveraging these indicators, auction platforms and sellers can instill greater trust, encourage participation, and optimize bidding outcomes.
(S. Li, Sun, and Montgomery 2011) Cross-Selling the right Product to the right Customer at the right time
Challenge:
- Companies seek to enhance the success rate of their cross-selling campaigns.
Research Objective:
The authors aim to formulate a customer-response model addressing:
The evolving customer demand for diverse products.
Multiple potential roles of cross-selling solicitations, such as for promotion, advertising, and education.
Varied customer preferences concerning communication channels.
Methodological Approach:
The authors employ a stochastic dynamic programming framework.
This approach is designed to maximize long-term profits from existing customers by considering the progressive nature of customer demand and the varied functions of cross-selling promotion.
The end goal of the model is to determine the best strategies for presenting the right product to the apt customer at the optimal time through the preferred communication channel.
Key Insights from a National Bank Data:
Households display different preferences and responsiveness towards cross-selling solicitations.
Beyond immediate sales, cross-selling solicitations accelerate households’ progression along the financial maturity curve (indicating an educational role) and also foster goodwill (indicating an advertising role).
A breakdown analysis reveals that the educational role of solicitations (83%) far outweighs its advertising (15%) and instant promotional (2%) roles.
Model’s Efficacy and Benefits:
Applying the formulated model leads to tailor-made and dynamic cross-selling campaigns.
The outcomes include:
A 56% boost in immediate response rate.
A 149% increase in long-term response rate.
A significant 177% improvement in long-term profitability.
Overall Implication:
- Adopting the proposed framework enables firms to better understand and cater to their customers, significantly enhancing the efficiency and impact of their cross-selling campaigns. The educational aspect, in particular, plays a dominant role in influencing customer behavior and driving long-term value.
(X. Zhang et al. 2014) Social Influence on Shopper Behavior Using Video Tracking Data
Objective:
- The study delves into understanding the role social factors play during a retail shopping experience and how these factors influence a shopper’s likelihood to interact with products and make a purchase.
Methodology:
A bivariate model is utilized to examine both the behavioral drivers leading to product interaction and purchase simultaneously. This model, implemented within a hierarchical Bayes framework, takes into account both individual shopper characteristics and the broader shopping context.
An innovative video tracking system is used to monitor each shopper’s journey and actions throughout the store.
Key Findings:
Direct social interactions, such as those with salespeople or conversations with other shoppers, lead to:
Longer shopping durations.
Enhanced product interaction.
An increased probability of making a purchase.
When shoppers are in larger groups, their buying behaviors are more influenced by discussions with their group members than interactions with strangers.
A store that has a moderate number of customers encourages more product interaction. However, beyond a certain threshold of crowd density, the shopping experience becomes less enjoyable and efficient.
The impact of social influences is also determined by factors like:
The similarity in demographics between a salesperson and the shopper.
The specific type of product category being considered.
There are particular behavioral signs that indicate when a shopper might be more inclined to make a purchase.
Implication:
- Understanding these social dynamics can help retailers strategize their in-store experiences. By optimizing salesperson interactions, understanding group dynamics, and managing store traffic, retailers can enhance the shopping experience and potentially boost sales.
(Ding, Li, and Chatterjee 2015) Learning User Real-Time Intent for Optimal Dynamic Web Page Transformation
Objective:
- The study aims to explore how understanding and adapting to online users’ real-time intentions and cart choices can help e-commerce websites increase their conversion rate of visitors into buyers.
Methodology:
A dynamic model is introduced which simultaneously learns a user’s real-time, unobserved intent from their cart actions and then instantaneously adapts the website page accordingly to improve the chances of a sale.
The model tailors its strategies based on:
Individual user’s browsing behavior.
Effectiveness of different marketing and web prompts.
Users’ comparison shopping activities on other sites.
The approach involves not only learning from the user’s behavior but also implementing optimal web page changes in real time.
Data was sourced from an online retailer and a controlled lab experiment to validate the model’s efficacy.
Key Findings:
Real-time learning and adapting to a user’s unobserved purchasing intent can significantly lower shopping cart abandonment rates and boost purchase conversions.
By starting optimal page adaptations based on intent after the user’s first page view, the shopping cart abandonment rate drops by 32.4%, and the purchase conversion rate rises by 6.9%.
The most efficient time to intervene and make web adaptations is after a user has viewed three pages. This balance ensures that the site has learned enough about the user’s intent while also intervening early enough to influence the purchase decision.
Implications:
- E-commerce platforms can benefit from implementing dynamic models that not only understand but also react in real time to users’ purchase intentions. By doing so, they can significantly improve their chances of converting visitors into buyers, optimizing their user experience, and boosting sales.
(Mallapragada, Chandukala, and Liu 2016) Impact of Product and Website Context on Online Shopping Basket Value
Background:
Managing consumer relationships and understanding the determinants of online shopping behavior pose challenges given the numerous influencing factors.
