Visualization, Intro

MGT 100 Week 1

Kenneth C. Wilbur and Daniel Yavorsky

Data Visualizations (Viz)

  • “The greatest value of a picture is when it forces us to notice what we never expected to see.”

        - Boxplot Inventor John Tukey
    - Great visualizations raise new questions

Data Viz are older than 0

Translated

SDPD Crime Reports Near UCSD

  • When is data zero vs. missing? Provenance is key
  • What kind of decisions could this inform?

Data Provenance

  • describes people, entities, and activities involved in producing, compiling, transforming & sharing data

    - Verifiability requires source data & cleaning scripts

  • Crucial in determining quality, reliability, trustworthiness

    - Investigating provenance requires and deepens domain expertise
- Typically turns up unexpected information, and sometimes errors

  • When can you trust provenance information?

      - Best to treat provenance as hypotheses to be verified in the data
  - Usually, provenance descriptions are missing or imperfect
  - Financial info is most likely to be accurate due (accounting, auditing)
  - Considerations: Consent; Privacy; Missingness; Permissible uses
  - Sometimes, provenance descriptions are marketing documents

Questions to ask

  • Data Origin, Measures, Quality, Verifiability

      -   How were the data produced?
  -   Who collected it, when and why?
  -   How is each variable measured? (surveys, APIs, sensors, etc; units)
  -   Is there a data dictionary or changelog?
  -   How can I verify individual datapoints?
  -   Certified by any third party? Who relies on them, for what?

  • Processing & Transformation

      -   What cleaning or manipulation was done?
  -   Who performed it and why? Can I see their scripts?
  -   Were missing values imputed? How?
  -   How were outliers handled?

Viz can build trust, understanding

  • Eyeballs can interpret pictures quickly

    - Human brains are great at interpreting visual patterns, predictions
- Can detect unknown errors

  • Understandable to managers

    - Viz choices enable narrative understanding, another common brain function

  • Viz are lingua franca across disciplines

    - Easily replicated -> more easily trusted
- Trust can is a major issue in some corporate cultures

  • Viz succeed when they raise deeper questions

    - Asking the next question indicates acceptance
- "I wonder why that is...." or "Maybe that's because..."
- Viz usualy won't settle the matter; hence, the first step

Honest Viz

  1. Usually start axes from 0, choose scales judiciously

  2. Show all relevant data, label accurately. Don’t edit, trim

  3. Discretize and smoothe judiciously

Visualize data before you analyze

  • 4 Datasets, Same \(\hat{\beta}^{OLS}\), i.e. same \(\frac{x'x}{x'y}\)

Customer Analytics

Customer

  • Receives good or service in exchange for payment (money, time, attention)
  • Has agency: Can say “no”
  • “The purpose of business is to create and keep a customer.”
    -Drucker

Analytics

  • Using data to improve decisions
  • Started by Charles Taylor in the 1880s (mostly)
  • Popularized by Moneyball (2011)
  • Measurement, Heuristics, Graphics, Models, Predictions, Automation, Optimization, Personalization, …
  • Can be deceptively difficult

First Law of Customer Analytics

  • No Customers, No Business

  • No Customers ->
    No Revenue ->
    No Profit ->
    No Business
    QED

Second Law of Customer Analytics

  • More Customers, More Profits

  • More Customers ->
    More Revenue ->
    More Profit
    QED

    - These are empirical tendencies, not logical necessities
- Individual customers can be unprofitable if (price-cost)<0

  • Marketing Objective: Maximize Long-term Profits

      - Long-term focus mostly aligns our interest with customers
  - Goal is not only to acquire customers; also, keep and develop them
  - Sidesteps or reduces most ethical dilemmas
  - Short-run objective may profit before liability; I won't teach

Example of Great Marketing: Netflix

  • Consumer surplus

      - Suppose customer pays $20/month, watches 60 hours: $0.33 per entertainment hour, or $0.13/hour with ads, or less with shared accounts
  - A la carte rentals are more like $1-2+/hour
  - Non-video entertainment tends to be much more expensive
  - Social benefits may accrue from shared viewing
  - Other streaming services may be competitive

  • Producer surplus

      - NFLX spent $13B on content in 2023, 230 million subscribers, about $4.70/user/month
  - Cost structure: High fixed, low marginal 
  - Ads likely earning around $4-5 per recipient/month
  - Net profit margin about 20-22% in a competitive category

How can we use customer data?

  • Businesses have 4, and only 4, ways to make money:
    Acquire, develop, retain and “fire” customers

    This is called Customer relationship management (CRM): week 9

  • Marketing mix (“4 P’s”): Improve product offerings, prices, promotion, distribution

  • Incorporate customer heterogeneity for targeting, personalization, recommendations, product development…

  • Privacy and security, e.g. misuse, theft, regulatory compliance

Example: Nielsen

CONSUMER PANEL DATA

      - The Consumer Panel Data include longitudinal data beginning in 2004 with annual updates. These data track a panel of 40,000–60,000 US households and their purchases of fast-moving consumer goods from a wide range of retail outlets across all US markets.

RETAIL SCANNER DATA

      - Retail Scanner Data consist of weekly pricing, volume, and store environment information generated by point-of-sale systems from more than 90 participating retail chains across all US markets. Data begin in 2006 and include annual updates.

E-commerce Analytics

Using Customer Data for Customer Analytics

How do we “do” customer analytics?

