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Companies Missing Big Opportunity to Turn Customer Pain Into Competitive Gain, Says CMO Council Report

Despite overwhelming agreement on the importance of customer experience and word-of-mouth, senior marketers admit their companies are failing to take decisive, company-wide action to integrate customer voice and experience into key business and marketing processes, according to a new study by the Chief Marketing Officer (CMO) Council.
Sponsored by Satmetrix, the Net Promoter Company, the study, titled "Giving Customer Voice More Volume," revealed that a surprising 58 percent of the 480 executives surveyed said their companies do not compensate any employees or executives based on customer loyalty, satisfaction improvements or analytics. Some 38 percent said their companies have no programs in place to track or propagate positive word of mouth among customers. In addition, only 29 percent said their companies rate highly in their ability handle and resolve customer problems or complaints.

The CMO Council study underscores critical deficiencies in the way companies measure, optimize and leverage customer experience to drive loyalty, improve brand value and increase business performance and growth, including:

-- Insufficient availability and aggregation of real-time customer
experience data across touch points that should be shared across the
organization

-- Poor use of customer interactions to collect insights and intelligence
or maximize up-sell and advocacy opportunities

-- Lack of Internet processes and systems to track online word of mouth
and drive customer advocacy

-- Intermittent or deficient monitoring of customer experience that fails
to provide true and timely insights into problems and opportunities

-- Too few compensation programs tied to customer experience, loyalty and
satisfaction gains

"Customer experience is one of the most critical determinants of brand strength and business growth. Yet, most organizations and senior marketers suffer from major blind spots and gaps in the way they interact, handle and respond to customer issues or problems," said CMO Council executive director Donovan Neale-May. "CMOs must assume ownership for the customer experience and establish enterprise-wide measures and disciplines to ensure continuous improvement. We are missing a major opportunity to turn customer pain into competitive gain at every touch point through better use of web and contact center technologies and processes."

Customer listening, learning and leveling are critical qualities that need to be part of an institutionalized corporate culture, notes the CMO Council. Yet, survey data demonstrates that most companies are not taking advantage of these opportunities to drive company-wide performance improvement and business growth. Instead, most companies treat customer interactions around service situations and incidents only as a problem that needs quick resolution:

-- Only 38 percent of companies gather customer insight from customer
engagement situations.

-- Just 32 percent look for ways to turn problems into new sales
opportunities, and only 15 percent introduce new products or services to
further monetize the relationship.

-- Merely 17 percent use the opportunity to identify and cultivate
potential customer champions and advocates.

While companies have a long way to go in turning detractors into brand advocates, senior marketers are clearly aware of the importance of customer experience. In fact, 83 percent of respondents said it is either "essential" or "increasingly important" in driving brand advocacy and business performance. In addition, 84 percent said positive customer experiences and word of mouth have helped their brands and businesses grow. There were 44 percent of respondents who admitted that high-profile negative customer experiences had at some time compromised their brands.

Only 31 percent rate their company's commitment to customer listening highly, but another 35 percent say it is "getting better." Although 34 percent of respondents said their companies have made no changes to the way they track and analyze customer experience in recent years, it can be seen as a positive development that 45 percent of respondents say their companies have taken steps to better integrate and analyze customer data. Another 39 percent said they have increased personalization and intimacy in their customer communications, 20 percent say they have embraced intelligent Internet analytics and 18 percent are capturing real-time information at the "point of pain."

"Companies must become more sophisticated and committed to both leveraging customer experience as a key business metric and instituting company-wide processes that drive improvement," said Laura Brooks, Ph.D. and vice president of research for Satmetrix. "The Net Promoter Score has been proven to be the most reliable customer metric for business growth, but measurement is not an end in itself. Companies need to commit themselves to understanding the key determinants of their score and continuously strive to improve their customer experience competitiveness." Brooks is co-author, with Satmetrix CEO Richard Owen, of a new book, titled "Answering the Ultimate Question," that offers a new operating model for NetPromoter success.

Other key findings of the study include:

-- Nearly two-thirds of companies do not have a formal Voice of Customer
program in place.

