Over the last decade, business intelligence has become an emerging phenomenon that has drastically changed the way companies make operational and strategic decisions. Business Intelligence through data mining provides managers unprecedented control and insight into various performance metrics of any business. Most businesses collect huge amounts of data that can be refined using various data mining techniques to give shape and meaning to its existence.
Today, data mining tools are extremely sophisticated and complex. Business managers can expect to receive predictive analysis on complex questions though the use of data mining techniques. Managers can ask questions as complex as, “How effective is the new marketing campaign going to be within a certain market segment?” and “What are the most valued attributes of a certain product or service?”
Simply put, data mining is the extraction of hidden predictive information from large volumes of data by the use of algorithms. The advancement in technology has enabled companies to refine their data and derive meaning that can be useful in the decision making process. Data mining tools have the ability to reliably predict future trends and behaviors, putting businesses in a position where they can proactively take action to capitalize business opportunities.
Data mining is being widely used in several industries to address strategic and operational challenges. For instance, in the retail industry, data mining is being used to collect and organize huge amounts of data on sales, customer purchase history, logistics, consumptions and after-sales services. The data is used to analyze customer buying patterns and trends. This multidimensional analysis leads to more effective and well-targeted advertisement campaigns, in addition to more accurate product recommendations and cross-referencing of products/services to customers and better customer experience.
Many companies are using data mining tools with a strong consumer focus in order to streamline retail, marketing and communication functions. The Drill Down approach enables companies to analyze data to get insights to customer preferences, effective market segmentation, and customer satisfaction levels. Data mining can help companies better understand customer preferences and predict future buying behavior. Based on this, a comprehensive customer journey map can be created in order to improve customer experience.
Although, market segmentation has been around for decades, but data mining techniques allow companies to dig deeper into buying patterns of customers and identify distinct segments more effectively, since even subtle patterns can be used to derive distinction between groups. This helps companies target customers with special offers by addressing their respective pain points with a more result oriented approach compared to conventional segmentation techniques. For example, Marks & Spencer implemented data mining to identify eleven distinct customer segments. The additional customer profile analysis helped the company ensure that the products customers wanted were in stock. This helped them enhance customer experience and improve conversion rates.
Data mining allows companies to construct models based on customer buying behavior which is based on their purchase history. This helps companies better understand the preferences and needs of customers. Many retail stores have revamped their store design or/and website layout based on their customers’ behavior. This approach has also been useful in product design process since it provides insight in terms of what customers need and what enhances overall customer satisfaction levels. Moreover, identification of sequences and correlations between purchasing activities have the potential to reveal a lot about future trends in the retail market. Predictive visualization can help managers plan their future course of action based on trends and prediction in a proactive manner.
Data mining can be instrumental in improving customer loyalty and building stronger relationships with customers. The data collected during customer interaction can be analyzed to get meaningful and actionable insights to improve customer experience. Since customer relationship management is essentially about acquiring and retaining customers, it be done through developing a strong relationship with customers that is based on trust and meeting their expectations. After the collection of data, businesses often find themselves confused as to how to address customer complaints and concerns. This is where data mining techniques can be extremely useful, as they have the potential to offer solutions to complex problems based on extensive data analysis.
Most companies invest a lot of time and energy to collect customer feedback through various means. It can be difficult to collect and organize unbiased feedback that is meaningful in terms of improving customer service experience. Data mining provides an opportunity for companies to make sense of the mammoth amounts of data and interactions with customers. Based on data banks, customer behavior, trends and customer profiling, companies can now derive feedback that pinpoints the problem areas that need work and investment. This allows companies to focus on improving customer service experience more effectively.
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