SELECT CASE STUDIES

Case Study:  Bank Merger Impact

The recent merger between two large banks is expected to significantly alter not only the retail and commercial operations, but also the consumer credit card operations of the newly formed institute. Prior to the merger, each bank issued its own credit card and had a unique customer base.  The merger of the two card families could take several different positions, which could result in varying levels of impact. Some suggestions include card name changes and extend to more significant changes, such as issuing new cards to replace all existing ones. The client is interested in assessing what effect, if any, the merger will have on its current credit card customer base and the subsequent impact they would have on future business.  Issues of acquiring new customers and retaining current customers have to be addressed from two perspectives: consumer loyalty and card usage. In addition, the client wants to assess in a more quantitative way the impacts of different proposals on its future competitive position.

Solution

First, we modeled the consumer’s acceptance of the merger and the impact of the merger on retention and usage of the credit cards.  Current card users are classified into four groups: committed users, regular users, opportunistic users, and committed switchers.  The market potential of each group is estimated.  We provided credit card group-usage mapping of attitudes and perceptions of each bank, which helped our client determine the consumer’s level of involvement, claimed behavior (interest), and choice behavior.  The results of the study are used by the client to target customers and design acquisition and retention strategies.  We also conducted a revenue impact study and found that the new merger would increase card operation revenues. In the consumers’ responses, we also found the factors that created the differentiated response to the name change.

Applied Methods

Clustering analysis was used to classify customers, which is based on consumer's response after evaluating acceptability, service features such as the rates, fees, and credit limits.

Correspondence analysis was conducted to reveal the cluster-product relationship.

Findings, Analysis & Recommendations

• Helped understand consumers' perceptions and attitudes as they relate to and affect consumers' attitudes toward the card issuer and their usage of cards;

• Assessed what impact a merger might have on a consumer's perception and operational revenues;

• Evaluated the possible influence of a card name change on the client's operations and finances; and,

• The results clearly addressed the merger impact on card usage, loyalty, and potential switching behaviour.

Case Study:  Telecommunications Company Strategy

A telecommunications company, providing a broad spectrum of communications services and products, seeks to identify customers who are responsive to its marketing programs.  As the communication industry is undergoing a global revolution, our client is entering new territories, such as internet, wireless, local, etc. and is competing with a whole new class of competitors.  Everyone is chasing the same pool of customers with similar products and services.  Hence a new, better targeting strategy should be developed and implemented to ensure a continuous growth.

Solution

We developed a response model that identifies prospects who are most likely to respond to the client’s marketing offers, using the wealth of the client’s data warehouse for each of the products and services, e.g. internet service, cellular service, etc., or by jurisdiction, such as communication services in local markets. These analyses are based on actual responses from our client’s most recent promotion activities and these procedures can be used to identify prospects that are most statistically similar to the client’s current customers, and that indicate better responses and more sales. These response models are designed to evaluate new markets or the potential of new products that our client plans to launch and to pinpoint precisely where the most responsive prospects are for more sales at a minimum cost. 

Applied Methods

Logistic and multinomial response models were estimated.  Outlier detection techniques are employed to eliminate any "exceptional" values in the data we prepared and analyzed.  We identified and evaluated a set of explanatory variables through conventional statistical tools, such as cluster analysis, discriminant analysis, ANOVA, and stepwise regression.

Findings, Analysis & Recommendations

• Helped the client to target the most responsive prospects before spending money on mailings or callings.  Validation results have proved that the top 30% of the prospects based on predicted response rates contain up to nearly 90% of the sales, which translates into a gain or cost savings of more than 85% over random targeting;

• Response models allowed our client to access the market potential and to identify which segments of the available market to emphasize, based on internal strengths and weaknesses.  The models can be used as guidelines for evaluating opportunities and allocating budget resources in a most cost-effective way.

• The implementation process is easy and can be applied to any size of customer base, which allows for further programme refinement and cost savings maximisation.

Case Study: Impact of a New Market Entrant -- Retail Petro

The marketing department of an leading energy company, the largest auto-gas station chains in the USA. The client wants to estimate a price-adjusting factor that is used to set price strategy.

Firms are constantly under the attack from new market entrants and must be able to respond to the threat by altering their marketing strategies.  Information on potential loss of short-term revenues and long-term financial implications is crucial to design effective competitive strategies.  Based on sales data collected from areas where new market entry had occurred, the client would like to know the likely impact on the client’s other marketplace, where new entry is expected to occur.  Specifically, the client wants to know how much sales loss is expected in the short-term and long-term and how to design its pricing strategy to counteract such an attack.

Solution

Based on the sales data provided by the client, we estimated the impact of a new market entrant on sales volume via intervention with ARIMA modeling.  Total new entry impact is decomposed into the transitory effect, or the short-term effect and a permanent effect which causes a shift of client’s revenues.  The result is used by the client to evaluate the potential impact on the client’s other locations where new entry is expected to occur and to develop optimal pricing policies under such conditions.

Applied Methods

Intervention ARIMA modeling is conducted to identify and estimate the combined effects of temporary changes and a permanent level shift resulting from new market entry.

Findings, Analysis & Recommendations

• Assisted the client to predict the financial consequences of a new market entrant and to design effective counteract strategy; and,

• Helped the client in budgeting, forecasting, and pricing.

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