Customer Conversion Research Articles

T-Learner Based on Machine Learning for Customer Conversion Prediction and Causal Effect Evaluation

T-Learner Based on Machine Learning for Customer Conversion Prediction and Causal Effect Evaluation

Impact of customer service on brand value and efficiency of a company’s marketing funnel

Customer service is a significant factor in creating brand value, however it is still poorly understood how it relates to customer behaviour and efficiency of a company’s marketing funnel. The paper attempts to examine this relationship within the Fintech industry. Methodologically, the study rests on the postulates of marketing theory on customer experience management and a company’s brand value co-creation. Among the research methods is primary data statistical processing through the A/B testing. Empirical data come from a Nigerian fintech company’s internal reports on customer behaviour and cover information collected by customer support. The A/B testing on a sample of 1,964 customers showed a 14.2% conversion in the test group provided with a tailored customer service, which is 3.96 percentage points in absolute terms, or 38.7% in relative terms, higher than in the control group with a conversion of 10.2%. Our experiment demonstrated a statistically significant difference (97.7%) between the groups, thus demonstrating the effectiveness of the targeted customer service in improving customer conversion metrics. The theoretical and practical findings confirm the hypothesis that customer service is a co-creator of brand value, and this impact can be utilized to enhance the efficiency of the company’s marketing funnel. The authors plan further research into the applicability of the methodology to customers in other countries and sectors.

Using customer feedback to prioritise remediation return on investment and improve customer experience

With the myriad of customer comments available on digital media, it is paramount for organisations to identify the breadth of compliments and complaints that customers are discussing, to analyse and understand drivers of customer sentiment and to know where to prioritise available resources such that the resolution of issues produces the biggest rewards for customers — and the business. Many organisations already successfully identify the issues that customers report (and there may be dozens of them). Often, they select the issues with the highest volume (impacting the most customers) and focus on resolving those first. Additionally, they may target the issues that have the highest negative sentiment. While both approaches are useful, they usually lack the ability to track newly developing issues/trends and, most importantly, are unable to accurately prioritise where to start (in the case of issues with similar volumes) and how to quantify the return on investment (ROI) associated with the remediation of each issue. This paper focuses on the technical capabilities needed to be able to identify the drivers of customer conversations and sentiment, the approaches needed to quantify both the importance of taking action from the customer's standpoint and the impact for the business in doing so, allowing a measured approach to improved customer experience. Analysing, prioritising and resolving cross-channel issues creates happier customers, higher rates of acquisition, increased repeat business and, ultimately, improves the bottom line.

Data Analytics and Marketing Communication Performance of Nigerian Organisations

This study investigated the effect of data analytics on marketing communication performance of Nigerian organizations based on insights from JCDecaux Nigeria in Lagos state. The methodology of the study was based on quantitative research approach based on sample size of one hundred (100) respondents selected through multistage sampling techniques was consisting of purposive sampling technique to select the organization of choice (JCDecaux Nigeria and its employees in Lagos state and convenience sampling technique was used to select individual respondents. The data in this study was analysed using frequency distribution, simple percentages and means as descriptive statistics while Pearson Correlation analysis was used to test the hypotheses using Statistical Package for Social Sciences (SPSS). The results indicated that there is significant effect of data analytics on conversion rate of marketing communication in JCDecaux Nigeria in Lagos state based on the hypotheses one tested and indicated by R square value of 84.6%. Also, there is significant effect of data analytics on advertising effectiveness in JCDecaux Nigeria in Lagos state, based on the hypotheses two tested and indicated by R square value of 71.4%. Finally, data analytics has significant effect on revenue generation in JCDecaux Nigeria in Lagos state, based on the hypotheses three tested and indicated by R square value of 92.2%. This study concluded that data analytics plays significant role in affecting marketing communication performance of JCDecaux Nigeria in Lagos state. This is based on the consideration that data analytics from customer information assists in improving customer conversion rate, advertising effectiveness and overall revenue generation, based on the fact that organizations can effectively tailor marketing communication to customers. In view of the findings and conclusion, this study recommended among other things that organizations and management should develop strategic marketing communication that are well aligned with the unique characteristics of customer information from data analytics.

