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Customer lifetime value (CLV) scoring is highly effective when applied to marketing databases. Some researchers have extended the traditional association rule problem by associating a weight with each item in a transaction. However, studies of association rule mining have considered the relative benefits or significance of “items” rather than “transactions” belonging to different customers. Because not all customers are financially attractive to firms, it is crucial that their profitability be determined and that transactions be weighted according to CLV. This study aims to discover association rules from the CLV perspective.

This study extended the traditional association rule problem by allowing the association of CLV weight with a transaction to reflect the interest and intensity of customer values. Furthermore, the authors proposed a new algorithm, frequent itemsets of CLV weight (FICLV), to discover frequent itemsets from CLV-weighted transactions.

Experimental results from the survey data indicate that the proposed FICLV algorithm can discover valuable frequent itemsets. Moreover, the frequent itemsets identified using the FICLV algorithm outperform those discovered through conventional approaches for predicting customer purchasing itemsets in the coming period.

This study is the first to introduce the optimum approach for discovering frequent itemsets from transactions through considering CLV.

Conventional frequent itemsets mining ignores the fact that the relative benefits or significance of “transactions” belonging to different customers are different in most of the relevant applied studies, which leads to failure to obtain some association rules with lower support but from higher-value consumers. Because not all customers are financially attractive to firms, it is necessary that their values be determined and that transactions be weighted. The purpose of this study is to propose a novel consumer preference mining method based on conventional frequent itemsets mining, which can discover more rules from the high-value consumers.

In this study, the authors extend the conventional association rule problem by associating the “annual purchase amount” – “price preference” (AP) weight with a consumer to reflect the consumer’s contribution to a market. Furthermore, a novel consumer preference mining method, the AP-weclat algorithm, is proposed by introducing the AP weight into the weclat algorithm for discovering frequent itemsets with higher values.

The experimental results from the survey data revealed that compared with the weclat algorithm, the AP-weclat algorithm can make some association rules with low support but a large contribution to a market pass the screening by assigning different weights to consumers in the process of frequent itemsets generation. In addition, some valuable preference combinations can be provided for related practitioners to refer to.

This study is the first to introduce the AP-weclat algorithm for discovering frequent itemsets from transactions through considering AP weight. Moreover, the AP-weclat algorithm can be considered for application in other markets.

The term ‘strategy’ is one of the most frequently used terms in business, and its application in marketing is particularly common. Company strategy, market strategy, marketing strategy, sales strategy, promotion strategy, distribution strategy, low pricing strategy – it would take a long time to list all of them. Although this term is so commonly in use, its definition is not as straightforward and it can be interpreted in different ways. In comparison with tactical decisions, strategy is much more significant for an organisation as it brings long-lasting consequences. It is implemented by higher level managers on a regular basis, and it is based on external, often subjective information, so decisions – especially at the time they are made – are difficult to evaluate.

Taking into consideration the fact that strategy refers to a long-term rather than a short-term period, strategic decisions serve as the basis for undertaking operational activities. However, marketing refers to the market and the competition. It is possible to claim that marketing strategy is trying to find an answer to the question to which path an organisation should follow in order to achieve its goals and objectives. If, for example, a company has a goal to generate a profit of PLN 1 million by selling 100,000 pieces of a product, the market strategy should answer at least the following two questions:

1. Who will be our target group, for example, who will purchase the 100,000 pieces of the product?

2. Why is it us from whom a potential buyer should purchase the product?

Who will be our target group, for example, who will purchase the 100,000 pieces of the product?

Why is it us from whom a potential buyer should purchase the product?

The target market will be defined if a reply to the first question is provided. The second question identifies the foundations of competitive advantage. These two issues, that is, target market and competitive advantage are the strategic marketing issues. You cannot change your target group unexpectedly while competitive advantage is the basis for changing decisions regarding prices, promotions and sales.

This chapter describes the measures of marketing activities which refer to strategic aspects and testify a company’s market position – the measures of the performance of target groups and competitive advantage. Readers’ attention should be also focused on the indices that are less popular in Poland and, therefore, may be underestimated. It seems that some of them, for example, the index of marketing resources allocation and the marketing risk index, provide a lot of valuable information and, at the same time, make it possible to show the value of marketing investments. Their wider use in the near future is only a matter of time.

The purpose of this paper is to propose a theoretical model for how consumers aggregate satisfaction with individual service encounters to form a summary evaluation of satisfaction, and further examines its effect on customers’ share of category spending (share of wallet (SOW)).

The data used consist of 10,983 completed surveys from 1,448 customers whose transaction-specific satisfaction with a retailer and their subsequent purchase behaviors in the category were tracked for more than four transactions. Mixed effects models were employed to test the relationship between the cumulative effect of satisfaction with multiple service encounters on SOW.

Cumulative satisfaction is a weighted average of satisfaction with specific encounters, with weights decaying geometrically so that more recent encounters receive more weight. More recent transaction-specific satisfaction levels tend to have greater influence on customers’ next purchase SOW allocations; this, however, is only the case for customers who are less than highly satisfied, with a rating of 8 or lower on a ten-point scale. Additionally, the impact of transaction-specific satisfaction on SOW is not linear. Highly positive transaction-specific satisfaction levels have a greater impact on SOW than negative levels.

Many companies monitor satisfaction across multiple service encounters. This study shows how one can aggregate these measures to arrive at a cumulative effect, and highlights the importance to discriminate between first, more and less recent encounters and second, low vs high levels of satisfaction to better understand customers’ spending among different providers.

Using a longitudinal data set with real customers, this paper identifies a new measure for taking into account the cumulative satisfaction, identifies the positivity bias, and shows how recency affects the relationship between satisfaction and SOW.

