What is data quality and why is it important, Customer database for small business.
Most of today's companies implement CRM principles that cover customer relationships in many aspects ?? sales, marketing, service, new product development, etc. But often, another, perhaps key component of the process is overlooked ?? these are market-oriented actions of the company.
It is noticeable that CRM is understood only as a technological tool, which in itself can be a solution or a combination of them, to retain existing customers, increase their satisfaction with the company and strengthen their loyalty. The message is, however, that any action must be “justified”. and "measure" (a prerequisite for today's innovative company), which can be achieved by having and using customer data.
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Data needed to realize any action ?? no one can deny the veracity of this dogma. However, collecting and consistently collecting data does not guarantee the right decisions. The action taken by the client may not achieve the intended result simply because the wrong data has been used. This means that the issue of data quality is becoming an important issue, even of strategic importance, in today's companies. The data quality management in healthcare is the best example which suits this context.
Incorrect data may be corrected in accordance with accepted company procedures. The following stages of cleaning up incorrect data can be proposed:
There is only one metaphor: "Bad foundation, strawberry house" (bad data, inaccurate information, wrong actions). We all know that “dialogue with the customer” is profit for the company.
Explaining this is easy ?? When communicating with a customer, the company collects data about the customer, which is then processed to obtain information through technological means. The resulting information is transformed into knowledge that can be translated into action. customer retention, loyalty increase, etc. A satisfied and loyal customer ensures a steady income and profit for the company. So, a conclusion based on logic, simple ?? in order for a company to gain financial benefits (to guarantee a steady income), it is necessary to collect and compile data on its customers, analyze it and use it in its dealings with consumers.
The issue of data quality becomes relevant when a company is preparing to base its decisions on data rather than experience? or "flair".
The problem of data quality implies that companies take care of data quality or could say customer leads database management, which consists of the following processes:
1. Identification and correction of false data;
2. standardization of different data types;
3. consolidation of identical data from different systems.
Customer database management starts with identifying the wrong types of data, ie understanding what data is called incorrect and what are its main attributes:
a) Errors ?? e.g. a 13-month period is entered which cannot logically be entered, or a date of production of the commodity which precedes the commencement of actual production is entered;
b) Homonyms(words with the same pronunciation but different meanings) ?? e.g. English word ?? No ?? can have different meanings ?? the opposite of the word "Yes", the abbreviation of the number, the abbreviation of "North".
c) Absence of standards ?? e.g. typing computer, PC, laptop and so on. ?? the same thing has different meanings and there is no one clear rule for how a computer should be called;
d) Unobservable data ?? this means that it is possible to enter erroneous data that does not exist, e.g. a street address or apartment number that does not exist on that street;
e) Pseudo data?? this means that pseudo data is entered in the required fields to fill in the empty values, e.g. the date of birth may not be specific digits, but e.g. XX / XX / XXXX.
a) Identification stage?? determines which fields identify the client, what data to collect, and what data is available in existing databases. Data records are reviewed, checked for data fields and correctness;
b) Standardization phase ?? defining the data to be collected requires standardization, ie description of the field length, format and systematic change. The standardization phase will be more effective if data error types are identified first;
c) Adjustment phase ?? select data to be corrected and errors to be corrected;
d) Identification phase ?? after arranging the data, data matches in different databases are searched. This step eliminates duplicate data;
e) Stage of improvement ?? Identifies what customer data is missing and what data should be collected in the future. Expected location, description of future field formats, etc.
The company must understand that identifying and correcting false data should not be a static but a dynamic process. With this in mind, it is to be expected that the quality of the data in the company will be under constant control, which will avoid additional costs for their repair in the future.
Research company Gartner Group ?? have found that companies lose as much as 5% of their customers every year due to incorrect data and actions taken on their basis. Analyzing this problem more broadly, two reasons can be identified that influence customer loss due to incorrect data:
1. Incorrect data required to communicate with the customer.This means that misrepresenting your customer's identity, address, etc. may not help you achieve your goals. e.g. incorrect invoice, offer, invitation.
2. Incorrect data analysis and misinterpretation of data. This is one of the most negative consequences of false data, which is related to misinterpreted data, which is the basis for customer actions.
It is imperative to understand that accurate customer data is an asset to the company and the lack of it or the occurrence of errors directly affects the iteration of the organization with the customers. Therefore, money for data processing and data quality management must not be perceived as an additional cost to the company, but as an investment that will allow for the right and reasonable. customer relationship decisions.
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