Personal acquaintances and salespeople in financial services: Differences between customers and friends

The existence, benefit and management of customer–salesperson relationships in the marketing of financial services are topics of increasing interest. Much of the sales and marketing literature implies that because of time spent together, salespeople and some of their customers develop close relationships that are akin to friendships. Evidence from social psychology confirms that strong relationships are founded in deep knowledge of others gained over long periods after sharing personal information. This paper reports on the results of a study of salespeople's assessments of their personal acquaintance with customers and friends in a financial services setting. The results indicate that salespeople do not classify customers as friends on all the dimensions of personal acquaintance. Furthermore, the nature of personal acquaintance differs between ‘good’ customers (those salespeople enjoy serving), and ‘bad’ (those they do not), with the exception of the personal acquaintance dimensions of interaction frequency and personal disclosure. We discuss the implications for practice and make recommendations for future research.     

Getting the most out of all your customers

Companies spend billions of dollars on direct marketing, targeting individual customers with ever more accuracy. Yet despite the power of the myriad data-collecting and analytical tools at their disposal, they're still having trouble optimizing their direct-marketing investments. Many marketers try to minimize costs by pursuing only those customers who are cheap to find and cheap to keep. Others try to get the most customers they possibly can and keep all of them for as long as they can. But a customer need not be loyal to be highly profitable, and many loyal customers turn out to be highly unprofitable. Companies can get more out of direct marketing if they see it as a single system for generating profits than if they try to maximize performance measures at each stage of the process. This article describes a tool for doing just that. Called ARPRO (Allocating Resources for Profits), the tool is essentially a complex regression analysis that can estimate the impact of a company's direct-marketing investments on the profitability of its customer pool. With data that companies already gather, the tool can show managers how much to spend on acquisition versus retention and even what percentage of their funds they should allocate to the different direct-marketing channels. Using the model, companies can easily see that even small deviations from the optimal levels of customer profitability are expensive. Applying it to one catalog retailer showed, for instance, that a 10% reduction in marketing costs would lead to a 1.8 million dollar drop in long-term customer profits. Conversely, spending 69% less on marketing would actually increase average customer profitability at one B2B service provider by 42%. What's more, the tool can show that finding the optimal balance between investments in acquisition and retention can be more important than finding the optimum amount to invest overall.     

INTEGRATION OF RANDOM SAMPLE SELECTION,SUPPORT VECTOR MACHINES AND ENSEMBLES FOR FINANCIAL RISK FORECASTING WITH AN EMPIRICAL ANALYSIS ON THE NECESSITY OF FEATURE SELECTION

Financial risk forecasting (FRF) is an effective tool to help people forecast whether or not a company will fail in future. Among all techniques of FRF, the support vector machine (SVM) is the most newly developed, and one of the most accurate and effective techniques. This study is devoted to investigating an ensemble model of FRF by integrating bagging with an SVM to generate a data‐driven SVM ensemble. Bagging is used to produce diverse training datasets on which multiple SVM classifiers are trained to make FRF for a target company. Simple voting is employed to produce a final decision from the SVM model committee. The empirical study has two objectives. One is to verify whether the data‐driven SVM ensemble can produce a more dominating performance than the most frequently used techniques in the area of FRF, i.e. multivariate discriminant analysis, logistics regression and a single SVM. The other is to verify whether feature selection is necessary to help the SVM make more precise FRF, although the SVM can handle high‐dimensional data. The results indicate that the data‐driven SVM ensemble significantly improves the predictive ability of SVM‐based FRF. Meanwhile, feature selection can effectively help the SVM achieve better predictive performance, which means that use of feature selection is necessary in SVM‐based FRF. Copyright © 2012 John Wiley & Sons, Ltd.     

Banks,customer relationship management and barriers to the segment of one

Technological advances are changing the nature of marketing channels and altering how consumers shop. The same changes are improving marketers' ability to capture and manipulate data, leading to an enhanced understanding of customers. Financial institutions are using these technological capabilities to target the individual needs of customers. As the benefits of customer relationship management (CRM) are brought to the fore, this focus on the ‘segment of one’ has become a feature of the service offered by some banks and financial services institutions. This paper reviews case study evidence of progress towards the segment of one and identifies the barriers that are impeding progress.     