-- Only 13 percent of companies have deployed real-time systems to
collect, analyze and distribute customer feedback.

-- While 74 percent say they receive customer feedback via e-mail, only
23 percent say they track and measure the volume and nature of these
messages.

-- Customer voice has gone online, but only 14.5 percent track word of
mouth on the Internet

-- Only 12 percent are using a word-of-mouth marketing platform to drive
online customer advocacy.

Andre Bramwell brings his innovative, customer-first approach to the auto transport industry with Bigfella Auto Express, offering flexibility and transparency in car shipping. [PR.com]

If you want to test in a channel where early adopters are being rewarded with both attractive CAC and scale, here’s what you need to know:

~ Podcast advertising is used very successfully as a direct-response channel with CAC on par with other consideration-stage activities. It is not just for awareness.
~ Podcast reach is very good, reaching 51% of US audiences aged 12+ monthly.
~ Ads read by hosts outperform canned programmatic ads.
~ Tracking is harder than most digital channels and the cost to test the channel is higher than most digital channels.

complete article

Today, SAP SE announced the next step in its long-standing partnership with Microsoft, using the latest in enterprise-ready generative AI innovation to help solve customers' most fundamental business challenges. The companies will collaborate on integrating SAPamp;reg;SuccessFactorsamp;reg; solutions with Microsoft 365 Copilot and Copilot in Viva Learning, as well as Microsoft's AzureOpenAI Service to access powerful language models that analyze and generate natural language. The integrations will enable new experiences designed to improve how organizations attract, retain and skill their people.

First Time Customers Get 10% off Orders.

[Liberian Investigator] Monrovia -- The Liberia Water and Sewer Corporation (LWSC) has raised alarm over the low number of water consumers paying their bills, compared to the many who reportedly access public water without payment.

December 21, 2005 – Last week a computer tape containing the names, addresses, account information, payment histories and Social Security Number of 2 million people disappeared while in transit. The tape, which contained data on all of LaSalle Bank’s mortgage customers, had been shipped via DHL to Experian by the bank in November. It then reappeared two days ago.

(Telecompaper) KDDI and Altius Link have launched a new customer support project harnessing the digital transformation (DX) expertise of the KDDI Customer Centre, including generative AI technology...

(Telecompaper) Customer communications cloud software company Sinch said that Rich Communication Services (RCS) can now be provided using its Sinch Customer Dashboard...

(Telecompaper) Vodafone Ireland ended Q2 FY25 with 2.15 million mobile customers, with contract customers accounting for 65.7 percent...

(Telecompaper) New Ugandan operator Talkio Mobile has launched a festive campaign giving over 10,000 customers who book a Talkio SIM online automatic entry to a...

This week, Google sent an email to some Google Ads advertisers warning that they may lose access to use Customer Match with their ads cause harm to users or create a poor user experience. Google said they are updating their customer match policy effective January 13, 2025, to enforce this. Google also posted this over here.

Navah Hopkins posted about this notice on LinkedIn after she received an email from Google about this update. The email says:

On January 13, 2025, Google will update its Customer Match policy to state that customers may lose Customer Match access if their ads have the potential to cause harm to users or create a poor user experience. We may take the following considerations, among others, into account when making this determination.

What are those considerations? Google wrote:

• User feedback
• Prevalence or severity of abuse
• Repeated violations of the Customer Match policy

Google added that "violations of this policy will not lead to immediate account suspension without prior warning. A warning will be issued, at least seven days, prior to any suspension of your account."

Here is the email:

Navah Hopkins added, "Google customer match allows advertisers to take customer lists and apply them for targeting and seed purposes in Google Ads. This is a great way to ensure folks get exactly the right messaging as well as giving us an option to exclude our existing customers from prospecting campaigns."

She explained what harm might be:

The first and most important consideration is using someone's email without their consent. I would take this signal from Google that gambling with privacy rules is getting harder.

Second, ad creative needs to pass the "not a creep" check. This means:
1. You're not targeting kids.
2. You're not making it seem like the ad is for any one specific user.
3. You're not including people in a target list who outright told you they didn't want to see your ads.

