Customer Data Mining
How to determine if an in-house or outsourced solution is right for you
February 2006 By Jim Wheaton
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Customer data mining is a complex process that involves highly trained professionals. Some companies handle data mining in house, while others farm it out, and still others follow a hybrid solution. Which option is right for you? Here are some factors to consider when you’re making this difficult decision.
What Can You Afford?
Most mid-size to large direct marketers have an in-house data mining department to handle at least some
of the analytics work. They feel it’s important to have total control of this critical function, and for the data miners to be continuously steeped in the business. Also, the cost of an in-house staff can be lower than an outsourced solution.
However, an in-house solution can be problematic for smaller direct marketers because employee turnover is an unfortunate fact of life. Having your own mining group is risky if your company is not large enough to support overlapping personnel.
Many smaller firms have been hurt badly when a key data miner has moved on to greener pastures. Generally, when this occurs, much or all “company memory” is lost forever. Therefore, when deciding whether to do customer data mining in house, you should reflect on how much staff you realistically can support.
For example, a well-known retail/catalog/e-commerce marketer recently lost its lone in-house data miner. Until it finds a replacement, the firm is in a tenuous situation in which predictive models are embedded in the production processes, driving decisions on whom to promote, but there is no statistician to interpret what’s going on.
Are You Seeking Ongoing Support or One-off Work?
Consistently excellent target marketing requires ongoing data mining support. However, some firms aren’t able to make that commitment. Instead, they pursue one-off assignments. Under such circumstances, the only logical choice is to outsource the work.
When farming out your data mining, beware that there is significant overhead the first time a vendor works with your customer file. The first project always is more expensive. To do an effective analytical job, it’s important for the analytics firm to totally understand your business and data.
For example, a major publisher with millions of subscribers across multiple titles decided to incorporate sophisticated data-driven marketing into its core business practices. As part if this initiative, it outsourced more than 40 custom-built predictive models over a one-year period. These models drove selections for the cost-effective micromarketing of dozens of products and services to the customer base.
What Can You Afford?
Most mid-size to large direct marketers have an in-house data mining department to handle at least some
of the analytics work. They feel it’s important to have total control of this critical function, and for the data miners to be continuously steeped in the business. Also, the cost of an in-house staff can be lower than an outsourced solution.
However, an in-house solution can be problematic for smaller direct marketers because employee turnover is an unfortunate fact of life. Having your own mining group is risky if your company is not large enough to support overlapping personnel.
Many smaller firms have been hurt badly when a key data miner has moved on to greener pastures. Generally, when this occurs, much or all “company memory” is lost forever. Therefore, when deciding whether to do customer data mining in house, you should reflect on how much staff you realistically can support.
For example, a well-known retail/catalog/e-commerce marketer recently lost its lone in-house data miner. Until it finds a replacement, the firm is in a tenuous situation in which predictive models are embedded in the production processes, driving decisions on whom to promote, but there is no statistician to interpret what’s going on.
Are You Seeking Ongoing Support or One-off Work?
Consistently excellent target marketing requires ongoing data mining support. However, some firms aren’t able to make that commitment. Instead, they pursue one-off assignments. Under such circumstances, the only logical choice is to outsource the work.
When farming out your data mining, beware that there is significant overhead the first time a vendor works with your customer file. The first project always is more expensive. To do an effective analytical job, it’s important for the analytics firm to totally understand your business and data.
For example, a major publisher with millions of subscribers across multiple titles decided to incorporate sophisticated data-driven marketing into its core business practices. As part if this initiative, it outsourced more than 40 custom-built predictive models over a one-year period. These models drove selections for the cost-effective micromarketing of dozens of products and services to the customer base.
Companies Mentioned:
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