Automatically searches through large amounts of data and discovers patterns and trends
that go beyond common analysis
What is Data mining
Drastic improvements in data recording and data storage technologies together with increased use of mobile devices led to a staggering growth rate of the amount of collected data. Companies now store enormous trails of information about their operations, customers, partners, suppliers. This raw data is massive, heterogeneous and unstructured. How to deal with it? At the moment less than 3% of all big data is used and analyzed. But let’s count: for a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional income, according to Forbes.
The term data mining (also popularly known as Knowledge Discovery in Databases, KDD) derives its name from two concepts: search for valuable information in large data sets, and mining rocks looking for a vein of golden ore. Like sifting earth and sand, data mining “digs” in and inspects raw data in order to reveal valuable insights: previously unknown tendencies, correlations, interrelationships and construct predictive models. Discovering hidden patterns and finding predictive models helps companies make better managerial choices and strategic business moves.
What Data mining can do for you
Data mining algorithms can be applicable to different types of data like: flat data, relational databases, data warehouses with data collected from different sources, transaction, multimedia, time-series, spatial databases and, of course, World Wide Web. Unlike traditional methods of intellectual information processing which only verify hypotheses formerly formulated by the user, data mining algorithms unearth previously hidden rules, patterns and relationships and provide predictive information.
So while traditional methods can only answer questions like: «How many customers received my new promotional mailings?», data mining methods deal with higher-order tasks, like: «Which category of clients is most likely to respond to my next marketing campaign e-mails, and what are its features (age, gender, shopping behavior etc.)». Such information is especially useful for predicting future trends, strategic planning, risk analysis and conducting a constructive market analysis.
How Data mining can help your business
Data mining use cases
- Detect and deter fraud: identify behavioral patterns in credit transactions that lead to fraud
- Prevent bad loans and defaults by examining profiles of high-risk borrowers
- Prevent attrition: define the logic of customer’s transactions previous to their shifting to a competitor bank
- Create highly personalized offerings: understand what bank products are often bought together and by which groups of customers
- Increase loyalty and customer retention by forecasting customer’s wishes: understand which services and benefits you customers would like to have at your organization
- Identify risk factors that predict profits, claims and losses to more accurate rates setting
- Segment customers to granularly choose which products to offer to which group and which marketing efforts would be most effective to each customer segment
- Do predictive modeling to find out policyholders that are likely to switch to a competitor insurance company
- Develop new product lines by understanding which services and benefits you customers would like to have at your organization
- Distinguish between profitable and unprofitable customers
- Identify high-risk customers who have not engaged with your brand for long time and create for them special «come back» offers
- Predict which customers may switch to an alternative supplier in the near future
- Define new customer groups for marketing purposes
- Forecast sales, determine a strategy of planned obsolescence or figure out complimentary products to sell
- Deeply understand your customer’s buying and Internet browsing habits
- Identify unexpected shopping patterns
- Run basket («affinity») analysis: discover seemingly unrelated products that are often purchased together and improve layouts in brick-and-mortar shops and/or recommendations of related products in online stores
- Segment your customers and predict the likelihood that a customer would do a certain action (open an e-mail, make a purchase)
- Create customized marketing campaigns, determine which types of special offers to provide which group and when
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