Wrapped input selection using multilayer perceptrons for repeat‐purchase modeling in direct marketing

In this paper, we try to validate existing theory on and develop additional insight into repeat‐purchase behavior in a direct marketing setting by means of an illuminating case study. The case involves the detection and qualification of the most relevant RFM (Recency, Frequency and Monetary) variables, using a neural network wrapper as our input pruning method. Results indicate that elimination of redundant and/or irrelevant inputs by means of the discussed input selection method allows us to significantly reduce model complexity without degrading the predictive generalization ability. It is precisely this issue that will enable us to infer some interesting marketing conclusions concerning the relative importance of the RFM predictor categories and their operationalizations. The empirical findings highlight the importance of a combined use of RFM variables in predicting repeat‐purchase behavior. However, the study also reveals the dominant role of the frequency category. Results indicate that a model including only frequency variables still yields satisfactory classification accuracy compared to the optimally reduced model. Copyright © 2001 John Wiley & Sons, Ltd.     

BUILDING AND EVOLVING DATA WAREHOUSING AND BUSINESS INTELLIGENCE ARTEFACTS: THE CASE OF SYSCO

This paper investigates use of data warehouse and business intelligence capabilities to integrate with customers in the supply chain and improve insights into customer sales. By making internal data warehouse sales information available to customers, additional value to those customers is created, eliminating asymmetries of information in the supply chain. In addition, the evolution of data warehousing into business intelligence is investigated, expanding sales information to include marketing associate performance analysis generated for internal use. Further, a methodology that was used for building a business intelligence system is also examined. Finally, what appears to be a business‐intelligence‐driven focus on enterprise resource planning systems is analyzed. These issues are illustrated using real‐world data warehousing and business intelligence artefacts developed at SYSCO. Copyright © 2012 John Wiley & Sons, Ltd.     

Adverse-Selection Costs and the Probability of Information-Based Trading

Prior studies offer various empirical models to decompose the observed bid‐ask spread into the adverse‐selection and transitory (order‐processing and inventory‐holding) components. There is limited evidence, however, on whether the spread components estimated from these models indeed measure what they purport to measure. In this study, we show that the estimates of the adverse‐selection component given by these models are positively and significantly related to the probability of information‐based trading (PIN), after controlling for the endogeneity of the PIN and other stock attributes. These results provide direct empirical support for the spread component models examined in the present study.     

Shareholders: An unexplored opportunity for Australian financial institutions

While Australian financial institutions have recognised for many years the need to segment their customer base and develop niche marketing strategies, those that are listed on the Australian Stock Exchange appear to have ignored a substantial and powerful pool of potential customers — their shareholders. This paper reviews the shareholder marketing strategies adopted by 12 listed Australian banks and building societies. It looks at the cost of customers, the value of converting shareholders to customers and reviews the shareholder loyalty programmes being offered. This paper argues that banks in Australia are ignoring this important pool of potential customers. The author develops the view that the banks and building societies have failed to maximise shareholder value as they have few processes in place to target this important sector.     

Modelling bank customer behaviour using feature engineering and classification techniques

This study investigates customer behaviour and activity in the banking sector and uses various feature transformation techniques to convert the behavioural data into different data structures. Feature selection is then performed to generate feature subsets from the transformed datasets. Several classification methods used in the literature are applied to the original and transformed feature subsets. The proposed combined knowledge mining model enable us to conduct a benchmark study on the prediction of bank customer behaviour. A real bank customer dataset, drawn from 24,000 active and inactive customers, is used for an experimental analysis, which sheds new light on the role of feature engineering in bank customer classification. This paper’s detailed systematic analysis of the modelling of bank customer behaviour can help banking institutions take the right steps to increase their customers’ activity.     

Microdata Evidence on the Empirical Importance of Selection Effects in Menu‐Cost Models

We use microdata on product prices linked to information on the producing firms that set them to study to what extent the timing of price changes reacts to changes in marginal cost. This self‐selection of price changes is a key feature in the canonical Menu‐Cost model a la Golosov and Lucas Jr. (2007), which may generate near monetary neutrality (Golosov and Lucas Jr. 2007, Karadi and Reiff 2016), but is absent in the Calvo (1983) model. We find that the microdata strongly favors the Calvo (1983) model. Thus, upstream in the supply chain, price setting is best characterized by a very low degree of self‐selection into price changes.     