Twitter Social Network Interaction As Customer Engagement In Competition For E-Commerce E-Health Performance In Indonesia

Background – The presence of e-commerce e-health is a societal solution to health needs in the era pandemic. Social distancing rules cause considerable restrictions on community socialization, so they use social media alternatives to share experiences. That matter causes customers to talk about the performance of digital e-health services and create consumer engagement. Public opinion and ratings on social media become data information analyzed to improve service quality. 
 Aim – This study aims to identify customer conversations on Twitter about e-commerce e-health to see its performance and compare e-health results networks in Indonesia.
 Design / methodology / approach – The research method in this study is the Social Network Analysis (SNA) approach and descriptive meaning to get the results of the formulated goals.
 Findings – The results of this study showed that pre-pandemic e-health users in Indonesia were more dominant in utilizing psychologist and psychiatric consultations and buying easy and practical medicines. During the pandemic, customers more often used doctor consultations related to symptoms and treatment of Covid-19, drugs and vitamins for self-isolation, supported by online payments. 
 Research implication – This research contributes to e-health companies in Indonesia regarding suggestions for utilizing SNA through customer interaction on social media to improve the performance and competition of e-health services and determine superior strategies. 
 Limitations – This research explores data only from social media Twitter, based on the material for data visualization is only in text. The scope of the study is only in the health business, so it can further develop in other sectors.

A necessary conversation to develop chatbots for HIV studies: qualitative findings from research staff, community advisory board members, and study participants

ABSTRACT Chatbots increase business productivity by handling customer conversations instead of human agents. Similar rationale applies to use chatbots in the healthcare sector, especially for health coaches who converse with clients. Chatbots are nascent in healthcare. Study findings have been mixed in terms of engagement and their impact on outcomes. Questions remain as to chatbot acceptability with coaches and other providers; studies have focused on clients. To clarify perceived benefits of chatbots in HIV interventions we conducted virtual focus groups with 13 research staff, eight community advisory board members, and seven young adults who were HIV intervention trial participants (clients). Our HIV healthcare context is important. Clients represent a promising age demographic for chatbot uptake. They are a marginalized population warranting consideration to avoid technology that limits healthcare access. Focus group participants expressed the value of chatbots for HIV research staff and clients. Staff discussed how chatbot functions, such as automated appointment scheduling and service referrals, could reduce workloads while clients discussed the after-hours convenience of these functions. Participants also emphasized that chatbots should provide relatable conversation, reliable functionality, and would not be appropriate for all clients. Our findings underscore the need to further examine appropriate chatbot functionality in HIV interventions.

COVID 19 Impact on the Consumer Consumption Pattern in the FMCG Sector

The research is focused on identifying the changes in the consumption pattern of the consumers in the FMCG sector due to the COVID 19 pandemic. The distribution channel disruption and the complete lockdown severely affected the manufacturing and the distribution of the FMCG products and the shifts that has taken place when it comes to purchase of FMCG goods, the medium used and whether the change in the behavior of the consumers are permanent or temporary. The research was conducted on the FMCG sector which provides huge opportunity in the rural markets of India. The pandemic has shifted the consumer consumption pattern from non- essential goods to essential products. Purpose – The main purpose of the research is to look into the consumption patterns for pre- corona, during the lockdown and the post lockdown and finds out the changes in the consumption pattern so as to understand the future of the FMCG sector. The customer conversion into e-commerce platforms and home delivery services for purchase of groceries, vegetables etc.

