Post by simranratry20244 on Feb 12, 2024 6:50:57 GMT
Predictive analytics uses the information contained in databases to make forecasts that help the business make decisions. The marketing area is one of those that benefits the most from these repositories of raw knowledge. However, not all databases can be used to support business intelligence. predictive analytics Analytical databases: characteristics and types Transactional databases are not the first option to carry out this predictive function, as they are not designed for analysis purposes , but for day-to-day operations. Analytical databases are the most suitable alternative as they are generally read-only systems that store historical data on business metrics. Analysts and business users turn to them to launch their queries prior to preparing reports. Among its main features are: The information collected there must be updated regularly. In addition to historical records, recent transaction data from corporate operating systems must be incorporated. They can be part of a data warehouse . They must be scalable. Analytical databases are not always the same nor are they worked with in the same way. Its structure, analytical orientation and qualities change depending on the type of database in question. The following types can be found on the market: Column-structured databases : use this structure to replace rows and reduce the number of data elements to be read in order to process each query.
Data storage devices : these are not databases themselves, but rather a hybrid format that combines these with BI hardware and tools in an integrated platform. Its main advantage is user accessibility and its intuitive interface, which makes it easy to use. In-memory databases: These databases load information into system memory in a compressed, non-relational format, which Colombia Telemarketing Data streamlines the workload involved in processing queries. MPP databases: their main characteristic is parallel processing, which is supported by different servers to share the workload associated with launching queries by different business users. OLAP databases : deal with online analytical processing based on multidimensional cubes of aggregated data for information analysis. How to use databases to improve marketing results Databases facilitate information management and considerably reduce the costs of marketing initiatives . The key is instant access to details on all current and potential customers. But, to be truly useful, databases must have a flexible design and the type of information they should collect must be clear from the beginning. The marketing area relies on databases to, among other things: Launch queries. Make customer recommendations. Know the results of the surveys carried out on clients. Databases allow : Identify the segments with the greatest market potential. Concentrate resources on the most profitable clients and potential clients with a similar profile. Carry out more effective campaigns with a guarantee of better results. However, to avoid confusion, unjustified costs and loss of effectiveness, it is necessary: Define marketing objectives and how you plan to achieve them. You have to know the goals to be able to determine what data is needed to achieve them.
Objectives should always be set that are aligned with the organization's general strategy. Decide what information will justify the costs of collection and what needs to be updated before creating the database. You should not collect what is not going to be used , since this would only reduce the agility of the processes. Take care of the design of the database: precision, flexibility, accessibility and a structure that allows filtering the records that are needed for analysis or to direct a marketing campaign. Without forgetting to use unambiguous descriptions for data, something that, in practice, if not applied, can lead to inefficiencies and errors. Establish categories and identifiers: divide the data into segments, categories and sub-categories. Encode the data. Create a business glossary . Data collection must be planned so that it occurs in the most orderly manner possible, only in this way can the risk in actions that, in the future, are based on knowledge extracted from the stored data be minimized. Keep databases clean: Investing effort in improving data quality is essential for effective marketing and analytics campaigns. Frequent updates, elimination of duplication, design of error checking systems and designation of data owners to be responsible for them is vital. Establish a data security and privacy policy that meets the organization's internal standards and also meets the minimum requirements established by applicable legislation.