Smart customers, dumb companies

Customers today are being bombarded with an overwhelming array of choices. To alleviate customer frustration, say Steven Cristol and Peter Sealey in Simplicity Marketing, companies should stop creating new brands and product extensions. Better to consolidate product and service functions by following a four R approach: replace, repackage, reposition, and replenish. That's an outmoded, dictatorial view of markets, says Christopher Locke. Far from being stymied by choices, customers are rapidly becoming smarter than the companies that pretend to serve them. In this networked economy, people are talking among themselves, and that changes everything. Locke predicts we'll see a growing number of well-defined micromarkets--groups of customers converging in real time around entertaining and knowledgeable voices--such as NPR's car guys and the Motley Fool investment site. "Micromedia" Web sites will replace traditional advertising because they'll provide credible user-supplied news about products and services. Locke contends that an open exchange of information solves the "problem" of choice much better than manipulative strategies like simplicity or even permission marketing. Companies can participate in micromarkets through what Locke dubs "gonzo marketing." If Ford, for example, discovers that a subset of its employees are organic gardeners, it may offer support to a big independent organic-gardening Web site with donations and employee volunteers. This marketing effort would be driven not by advertising managers but by people with genuine interest in each micromarket, so it would have credibility with customers. With gonzo marketing, both companies and their markets will benefit.     

ACCOUNTING DETERMINED AND MARKET DETERMNED RISK MEASURES: SOME TESTS OF ASSOCIATION AND PREDICTIVE ABILITY

This paper examines the degree of association between certain measures of risk that commonly feature in the accounting literature, and the β coefficient, the risk measure given by the capital asset pricing model. Tests were applied to a subset of New Zealand listed public company securities. A weak association was found. Tests of the predictive ability of an accounting based regression model showed that its performance was inferior to other commonly assumed predictive models.     

Twenty‐five years of the Taffler z‐score model: Does it really have predictive ability?

Although copious statistical failure prediction models are described in the literature, appropriate tests of whether such methodologies really work in practice are lacking. Validation exercises typically use small samples of non‐failed firms and are not true tests of ex ante predictive ability, the key issue of relevance to model users. This paper provides the operating characteristics of the well‐known Taffler (1983) UK‐based z‐score model for the first time and evaluates its performance over the 25‐year period since it was originally developed. The model is shown to have clear predictive ability over this extended time period and dominates more naïve prediction approaches. This study also illustrates the economic value to a bank of using such methodologies for default risk assessment purposes. Prima facie, such results also demonstrate the predictive ability of the published accounting numbers and associated financial ratios used in the z‐score model calculation.     

FORECASTING FIRM RISK IN THE EMERGING MARKET OF CHINA WITH SEQUENTIAL OPTIMIZATION OF INFLUENCE FACTORS ON PERFORMANCE OF CASE‐BASED REASONING: AN EMPIRICAL STUDY WITH IMBALANCED SAMPLES

With the development of the Chinese economy, how to make the right decision regarding firms’ risk is becoming more and more important. Case‐based reasoning (CBR) is a potential method that can help forecast business risk status in advance; it is easy to apply and is able to provide good explanations of output. In order to obtain more accurate prediction with CBR, it is essential to investigate factors that influence CBR's performance, and to optimize these factors sequentially for the improvement of CBR's performance in firm risk prediction in emerging markets under a more practicable assumption. We verified that sequential optimization of feature selection, feature weighting, instance selection and the number of nearest neighbours is a possible alternative for improving predictive performance of CBR forecasting under the assumption that the number of failed samples is smaller than that of nonfailed samples. The detailed implementation includes: (1) selecting significant features through a correlation matrix and reducing feature dimensions with factor analysis; (2) using variance contribution ratios of features from factor analysis as feature weights; (3) eliminating noisy cases via a state matrix; and (4) obtaining the optimal number of nearest neighbours from empirical results among different numbers of nearest neighbours. To validate the usefulness of the sequential optimization approach, we applied it to a real‐world case: firm risk prediction with imbalanced data from the emerging market of China. Experimental results show that predictive accuracy of CBR applied in the emerging market was improved with the sequential optimization approach. Insightful thoughts from the results of the sequential optimization of the CBR forecasting system on modelling social tasks are also provided. Copyright © 2013 John Wiley & Sons, Ltd.     

Why are some modes of acquisition more profitable? A study of the credit card industry

A key challenge in financial services marketing is attracting good customers to the firm. For most financial services firms, including credit card firms, a good customer is also a profitable customer. Managers would like to use marketing tactics that attract the most profitable customers while closely monitoring and perhaps limiting expenditures on marketing tactics that tend to attract relatively less profitable customers. Therefore, managers need to understand the relative effectiveness of different modes of new account acquisition and the impact that the various modes of acquisition may have on overall account profitability. To date, there have been very few studies that have calculated individual level customer profitability and then investigated the relationship between new customer acquisition source and customer profitability. That is, how do modes of acquisition differ in their ability to attract profitable customers? We answer this question using a proprietary and novel data set from the credit card industry. Of the four modes of acquisition used in this industry, we find that Internet and direct mail efforts generate more profitable customers than telemarketing and direct selling. We provide possible explanations for these findings. Our work adds to the growing literature in customer relationship management and our results have important managerial implications for resource allocation among acquisition strategies.     

