Marketing Mix and Product Design Quiz

Study Notes

Marketing Mix: A Comprehensive Overview

The marketing mix is a foundational model for businesses, historically centered around product, price, place, and promotion (also known as the "4 Ps").
Services marketing uses an extended marketing mix, typically comprising 7 Ps (product, price, promotion, place, people, process, physical evidence), made up of the original 4 Ps extended by process, people, and physical evidence.
Occasionally, service marketers refer to 8 Ps (product, price, place, promotion, people, positioning, packaging, and performance), comprising these 7 Ps plus performance.
The model of 4 Cs was introduced in the 1990s as a more customer-driven replacement of the 4 Ps. There are two theories based on 4 Cs: Lauterborn's 4 Cs and Shimizu's 4 Cs.
The correct arrangement of marketing mix by enterprise marketing managers plays an important role in the success of a company's marketing.
The origins of the 4 Ps can be traced to the late 1940s and were first proposed in 1960 by E. Jerome McCarthy.
In 1981, Booms and Bitner proposed a model of 7 Ps, comprising the original 4 Ps extended by process, people, and physical evidence, as being more applicable for services marketing.
Robert F. Lauterborn proposed a 4 Cs classification in 1990, which is a more consumer-orientated version of the 4 Ps.
The 7Cs Compass Model is a framework of co-marketing (commensal marketing or Symbiotic marketing) and includes commodity, cost, communication, and channel.
Service 7 is an alternative approach suggested by Australian author Peter Bowman, which suggests implementing seven service marketing principles.
Digital marketing mix is fundamentally the same as Marketing Mix, which is an adaptation of Product, Price, Place, and Promotion into digital marketing aspect.
The Internet Mix consists of three elements: content, commerce, and community.The Challenge of Product Design Summary
Maximizing the number of customers who will prefer a product is a difficult problem.
The problem depends on the preferences of customers, the attributes of competing products, and the attributes of the product being designed.
The problem of deciding the attributes of a product to maximize customer preference is Poly-APX-complete.
No efficient algorithm can guarantee a constant ratio between the number of customers preferring the product returned by the algorithm and the actual optimal product.
The problem of finding a strategy to always reach a minimum average number of customers over time is an EXPTIME-complete problem.
Heuristic solutions can be found using genetic algorithms, particle swarm optimization, or minimax algorithms.
Product design depends on customer preferences, which can vary by demographics such as age and gender.
Competing products can also affect the design of a new product.
A product's attributes, such as color and payment options, can influence customer preference.
The problem of product design is a complex optimization problem.
Efficient algorithms for product design have not been found, but heuristic solutions are available.
Heuristic solutions can use methods such as genetic algorithms, particle swarm optimization, or minimax algorithms.