The factors of interest include the nature of the product being shopped for (the “what”) and the specific context or attributes of the website where shopping takes place (the “where”).
Research Objective:
This study delves into the effects of these characteristics on the value of an online transaction’s basket.
Additionally, the study considers the influence of other facets of online browsing, such as the number of page views and the duration of the visit.
Methodology:
A multivariate mixed-effects Type II Tobit model is used, featuring a system of equations to elucidate the variance in shopping basket value.
The dataset used consists of 773,262 browsing sessions that led to 9,664 transactions. These transactions span 43 product categories and come from 385 distinct websites.
Key Findings:
Contextual factors play a crucial role in online browsing.
A website’s product variety breadth is:
Positively related to both the length of visits and the basket values.
Negatively linked with the number of page views.
The ability of a website to communicate or its communication functionality elevates the basket value, particularly when shopping for hedonic or pleasure-driven products.
Relevance:
- The research offers actionable insights for online retailers, emphasizing the importance of tailoring product offerings and website strategies based on the observed influences on basket value.
(X. Zhang, Li, and Burke 2018) Group Interaction on Shopper Behavior
Objective:
- To understand how social interactions and group discussions influence shopper behavior in retail contexts.
Methodology:
Creation of a dynamic linear model in three stages, analyzing the effects of group discussions on shopper choices and purchases.
Empirical study using a unique video tracking and transaction dataset obtained from a specialty apparel store.
Key Findings:
Influence on Shopper Decisions: Group conversations profoundly impact where shoppers decide to browse (department or “zone” choice), their likelihood to make a purchase, and the amount they spend over time.
Size Matters: The size of the shopping group amplifies the influence, especially when deciding where to browse and the conversion from browsing to buying.
Group Dynamics: Group composition and how cohesive the group is (whether they stick together or split up) moderate the effects of group discussions on shopping behavior.
Mixed-Age vs. Adult Groups: Mixed-age group discussions tend to influence shopping behavior more strongly across all stages, while adult groups show a slight lingering effect on the final purchase decision.
Repeated Discussions: Shoppers who engage in multiple conversations in a specific department tend to revisit and buy from that department.
Cumulative Discussions: The more discussions held store-wide, the higher the overall spending.
Comparing Group vs. Solo Shoppers: Group shoppers tend to visit more departments than those shopping alone. Mixed-age groups and solo shoppers show a higher buying propensity than groups of just adults or teens.
Implications for Retailers:
Engagement Strategy: Retailers can create zones or areas designed to promote group interactions, given the observed influence on shopping behavior.
Store Layout: Recognizing that group shoppers tend to explore more, store layouts can be optimized to encourage group wanderlust and engagement.
Targeting Strategy: Since mixed-age groups and solo shoppers are more inclined to make a purchase, promotional efforts or assistance can be tailored for these segments.
(Ding and Li 2019) Rational Herding in Digital Goods Consumption and Purchase
Objective:
- Understand whether users display herding behavior in the consumption and purchase of serialized digital goods and identify the factors influencing this behavior.
Background:
With the rise of digital goods, average individuals increasingly produce serialized content.
There’s uncertainty regarding the presence and degree of herding behavior in the consumption and purchase of these digital goods.
Methodology:
A simultaneous equations model, rooted in herding theory, was designed.
The model assesses two competing effects:
Private Signal Effect: Impact of individual private signals on quality inference.
Sequential Actions Effect: Influence of observed sequential actions of others on herding.
The model utilizes a hierarchical Bayes framework.
Data from China’s leading literature website was used for empirical analysis.
Key Findings:
Presence of Rational Herding: Users display rational herding in both the consumption and purchase of digital books on the platform.
Herding in Purchase: The tendency to herd is notably stronger when purchasing than when merely consuming.
Factors Affecting Herding:
Product Features: These reduce the herding bias.
Reputation of the Producer: Amplifies the herding effect.
Competition: Intensifies the herding behavior.
P2P platforms (e.g., Uber, Lyft, Airbnb): mediate good and service flow between providers and consumers.
When P2P platforms focus on providers, then consumers will increase purchase-related outcomes because of increased “empathy lens” (tendency to think about how one’s purchase affects the provider), in contrast to “exchange lens” (the reciprocal exchange of money with a firm in return for a good or service).
4 pilot studies confirm the prediction that P2P firms are perceived to have greater provider-firm independence.
Study 1: Field study with a P2P firm (Borrow’d)
Study 2:
a. mediation step via increased perceptions that a purchase helps an individual (P2P - Reliable ride vs. traditional - Reliable Cab).
b. the mediation step and outcome do not occur in traditional business model (whether a service provide was an employee or independent contractor)
Study 3: how study 1 continues to affect willingness to pay for a gift card for a P2P ride service.
Study 4: compare the prototypical for-profit matchmaker P2P model to (a) P2P forums and (b) cooperatives.