Search results

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.

It is generally recognised that companies spend approximately 50% of their marketing budget on promotional activities. Advertising belongs to the most visible areas of a company’s activity. Therefore, it should not be surprising that the average recipient associates marketing with advertising, competitions and leaflets about new promotions delivered to houses or offices. Advertising, especially Internet advertising, is one of the most effective forms of marketing and one of the fastest developing areas of business. New channels of communication are emerging all the time – the Internet, digital television, mobile telephony; accompanied by new forms, such as the so-called ambient media. Advertising benefits from the achievements of many fields of science, that is, psychology, sociology, statistics, medicine and economics. At the same time, it combines science and the arts – it requires both knowledge and intuition. Contemporary advertising has different forms and areas of activity; yet it is always closely linked with the operations of a company – it is a form of marketing communication.

The indices of marketing communication presented in this chapter are generally known and used not only by advertising agencies but also by the marketing departments of many organisations. Brand awareness, advertising scope and frequency, the penetration index or the response rate belong to the most widely used indices; others, like the conversion rate or the affinity index, will get increasingly more significant along with the process of professionalisation of the environment of marketing specialists in Poland and with increased pressure on measuring marketing activities. Marketing indices are used for not only planning activities, but also their evaluation; some of them, such as telemarketing, mailing and coupons, provide an extensive array of possibilities of performance evaluation.

Companies spend millions on training their sales representatives. Thousands of textbooks have been published; thousands of training videos have been recorded. Hundreds of good pieces of advice and tips for sales representatives have been presented along with hundreds of sales methods and techniques. Probably the largest number of indicators and measures are applied in sales and distribution. On the one hand, this is a result of the fact that sales provide revenue and profit to a company; on the other hand, the concept of management by objectives turns out to be most effective in regional sales teams with reference to sales representatives and methods of performance evaluation. As a result, a whole array of indices has been created which enable the evaluation of sales representatives’ work and make it possible to manage goods distribution in a better way.

The indices presented in this chapter are rooted in the consumer market and are applied most often to this type of market (particularly in relation to fast-moving consumer goods at the level of retail trade). Nevertheless, many of them can be used on other markets (services, means of production) and at other trade levels (wholesale).

Although the values of many indices presented herein are usually calculated by market research agencies and delivered to companies in the form of synthetic results, we have placed the emphasis on the ability to determine them independently, both in descriptive and exemplifying terms. We consider it important to understand the genesis of indices and build the ability to interpret them on that basis. What is significant is that the indices can be interpreted differently; the same index may provide a different assessment of a product’s, brand or company’s position in the market depending on the parameters taken into account. Therefore, we strive to show a certain way of thinking rather than give ready-made recipes and cite ‘proven’ principles. Sales and distribution are dynamic phenomena, and limiting them within the framework of ‘one proper’ interpretation would be an intellectual abuse.

The purpose of this paper is to split loan customers to different credit ratings to ensure the results that show that customers with lower credit ratings have higher loss rates, and the number of customers that satisfies the bell-shaped distribution. Hence, the number of credit ratings, the distribution of the rated obligors among ratings can achieve a meaningful differentiation of risk, which can avoid the loan pricing confusion.

The authors introduce a multi-objective programming to establish the credit rating model. Objective function 1 minimizes the absolute difference between the obligor number proportion and perfect client proportion, following a standard normal distribution. Objective function 2 minimizes the total difference of the deviation between two adjacent credit ratings’ loss rates. This study combines the two objective functions to ensure the obligor number distribution and the monotonicity of the loss rate, and applies genetic algorithm to solve the model.

This study’s analysis is based on data from 6,155 enterprises, provided by a Chinese bank and Prosper P2P loan data. The empirical results reveal that the proposed approach can ensure the balance between both criteria and avoid undue concentration of obligors in particular grades.

The proposed credit model could help building a reasonable credit rating system, which is the prerequisite of loan pricing; thus, inaccurate credit rating can cause incorrect loss rate estimates and loan pricing.

The essential investments in new product development (NPD) made by industrial companies entail effective management of NPD activities. In this context, performance measurement is one of the means that can be employed in the pursuit of effectiveness.

Scanning both the academic and popular business literature of the last 40 years puzzles the alert reader. The variety of prescriptions of how to be successful (effective, performing, etc.) 1 Organizational performance, organizational success and organizational effectiveness will be used interchangeably throughout this paper.1 in business is hardly comprehensible: “Being close to the customer,” Total Quality Management, corporate social responsibility, shareholder value maximization, efficient consumer response, management reward systems or employee involvement programs are but a few of the slogans introduced as means to increase organizational effectiveness. Management scholars have made little effort to integrate the various performance-enhancing strategies or to assess them in an orderly manner.