Third, and most importantly, violating customer match rules will get your account suspended. Don't mess around with it.

Forum discussion at LinkedIn.

During lockdown Tony Fowler has offered help to isolated older customers who have been advised to stay indoors.

Bosses must pass on all tips and service charges to staff under new employment rules.

VW Finance showed "a lack of empathy", according to the Financial Conduct Authority.

People can attend a virtual meeting to ask questions about the firm and its £1.9m five-year plan.

Customers are some of your best leads. According to a 2022 HubSpot Blog survey of more than 500 sales professionals, more than 70 percent said that upsell and cross-sell drives up to 30 percent of their revenue.

Personalisation in marketing works because personal connections matter. That extends beyond your customers, too. Personal connections include the relationships you have with your suppliers, your stakeholders, your employees, industry peers… the list goes on.

Drawing on the MOA framework, this study establishes a research model that explains the usage intention of robo-advisors. In the model, three predictors that consist of technology relative advantage, technology herding, and technology familiarity influence usage intention of robo-advisors directly and indirectly via the partial mediation of trust. At the same time, the effects of the three predictors on trust are hypothetically moderated by learning goal orientation and perceived performance risk respectively. Statistical analyses are provided using the data of working professionals from the insurance industry in Taiwan. Based on its empirical findings, this study discusses important theoretical and practical implications.

Physical and social aspects each make up a separate part of servicescape. Together, these make up the servicescape. Although previous research has frequently investigated these aspects separately, the purpose of this study is to simultaneously find out the impact of both aspects within the casual dining restaurants' context. In total, 462 customers in Delhi were polled for this study, and structural equation modelling was used to analyse the data. According to the results, both the social and physical parts of the servicescape have the ability to affect how satisfied customers are, which in turn can affect how they behave in the future.

This study examines the relationships between ICTs (e.g., chatbots, virtual assistants, social media platforms, e-mail marketing, mobile marketing, data analytics, interactive voice response, big data analytics, push notifications, cloud computing, and augmented reality) and e-customer relationship management (e-CRM) from the banking industry of China. Similarly, this study unfolds the moderation interference of trust and risk between the association of ICTs and e-CRM, respectively. The study provided a positive nexus between ICTs and e-CRM. On the other side, a significant moderation of trust, as well as financial risk was observed between the correlation of ICTs and customer relationship management. This study endows with insights into ICTs which are critical for achieving e-CRM by streamlining interactions and enhancing their experience. Similarly, trust and financial risk were observed as potential forces that sway the association between ICTs and e-CRM.

Human behaviour, which is led by the human, emotional and occasionally fallible brain, is highly influenced by the environment in which choices are presented. This research paper explores the synergistic potential of the Internet of Behaviours (IoB) and digital nudges in the financial sector as new avenues for intervention while shedding light on the IoB benefits and the digital nudges' added value in these financial settings. Afterward, it proposes an IoB-Nudges conceptual model to explain how these two concepts would be incorporated and investigates their complementary relationship and benefits for this sector. Finally, the paper also discusses key challenges to be addressed by the IoB framework.

In order to overcome the problems of poor evaluation accuracy and long evaluation time in traditional customer relationship evaluation methods, this study proposes a new customer relationship evaluation method for e-commerce platform based on decision tree algorithm. Firstly, analyse the connotation and characteristics of customer relationship; secondly, the importance of customer relationship in e-commerce platform is determined by using decision tree algorithm by selecting and dividing attributes according to the information gain results. Finally, the decision tree algorithm is used to design the classifier, the weighted sampling method is used to obtain the training samples of the base classifier, and the multi-period excess income method is used to construct the customer relationship evaluation function to achieve customer relationship evaluation. The experimental results show that the accuracy of the customer relationship evaluation results of this method is 99.8%, and the evaluation time is only 51 minutes.

