Churn Analysis Using Selected Structured Analytic Techniques

DOI: 10.4018/978-1-4666-6288-9.ch006
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Abstract

After explaining how to prepare data, an introduction to structured analytic techniques is covered in this chapter. The importance of structured techniques comes from their simplicity and wide usage, making them fast to use and efficient to structure in even complex environments. For further explanation of how those techniques could be applied in the business environment, analysts (readers) should look closer into the case studies in last chapter of this book. Besides the ability to structure logic problems, analysts (readers) also needs to be aware of the different motivators influencing market conditions, especially from the customers' perspective. The chapter ends with a brief introduction of consumer behavior, making it part of the churn topic.
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6.1 Simple Hypothesis

A hypothesis is, widely, potential explanation or conclusion which is tested by collecting and presenting evidences (Heuer, Pherson, 2010.). It is s statement which was not confirmed to be true or, in other words, temporary explanation based on observation which has to be confirmed or rejected. A hypothesis has to fulfill following terms:

• To be written as clear statement, not question,

• To be based on observation and knowledge,

• It can be confirmed or rejected,

• It clearly predicts expectable results,

• It contains dependent and non-dependent variable.

While using hypotheses, common question is which hypothesis is true or most probable. Analyst will choose most probable answer using intuition and then will look for hypothesis confirmation using available data. By using hypotheses, analyst must be aware of wider perspective, avoiding to be focused only on one hypothesis and always looking for approval and alternatives. Hypothesis testing is used in situations:

• When we need to analyze all alternatives in details,

• When we have large number of variables,

• When there is uncertainty regarding final outcome,

• When analysts or decision maker do not have same perception for same problem.

In order to generate hypotheses, different techniques are used like brainstorming, scenario analysis, quadrant crunching or Delphi method. Also, for purpose of hypotheses development, techniques like simple hypothesis, multiple hypotheses generator, quadrant hypothesis generation (Heuer, Pherson, 2010.).

While using hypothesis testing analyst can efficiently avoid several common problems:

• Rushing into conclusions,

• To be blinded with first impression,

• To be misled with first answer which looks good enough,

• To focus on limited alternatives rather than full project scope,

• To use answer suitable for most target audience,

• To use certain answers in order to protect him from error.

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6.2 Ach (Competitive Hypotheses Analysis)

While hypothesis is a form of a statement yet to be confirmed, its trueness has to be confirmed using evidence. If we knew that any statement is true, it won’t be named hypothesis (Heuer, Pherson, 2010.).

Hypotheses plays key role in analysis because of its capability to narrow down number/area of results, very efficient characteristic for understanding and managing complex environment. Although narrowing of problem space can be efficient in order to focus on important rather than everything, it can be a limitation if not used with caution. It is up to analyst to choose several hypotheses and then to look for most appropriate one(s) in order to analyze consequences. ACH (competitive hypotheses analysis) is simple and efficient model which helps in solving complex problems especially useful when many alternatives need to be evaluated in past, present and future. This technique helps analyst to overcome or to minimize some of common cognitive limitations by creating proofs and hypothesis matrix layout in order to visualize problem structure and understand correlations between complex environment parts. At the same time matrix allows us to record process steps towards solution.

ACH can be very effective in collaboration between analyses since it can avoid subjective influence to analysis process. Visualization matrix can be used to present areas of disagreement between analyses, if any.

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