In the age of analytics, The Marketing Analytics Practitioner’s Guide serves as a comprehensive guide to marketing management, covering the underlying concepts and their application.
As advances in technology transform the very nature of marketing, there has never been greater need for marketers to learn marketing.
Essentially a practitioner’s guide to marketing management in the 21st century, the guide blends the art and the science of marketing to reflect how the discipline has matured in the age of analytics.
Application oriented, it imparts an understanding of how to interpret market intelligence and use analytics and marketing research for taking day-to-day marketing decisions, and for developing and executing marketing strategies.
Product ideas are sometimes self-evident. Success in innovation often lies in the design, development and the execution. Once the ideas and concepts have been generated and screened, it is time for R&D to design and craft the new products. They do so by combining art, science, and technology to transform the concepts into new products.
This chapter imparts an understanding of the product design techniques and the processes, including sensory research, the Kano model, conjoint analysis and the house of quality. You will learn how ideas are turned into reality, from concepts to products.
To begin, consumer requirements need to be translated into engineering and manufacturing parameters that are measurable and controllable. This usually results in some tension between marketing and R&D. Marketers spell out what consumers want, but, to create the new product, what engineers require are technical specifications. A set of methods called quality function deployment (QFD) helps to translate the marketer’s description of consumer needs into the engineer’s language of technical specifications.
The house of quality is a prominent QFD technique that brings together in a “house” configuration the attributes that consumers need and the engineering characteristics that will influence those attributes. Utilizing an inter-relationship matrix to relate the consumer attributes to the engineering characteristics, the technique is able to translate a product concept into the technical specifications for a prototype. It also improves cross-department communication by serving as “a kind of conceptual map that provides the means for inter-functional planning and communication” (Hauser and Clausing, 1988).
Food technologists seek to alter their recipes to achieve what they refer to as the “bliss point”, the point at which the composition of ingredients creates the maximum amount of crave. Aeronautical engineers seek to design airplanes that cater to the preferences of the major airlines. Credit card managers seek the combination of features that would incentivize their card holders to spend more on their credit cards. They all are in search of the optimum combination of product features to gratify their target customers.
The optimum combination of features, however, is not the final answer. The key issue in product design is the trade-off between product features, and the trade-off between quality and cost: Are consumers willing to pay for the optimum combination of features that gratify their needs? Are they prepared to pay the additional cost for an improvement in performance?
Designers are also confronted with the challenge of negatively correlated characteristics. For instance, taste versus low calories, taste versus nutrition, power versus safety, ubiquitous versus exclusive, quality versus price and so on. Sometimes a creative solution can satisfy multiple needs. Most of the time, however, a trade-off is called for. To make an informed decision, the designer needs to know the combination of price and features that target consumers finds most desirable.
Conjoint analysis, a technique developed by Paul Green at the Wharton School, answers these questions. It is a predictive technique used to determine customers’ preferences for the different features, including price, that make up a product or service.
Conjoint analysis works well for consumer durables and has also been used for FMCG products. Yet sometimes it may become challenging to convey tastes, as in food products, or fragrances, in a conjoint analysis. Sensory research and consumer product testing offer an alternative methodology where preference ratings by consumers are modelled to determine the importance of each attribute.
“We called this project ‘You bet your company’.” — Bob Evans, IBM
One of the greatest feats in the design of computer systems dates back to the mainframe age, at a time when computers were created incompatible. Upgrading to a new computer in the early ‘60s, meant you needed to redevelop all software, rewrite all programmes. There were, at that time, too many different computers requiring different supporting programmes and different peripheral equipment. IBM’s own business divisions produced a “wildly disorganized” array of offerings, as CEO Thomas J. Watson Jr. recalled in his autobiography, Father, Son & Co.
It took a “$5,000,000,000 Gamble,” as Fortune called it, to change the status quo. In addition to the investment risk, gigantic by 1960 standards, the IBM 360 posed major structural risks. It would disrupt every facet of the computer industry. All of IBM’s existing computers and peripherals would become obsolete overnight. IBM’s head count would grow by 50%. The company, which was essentially an assembler of computer components, and a business service organization, would open five new plants and become the world’s largest maker of integrated circuits. Bob Evans, the chairman of the committee that conceived of the IBM 360, said: “We called this project ‘You bet your company’.”
System 360 was launched on 7 April 1964 as a single compatible line consisting of six computers and 44 peripherals. Besides displacing all existing models, it opened up new fields of computer applications, and served both business and scientific applications.
For the first time, all input, output and other peripheral equipment had standard interfaces. For the first time if you bought a new computer you did not need to rewrite your programmes.
The gamble paid off — IBM transformed the world of computing establishing a new global standard. System 360 gave Big Blue a competitive advantage in computer systems that prevailed for decades.
IBM computers that are better known today include Deep Blue which beat Garry Kasparov in 1997, and Watson which defeated champions in the Jeopardy! quiz show in 2011. Along with the IBM 360, they exemplify the legacy of innovation at a corporation that has topped the US patent list for the past 21 consecutive years.... less