In order to solve the problems of large generalisation error, low recall rate and low retrieval accuracy of customer evaluation information in traditional trust evaluation methods, an evaluation method of customer trust in e-commerce market based on entropy weight analytic hierarchy process was designed. Firstly, build an evaluation index system of customer trust in e-commerce market. Secondly, the customer trust matrix is established, and the index weight is calculated by using the analytic hierarchy process and entropy weight method. Finally, five-scale Likert method is used to analyse the indicator factors and establish a comment set, and the trust evaluation value is obtained by combining the indicator membership. The experiment shows that the maximum generalisation error of this method is only 0.029, the recall rate is 97.5%, and the retrieval accuracy of customer evaluation information is closer to 1.

Aim/Purpose: This work aims to understand why a disparity between the popularity of smart phones and the limited adoption of m-banking exists. Accordingly, this study investigates factors that affect a person’s decision to adopt m-banking services. Such an investigation seeks to determine if and to what extent customers’ mobile phone experience as well as their awareness of m-banking services influence their intention to use such services? Background: This study developed a conceptual model to determine the influence that users’ mobile phone experience as well as users’ awareness of m-banking services had on users’ behavioral intention to use m-banking in Saudi Arabia. Methodology: The quantitative method used to collect data was a survey questionnaire tech-nique. A questionnaire with non-structured (close-ended) questions was formulated. A random sample, targeting banking customers in Saudi Arabia, was selected. This study collected data using a cross-sectional survey. Of those surveyed, 389 provided valid responses eligible for data analysis. SPSS v.22 was used to analyze the data. Contribution: This study produced helpful results and a new m-banking conceptual model. The developed conceptual model focused integrally on users’ awareness and experience as antecedents of m-banking adoption and highlighted the im-portance of differentiating between measuring the users’ characteristics in adopting e-banking in general and m-banking services in particular. In addition, this type of model has the ability to synthesize new control variables as well as to study technology acceptance in developing countries. This study, based on an extended UTAUT model, set out to discover what factors might affect customers’ intentions to use m-banking in Saudi Arabia. Findings: The results show that service awareness has a direct effect on performance and effort expectancy, but not on perceived risk. Moreover, mobile phone experience fails to impact the relationships in the same hypothesized direction. As anticipated, performance expectancy, effort expectancy, and perceived risk have direct and significant effects on behavioral intentions to use m-banking. However, customer awareness fails to impact the relationships of performance expectancy, effort expectancy, and perceived risk on behavioral intentions to use m-banking. Recommendations for Practitioners: Banks should target customers by distributing useful information and applying measures to increase acceptance. Banks need to introduce something imaginative to convince bank customers to abandon existing service channels and adopt m-banking services. Banks should make m-banking services the easiest service for conducting bank transactions and/or help customers conduct transactions that they cannot do any other way. Recommendation for Researchers: Other factors, such as trust, culture, and/or credibility should be investigated along with user’s awareness and experience factors in m-banking services. There is a need to focus on a specific type of m-banking. Thus, it may be fruitful to study the adoption of different systems of m-banking services. Impact on Society: This study suggests that m-banking services should be designed and built based on a deep understanding of customers’ needs using extensive testing to assure that applications and sites function well in a mobile setting. Future Research: Future researchers should apply the conceptual model developed in this study in different settings, different countries, and to different technologies.

