Optimizing price levels in e-commerce applications with respect to customer lifetime values

Price dispersion in the Internet has attracted attention from practitioners and academics alike, since it enables companies to adjust prices to a level appropriate to their strategy. This paper demonstrates how Internet retailers can optimize short-term profitability by determining the price elasticity of demand based on empirical price tests. For this purpose visitors of an Internet retailer are divided into subgroups of approximately same size and identical characteristics. Using A-B tests different prices are shown to each subgroup and the conversion rate as a function of price is calculated. We describe the organizational requirements, the technical approach, and the statistical analysis applied to determine the price optimizing the per-order profit. A field study carried out with a large Internet retailer is presented and shows that the company was able to optimize the analyzed price component and thus increase the contribution margin per visitor by about 7%. We conclude that the discussed method could be applied to answer further research questions such as the temporal behavior of demand curves.

Subject area The focus of the case is on the concepts of customer lifetime value (CLV) and customer equity (CE). Monitoring, measuring and maximizing CLV and CE have become a key priority for all marketers. Instructors can introduce these concepts and its key components. The main focus of the case is a quantitative assignment that asks students to analyze the convenience for the existing five AFPs (Administradora de Fondos de Pensiones, Pension Fund Administrator) of winning the tender. The use of CLV and CE measurements is particularly relevant. Students need to estimate the impact of pricing on the CLV and CE of the existing five AFPs. Study level/applicability BA, MSc, MBA Courses: CE, Marketing Metrics, Pricing. The case can also be used in courses that focus on Marketing Plan, Marketing Research or Services Marketing. Case overview In early 2009, Valentina Vial was given the assignment to develop the pricing strategy of Alianza to enter the pension industry. The company will propose a commission fee to compete with the country's existing five AFPs. Whichever AFP presents the lowest commission will be awarded the tender. When there are several competitors, the company must guess each competitor's likely pricing decision. In the analysis of the convenience for the existing five AFPs of winning the tender, the use of CLV and CE measurements is particularly relevant. Valentina Vial needed to estimate the impact of pricing on the CLV and CE of the existing five AFPs. Expected learning outcomes Understand the concepts of CLV and CE and the importance of maximizing a customer's lifetime value for the firm by calculating the CLV and the CE based on a combination of financial and non-financial data. Illustrate the importance of adopting a long-term strategic perspective (using CLV and CE) in choosing a pricing strategy. Once a firm commits to a pricing strategy, it is difficult to shift course. Given this, the choice of pricing levels should be informed by long-term strategic thinking, including consideration of potential competitive pricing decisions. Supplementary materials Teaching Notes are available for educators only. Please contact your library to gain login details or email support@emeraldinsight.com to request teaching notes.

由於市場競爭激烈，如何留住顧客是每個商家所關心的；因此，關係行銷也越形重要。本研究主要以實證分析的方式，探討對於網路書店不同的顧客關係結合方式，包「財務性」、「社交性」與「結構性」，與顧客終身價值的關聯性，以及不同消費者屬性的區隔市場是否對此關聯性產生干擾的效果，試圖提出有效的顧客關係連結策略。研究範圍主要為國內的網路書店以及國外網路書店有在台灣登錄者，而研究對象限定為「曾經在網路書店購書者」，並採用傳統問卷與網路問卷並行發放的方式進行問卷訪談；最後傳統有效問卷為44份，網路問卷244份。研究發現，網路書店其與顧客關係的結合方式中，「財務性」方式雖然對顧客終身價值的承諾無顯著性的影響，但卻是網路購物最基本的結合方式，而「社交性」相對於「結構性」的結合方式對於顧客的終身價值影響較高。另外，網路書店的消費者可以區分為三群，分別是市場規模最大的「便利性與網頁設計並重群」，對於特定網站的終身價值承諾最高的「整體考量群」，以及「自我經驗與互動導向群」。對於「自我經驗與互動導向群」與「整體考量群」應予以「社交性」的關係結合方式，尤其是「自我經驗與互動導向群」。而針對「便利性與網頁設計並重群」則應予以投入在「結構性」的關係結合方式，以有效與顧客建立長期的關係以及較高的顧客終身價值。研究另外還發現，越高價值的顧客群，應該越予以著重在「社交性」的結合方式策略，而逐漸降低強調在「財務性」與「結構性」的結合方式。此外「網頁的設計」因素，在競爭激烈的環境之下，已逐漸成為決定消費者購買的重要因素之一。

This article provides an empirical statistical analysis and discussion of the predictive abilities of selected customer lifetime value (CLV) models that could be used in online shopping within e-commerce business settings. The comparison of CLV predictive abilities, using selected evaluation metrics, is made on selected CLV models: Extended Pareto/NBD model (EP/NBD), Markov chain model and Status Quo model. The article uses six online store datasets with annual revenues in the order of tens of millions of euros for the comparison. The EP/NBD model has outperformed other selected models in a majority of evaluation metrics and can be considered good and stable for non-contractual relations in online shopping. The implications for the deployment of selected CLV models in practice, as well as suggestions for future research, are also discussed.