Clicks and Conversions: Unveiling the Power of Machine Learning in Social Media Ad Classification

The classification of social media ads plays a crucial role in determining the likelihood of target audience engagement and product purchases. This application of data science in marketing offers significant potential for improving ad targeting strategies. This article aims to provide insights into the analysis of social media ads for effective audience classification. By understanding the factors that influence target audience behavior, marketers can optimize their ad campaigns and enhance customer conversion rates. Through the utilization of data science techniques, this study offers valuable guidance on how to analyze social media ads and classify target audiences more accurately

Pdf The Challenger Sale: Taking Control of the Customer Conversation

pdf The Challenger Sale: Taking Control of the Customer Conversation

Prediction of customer’s perception in social networks by integrating sentiment analysis and machine learning

Understanding the customer behavior and perception are important issues for motivating customer satisfaction in marketing analysis. Customer conversation with customer support services through social networks channel provides a wealth of information for understanding customer perception. Therefore, in this paper, a hybrid framework that integrated sentiment analysis and machine learning techniques is developed to analyze interactive conversations among customers and service providers in order to identify the change of polarity of such conversation. This framework aims to detect the conversation polarity switch as well as predict the sentiment of the end of the customer conversation with the service provider. This would help companies to improve customer satisfaction and enhance the customer engagement. The effectiveness of the proposed framework is measured by extracting a real dataset that expresses more than 5000 conversational threads between a customer service agent of an online retail service provider (AmazonHelp) and different customers using the retailer’s twitter public account for the duration of one month. Different classical and ensemble machine learning classifiers were applied, and the results showed that the decision trees outperformed all other techniques.

Deteksi Intensi Chatbot Berbahasa Indonesia dengan Menggunakan Metode Capsule Network

Intent detection is a process of classifying customer intention from given sentence or chatting. One of the uses of intent detection is in a chatbot. With intent detection, the chatbot can detect the customer intent. However, currently the use of intent detection has not been implemented by most companies in Indonesia. A good intent detection method for chatbots is one that is able to classify user intentions accurately and quickly. This study aims to perform intent detection of messages from the Indonesian language chatbot dataset obtained from customer conversations of PT. Kazee using the Capsule Network(CapsNet) method. With this research, it is hoped that the chatbot of PT. Kazee can respond to customers more appropriately. On this study we conducted experiments and analyze the use of Capsule Network (CapsNet) method in detecting the intent of PT. Kazee customers conversation. The dataset of the experiments contains questions about PT. Kazee. There are two types of datasets—a dataset with six intentions and a dataset with 18 intentions. We compare the result of this experiment with the BERT intent detection model we used previously. The experiment show that the execution time of CapsNet method is faster than that of BERT. However, BERT is still superior to the CapsNet for the ability to respond appropriately. The CapsNet method can be considered for use on chatbots that are more concerned with execution speed.

A novel semi-supervised framework for call center agent malpractice detection via neural feature learning

This work presents a practical solution to the problem of call center agent malpractice. A semi-supervised framework comprising nonlinear power transformation, neural feature learning, and clustering is outlined. We put these building blocks together and tuned the parameters to obtain the best performance. The data used in the experiments is obtained from our in-house call center. It is made up of recorded agent–customer conversations, which have been annotated using a convolutional neural network (CNN) based segmenter. The methods provided a means of tuning the neural network parameters to achieve a desirable result. Using our proposed framework, we show that it is possible to significantly reduce the malpractice classification error of k-means and agglomerative clustering models, which would serve the same purpose. By presenting the amount of silence per call as a key performance indicator, we show that the proposed system has increased the efficiency of quality control managers, thus enhancing agents’ performance at our call center since deployment.

Conversion paths of online consumers: A sequential pattern mining approach

As the number and variety of online channels increase, businesses and advertisers must realize efficient marketing strategies and marketing mix. Customers' online channel usage patterns can be heterogeneous depending on various variables. If they understand this behavior correctly and use them for channel inflow or advertisement toward customers, they can establish an effective marketing strategy. In this paper, we utilize sequential pattern mining to find homogeneous patterns in customer's conversion paths by using conversion path data of customers who purchase diapers and find related insights. Furthermore, by analyzing the customer's channel movement pattern by dividing it into demographic information, we examine how demographic factors affect customer's online channel usage behavior.

