Introduction
Data collection is not a new practice in the retail industry although data mining is a new term that has caveats of modern tools and methodologies used for implementing it. Earlier, data gathering was done with the help of surveys, point-of-sale, order books and invoices depending on the decade under consideration. Today, data mining is developing to be a sophisticated and organized means of data collection. It used for extracting and generating meaningful insights with the help of computing power and harnessing the power of the internet. Large corporations are spending millions of dollars on enterprise solutions for data mining and using Artificial Intelligence and Business Intelligence tools such as Neural networks and predictive algorithms to better guide their business decisions.
Uses of Data Mining
Data mining is widespread today in the retail industry and primarily in focus for identifying the behavior of the consumer. It is useful for finding trends with the help of computing power, which may not be obvious by empirical human observation. It serves number of additional purposes such as:
- Identifying consumer retention and contentions
- Cutting operational costs
- Increasing revenue
- Improve customer satisfaction
- Identifying potential patrons
- Decrease the overall cost of operations
It is also used to drive innovation and product development, improving customer services, increasing the social outreach by means of social media and in-person, finding potential points-of-sale locations both digitally and in real-world, etc. In case of digital outreach, it is helpful in building efficient SEO (Search Engine Optimization) which will aid in making the product or website more accessible and visible.
Use cases
There are several use-cases that can be easily considered as a part of retail industry sales and marketing with almost the entire retail industry trying to stay in the competitive edge. All departmental stores today such as Walmart, Target, Walgreens, etc. heavily invest in Data Science and Big Data methodologies for several reasons such as
- inventory management,
- supply and demand assessment,
- identifying potential audience,
- determining user preferences, etc.
Loyalty programs are setup with incentives that tempt consumers to sign-up to assess their shopping carts and provide better suggestions. Behavioral analysis is done based on data models generated solely for this purpose. Similar patterns can be observed in other industries which are primarily focusing on website engagements. Social media platforms are widely used for determining user behavior and trends which help companies market their products to appropriate audience. Websites such as Pinterest, Facebook, Instagram, etc. have ad campaigns that are tailored according to the specific audience groups.
The overall incentive is to increase consumerism in such cases. The most successful example perhaps is that of Amazon which rose to prominence with algorithms that studied trends and behavior accurately in addition to quick implementation which was possible due to well-studied inventory management all because of the Prime membership which helped acquire large amount of data and customers.With increasing use of data mining in retail, the behavioral and analytical models are getting increasingly better, and it remains to be seen how it will impact the overall user experience.
