Chapter 2 General principles

2.1 The Marketing Mix

The Marketing Mix (the 4Ps of Marketing) is a heuristic tool to organize marketing activities and ensure that the business considers all relevant aspects for better decision-making. The model is most suitable for the design, production, distribution, and sales of products. The 4Ps of marketing are:

  • Product (creating)
  • Promotion (communicating)
  • Place (distribution)
  • Price (exchange)

2.2 Understanding your consumers

The first law of consumer behaviour is that “customers buy stuff for what it means to them, not what it does”(Prevos, 2018). Consequently, this law implies that “creating new products not only requires knowledge of engineering; it also requires engineers to understand psychology” (Prevos, 2018).

  • Information Processing

It is said that people store brand information in their brain as a format of pictures in 2010 (Koll et al., 2010).

Aunt Jemima syrup and pancake mix will get a new name and image after the BLM movement. How do you evaluate this strategic marketing decision?

2.3 The omni-channel environment

A fundamental question you want to ask in this section is probably “what are the key differences between traditional retailers and new( or mordern) retailers?” There will be a channel design strategy tutorial to help you understand the complexity of the omni-channel environment.

  • Information/Data exchange

  • A touchpoint can be defined as any way a consumer can interact with a business, whether it be person-to-person, through a website, an app or any form of communication (“Touchpoint Glossary”, n.d.). When consumers come in contact with these touchpoints it gives them the opportunity to compare their prior perceptions of the business and form an opinion (Stein, & Ramaseshan, 2016). We could classify these touchpoints into three groups: Pre-purchase, purchase and post-purchase. A critical question for you to think is how brands could strategically approach touchpoint management to successfully attract consumers of different ages along the customer journey?

  • Order fullfillment
    • The Information and Fulfillment Matrix (Bell, et al., 2014) This matrix shows how different information exchange strategies and fullfillment strategies are being used by different retailers.
The Information and Fulfillment Matrix

Figure 2.1: The Information and Fulfillment Matrix

To understand how order fullfillment works, you will be having a sneak peek of an interactive map here:

Retail dashboard - the most and least profitable states (overall) - https://public.tableau.com/profile/zhenning.xu#!/vizhome/Retaildashboards-themostandleastprofitablestatesoverall/Sheet1

Which states are the most profitable? Why?

2.4 The Big Data Revolution

Consider the following scenarios: - Netflix has a profile with customized recommendations (Netflix, 2011). The company uses both behavioral experiments and real-time Big Data to facilitate decision-making when it comes to introducing a new genre.

  • Google and Facebook announced new technologies which allow companies to generate online real-time ads based on trends or topics detected on social media (Levine, 2016; Wright, 2016).

  • Using big behavioral models with 300 to 400 variables, Lenovo systematically upgraded its rule-based targeting to algorithmic retargeting which drive additional conversions at a lower cost (Marwaha, 2016).

  • The decision-tree analysis adopted by a rural Michigan hospital for heuristic knowledge purposes, achieved a surprisingly high level of performance, and actually outperformed the decision-making mode either without any analytics and or with complex algorithms (Marewski & Gigerenzer, 2012).

  • In Emory University Hospital, a typical twenty-bed health care unit usually sends out an estimated 16,000 data points a second. After receiving the data, doctors and nurses make about 100 decisions per day for one patient (Lohr, 2015).

  • The web services company Baidu uses online activities to discover trends to predict box office performance. Recently, Baidu predicted that the box office for the movie The Golden Era would be 200 million RMB. However, the box office results were only about 50 million (Economic information daily, 2014).

Most of these organizations have been taming the Big Data tidal wave to drive their performance. As a matter of fact, the majority of traditional marketing decisions relies on analytics dealing with small data sets (Megabytes or Gigabytes, or even Kilobytes scale), and the analytic platforms and capacity deployed are limited. These fixed-scale data sets are commonly available to the manager or researcher’s computer where the analysis is performed locally, the analysis is not easily replicable, and the decision-making is centralized. However, the underlying changes in recent marketing and information technology featured high magnitude, mobile, and versatile solutions for strategic marketing activities. For instance, Netflix analyzes millions of real-time data points produced by its viewers helping the firm determine if a pilot will become a successful new show.

Due to the availabilities of big data technologies, companies will be able to make better channel decisions with cloud-based solutions and technical platforms. Our world is constantly being influenced by technology and I believe it is crucial to use that technology to facilitate learning and teaching. Technical know-how plays an important role in understanding how technology works. Specifically, technical know-how represents practical expertise, while business know-how represents an overall view of a business organization functions as a whole. It has long been recognized that an organic combination of the two is good for success. However, the era of ‘big data’ makes us rethink how we live, work, and think. Moreover, in the era of ‘big data,’ we are increasingly required to be quantitatively literate—to be able to build a tight alignment of critical thinking processes across big data, technical know-how, and business know-how for reasonable conclusions. We face new claims or realities, ostensibly supported by big data, meant to inform and direct our choices or guide our behavior.

References

https://en.wikipedia.org/wiki/Touchpoint

2.5 Week 1 - To do list

2.5.1 Form groups (2-3 students per group)

2.5.2 Install Tableau on your own laptop/PC

2.5.3 Syllabus Quiz (Individual work)

2.5.4 Discussion Questions (Individual work)

Please find the discussion questions for week 1 below:

  • 1.How could brands strategically approach touchpoint management to attract Millennials along the customer journey? Do a Google search and describe how businesses should analyze these touchpoints? List at least 3 technologies or tools. Make sure to cite.
    1. What is the Information and Fulfillment Matrix (Bell, et al., 2014)? List three of your favorite brands and try to see where they could operate in the 4 quadrants defined by Bell, et al., (2014).
    1. List the fullfillment strategies mentioned in Bell, et al. (2014) and describe the primary fullfillment strategies adopted by each of the favorite brands you mentioned in Q3. Make sure to cite sources.
    1. List at least 2 Big Data technologies or tools that are being used by each of the favorite brands you mentioned in Q3. Make sure to cite sources.
    1. Aunt Jemima syrup and pancake mix will get a new name and image after the BLM movement. How do you evaluate this strategic marketing decision? How does the strategic move affect’ consumers purchase intention?

2.6 Readings

Bell, et al., 2014. How to Win in an Omnichannel World. https://sloanreview.mit.edu/article/how-to-win-in-an-omnichannel-world/