Gaining Competitive Intelligence with Data Mining

Gaining Competitive Intelligence with Data Mining

Gaining Competitive Intelligence with Data Mining

Twitter added $47.5 million to its coffers in 2013. This was not advertising revenue, but income generated by selling user data to businesses hungry to find new consumers. By researching target consumer buying habits, businesses can find more consumers and invariably increase profits. Because of this, big data is a highly sought after business asset; however, mining that data is a complex task that is only possible using powerful software.

How Data Mining Works

Data mining is when a financial analyst gathers consumer information and looks for patterns that a business can exploit. A simplified data mining example is when a restaurant manager knows the local yearly convention schedule based on experience. The manager can cross-reference that information with historical sales results to predict such things as forecasted profit or labor demand. With this information, the manager can estimate an advertising budget or hire temporary staff to handle anticipated workload. When medium to large-sized businesses use data mining, they uncover these same information points; however, revenue gains can range from millions to billions of dollars. There are several techniques that firms frequently employ to find gold in information.

Researching Retail Shopping

When an analyst conducts a basket analysis, they research information collected from a company’s sales, looking at factors such as what items a consumer purchased in a store or what items they looked at online. In physical retail stores, businesses can use this information to group these products near each other. This results in increased consumer purchases, because the consumer can more easily see how buying these products can better solve their needs. For online shoppers, retailers can take this process further by displaying items similar shoppers have viewed. The ability to do this increases consumer convenience and the possibility that the shopper will make additional impulse purchases.

Forecasting Profits

In the data mining field, sales forecasting is the natural evolution of hand written sales ledgers that businesses have used to record historical sales figures and predict future profits. Now businesses can predict future sales with pinpoint accuracy. Not only can retailers pull information directly from POS systems, reducing labor costs and human error, but they can recall this information in customizable reports. Additionally, these information collection systems can drill down further than overall sales. A firm can put systems in place to collect detailed information such as a buyer’s gender, age or residence. With this information, it is possible to discover who and where a business’s ideal consumers are and attempt to find more like them.

Marketing to Current Consumers

Database marketing is another evolved data mining discipline. In the past, a business collected contact information and blindly attempted to solicit further sales from raw data. Data mining gives retailers the power to identify consumers who actually generate revenue and even make decisions about which consumers are profitable to target for repeat business. Contemporary data handling software allows marketers to gather more data, and in turn make better decisions, than was ever possible with legacy databases that simply hold information. While these databases are invaluable, without data mining’s processing power, firms cannot tap into the full utility of these information sources. Data mining programs allow analysts to investigate a client base on a microscopic level. With this power, the analysts can find consumer buying patterns that are not readily apparent.

Managing Retail Inventory

Retailers can also harness data mining insights for merchandise planning. It is easy to imagine that a retailer can use this tool to manage such tasks as maintaining inventory levels to meet demands. Thinking on a higher level, data mining is also a boon to executive buyers who have to navigate complex merchandising networks and plan complicated shipping logistics. Some corporate buyers set up data mining systems to automatically replenish inventories from various suppliers as needed. Analysts can also use data mining to protect assets by identifying risks such as shrinkage. By using data in concert with radio frequency identification technology (RFID), an analyst can identify exactly where merchandise is disappearing and reduce or eliminate theft. Businesses are increasing in size and data mining is also a way for firms to increase security while reducing staff members.

Marketing with Credit

Firms that issue consumer credit cards are especially privy to detailed buyer information. To facilitate mutual trust, a consumer must release more personal data to secure brand specific credit. This is the perfect opportunity for a retailer to segment its consumer base and take a deep look at their buyers’ spending characteristics. With this information, a retailer can create customized marketing campaigns for their consumers. The modern shopper responds impressively to these targeted campaigns. While a retailer cannot cost-effectively create customized marketing strategies for each consumer, they can identify the largest profit opportunity group and pursue their spending dollars. For a company that sells millions – or more – in merchandise, even a small percentage revenue increase can equal huge profits.

Analyzing Consumer Call Activity

Firms that operate consumer call centers can gather detailed information. Call centers typically use such data to track customer service representative effectiveness. A business can track such metrics as calls in queue and average call wait time. With this information, a call center manager can set policies as to how long an operator should spend on a call. This information can also identify the need to set up special teams based on contact center call volume. Using customer relationship marketing software to identify problem escalations across disparate business units can help geographically dispersed organizations identify major issues early. Big data helps companies earn more by taking overwhelming datasets and organizing the information in a useful manner.

Customer Loyalty Marketing

Customer loyalty initiatives are a longstanding, proven marketing practice. Big data takes customer loyalty programs to another level by intimately tracking consumer details. Modern loyalty programs go further than offering a free good for every tenth consumer purchase. Some firms, whose sole service is customer loyalty, create service provider networks. These networks gather consumers who they then reward for making purchases from network businesses. This may seem like just a computerized version of the old-fashioned, hole punch consumer loyalty card, but these memberships can also track details such as when a consumer makes a purchase and how much they spent. Aggregated with data compiled from other consumers, these loyalty card service providers are building enormous data warehouses filled with consumer buyer data – a valuable commodity in today’s marketing arena.

By exploiting big data, United States firms are turning a hefty profit. Companies find this profit by selling consumer data and finding new business opportunities. Businesses can also generate profit by reducing liabilities such as fraud, embezzlement and shrinkage. In a data driven environment, the power to manage massive data caches can separate winners and losers in the marketplace. Software manufacturers have developed free or inexpensive data mining programs as smaller business are beginning to see the value in big data, but these entry-level platforms generate fees once companies begin to add features such as legacy system integration and real-time analytics. Despite this, researchers are making evolutionary progress for businesses of all sizes in the race to monetize information.

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Sources:

https://blog.kissmetrics.com/data-mining
http://www.wsj.com/articles/SB10001424052702304441404579118531954483974
http://documents.software.dell.com/statistics/textbook/data-mining-techniques#concepts
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