Class 1 Intro to Marketing Analytics

Author

Affiliation

Dr Wei Miao

UCL School of Management

Published

October 4, 2023

1 Module Overview

1.1 About Me

Hi there, I’m Wei Miao!
I did my PhD in quant Marketing at NUS Singapore and a undergrad in Finance from Fudan
I love musical instruments, video games, travelling, and food (bubble tea is my fav)!
My research focuses on digital marketing, sharing economy, and platform design.

1.2 Weekly Arrangements

Detailed weekly arrangements can be found in this link
- Add bookmark for easier reference
Each week, we have a 3-hour lecture on Wednesday, divided into two sessions
- A case study workshop session, to practice the tools learned in the previous week
- A methodology session, in which we learn a new analytics tool
Class recordings are available on Moodle “LectureCast Recordings” section.

1.3 Assignments

No exams; 3 individual assignments, which are similar to case studies in class, and you will need to use what you learned in class to solve these real-life marketing analytics problems.
- 1st assignment, 30% weight, 1500 words, due on Oct 30
- 2nd assignment, 30% weight, 1500 words, due on Nov 24
- 3rd assignment, 40% weight, 2000 words, due on Dec 15
How to submit?
- Only submit PDF rendered from R Quarto with codes printed; don’t submit any other file format or your submission won’t be marked.
- The quarto-based answer sheets (qmd files) will be given to you.
Word count and late submission penalties will be applied by BA admin. For related queries and EC applications, please directly contact BA admin.
Your marks will be released in due time (no later than 4 weeks).
We have random second marking in place to mitigate marking errors. Please refrain from emailing teaching assistants for re-marking as this is not allowed by school policy.

2 What is Marketing?

2.1 Role of Marketing

Finance (finance a company’s business activities)
Accounting (bookkeeping of transactions)
Operations (supply chain, manufacturing, inventory management)
Marketing (directly deal with consumer; value exchange and realization)

2.2 What is Marketing?

Kotler (1991): “Marketing is a social and managerial process by which individuals and groups obtain what they want and need through creating, offering and exchanging products of value with others.”
British Chartered Institute of Marketing (2000s): “Marketing is the management process responsible for identifying, anticipating and satisfying customers’ requirements profitably.”
American Marketing Association (2017): “Marketing is the activity, set of institutions, and processes for creating, communicating, delivering, and exchanging offerings that have value for customers, clients, partners, and society at large.”

2.3 What is Marketing? A Word Cloud Approach

[link to code]

install.packages('pacman')
pacman::p_load(tm, wordcloud, RColorBrewer,
               wordcloud2, data.table)
# generate text corpus
df_mkt <- 'Marketing is a social and 
managerial process by which individuals and groups 
obtain what they  want and need through creating, 
offering and exchanging products of value with others.
Marketing is the management process responsible for 
identifying, anticipating and satisfying customers 
requirements profitably.
Marketing is the activity, set of institutions, 
and processes for creating, communicating, delivering, 
and exchanging offerings that have value for customers, 
clients, partners, and society at large.'
df_mkt_corpus <- Corpus(VectorSource(df_mkt))
df_mkt_corpus <- df_mkt_corpus |> 
  tm_map(removePunctuation) |>
  tm_map(stripWhitespace) |>
  tm_map(content_transformer(tolower)) |>
  tm_map(removeWords, stopwords("english"))
# Create a document-term-matrix
df_mkt_dtm <- TermDocumentMatrix(df_mkt_corpus)
df_mkt_matrix <-  as.matrix(df_mkt_dtm)
df <- data.table(words = rownames(df_mkt_matrix),
                 freq = df_mkt_matrix[,1])
# draw wordcloud
set.seed(888)
wordcloud(words = df$words, freq = df$freq, 
          min.freq = 1, max.words=200, random.order=FALSE, 
          colors=brewer.pal(8, "Dark2"))

Marketing is a management process that creates and exchanges values for the company by selling the right products to the right customers. - Wei, 2023

2.4 Marketing Process

We will go through the above concepts quickly in the case of Uber.

2.5 Situation Analysis: 5 C’s

Any marketing decision can benefit from a deep understanding of the players within the market ecosystem—your own company, current and potential customers, collaborators and competitors—and the context they interact within: the 5Cs for short.

2.6 Situation Analysis: Competitors

Managers tend to exhibit a supply-side bias, largely paying attention towards more salient direct (industry) competitors.
- Indirect competitors are defined by who your customers consider to satisfy the same goals.
- Potential competitors are those who might pose a competitive threat in the future; who possess equivalent resources that would allow them to enter the market

2.7 Situation Analysis: Context/Climate

Context/Climate analysis is a strategic planning method used to assess major external factors that influence the market ecosystem, and is often referred to as PESTLE analysis.

2.8 Strategy: STP

Situation analysis is a critical input into marketing strategy design, the sequential application of the processes of segmentation, targeting, and positioning. Marketing strategy is inherently related to the concepts of the business model and business strategy—who you are serving, with what value proposition, and how you do so.

Segmentation, Targeting, and Positioning

2.9 Tactics: 4P’s

The marketing mix provides an implementation of your positioning. Segmentation is here applied at the tactical level, to optimally design the marketing mix or 4Ps.

3 Marketing Analytics

3.1 Big Data Era

With the advancement in information and communication technologies (ICTs), data scientists nowadays are equipped with data analytics tools powerful than ever!

Firms now have access to enormously rich information trail of customers

Demographic profiles (DoB, gender, ethnicity, income)
Purchase history (recency, frequency, monetary value, spending behavior)
Online browsing and search history (browsing, click through, add to cart, purchase)
GPS data from mobile phones for offline store visits
Social media (location, consumer preference, social network)

3.2 What Marketing Analytics Can Do?