Understanding the Influence of Mobile Interface of Shopee and Lazada on Customer Conversion

Mobile commerce is an industry that has rapidly grown in value over the years. Fewer customers now use their mobile devices only as a single functioning unit, expanding their purpose even to shopping. Despite its increasing popularity, recent literature reviews did not reveal a definitive study on m-commerce app interface quality and its relation to customer conversion. This paper aims to understand the influence of mobile interface on customer conversion by examining the top 2 e-commerce apps in the Philippines – Shopee and Lazada. The Regression analysis method is applied in determining relationships between variables. From a total of 385 participants, results show that dimensions of the mobile interface (visual appeal, layout quality, information quality) influence credibility and usability, except for in-app banners, which has a negligible effect on customers. The study has also found that all aspects of mobile interface positively influence customer conversion. On top of that, the researchers have also taken steps to discover that perceived trust and ease of use greatly affect customer conversion. Hence, when a customer finds an e-commerce platform to be trustworthy and user-friendly, the more they will be likely to purchase a product.

Business models competition based on knowledge assets

All organisations have trouble pointing out exactly what makes their customers choose their services. During great uncertainty, knowing all the details count as to be successful and to gain a new business. This research was designed to understand the main causes to close a deal in B2B, in the management consulting around Europe. The aim was to determine if it is possible to isolate the relevant features responsible for the acceptance of service proposals in management consulting context based on historical data. The objectives were to highlight these variables, use them to construct a predictive model, and suggest performance improvements, to allow organisations to manage their time more effectively and efficiently. The sample was used to train a logistic regression model which achieved excellent classifications. The findings of the research rank the most critical success factors in B2B consulting which determines customer conversion and shows that variables' technical credibility at the proposal stage and budget availability are the ones more influential to close a deal.

Role of psychological and technological attributes on customer conversion to use food ordering apps

PurposeToday the mobile apps are helping customers in every means of their life by exploring information related to electronic items and even for food items also. Therefore, the main aim of this study is to examine the influencing role of psychological and technological attributes on customer conversion to use food ordering apps.Design/methodology/approachConvenience sampling method was used to collect responses from the customer's age between 18 and 35 years to get more representation of society. 374 customer's data has been used for structural equation model analysis with the help of SmartPLS 3.0 and SPSS-20 software.FindingsFindings showed that customer conversions are positively influenced by perceived ease of use, perceived usefulness, perceived incentives, perceived information, customer relationship management and order management system. In contrast, the perceived price and visual design show insignificant influence.Research limitations/implicationsResearch outcome provides some valuable insights to provide direction to all online food aggregators to design their apps according to customers need. Thus, the authentic customer reviews and the facility of providing feedbacks are very fruitful for attracting new user while using and placing orders through theses apps.Practical implicationsStudy suggested that observing satisfaction and dissatisfaction levels of the customer helps in enhancing the facilities of food ordering apps, so that the customer would enjoy the whole order process seamlessly, which automatically leads to customer conversions.Originality/valueMajor contribution of this study is the empirical analysis of psychological and technological attributes on customer's conversion towards food ordering app.

СТРАТЕГІЯ ІНТЕРНЕТ-МАРКЕТИНГУ ІТ-ПРОДУКТУ НА РИНКУ B2C

The article discusses the issues of developing an effective strategic approach to promoting IT products via the Internet on a B2C market to attain marketing goals. The study provides insights into the peculiarities and essential differences in marketing activities on a B2B and B2C markets along with revealing the specifics of using Internet marketing tools in a consumer market subject to specific features of an information technology product and a particular phase of a consumer’s purchase decision. The findings demonstrate that IT products are characterized by high degree of innovation and technological complexity which differentiates them from products in other areas of business. It is argued that the promotion of IT products on the Internet imposes a strategic approach to the selection of advertising channels and formats to ensure effective communication and achieving marketing goals. It is important that this process shall rely on the results of marketing research including the research on target audiences, competitors activities, and the effectiveness of particular tools for selecting advertising channels and formats that will ultimately contribute to reaching marketing goals. In this context, using Internet marketing tools for IT products is critical to build an effective communication system in the framework of a customer conversion funnel. It is noted that the use of Internet marketing instruments by software companies also has certain specifics driven by the conversion funnel and differentiation characteristics which make it differ from other areas of the IT market. The study findings highlight a significant role of a video format in building brand knowledge as well as in achieving conversion goals. The research results have important implications to IT companies, in particular to mobile applications developers, which can be utilized in designing product promotion strategies or while launching a new product into the market.