It is important for online stores to improve customer lifetime value (LTV) if they are to increase their profits. Conventional recommendation methods suggest items that best coincide with user's interests to maximize the purchase probability, and this does not necessarily help improve LTV. We present a novel recommendation method that maximizes the probability of the LTV being improved, which can apply to both measured and subscription services. Our method finds frequent purchase patterns among high-LTV users and recommends items for a new user that simulate the found patterns. Using survival analysis techniques, we efficiently find the patterns from log data. Furthermore, we infer a user's interests from the purchase history based on maximum entropy models and use the interests to improve recommendation. Since a higher LTV is the result of greater user satisfaction, our method benefits users as well as online stores. We evaluate our method using two sets of real log data for measured and subscription services.

As there are a lot of booming online retailers in e-commerce industry in the Internet age, the need of maintaining competitive advantages has become to pay attention to customer relationship management (CRM). To build a successful CRM strategy, it is needed to know individual customer class which can be calculated from Customer Lifetime Value (CLV): the monetary value of customers purchased from the business during their lifetime. CLV modelling allows us to identify customer's predicted business value. It provides the retailers for effectively allocating the resource in their business. This predictive model has been taken on the global Super Store Retail dataset with almost ten thousand transactions. Our model will predict the customers' class of the next year based on their CLV that will help the online retailer to decide which customer should be invested to get long term CRM. Random Forest (RF) algorithm is utilized to train our model and Random Search tuning is conducted to get the best predictive accuracy. The experimental analysis is performed to compare with AdaBoost algorithm on the same dataset.

Salesperson CLV orientation's effect on performance

The selection of a suitable customer lifetime value (CLV) model is a key issue for companies that are introducing a CLV managerial approach in their online B2C relationship stores. The online retail environment places CLV models on several specific assumptions, e.g. non-contractual relationship, continuous purchase anytime, variable-spending environment. The article focuses on empirical statistical analysis and predictive abilities of selected probabilistic CLV models that show very good results in an online retail environment compared to different model families. For comparison, eleven CLV models were selected. The comparison has been made to the online stores’ datasets from Central and Eastern Europe with annual revenues of hundreds of millions of euros and with almost 2.3 million customers. Probabilistic models have achieved overall good and consistent results on the majority of the studied transactional datasets, with BG/NBD and Pareto/NBD models that can be considered stable with significant lifts from the baseline Status quo model. Abe's variant of Pareto/NBD have underperformed multiple criterions and would not be fully useful for the studied datasets without further improvements. In the end, the authors discuss the deployment implications of selected CLV models and propose further issues for future research to address.

As a fundamental concept of customer relationship management, customer lifetime value (CLV) serves as a crucial metric to identify profitable retail customers. Various methods are available to predict CLV in different contexts. With the development of consumer big data, modern statistics and machine learning algorithms have been gradually adopted in CLV modeling. We introduce two machine learning algorithms—the gradient boosting decision tree (GBDT) and the random forest (RF)—in retail customer CLV modeling and compare their predictive performance with two classical models—the Pareto/NBD (HB) and the Pareto/GGG. To ensure CLV prediction and customer identification robustness, we combined the predictions of the four models to determine which customers are the most—or least—profitable. Using 43 weeks of customer transaction data from a large retailer in China, we predicted customer value in the future 20 weeks. The results show that the predictive performance of GBDT and RF is generally better than that of the Pareto/NBD (HB) and Pareto/GGG models. Because the predictions are not entirely consistent, we combine them to identify profitable and unprofitable customers.

The article contributes to the knowledge of customer lifetime value (CLV) models, where extensive empirical analyses on large datasets from online stores are missing. Based on this knowledge, practitioners can decide about the deployment of a particular model in their business and academics can design or enhance CLV models. The article presents predictive performance of selected CLV models: the extended Pareto/NBD model, the Markov chain model, the vector autoregressive model and the status quo model. Six large datasets of medium and large‑sized online stores in the Czech Republic and Slovakia are used for a comparison of the predictive performance of the models. Online stores have annual revenues in the order of tens of millions of euros and more than one million customers. The comparison of CLV models is based on selected evaluation metrics. The results of some of the models which use additional non‑financial data on customer behaviour - the Markov chain model and the vector autoregressive model - do not justify the effort which is needed to collect such data. The advantages and disadvantages of the selected CLV models are discussed in the context of their deployment.