Aim/Purpose: In this paper, we present an RFM model-based telecom customer churn system for better predicting and analyzing customer churn. Background: In the highly competitive telecom industry, customer churn is an important research topic in customer relationship management (CRM) for telecom companies that want to improve customer retention. Many researchers focus on a telecom customer churn analysis system to find out the customer churn factors for improving prediction accuracy. Methodology: The telecom customer churn analysis system consists of three main parts: customer segmentation, churn prediction, and churn factor identification. To segment the original dataset, we use the RFM model and K-means algorithm with an elbow method. We then use RFM-based feature construction for customer churn prediction, and the XGBoost algorithm with SHAP method to obtain a feature importance ranking. We chose an open-source customer churn dataset that contains 7,043 instances and 21 features. Contribution: We present a novel system for churn analysis in telecom companies, which encompasses customer churn prediction, customer segmentation, and churn factor analysis to enhance business strategies and services. In this system, we leverage customer segmentation techniques for feature construction, which enables the new features to improve the model performance significantly. Our experiments demonstrate that the proposed system outperforms current advanced customer churn prediction methods in the same dataset, with a higher prediction accuracy. The results further demonstrate that this churn analysis system can help telecom companies mine customer value from the features in a dataset, identify the primary factors contributing to customer churn, and propose suitable solution strategies. Findings: Simulation results show that the K-means algorithm gets better results when the original dataset is divided into four groups, so the K value is selected as 4. The XGBoost algorithm achieves 79.3% and 81.05% accuracy on the original dataset and new data with RFM, respectively. Additionally, each cluster has a unique feature importance ranking, allowing for specialized strategies to be provided to each cluster. Overall, our system can help telecom companies implement effective CRM and marketing strategies to reduce customer churn. Recommendations for Practitioners: More accurate churn prediction reduces misjudgment of customer churn. The acquisition of customer churn factors makes the company more convenient to analyze the reasons for churn and formulate relevant conservation strategies. Recommendation for Researchers: The research achieves 81.05% accuracy for customer churn prediction with the Xgboost and RFM algorithms. We believe that more enhancements algorithms can be attempted for data preprocessing for better prediction. Impact on Society: This study proposes a more accurate and competitive customer churn system to help telecom companies conserve the local markets and reduce capital outflows. Future Research: The research is also applicable to other fields, such as education, banking, and so forth. We will make more new attempts based on this system.

Aim/Purpose: In this study, the research proposes and experiments with a new model of collecting, storing, and analyzing big data on customer feedback in the tourism industry. The research focused on the Vietnam market. Background: Big Data describes large databases that have been “silently” built by businesses, which include product information, customer information, customer feedback, etc. This information is valuable, and the volume increases rapidly over time, but businesses often pay little attention or store it discretely, not centrally, thereby wasting an extremely large resource and partly causing limitations for business analysis as well as data. Methodology: The study conducted an experiment by collecting customer feedback data in the field of tourism, especially tourism in Vietnam, from 2007 to 2022. After that, the research proceeded to store and mine latent topics based on the data collected using the Topic Model. The study applied cloud computing technology to build a collection and storage model to solve difficulties, including scalability, system stability, and system cost optimization, as well as ease of access to technology. Contribution: The research has four main contributions: (1) Building a model for Big Data collection, storage, and analysis; (2) Experimenting with the solution by collecting customer feedback data from huge platforms such as Booking.com, Agoda.com, and Phuot.vn based on cloud computing, focusing mainly on tourism Vietnam; (3) A Data Lake that stores customer feedback and discussion in the field of tourism was built, supporting researchers in the field of natural language processing; (4) Experimental research on the latent topic mining model from the collected Big Data based on the topic model. Findings: Experimental results show that the Data Lake has helped users easily extract information, thereby supporting administrators in making quick and timely decisions. Next, PySpark big data processing technology and cloud computing help speed up processing, save costs, and make model building easier when moving to SaaS. Finally, the topic model helps identify customer discussion trends and identify latent topics that customers are interested in so business owners have a better picture of their potential customers and business. Recommendations for Practitioners: Empirical results show that facilities are the factor that customers in the Vietnamese market complain about the most in the tourism/hospitality sector. This information also recommends that practitioners reduce their expectations about facilities because the overall level of physical facilities in the Vietnamese market is still weak and cannot be compared with other countries in the world. However, this is also information to support administrators in planning to upgrade facilities in the long term. Recommendation for Researchers: The value of Data Lake has been proven by research. The study also formed a model for big data collection, storage, and analysis. Researchers can use the same model for other fields or use the model and algorithm proposed by this study to collect and store big data in other platforms and areas. Impact on Society: Collecting, storing, and analyzing big data in the tourism sector helps government strategists to identify tourism trends and communication crises. Based on that information, government managers will be able to make decisions and strategies to develop regional tourism, propose price levels, and support innovative programs. That is the great social value that this research brings. Future Research: With each different platform or website, the study had to build a query scenario and choose a different technology approach, which limits the ability of the solution’s scalability to multiple platforms. Research will continue to build and standardize query scenarios and processing technologies to make scalability to other platforms easier.

