Scoring and its role in segmentation and targeting

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Scoring and its role in segmentation and targeting

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Abstract

There are various ways to determine consumer behavior. One such method is the use of a numerical variable called the score to assess a particular type of customer behavior. The score is assigned based on the "Predictive model". The construction of a score implies the implementation and study of a large amount of different data. As stated by Rene Lefebvre in his book "Managing customer relationships", the predictive model uses mathematics and statistics to analyze a database and uses formulas which explain the customer's behavior. It therefore interprets the data and builds a feature that makes it a probable response of the client or prospect to an offer.

Introduction
Definition
The Method of Calculation
1. Data
2. Variables
3. Method
4. The sectors affected by the scoring
The different types of scores
1. SCORE RFM
2. SCORE Palatability
3. SCORE OF ATTRITION
4. RISK SCORE
5. GRANT SCORE
6. SCORE OF RECOVERY
The Effectiveness of Scoring
The limits of scoring
Conclusion

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[...] Subsequently, according to its constraints (budget) and its objectives (market share), the bank can determine the number of customers it should contact. More generally, the effectiveness of scoring is measured using "Charter gain" (see illustration), a chart showing the differential between what has been achieved, and what would have been, taking into account only the number of people contacted. The population is sorted according to its rating, horizontally, from the best to the worst. Vertically, it counts the number of orders made for each population. [...]

[...] By relying primarily on information and behavioral data bank of applicants, the risk score determines the likelihood of its milestones getting completed. Field Survey: Mrs. responsible for business customers of the Bred Bezons told me how she proceeded to get the risk score. The internal display ECS5 shows the funding like overdraft facilities, equipment loans, personal loans, etc by the company. This screen refers to the original amount, outstanding amount and loan maturity. The residual risk can be calculated from this screen. [...]

[...] The sectors affected by the scoring Banks, for marketing purposes Telecom operators and Internet service providers, for analysis of the phenomenon of attrition. Life insurance, mail order, the consumer credit etc are among the sectors that score applicants. However, some markets such as automotives did not need scoring. Professionals in the automotive sector have access to vehicle registrations. For them, it is easier to consider this information than doing the scoring. The banking sector was among the first users of the scoring. [...]

[...] Specific costs: that of compiling data, computing, data processing costs and also their operational procedures for acquiring and implementing the results of scoring Distrust of the tool from the commercial point of view. People need to be trained in scoring, which makes the use of technology a better prospect. The scoring steps predict risk sometimes when there may none in the real deal. There may be a misuse of scoring if it is used to exclude all risks by not lending to the poor, and the same becomes a tool of negative discrimination. [...]

[...] An example of this type of score is planning a party balance: we will select the people to whom the invitation has to be sent, based on their interest in this type of parties and proposed products. Their estimated ability spend an amount substantial enough, is also taken into account. Several variables will have to be combined to get the most relevant file. SCORE OF ATTRITION Unlike the palatability score, the score of attrition is based on medium or long term. [...]

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