1. Data excavation does not really work in the B2B environment
Typically, managers believe that data mining cannot work well in the business-to-business (b2b) environment, because the unique difference in the business model, such as the accounts designated by the financial manager, the product gross profit, sometimes long Not frequent purchase cycle, the product line detailed directory extension. Important in successful data mining is the data you are dealing with, rather than commercial features, especially those who produce goods transactions more significant. These data include what customers buy, what time is purchased, total sales, and so on. Long-term basic archive data, total sales, total profits, etc., have brought huge value to the analysis. Finally, the sales history such as effective directory total and valid mailing list can also bring great value to the analysis. With the data already owned, data mining can work well in the B2B environment.
2. There is no sufficient data to develop excellent models for business activities.
There is a wrong understanding: good database marketing needs to analyze millions of customers. Obviously not that. The development of a good model is more dependent on high quality data, not the number of data. For example, if you are developing a predictive reaction model to determine who is most likely to respond to the company's mail directory, the key standard is the last mail directory that responds. If you have thousands of responses, you can develop a good predictive model. Some data mining products only require less data.
3. Effective mining B2B data requires updates and better technologies
Data Mining This science has evolved for a long time, reaching a maturity and stable level. Many useful commercial products can analyze B2B data well. Different whether the product is refined by the commercial practical application. Suppliers continue to upgrade their products to attract the audience and managers in the market. For example, data mining products should be able to deal with lack of data and bad data in a real environment, and provide easy to understand and easy to use output results. And pay attention to your data mining software provider if there is important business activity history, if yes, then these vendors will know the data processing of input and output. For where data mining provides the largest investment return, they should have rich experience. Therefore, the product that is actually inspected is appropriate, not necessarily the latest data mining tool.
4. Need a statistically to perform data mining
Many software and service sellers make data mining tools complicated. It is meaningful to understand the difference between the structural neural network, or the regression model and related analysis, but it is not necessary to produce the correct result. Many products in this field are very complicated, and some products are concentrated to solve the needs of commercial users, such as convenience and speed, which can be submitted to very powerful results. For example, some companies automatically fuse the data mining and linear return neural network in the product, which focuses on the use of complex statistical processing steps.
5. It takes too much relative to expected revenue.
You may be surprised to find the minimum investment. In the world of B2B, customers' cost, the value of the entire life cycle and product profit usually higher than consumer. There is no need to improve the return on investment in maturity or cross-selling results. If you are very cautious, you can take a small step to demonstrate the work of data mining in business activities. Find an experienced vendor to build a small predictive model to test them. Typically, the test work can be completed for $ 10,000 - $ 15,000, but the results may be worth $ 100,000 or more. Data mining can be easily generated from 5 to 10 times.
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