Aim/Purpose: This study aimed to increase our understanding of how the stages of the customer purchase journey, privacy trade-offs, and information sensitivity of different business service sectors affect consumers’ privacy concerns. Background: The study investigated young consumers’ willingness to provide consent to use their personal data at different phases of the customer journey. This study also examined their readiness to provide consent if they receive personal benefits, and how information sensitivity varied between different individuals and business sectors. Methodology: Data was collected by a quantitative survey (n=309) and analyzed with R using the Bayesian linear mixed effect modeling approach. The sample consisted of university students in Finland, who represented a group of young and digitally native consumers. The questionnaire was designed for this study and included constructs with primarily Likert-scale items. Contribution: The study contributed to data privacy and consent management research in information sensitivity, privacy trade-off, and the customer journey. The study underlined the need for a stronger user experience focus and contextuality. Findings: The results showed that readiness to disclose personal data varied at different phases of the customer journey as privacy concerns did not decrease in a linear fashion throughout the purchase process. Perceived benefits affected the willingness to provide consent for data usage, but concerned consumers would be less trade-off oriented. Self-benefit was the most relevant reason for sharing, while customization was the least. There is a connection between the information sensitivity of different business sector information and privacy concerns. No support for gender differences was found, but age affected benefits and business sector variables. Recommendations for Practitioners: The study recommends approaching consumers’ data privacy concerns from a customer journey perspective while trying to motivate consumers to share their personal data with relevant perceived benefits. The self-benefit was the most relevant benefit for willingness to provide consent, while customization was the least. Recommendation for Researchers: The study shows that individual preference for privacy was a major factor directly and via interaction for all three models. This study also showed that consumers’ subjective decision-making in privacy issues is both a situational and a contextual factor. Impact on Society: This study could encourage policymakers and societies to develop guidelines on how to develop privacy practices and consent management to be more user centric as individuals are increasingly concerned about their online privacy. Future Research: This study encourages examining consumers’ motivational factors to provide digital consent for companies with experimental research settings. This study also calls to explore perceived benefits in all age groups from the perspective of different information in various business sectors. This study shows that privacy concern is a contextual and situational factor.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: This study investigates the factors that affect the repurchase intentions of Generation Z consumers in India’s online shopping industry, focusing on combining the Expectation-Confirmation Model (ECM) and Extended Technology Acceptance Model (E-TAM). The aim is to understand the intricate behaviors that shape technology adoption and sustained usage, which are essential for retaining customers in e-commerce. Background: Social media and other online platforms have significantly influenced daily life and become essential communication tools owing to technological advancements. Online shopping is no exception, offering a range of product choices, information, and convenience compared with traditional commerce. Indian retailers recognize this trend as an opportunity to promote their brands through e-shopping platforms, leading to increased competition. Generation Z comprises 32% of the world’s population and is a significant emerging customer base in India. Numerous studies have been conducted to study customers’ repurchase intention in the online shopping domain, but few studies have explicitly focused on Generation Z as a customer base. This study aims to comprehensively understand the topic and investigate the variables that impact consumers’ online repurchase intention by examining their post-adoption behavioral processes. Methodology: The study employed a quantitative research design with structural equation modeling using AMOS to analyze responses from 410 participants. This method thoroughly examined hypotheses regarding factors affecting repurchase intention (security, ease of use, privacy, and internet self-efficacy) and the mediating role of e-satisfaction. Contribution: This study makes a unique contribution to the field of e-commerce by focusing on Generation Z in India, a rapidly growing demographic in the e-commerce industry. The results on the mediating role of e-satisfaction have significant implications for e-retailers seeking to enhance customer retention strategies and gain a competitive edge in the market. Findings: The research findings underscore the significant influence of security, ease of use, and internet self-efficacy on repurchase intentions, with e-satisfaction playing a pivotal role as a mediating factor. Notably, while privacy concerns did not directly impact repurchase intentions, they displayed considerable influence when mediated by e-satisfaction, highlighting the intricate interplay between these variables in the context of online shopping, which is the unique finding of this study. Recommendations for Practitioners: This study has several significant implications for practitioners. Effectively addressing computer-related individual differences, such as computer self-efficacy, is crucial for boosting online customers’ repurchase intention. For instance, if an e-retailer intends to target Generation Z customers, they should collaborate with IT professionals and develop various computer literacy programs on online streaming platforms, such as YouTube. These programs will enhance target customers’ confidence in online shopping portals and increase their online repeat purchases. Additionally, practitioners should strive to improve the online shopping experience by making the portal user-friendly. Generation Z is accustomed to a fast Internet experience, so they prefer that the process of completing online transactions is swift with fewer clicks. The search for products, payments, and redress should not be tedious. Furthermore, the primary objective of the e-retailer should be to satisfy customers, as satisfied customers repeat their purchases and increase overall profitability. Recommendation for Researchers: The current study was conducted in the Delhi-NCR region of India, and its findings could serve as a basis for future research. For instance, the scale devised in this study could be utilized to examine the impact of cash-on-delivery as a payment method on purchase intention across the country. Alternatively, a comparative analysis could be conducted to compare cash-on-delivery effects in various countries. Impact on Society: The study’s findings enable stakeholders in the online shopping industry to comprehend the post-adoption behavior of Generation Z users and augment existing literature by establishing a correlation between determinants that impact repurchase intention and e-satisfaction, which serves as a mediator. Future Research: This study examines the factors that impact the propensity of Generation Z shoppers to engage in repeat online purchases. This study focuses on India, where the Generation Y (millennial) customer base is also substantial within the online shopping market. Future research could compare the shopping habits of Generation Z and Generation Y customers, as the latter may place greater importance on privacy and security. Additional studies could broaden the scope of this research and explore the comparative viewpoints of both generations. Also, it would be advantageous to conduct in-depth interviews and longitudinal studies to acquire a more in-depth comprehension of the evolving digitalization of shopping.