This paper examines commonly used property price indices in several Commonwealth countries. It finds that many of the measures may be flawed owing to two issues relating to the index construction methodology: the quality change problem and the choice of an index number algorithm. Using data that comprises the universe of transactions for the Singapore residential market, alternative indices based on more rigourous estimation models are constructed that aim to mitigate these problems. When compared to the official benchmark indices, deviations in time series price behaviour are evident particularly for short‐run dynamics. A key implication of the results is the importance of explicitly recognizing the biases that can arise from using extant indices. Otherwise, a reliance on flawed index signals for decision‐making may result in distorted allocations.

Current models of transaction credit in the e-commerce network face many problems, such as the one-sided measurement, low accuracy and insufficient anti-aggression solutions. This paper aims to address these problems by studying the transaction credit problem in the crowd transaction network.

This study divides the transaction credit into two parts, direct transaction credit and recommended transaction credit, and it proposes a model based on the crowd transaction network. The direct transaction credit comprehensively includes various factors influencing the transaction credit, including transaction evaluation, transaction time, transaction status, transaction amount and transaction times. The recommendation transaction credit introduces two types of recommendation nodes and constructs the recommendation credibility for each type. This paper also proposes a “buyer + circle of friends” method to store and update the transaction credit data.

The simulation results show that this model is superior with high accuracy and anti-aggression.

The direct transaction credit improves the accuracy of the transaction credit data. The recommendation transaction credit strengthens the anti-aggression of the transaction credit data. In addition, the “buyer + circle of friends” method fully uses the computing of the storage ability of the internet, and it also solves the failure problem of using a single node.

Congress passed the Stop Trading on Congressional Knowledge (STOCK) Act of 2012, vesting the Securities and Exchange Commission with the clear legal authority to prosecute members of Congress (politicians) if they engage in insider trading. This paper aims to investigate whether members of Congress are informed traders even before they get elected to Congress, and thus helps assess whether the STOCK Act was a necessary piece of legislation.

This study compares the performance of politicians’ portfolios before and after they are elected to Congress using data from the 2004-2010 period. The authors use an event-study method to construct transactions-based calendar-time portfolios and use standard asset pricing models including capital asset pricing model (CAPM) to determine whether these portfolios earn abnormal returns (i.e. outperform the market).

The authors find weak and inconsistent evidence of abnormal returns in politicians’ portfolios that precede their election. They also find that it takes two consecutive terms in Congress for members to start making informed trades that earn themselves abnormal returns. However, these abnormal returns only accrue to those who serve on powerful committees.

The results in this paper provide support for the STOCK Act of 2012 by showing that members of Congress become informed traders while they serve in Congress. However, these results do not imply any wrongdoing for members of Congress, because the paper uses the pre-STOCK Act data (2004-2010 period).

This study is the first academic work that compares politicians’ portfolios before and after they get elected.

The purpose of this paper is to construct a price index for Chinese oil paintings and analyze the financial performance of investing in Chinese oil paintings and its potential for portfolio diversification in Chinese financial markets.

A hedonic regression model is applied to construct a semiannual price index for Chinese oil paintings from 2000 to 2014. The CAPM model, downside β and standard portfolio optimization are used for analyzing portfolio diversification.

The hedonic regression shows that the majority of hedonic variables, such as dimension, artist’s reputation, living status, medium and auction houses, are statistically significant in estimation. Not only the return from oil painting investments is higher than other equities, but also the β coefficient of the CAPM model and downside β indicate that Chinese oil painting may be a good hedging instrument against stock market risk. Furthermore, the portfolio optimizations under standard assumptions suggest that oil paintings as an alternative investment provide diversification benefit.

This paper provides a new and comprehensive analysis of characteristics and risks of investing in the Chinese oil paintings.

HSBC (The Hong Kong and Shanghai Banking Corporation Limited) Holdings Plc. is a part of various trade finance consortia which aimed to digitise the traditional paper-based trade finance process. It had successfully executed multiple trade finance pilots using a blockchain based platform Voltron and was launching its Contour blockchain trade finance platform as a service to its clients. The trade finance market was estimated to be USD 18 trillion on an annual basis and HSBC had a 12% share in the trade finance transactions worldwide. This case revolves around the challenges facing banks/consortia while porting the traditional trade finance process to the blockchain based system. The crux is how the banks form the consortia, implement blockchain and facilitate trading globally given that it is a new technology and will require bringing all the stakeholders involved in the trade finance value chain to the blockchain based platform. HSBC is facing some decision questions on the formation, governance and management of the consortium, on the interoperability between consortia and on how to price its services to its customers.

This paper aims to investigate Lithuanian house price changes. Its twin motivations are the importance of information on future house price movements to sector stakeholders and the limited number of related Lithuanian property market studies.

The study employs ARIMA modelling approach. It assesses whether past is a good predictor of the future. It then examines issues relating to an application of this univariate time-series modelling technique in a forecasting context.

As the results of the study suggest, ARIMA is a useful technique to assess broad market price changes. Government and central bank can use ARIMA modelling approach to forecast national house price inflation. Developers can employ this methodology to drive successful house-building programme. Investor can incorporate forecasts from ARIMA models into investment strategy for timing purposes.

Certainly, there are number of limitations attached to this particular modelling approach. Firm predictions about house price movements are also a challenge, as well as more research needs to be done in establishing a dynamic interrelationship between macro variables and the Lithuanian housing market.

Although the research focused on Lithuania, the findings extend to global housing market. ARIMA house price modelling provides insights for a spectrum of stakeholders. The use of this modelling approach can be employed to improve monetary policy oversight, facilitate planning for infrastructure or social housing as a countercyclical policy and mitigate risk for investors. What is more, a greater appreciation of Lithuania housing market can act as a bellwether for real estate markets in other trade-exposed small country economies.