This study classifies organizational strategies by the importance each strategy attaches to different constituencies in the firm’s environment. A number of researchers divide an organization’s environment into various constituency groups and argue that these groups constitute – as providers and recipients of resources – the basis for organizational survival and well-being. Some theoretical schools argue for the foremost importance of responsiveness to certain constituencies while stakeholder theory calls for a – situation-contingent – balance in these responsiveness levels. Given that maximum responsiveness levels to different groups may be limited by an organization’s resource endowment or even counterbalanced, the need exists for a concurrent assessment of these competing claims by jointly evaluating the effect of the respective behaviors towards constituencies on performance. Thus, this study investigates the competing merits of implementing alternative business philosophies (e.g. balanced versus focused responsiveness to constituencies). Such a concurrent assessment provides a “critical test” of multiple, opposing theories rather than testing the merits of one theory (Carlsmith, Ellsworth & Aronson, 1976).

In the high tolerance level applied for this study (be among the top 80% of the industry) only a handful of organizations managed to sustain such a balanced strategy over the whole observation period. Continuously monitoring stakeholder demands and crafting suitable responsiveness strategies must therefore be a focus of successful business strategies. While such behavior may not be a sufficient explanation for organizational success, it certainly is a necessary one.

This paper sets out to explore how the principles developed by Sir Ronald Aylmer Fisher, FRS (1890‐1962), a gifted British evolutionary biologist, geneticist and statistician, can be applied in today's retail environment to estimate new customers becoming visible through your loyalty or database marketing program – customers whom we like to call “faux‐new” customers. Here, we have used the word “faux” to mean “fake” or “false” – customers who look new in the next month (because we did not observe them in the first month), but are indeed customers (because they made a purchase before the first month, a month for which we do not have data). That is, there are “faux‐new” customers and “actual‐new” customers whom we will observe in the second month.

The paper uses data and statistics from numerous loyalty‐marketing programs to support its conclusions. It investigates a technique discussed by R.A. Fisher in counting species and applies the technique toward counting customers. It showcases the Poisson distribution assumption in modeling customer frequency of purchases.

The study found the technique to be robust against a large real‐world data set: the technique was predictive despite some dubious assumptions.

When statisticians and marketers attempt to estimate new customer growth rates, they must be careful. It is tempting to estimate the monthly growth in your customer base by observing the number of customers in July, and then observe the number of “new” customers in August. In this case, however, the results would be overestimated. Many customers who transacted in August but not in July are not actually “new” customers – they are “faux‐new” customers. They could very well have transacted in April (assuming that one does not have the luxury of observing months before July). To estimate the actual number of new customers, it is necessary first to need to strip out the number of customers that were estimated “zero‐purchase,” or “faux‐new,” customers in July.

This paper explores innovative statistical techniques for marketers and business analysts who estimate customer growth based on multiple periods of customer transactions.

Preface The functions of business divide into several areas and the general focus of this book is on one of the most important although least understood of these—DISTRIBUTION. The particular focus is on reviewing current practice in distribution costing and on attempting to push the frontiers back a little by suggesting some new approaches to overcome previously defined shortcomings.

The purpose of this paper is to investigate the effects of capacity decisions regarding the number of servers/chefs and tables on identifying a change in the number of wait-related anxious customers, customer losses and customers served to meet the waiting time standards of an actual upscale restaurant.

The authors applied a simulation model to present the consequences of restaurant capacity decisions based on waiting time standards. Arena Simulation Software, licensed by Rockwell Automation, was used for modeling and identifying distributions of the data set provided by the restaurant. An experiment was designed for an upscale restaurant with existing five servers/chefs and 50 tables by changing these resources to measure the changes in customers' wait-related anxiety and other service performance indicators.

The results showed that an additional server/chef on weekends decreases the daily average number of anxious customers by nearly 33% and increases the daily average number of customers served by nearly 3% and has a little positive effect of decreasing customer losses. Table insertion for high- and low-requested seating areas had an only positive effect on decreasing customer losses.

In this study, the service capacity is dependent on waiting time, and it is addressed to study the relationship with customers' wait-related anxiety, which is a subjective metric. This study developed a point of view for identifying anxious customers whose waiting times are much longer than their cooking and delivery duration expectations regarding their meal preferences in the cooking stage and waiting experiences in the service entry.

The purpose of this article is to identify policy issues arising from discrepancies between the various sources of numbers of customers for mobile networks.

This paper reviews and analyzes a range of statistics, and then draws out policy implications.

Survey data report much lower numbers of customers than operators, indicating delays in achieving MDGs and market distortions.

There is a lack of data on the reasons for ownership of multiple mobile SIM cards.

There is a need for more extensive surveys to identify reasons for ownership of multiple mobile SIM cards. The analysis can be incorporated into policy and regulatory decision making.

The paper takes an analytic approach, suggesting areas for further work.