Aim/Purpose: This study focuses on two targeting strategies of out-store Location-Based Mobile Advertising (LBMA): the geo-fencing strategy (i.e., targeting customers who are near the focal store) and the geo-conquesting strategy (i.e., targeting those who are near competitors’ stores to visit the focal store). To the authors’ knowledge, no previous studies have compared the perceived intrusiveness of advertisements (ads) in geo-fencing and geo-conquesting settings, despite the accumulating literature on out-store LBMA. Hence, the aim of this study is to determine which targeting strategy is more effective in terms of reducing the perception of ads’ intrusiveness and increasing positive customers’ attitudes and purchase intention. Background: The intrusive nature of LBMA is perceived negatively by some customers, impacting their attitudes toward the ad, purchase intention, and even their perception of the brand. Therefore, identifying the targeting strategy under which ads are perceived as less intrusive is essential. Additionally, brick-and-mortar clothing stores in Jordan are facing challenges due to the rise of online shopping and increased competition from nearby stores. Thus, examining geo-fencing and geo-conquesting might tackle these challenges and encourage local clothing retailers to adopt these strategies. Methodology: A quantitative method was used in this study. A between-subjects experimental design was used to collect the data using a scenario-based survey distributed to Jordanians aged 18 to 45. A total of 531 responses were collected. After excluding those who do not belong to the targeted age group and those who did not pass the manipulation check, 406 responses were analyzed using the Statistical Package for the Social Sciences (SPSS) software version 28 and the Analysis of Moment Structures (AMOS) software version 26 to conduct Structural Equation Modeling (SEM). Contribution: This work offers valuable contributions by investigating the impact of the perceived intrusiveness of ads on purchase intention in the contexts of geo-fencing and geo-conquesting, which has not been studied before. Additionally, it fills a gap by examining this phenomenon in Jordan, a developing country in which attitudes toward LBMA have not been previously explored. Findings: The results revealed that location-based mobile ads sent under a geo-fencing strategy are perceived as less intrusive than those sent under a geo-conquesting strategy. In addition, customers’ attitudes fully mediate the relationship between intrusiveness and purchase intention only under the geo-fencing strategy. Ultimately, neither of the strategies is more effective in terms of increasing positive customer attitudes and purchase intentions in the context of clothing retail stores in Jordan. Recommendations for Practitioners: Clothing retailers in Jordan should consider adopting geo-fencing and geo-conquesting strategies to boost purchase intentions and tackle industry challenges. Additionally, to increase purchase intentions with geo-fencing, practitioners should focus on fostering positive customer attitudes toward ads, as simply perceiving them as less intrusive is not sufficient to drive purchase intention without the mediating effect of positive attitudes. Recommendation for Researchers: This research is crucial for academics and researchers as geolocation technology and LBMA are expected to advance significantly in the future. Researchers can investigate this topic through a randomized field experiment, followed by a research questionnaire to collect data from a real-world setting. Impact on Society: Utilizing LBMA is essential for local clothing retail stores that are trying to effectively reach and connect with their customers because searching the Internet for local goods and services is done primarily on mobile devices. Indeed, this study revealed that customers in both settings (i.e., geo-fencing and geo-conquesting) reported a high intention to visit the promoting store and to purchase from the advertised product category. Future Research: Future research can apply this topic to different industries and cultural contexts, as the results may vary across industries and regions. Moreover, future research could build on this study by investigating additional constructs, such as product category involvement, customization, and content type of the message (e.g., informative, entertaining).