Lead management optimization using data mining: A case in the telecommunications sector

The growing competitiveness of the market has put pressure on companies to improve their customer relationship management strategies. In an era where mass marketing techniques are inadequate, lead management is at the forefront to provide a customized approach to customer acquisition. For this, lead management depends on the correct selection of leads and decision making on what type of approach to take to satisfy the requirements of customers. However, currently, firms are faced with massive quantity of data regarding customers and prospects. Data mining is a solution to cope with this problem, providing a robust approach to massive quantity of data and its complexity. In literature, there is a lack of documented applications of these techniques in lead management. In this sense, we propose a methodology that aims to improve efficiency on the distinct maturity stages of leads management. Also, the methodology aids in support the decision-making regarding the segmentation of leads. This research suggests the application of data mining techniques in the optimization of leads management processes, from capture to conversion, with the objective of improving customer conversion effectiveness. A case study was conducted in a telecommunications company. It was possible to implement the proposed method to estimate the probability of conversion for each lead. With this, was possible to segment the offer to each type of lead.

A Closer Look at Consumer Conformity in Industry 4.0: Purchase Intention Redefined

This research is based on the observations of the five major phenomena. The first is the digital world that has given impacts on the social behavior of people. The more opened the people to information, the easier for them to absorb information from external sources and become influenced (Afonasova et al., 2019; Joireman & Durante, 2016; Kurniasih, 2019). Second, the dynamic world of fashion is unpredictable. The latest fashion trend is primarily affected by the taste of market. The trend can come from all sources, such as TV shows, dramas, actors, actresses and games (AlGeddawy, Abbas, & ElMaraghy, 2014; Eastman, Iyer, & Thomas, 2013; Khan et al., 2015; Knight & Kim, 2007). Figure 1.6, 1.7 and 1.8 shows the ongoing process of fashion segment development in Indonesia projected by the Global Consumer Survey by 2024. The analyst opinion is as follow 'In the Fashion segment, the global online share of revenues already reached 20% in 2018, but the segment's growth will continue in the next years, driven by East and Southeast Asia regions with their growing middle class and lagging offline shopping infrastructure. One of the leading trends is the direct integration of shopping functionality into the content display on social media, enabling high-speed, "inspire and sell" customer conversion (e.g., Instagram Checkout). The audience is increasingly shifting from desktop to mobile, and improving the mobile browsing and checkout experience is becoming crucial.' (Fashion - Indonesia Statistica Market Forecast, 2020). The analyst' prediction matches the global consumer trend that solidifies the importance of this dissertation. As the biggest market share among fashion products, this research focuses on the fashion apparel, known as clothing in general. Keywords: Consumer Conformity; Social Ties; Social Comparison; Image Related Concerns; Purchase Intention

Third party ancillary revenues in the airline sector: An exploratory study

Some airlines, especially Low Cost Carriers (LCCs), have earned significant profits from revenues derived from ancillary revenues. However, to date few have been able to derive a meaningful portion of revenues from 3rd party ancillaries. The key to increasing these revenues comes from an understanding of passenger willingness to pay for 3rd party products/services, coupled with increasing customer awareness and consequently the all-important customer conversion. This study assesses the 3rd party ancillary services that passengers are more willing to purchase, along with the potential offers that might increase their willingness to purchase specific 3rd party services. A mixed methods approach was used consisting of a passenger survey and nine expert interviews. The main findings are that car hire, airport parking, and the sale of hotel rooms had the most significant associations with customer willingness to pay. It was also found that there was a significant association between specific offers and increased willingness to purchase. The 15% discount off a future flight, and to a lesser extent, hotel price guarantees were the most significant. Expert interviewees confirmed that the future sustainability of this revenue stream for airlines is centred on mobile digital devices, customer conversion, and exploiting potential market opportunities.