This chapter is from the book
This chapter is from the book
This chapter is from the book
Exercises
[Level 1] Business internal databases are repositories of data and information gathered by a company, typically during the course of business transactions. They could be augmented by external, secondary data sources. Companies gather information about customers when they purchase a product, inquire about a service, or have a product serviced. Business internal databases are used by the companies to strengthen their relationships with customers and for direct marketing. Those databases could become quite large over time, and dealing with the vast quantity of data poses quite a challenge for decision makers. The managers are often further hindered by the relational nature of a database.
Computational intelligence software helps managers make sense of the enormous mass of information contained in non-relational data warehouses. The software is capable of creating new knowledge that is actionable for decision makers, as it does not just have the ability to explain the past, but also possesses predictive power. Micromarketing refers to using a differentiated marketing mix for specific customer segments, sometimes fine-tuned for the individual shopper. Data analytics make micromarketing drive sales and business profits.
Like many businesses nowadays, Target has utilized data analytics to micromarket—that is, to target each consumer with promotional materials designated for that individual. The New York Times reported on those practices on February 16, 2012 [Duhigg, 2012]. According to the report, titled “How Companies Learn Your Secrets,” Target collects data on every person who shops at its department stores, assigning a unique code known as customer ID. If the consumer uses a credit card or coupon, fills out a survey, mails in a refund form, calls the customer center, or opens an e-mail, Target links those interactions with the customer ID. The company also collects demographic information, such as age, gender, ZIP Code, marital status, number of children, place of residence, and income, and has the ability to purchase additional data. Like many other innovative businesses, Target utilizes data analytics in its marketing research. One avenue of these analyses is to relate to the customers during major life events, such as having a baby, graduating from college, moving from state to state (or coast to coast), and so on.
How does Target identify specific customer segments, such as pregnant female consumers, for example? Target data scientists use the store’s baby registry to identify consumers who have used it, and then backtrack to find out what products they had bought early in their pregnancy. The researchers discovered unique patterns that became clusters, and subsequently classes of consumers, and associated them with products. It appeared that women in their first 20 weeks of pregnancy purchased supplements such as calcium, magnesium, and zinc, and subsequently bought a lot of unscented lotions in their second trimester. In their third trimester, they purchased washcloths, hand sanitizers, soap, and cotton balls. Overall, Target data scientists identified 25 products, and came up with a “Pregnancy Prediction Score.” This is just one of the metrics Target uses. It applies those metrics to all customers, and those who score high enough are contacted. In this particular “Pregnancy Prediction Score” case, the high-scoring customers are assumed to be pregnant, and receive targeted promotions on products Target predicts they will need. Reportedly, Target sales on mom-to-be and baby products have increased since the data analytics tools were applied.
Similarly, many of the company’s online customers’ data browsing habits are collected and analyzed. Do you think these data analytics practices are ethical?
- 1.1 [Level 1] Provide documented examples of data analytics usage by businesses. How many of these companies gained a competitive edge in the marketplace through harnessing the power of computational intelligence approaches?
If you do an Internet search on the phrase “faith-based businesses,” the results direct you to companies that pursue a religious agenda. But, according to Fast Company [Safian, 2014], there is another kind of faith in business nowadays: the belief that a product or service can perform a radical industry makeover, completely change consumer habits, challenge economic assumptions, and enable city, county, and state officials to be proactive about health care, weather, and traffic emergencies. With $452 million distributed in 2013, Bloomberg Philanthropies is among the largest philanthropic foundations in the United States. It differentiates itself by utilizing innovative and sophisticated data-driven solutions in its business processes. As a result, the foundation has been extraordinarily effective, as it positions itself for maximum impact [Safian, 2014].
- 2.1 [Level 2] Examine the data analytics tools used by Bloomberg and write a comprehensive report on them. Identify avenues of possible use of computational intelligence to further Bloomberg’s philanthropic pursuits.
Fierce competition, time-to-market pressure, and an increasing demand for product differentiation call for more sophisticated, yet rapid product design. Businesses are increasingly seeking more efficient ways to integrate consumers’ preferences into the product design process. Taking advantage of techniques from the field of computational intelligence, it is possible to construct systems that can computationally design products with specified desirable consumer characteristics.
- 3.1 [Level 3] Conduct research on techniques from the field of computational intelligence that deal with optimal design of products. Write a case study to illustrate the application of the computational intelligence approaches in your industry.
- 3.2 [Level 4] Propose a research project for your Ph.D. dissertation that will support the construction and deployment of a sophisticated, computational intelligence model to design products that your business unit is involved with.