Unmanned stores are one of the latest conceptual developments in retail and have received much attention, especially in the context of COVID-19-related social restrictions and the associated changes in consumer behaviour. The concept considers the latest technological developments and promises to offer various benefits to consumers and retailers based on artificial intelligence and automation. Using a German sample, this paper aims to evaluate consumers' acceptance of and intention to use the most prominent innovative solutions in unmanned stores. A modified technology acceptance model (TAM) as a theoretical framework was applied to the study. The results of the structural equation modelling make two contributions to the existing literature: First, the acceptance criteria for unmanned stores have not been analysed previously. Second, the modified TAM could be confirmed in this study. We provide empirical evidence suggesting that significant numbers of consumers accept unmanned stores, especially if the stores are strategically located and when individuals have a high innovation affinity.

This paper discusses applications of the measures of the risk of misinforming and the role of the warranty of misinforming in the context of the informing component of Customer Related Management (CRM) issues. This study consists of two parts. Firstly, we propose an approach for customers’ grouping based on their attitude toward assessing product's properties and their expertise on the terminology/domain of the seller’s message describing the product. Also we discuss what the most appropriate personal/group warranty is for each of these group/clusters. 

If you’re in the market for a laptop or desktop PC, you know that pretty much all of them come with impressive features. But which brands stand out from the rest? The American Customer Satisfaction Index (ACSI) Household Appliance and Electronics Study 2024 rates the best computer brands and types of devices according to customer […]

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The house doesn't always win.

Verizon Fios internet went out for four hours early Tuesday along a swathe of the East Coast, with thousands of customers reporting issues online.

I know that it has been a while since I was a small remodeling contractor, but has the state of the art really fallen this far?

This September, South Valley Drywall held its 29th Annual Customer Appreciation Golf Tournament for 160 of its valued customers and trade partners at Omni Interlocken Golf Club in Colorado. For the event, it partnered with National Sports Center for the Disabled and raised more than $18,000.

East Lansing, MI – Do you find shopping therapeutic after a tense day at work? It may be rooted to your occupation. Service workers who are verbally abused by customers are more likely to indulge in stress-related shopping sprees, according to a recent study from Michigan State University.

Washington — The Centers for Disease Control and Prevention has released guidance intended to help employers in the retail and service industries protect workers from violence that may occur when they ask customers or co-workers to comply with store policies aimed at preventing the spread of COVID-19.

Boca Raton, FL — Retail workers “are being asked to do too much,” and many are “leaving or throwing up their hands,” says a researcher from Florida Atlantic University.

Amherst, MA — The long-standing approach that “the customer is always right” can take a toll on workers’ mental health and limit their capacity to serve customers, according to a recent study.