Purpose:  A number of customer metrics allow estimating customer profitability with methods such as the Customer Lifetime Value (CLV). However, investments in customer relationships carry the potential risk to destroy value and reduce profitability when based on incorrect estimates of customer profitability. Therefore, estimating future customer value correctly is essential to allocate marketing expenditures in the most effective way. In this article recent literature about the CLV is reviewed in order to assess its ability as a customer profitability measure. Besides the financial perspective of the CLV, non-financial perspectives such as customer advocacy, (customer or open) innovation and learning have been identified to have an impact on customer profitability. How to properly estimate a customer’s value taking all relevant value creating factors, financial as well as non-financial, into account is the underlying research question. Design/methodology/approach:  This research is based on the review of a number of theoretical and empirical articles published between 1990 and 2010. The aggregation of measures, key-drivers and risks of each key-perspective of the customer relationship contributes to the development of a more systematic understanding of the value creation process and provides answers to the research question. Indirect effects of the CLV as a source of value have received increasing attention in previous research but are not sufficiently accounted for by mainstream methods for valuing customers (Ryals, 2008). Therefore, the attempt to structure available knowledge on indirect effects of the CLV in its contextual setting is made. Findings:  This research is concluded providing evidence that one-dimensional calculations of the CLV deliver an incomplete picture of the customer relationship and estimate customer profitability incorrectly. This supports the idea of a multidimensional CLV approach that accounts for interrelated key-perspectives and results in superior resource allocation. Originality/value:  Seeing customers in a comprehensive way helps to better understand their needs and potential contributions, so that long-term overall profitability can be advanced through the consideration of indirect effects. Indirect effects are usually not reflected in common accounting methods but might result in benefits for the firm. In this research, evidence is provided for the importance of indirect effects offered by customers. This makes the consideration of all relevant dimensions in the value creation process fundamental in order to allocate marketing resources in the most effective way.

Non-parametric estimation of mean customer lifetime value

Inorganic arsenic (iAs) is one of the most toxic metalloids which could accumulate in marine species, especially in clams, causing serious ecological risk. Marine clams accumulate high level of iAs in different tissues. Currently, Fluorescence Lifetime Microscopy (FLIM) technique has provided quantitative information in biochemical diagnosis. In this study, we applied FLIM method into analyzing Hematoxylin and eosin (H&amp;E) stained sections of arsenic exposed <i>Ruditapes philippinarum</i>. The clams were exposed under different concentrations of As(Ⅲ) and As(V) for thirty days. Fluorescent images of the H&amp;E stained hepatopancreas tissue were obtained with FLIM system, followed with data analysis for fluorescence lifetime values. The average fluorescence lifetime of sections in the control group was around 250 ps. The average lifetime value in the As(V) group was slightly increased to around 280-300 ps. The average lifetime value in the As(III) group achieved a significant increase to around 340 ps. These results suggested a higher extent of structural change in As(Ⅲ) exposed group than As(V) group. As a result, this work has provided quantitative evaluation standard for the toxicity of marine mollusk based on fluorescence lifetime imaging method.

To maximize customer lifetime value (LTV), we conduct sales promotions periodically for internet shopping, such as coupons and points, or recommend attractive products to stimulate purchase motivation. For recommendation, it is essential to narrow the target to effective customers and choose appropriate recommended products to maximize the effect with little cost. We formulated this problem as a product portfolio optimization problem to maximize LTV using Markowitz’s mean-variance model, which we generally use for deciding the portfolio of diversified investment stocks. This study proposes a method to increase sales and improve LTV by adding optimal products to the customer’s portfolio. First, we build an LTV mean-variance model for each customer based on their purchase history and select a group of customers with good performance as the sales promotion target. We then estimate which product recommendations from the product groups not purchased by those customers improve the performance on the product sales mean-variance model. Thus, we can obtain the expected LTV while reducing the fluctuation risk. As a result of verification with actual data, we confirmed the effectiveness of the proposed method.

This study investigates the effectiveness of different customer acquisition channels—social media, content marketing, SEO, paid advertising, influencer partnerships, and email marketing—on key performance metrics such as return on investment (ROI), customer conversion rates, and customer lifetime value (CLV) for tech start-ups. Additionally, it explores the moderating role of advanced analytics in enhancing these channels' performance. Using a quantitative research design, data were collected from 200 respondents from tech start-ups via a structured questionnaire. The results indicate that SEO and email marketing have the strongest positive impact on ROI, conversion rates, and CLV, followed by social media and content marketing. Paid advertising and influencer partnerships, though effective, had comparatively smaller effects. The study also confirms that advanced analytics significantly enhances the effectiveness of all acquisition channels, particularly SEO and social media. These findings provide critical insights for tech start-ups, suggesting that prioritizing organic channels like SEO and email marketing, combined with data-driven decision-making, can yield higher returns. Theoretical implications include an expanded understanding of multi-channel strategies in start-ups, while practical implications highlight the importance of advanced analytics in optimizing customer acquisition efforts. Future research could explore how start-ups in diverse industries can further optimize these strategies.