  • Decide objectives & outcome metrics
  • Collect, wrangle, clean & verify relevant data
  • Analyze data
  • Communicate analyses and recommendations
  • Make decisions
  • Implement data-driven decisions
  • Retrospectively evaluate and improve
  • Repeat
  • …Once you have a stable process, automate carefully & monitor

Challenges: Executives

  • May be territorial, or incentivized to be
  • May worry that analytics will constrain or replace them
  • May think data == magic
  • May prefer hunches or misunderstand uncertainty

Challenges: Analysts

  • Expensive
  • Hard to find
  • Not always current
  • Not always interested in business

Challenges: Cultural

  • Do analytics make or justify decisions?

  • High- or low-trust environment? Tolerance for uncertainty?

    - You have limited credibility. You may only get a few strikes

  • Do messengers get rewarded or shot?

  • Are data available and integrated?

  • Do teams work together or compete?

Signs of a great analytics org

  • C-level champion(s), i.e. C{D,E,A,F,M,O}O
  • Centralized team regulates data, arch., standards & tools
  • Decentralized analysts collaborate with execs
  • Analytics career tracks are well established
  • Careful in-housing/outsourcing decisions about analytics
  • Good examples?

Analytics truisms

  • Analytics matters more in B2C than B2B (why?)

  • Selection effects are usually large

      treatment effects are usually small 
  Key exceptions: Price or "free" giveaways

  • Demographics don’t predict behavior very well

  • Agencies lie about data sometimes

  • “If it’s written in LaTeX, it’s probably correct”

MGT 100

        - I am assuming you read the syllabus carefully

Course Design Principles

  • Survey the field broadly, pointers for deeper learning

  • Focus on conceptual understanding:
    “When it comes to LLMs, skillful prompting leaves amateurs in the dust.”

  • Strict communication policies

    1. Website for class content
2. Canvas for study groups & grades
3. Piazza for all asynchronous interaction. No email or canvas messages
4. After class, break or office hours for live discussions

  • Course site & schedule

Gen AI effects on productivity

Tips to max your learning

  • Read the syllabus carefully

  • Attend and contribute as suggested in the syllabus

  • Budget 5-10 hours/week

  • Between classes:

    1. Step through script carefully, understand everything.
2. Do homework questions on your own
3. Check homework with group, resolve differences
4. Monitor Piazza and read for the next class
5. Compile notes and homework answers to facilitate exam prep

Studying and Integrity

  • We assign attending students to study groups in week 2

  • It is OK to share homework scripts and answers

    - We do not collect or assess homeworks. 
- We do not provide homework answers.
- Exam questions will test your familiarity and understanding of homework answers. More details forthcoming

  • We encourage you to use Gen AI thoughtfully; we use it too

    - LLMs are poor substitutes for human understanding
- Be advised, you may get what you pay for
- Free models are sometimes worse than useless

  • Please write down your intentions for this class.
    How will you measure your effort?

Vocab

Common language helps communication

Customer level

  • Core need: identifiable problem a customer wants to solve. Could be functional, emotional, social, profit-motivated, etc. Related: desire, want, pain point

  • Core benefit: Customer’s desired outcome of a purchase. E.g., commuters need to get to school, not necessarily cars

  • Consumer: Entity that experiences the core benefit

  • Customer: Entity that purchases and pays

Product level

  • Product/service/experience: Distinct offering that provides the core benefit

  • Features: Aspects of a product that provide additional tangible or intangible benefits

  • Value proposition: utility( Core benefit + features - price )

  • Contribution margin: Price — marginal cost

  • Competitor: Any paid or free alternative that addresses the core need. E.g., commute by bike, walk, bus, trolley, Uber, scooter, skateboard; work from home

Market level

  • Market: Potential customer group with common core need

  • Segment: Distinct subgroup of similar customers

  • Targeting: Which segment(s) a firm tries to serve

  • Positioning: Specification of product features to suit targeted segments

  • Marketing: Practice of meeting customer needs profitably

      - Marketing: Business discipline that focuses most on customers
  - Ads & sales: Worthless without good value prop and positive margin
  - Poor implementation commonly leads to confusion with bullshit ("persuasive speech without regard for the truth" --Frankfurt 2005)

Legacy Terms

  • 3/4/5 C’s: Customer, Competitor, Company;
    Context; Complementors

  • STP: Segmentation, Targeting, Positioning AKA Marketing Strategy

  • 4/…/10 P’s:
    Price, Product, Promotion, Place AKA distribution AKA Marketing Tactics

        - You need to know these well if you interview for marketing roles
    - Generations of marketing professionals were educated to think this way, e.g. MGT 103 and Harvard MBAs
    - Still relevant, but less, thanks to customer data abundance & analytics

Coding & Script

Coding errors

  • “The good news about computers is that they do what you tell them to do. The bad news is that they do what you tell them to do.”

  • Conjecture:
    (debugging difficulty) is exponential in (lines of code)

  • We can code fast or slow

Coding Habits

  • Good habit: Test chunks as you code

  • Test = Manipulate text, verify output matches expectation

  • “Go slow to go fast”

Pipe |>

  • y <- f(g(x)) is the same as

  • y <- x |>

    g |>

    f

  • Why?

  • Old pipe was %>% ; remains widely used

Today’s script

  • Install/update R and Rstudio
  • Posit.Cloud
  • Data Import/export
  • Data manipulation, summarization
  • 5 verbs: Summarize, select, filter, arrange, mutate, group_by
  • Univariate statistics
  • Univariate plots
  • Bivariate statistics
  • Bivariate plots

Wrapping up

Recap

  • Customer analytics :
    Using customer data to improve decisions

  • Data Viz should be the first step in customer analytics

  • Marketing : Meeting customer needs profitably

  • Analytics types:
    Descriptive, Diagnostic, Predictive, Prescriptive

  • Summarize, select, filter, arrange, mutate, group_by

Going further