research topics

Layer 3 in 2011: Customer Management in E-Finance

(Prof. Dr. Andreas Hackethal, Prof. Dr. Bernd Skiera)

 

Motivation

Research in layer 3 proceeds from the basic premise that the online environment will further gain in importance for financial service providers and that customer management in such an online environment poses substantial challenges. More and more banking and insurance managers need to deal with technologies that are related to Web 2.0, such as widgets, iPhones, microblogging, wikis, social networks and avatars. Internet firms such as Google and Yahoo have achieved much higher stock market values than most financial service providers.

The aim of our work is to analyze these technological trends carefully and to evaluate how technology and data intelligence can create value for financial service providers and their customers. We posit that customers should be viewed as assets, which requires to evaluate their current and future values. The measurement of the value of a customer via a customer lifetime analysis and the value of the customer base via the determination of customer equity are particularly useful because they can also be linked to the financial value of a financial service provider. The value of technological trends for financial service providers is best evaluated by linking them to customer metrics, in particular customer profitability and loyalty measures such as customer lifetime, and analyzing how they can enhance the decision support and investor coaching by financial service providers. As improvements to customer decisions provide particular strong incentives for customers to stay with a financial service provider, we put substantial emphasis on analyzing factors that influence the quality of those decisions.

Module 1 focuses on investments in customers, in particular those that are related to the internet and (online) financial advice as well as decision support provided to the consumers.

Module 2 concentrates on customer decision making and in particular on individual characteristics that influence customer preferences, beliefs and decisions.

Module 3 focuses on the five key customer metrics for financial service institutions. Multiplying the number of customers with the profit per customer leads to the current (short-term value of) profitability. The consideration of customer lifetime values allows for measuring the long-term values of customers. The two key investment measures are acquisition and retention (including customer development) costs per customer. We develop models to appropriately measure those five customer metrics and analyze the interdependencies among those key metrics. We then identify the effects of investments in customers and customers' decision on those customer metrics.

Module 4 analyzes how to link customer metrics to customer value metrics, such as customer lifetime value or customer equity.

Module 5 links customer value to shareholder value as the key success metric of financial markets. In contrast to most discounted cash flow models, we use the five key customer metrics as the building blocks of our shareholder value model. 


Five key customer metrics

Our model of linking customer metrics to shareholder value indicates that financial service institutions should consider five customer metrics as key performance indicators for firms with contractual customer relationships, three on the revenue side: number of customers, customer cash flow and retention rates, and two on the expenditure side: acquisition as well as retention expenditures (Skiera / Wiesel / Schulze 2009). Thereby, changes in customer retention have by far the greatest impact on customer equity and shareholder value. Yet, retention rates are hardly reported and, most likely, not carefully tracked by at least some financial institutions.


More emphasis on customer equity reporting

Customer equity reporting describes the value of a financial service institution's customer base. As such, it bridges the gap between financial statement capabilities and financial reporting objectives and aligns customer management with corporate goals and investors’ perspectives (Wiesel / Skiera / Villanueva 2008). It also provides a mean improve the understanding of securitization activities of financial service institutions (Skiera / Bermes / Horn 2011).


Viral marketing campaigns work

Seeding strategies have a major influence on the success of viral marketing campaigns in social networks such as facebook. Our comparison of four different seeding strategies in two complementary small-scale field experiments and one real-life application shows that they work very well and that seeding strategies that target either well-connected individuals ("high degreeness") or individuals who connect different parts of the network ("high betweenness") lead to a success rate twice as high as that of a random seeding strategy (Hinz / Skiera / Barrot / Becker 2010).


Forecasting risk in customer portfolios can protect them from losses

We forecast portfolio risks with respect to correlation meltdowns during market downturns. Our results show that our framework predicts future portfolio risks significantly better than commonly used approaches. Our model can aid financial institutions to better manage the market risk in the portfolios of their customers (Braun / Hackethal 2010).


Financial advisors need to consider labour income and real estate property when optimizing investor portfolios

We build optimal portfolios with respect to investors' income structure and real estate ownership. We can show that those two variables substantially decrease the optimal equity ratio for investors. In addition, mortgage loans significantly affect portfolio structure (Hackethal / Litty / Meyer 2010).


The impact of financial product innovations on customer behavior

We find that transaction volumes of investors double on average after their first purchase of innovative structured retail products. This is compatible with product innovations reducing returns through increased trading volumes. The effect of financial innovations is therefore ambiguous (Stuber / Meyer / Hackethal 2009).


How to enhance customer benefits from online advisory tools

We analyze how online investors adopt a new and unbiased portfolio optimization tool and find that only a very few investors implement the recommendations from the tool although most other investors would also benefited substantially. We conclude that transparancy with respect to risk and return profiles of individual portfolios would help providers producing customer benefits (Bhattacharya / Hackethal / Kaesler / Loos / Meyer 2010).

 

Finished projects (PDF):

Former Cluster 3: 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012

Former Cluster 4: 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012