Developing an index of online customer satisfaction

This paper integrates and applies the concept of ‘satisfaction’ from three fields: management information systems (MIS), marketing and e-commerce. E-customers are viewed as both computer users and customers and as a result researching customer satisfaction in e-commerce needs to integrate aspects of satisfaction from each of these fields. This study defines e-customer satisfaction and develops an index using a weighted sum model. The index is tested using a Korean sample. Analyses suggest an acceptable model and a generalisable set of measures from which managers and researchers evaluate online satisfaction and, especially, marketing strategies in financial services industries.     

Customer account relationships and e-retail banking usage<Superscript>1</Superscript>

Financial institutions are actively developing new electronic banking products for their retail customers. To date, the market leaders have drawn a disproportionably higher share of e-retail banking customers. In response, smaller institutions have become quite active in exploring ways to participate profitably in online banking. A major influence is from a customer relationship management (CRM) perspective, where institutions try to limit the outflow of current customers and direct high-value customers to potential products from a multi-product service offering array. These efforts can succeed only if retail bank marketers focus the promotion of the new products and services that can utilise this channel toward those customers who are most likely to find them attractive. The first aim of this study was to examine the role that online and electronic banking play in defining the customer's primary financial relationship. The analysis of 701 retail customers of a financial institution presented in this study suggests that banks and other institutions are highly vulnerable to loss of customers to rivals with extensive online services. A second aim was to examine to what extent information on banking relationships is able to extend CRM analysis beyond that offered by typical demographic and income data. Current customer account relationships are found to be highly predictive of use of electronic services use in general. And, interest in the use of specific online services is related to differing customer relationships in addition to ordinary demographic and balance information. These findings can be useful for retail banking in identifying potential high-value users from a customer relationship management perspective.     

Lock‐In Effects in Relationship Lending: Evidence from DIP Loans

Do prior lending relationships result in pass‐through savings (lower interest rates) for borrowers, or do they lock in higher costs for borrowers? Theoretical models suggest that when borrowers experience greater information asymmetry, higher switching costs, and limited access to capital markets, they become locked into higher costs from their existing lenders. Firms in Chapter 11 seeking debtor‐in‐possession (DIP) financing often fit this profile. We investigate the presence of lock‐in effects using a sample of 348 DIP loans. We account for endogeneity using the instrument variable (IV) approach and the Heckman selection model and find consistent evidence that prior lending relationship is associated with higher interest costs and the effect is more severe for stronger existing relationships. Our study provides direct evidence that prior lending relationships do create a lock‐in effect under certain circumstances, such as DIP financing.     

Tiered Information Disclosure: An Empirical Analysis of the Advance Peek into the Michigan Index of Consumer Sentiment

This paper studies market microstructure implications of informed high‐frequency traders (HFTs) from two seconds of advance peek into the Michigan Index of Consumer Sentiment (ICS), provided by Thomson Reuters to its elite customers. Using individual stocks in the NASDAQ data set, we show how HFTs trade around ICS events. We find that liquidity demanders during two seconds of advance peek earn substantive profits, which are consistent with the notion that HFTs’ informational advantages may increase adverse selection costs for other market participants. This evidence elucidates the debate on regulatory oversight and its role in circumventing the potentially adverse effects from an advance peek into ICS.     

Internal control quality and relationship‐specific investments by suppliers and customers

This paper investigates the association between the quality of internal control over financial reporting (ICOFR) and relationship‐specific investments by suppliers/customers. I find a significant negative association between the disclosure of a material weakness (MW) in ICOFR and changes in the relationship‐specific investments of suppliers/customers. Relationship‐specific investments of suppliers/customers have a stronger negative association with inventory‐related MW. The negative association between MW disclosures and the relationship‐specific investments of suppliers/customers is stronger when economic dependence between the firms and the suppliers/customers is more important and when the suppliers/customers have greater bargaining power. Also, suppliers/customers significantly increase their relationship‐specific investments following the remediation of ineffective ICOFR. Overall, my findings show that a firm's quality of ICOFR affects the relationship‐specific investment decisions of suppliers/customers.