Data Mining: The Benefits

As a process, data mining keeps your business profitable. As a software application, it empowers you to uncover previously undetected facts about customers in your databases.

According to IBM, "Data mining offers firms in many industries the ability to discover hidden patterns in their data patterns that can help them understand customer behavior and market trends. The advent of parallel processing and new software technology enable customers to capitalize on the benefits of data mining more effectively than had been possible previously."

For example, to quote from a paper on the benefits of data mining, "Retailers often wish to know what products typically sell together. Knowing that 72 percent of their customers who buy a certain brand of soda also buy a certain brand of potato chips can help the retailer determine appropriate promotional displays, optimal use of shelf space, and effective sales strategies. As a result of doing this type of affinity analysis, the retailer might decide not to discount the potato chips whenever the soda is on sale, as doing so would needlessly reduce profits. Knowing such information can help a mail-order firm increase sales by narrowing down the scope of a given mailing campaign or perhaps creating more custom mailings that each yield higher success rates."

There are a number of ways that data mining saves money and improves efficiency. It adds the benefits of automation to your existing software and hardware. It can be carried out on new technology eve if your current system is being upgraded. And today's technology can also analyze terabytes in minutes.

These are some of the ways data mining can take customer-service to the ultimate level, from attracting new prospects to re- and cross-selling existing customers to retaining the blue-chip ones. A company can minimize losing customers who are a flight risk by studying customers they have lost. Or a company can target prospects with just the right specials if they know their buying traits. If one out of a hundred consumers who had a similar profile bought product A, then marketing to the remaining 99 is imperative. Chances are many of those people will purchase product A as well.

The Two Crows Corporation queried some of the top CFOs in the country about data mining and made these enterprise-wide recommendations on how data mining can groove with your existing CRM system:

Your existing CRM strategy must be enterprise wide.
CRM implementation and management must be aligned with business processes and follow a segmented plan that reflects financial, organizational and market realities.
CRM data must be used and shared among processes and applications.
Technology acquisitions and implementations must address specific business issues, and responsibility for these must be shared among all parties involved.
Data warehousing and data mining must be at the core of any CRM technological efforts.
The data is less valuable if it isn't consolidated, understood and then shared.
Management measures must be in place to encourage and enforce the effective sharing and application of data.
Data must be tracked against standardized metrics that enable comparison over time. Data mining and marketing efforts must be measured consistently and regularly.

To emphasize the link between data mining and CRM, their report continued, "This is not to say that the acquisition and implementation of CRM technologies and applications cannot be pursued on a non-enterprise, departmental basis. Valuable data can be gained from almost any CRM implementation, especially through the short-term reduction of specific process costs. Overall, however, the cost of piecemeal implementations will likely be greater to the enterprise - and the potential value of the strategy, system and data will be less. As CFOs are charged with optimizing the value of the enterprise, it behooves them to make CRM - and its data - an enterprise imperative."

Found on the Internet

Here are a few snapshots how data mining can benefit certain industries.

Retail / Marketing

Identify buying behavior patterns from customers.
Find associations among customer demographic characteristics.
Predict which customers will respond to mailing.

Banking

Detect patterns of fraudulent credit card usage.
Identify "loyal" customers.
Predict customers that are likely to change their credit card affiliation.
Determine credit card spending by customer groups.
Find hidden correlations between different financial indicators.
Identify stocks trading rules from historical market data.

Insurance and Health Care

Claims analysis - determine which medical procedures are claimed together.
Predict which customers will buy new policies.
Identify behavior patterns of risky customers.
Identify fraudulent behavior.

Transportation

Determine the distribution schedules among outlets.
Analyze loading patterns.

Medicine

Characterize patient behavior to predict office visits.
Identify successful medical therapies for different illnesses.