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Data Mining Tools Can Fuel the Evolution of Retail Strategies

With the advancement of AI and significant rate of technology adoption among various sectors, data mining tools are exhibiting the potential to drive positive reinforcement across business strategies

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A faster penetration and growth in internet usage worldwide have caused a veritable explosion in data generation in recent years. In 2006, Clive Humby, a noted data scientist, hailed data as the new oil, a phrase that is seemingly supported by  the vast amount of digital data being created, captured, and replicated over the years. 

According to big data estimates from Domo, each person worldwide was anticipated to generate almost 1.7 megabytes of data per second by 2020. Given this massive explosion of big data over time, a lot of research efforts have begun to emerge in recent years targeted toward extracting useful insights from a vast information base. 

Forbes suggests that there will be over 150 trillion gigabytes of data that will require analysis by the year 2025. This, in turn, has resulted in a strong focus on the development of advanced data mining tools and machine learning technologies.

What Is Data Mining?

Data mining refers to the process of obtaining useful insights from huge data accumulations, such as linked dataset collections or data warehouses. These processes require the use of specific and powerful tools that possess robust mathematical, analytic and statistical capabilities, and are designed to sift through massive data sets in order to detect anomalies, patterns, trends and other insights that can facilitate better business planning and decision-making.

One of the most common use-cases of data mining technologies is weather forecasting, which involves deep analysis of large collections of historical data to detect patterns and make future weather predictions based on variables like climate, time of year, etc. 

Tech companies like Spotify also leverage tools for data mining, for solutions such as its AI recommendation engine which can direct the user towards new artists, genres and songs after gaining a deeper understanding of their music preferences using proprietary algorithms.

While the data mining tools industry is most often associated with inquiries pertaining to the marketing department, the process provides significant benefits to other business applications as well. For instance, helping designers and engineers study product success or failure by analyzing how, where and when they are used. Or, allowing service organizations from industries such as retail to identify new business prospects from evolving demographics and economic trends.

Veering Toward Automation: Evolution of Data Mining Technologies 

Data mining was predominantly an intensive manual coding procedure, requiring specialists to have strong statistical knowledge as well as programming language know-how. However, the emergence of next-gen technologies such as AI and machine learning proved to be game-changing for the industry and allowed most intrinsically manual processes to be automated.

Most organizations in the modern world make use of advanced data mining technologies to attain comprehensive insights into their business processes, a trend that is proving to be particularly beneficial in light of the pandemic situation caused by the COVID-19 virus. 

In July 2020, for instance, Microsoft introduced a novel AI-based data mining tool for its Azure cloud platform, designed to enable developers to examine unstructured medical data, from clinical trial protocols to clinical notes to medical publications. The Text Analytics for health tool allows data analysts, medical professionals and researchers to identify phrases and words from unstructured data, and connect them with relevant biomedical and healthcare concepts, including medication names, diagnoses, and treatments.

The tool is being leveraged by medical researchers and analysts from noted institutions such as the Allen Institute for AI in Seattle, which is using it to create a COVID-19 search engine designed to enable researchers to analyze coronavirus-related information even faster.

Data Mining in the Retail Sector Remains On-Trend

Retail has long been one of the most prominent contributors to the global economy. National Retail Federationestimates suggest that retail sales have surged by over 4% each year since 2010. Given this increasingly competitive market landscape, players operating in the space are focusing on gleaning as many insights as they can about their customers, seeking answers for questions such as why they buy, when they buy, what they buy and who they are.

The proliferating shift of the physical retail domain to the online domain has made this easier since more and more data about consumer behavior is produced with each new purchase. However, to gain these insights, retailers need to leverage sophisticated tools for data mining in order to collect, organize, clean and analyze this massive influx of information.

Data mining tools or knowledge discovery in databases (KDD) tools bring myriad benefits to retail enterprises. They can allow for more precise production and sales procedures that place emphasis on repeated product purchases by buyers, as well as enable retailers to promote products, discounts or other strategies that are more attractive to consumers.

Supermarkets, especially, are a lucrative application area for data mining tools and techniques. To illustrate, noted US-based retail company Target developed a means to predict possible pregnancy among their shoppers, using a data-mining program. 

The company devised a tool to look through the customers’ shopping baskets to spot customers that showed the most likelihood of being pregnant, and subsequently began to target promotions for associated products including cotton wool and diapers, among others. The data mining tool proved to be highly accurate and earned Target substantial visibility in relevant areas.
 
Although the concept began to come into its own around the 1960s-1980s, the data mining tools industry gained significant attention in the 1930s, particularly following the introduction of a universal machine concept by Alan Turing in 1936 that could perform computations similar to modern-day computers. Data mining has seen massive development since then and is quickly becoming a core component of most technologically charged business processes in the modern world.
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