Customer Attributes

UCSD MGT 100 Week 02

Kenneth C. Wilbur and Dan Yavorsky

Let’s reflect


  • What is it
  • Measurable, Accessible, Substantial, Actionable


  • A fancy way to say that consumers differ, e.g.

  • Product needs–usage intensity, frequency, context; loyalty

  • Demographics–often overrated as predictors of behavior

  • Psychographics–Orientation to Art, Status, Religion, Family, …

  • Location

  • Experience

  • Information

  • Attitudes

  • Differences often predict purchases, wtp, usage, satisfaction, retention, …

Market Segmentation

  • Segments: distinct customer groups with similar attributes within a segment, different attributes between segments

        - Fundamental since the 1960s
        - Numerous segmentation techniques exist; major recent improvements
        - Customer Response Profiles embody segments
        - B2B segments: customer needs, size, profitability, internal structure
  • Decision drivers: product attributes, extensions, bundling, packaging; advertising targeting, content, media; price discrimination, discounts; social media, loyalty programs, …

  • Segments should be Measurable in relation to firm objectives, Accessible, Substantial, Actionable

Segments in this class, ranked by size

  1. Business Econ majors who won’t like Marketing

  2. Business Econ majors who will like Marketing

  3. Non-technical majors (ISIB, Biz Psych, Comm, etc)

  4. Data Science, Computer Science, other technical majors

Segments inform our course content for 2 objectives:

    - Broad survey of analytics, customers and marketing topics

    - Deep dive into demand estimation with pointers to further learning



Is segmenting by gender sexist?

Customer demographics

  • In some markets– makeup, diapers, sports, shoes– demographics correlate strongly with behaviors

  • In most markets– smartphones, universities, software, cars– demographics correlate weakly with behaviors

  • Demographics don’t typically cause purchases, except when they predict real differences in customer needs

  • Why do we so often overrate demographics as predictors of behavior?

Segmentation in Action

  • Who does it
  • Browser users
  • Why we keep it quiet

  • Nearly every large business segments its markets

Firefox User Types

Firefox User Types

Firms don’t publicize segments

  • UO website: “We stock our stores with what we love, calling on our — and our customer’s — interest in contemporary art, music and fashion. …
  • “We offer a lifestyle-specific shopping experience for the educated, urban-minded individual in the 18 to 30 year-old range…’’

Firms don’t publicize segments

  • Earnings call: “Our customer is from traditional homes and advantage, but this offers them the benefit of rebellion…
  • Our customer is exposed to new ideas and philosophies. This can be a real involvement and work, or it could be just talk.
  • Irreverence and concern can live together. Often products sell well that represent the concerns they have but also can speak to their irreverence.
  • Our customer leads a pretty cloistered existence although they deem themselves worldly…they believe that they’re right and they believe that everything that’s happening to them is what’s happening everywhere.
  • Our customer is highly involved in mating and dating behavior…one of the primary drives for their spending behavior…they work hard to postpone adulthood… ’’

Firms don’t publicize segments

  • A. website: “a lifestyle brand that catered to creative, educated and affluent 30-45 year-old women…
  • “Our customer is a creative-minded woman, who wants to look like herself, not the masses. She has a sense of adventure about what she wears, and although fashion is important to her, she is too busy enjoying life to be governed by the latest trends.’’

Firms don’t publicize segments

  • Earnings call: “We don’t think of her in terms of age or affluence or even location. We try to think of her in her life stage and her sensibilities.
  • “She’s recently wed. She’s settling down. She’s very interested less in the mating rituals and actually has been trying and building and creating an environment she wants to live in for herself and family.
  • “She loves art and culture… And clothing and her living environment to her are canvases in which she’s able to express and control her life, whereas workplace and those things around her, she may not control.
  • “We believe in many ways that’s what’s touched her and connect her to Anthropologie and why she is more loyal to us than to most retailers.’’

The Nuts and the Bolts

  • Customer Data Platform (CDP)
  • Data Marketplaces

Customer Data Platform (CDP) - 4 jobs

  1. Data collection

      Intake data from numerous disparate sources:
      In-house, direct partners, data brokers, public data
  2. Data unification or harmonization

      Authenticate and de-duplicate rows and columns
  3. Data comprehension

      Generate inferences, test hypotheses, make predictions, estimate models
      Covers descriptive, diagnostic & predictive analytics
  4. Data activation

      Prescriptive analytics: Use data to inform and automate marketing actions

Data Marketplaces

  • Relatively new phenomenon:
    Automated platforms for transacting & transmitting data

  • E.g., Snowflake Marketplace

  • Upsides

      More data types and sources
      Easy subscriptions, automatic updates
      Competitive marketplace may lower prices
      Less data wrangling 
  • Some caveats

      Low barriers to entry
      Most datasets are not audited or externally validated (for now)
      Many buyers don't really know how to check data quality
      These conditions can lead to a lemons/peaches market
      Buyer beware: Always try before you buy

Recent evidence

  • Comparing measurability: Demographics vs. Behavior
  • Comparing performance of 8 demand models

Research question

  • Suppose we

    1. Train demand model \(M\) to predict mayonnaise sales …

    2. … using information set \(X\)

    3. … & choose targeted discounts for each consumer to maximize firm profits

       - Essentially 3rd-degree price discrimination
  • Separately, using different data, we nonparametrically estimate how each individual responds to price discounts

          - This gives us ground-truth to assess each household's response to price discount
          - But, the nonparametric estimate can't give counterfactual predictions; we need M for that
  • How do targeted coupon profits depend on \(M\) and \(X\)?

          - We use model $M$ and $X$ to predict profits of offering a price discount to each individual household
          - We use ground-truth to calculate household response, then calculate profits across all households
          - We'll also compare to no-discount and always-discount strategies

A little bit of theory

  • For any price discount < contribution margin, giving a coupon to…

    • … our own brand-loyal customer directly reduces profit

    • … a marginal customer may increase profit

    • … another brand’s loyal customer does not change profit

  • So the demand model’s challenge is to correctly identify the marginal customers without accidentally identifying our own brand-loyal customer

        - This research disregards the `post-promotion dip' for simplicity

Information sets \(X\)

  1. Base Demographics:
    Income, HHsize, Retired, Unemployed, SingleMom

  2. Extra Demographics: Age, HighSchool, College, WhiteCollar, #Kids, Married, #Dogs, #Cats, Renter, #TVs

  3. Purchase History: BrandPurchaseShares, BrandPurchaseCounts, DiscountShare, FeatureShare, DisplayShare, #BrandsPurchased, TotalSpending

Demand Models \(M\)

  1. Bayesian Logit models (3)

         - Based on utility maximization in which consumers compare utility and price of each available product
         - Includes Hierarchical and Pooled versions
  2. Multinomial Logistic Regressions (2)

         - Estimated via Lasso and Elastic Net
  3. Neural Network (2)

         - Including single-layer and deep NN
  4. KNN (Nearest-Neighbor Algorithm) (1)

  5. Random Forests (2)

         - Including standard RF for bagging and XGBoost for boosting


  • To predict behavior, use past behavior

  • Economic theory can help demand models to perform well with limited behavioral data

  • ML performance depends critically on data quality. Predictions do not always outperform economic models

  • Statistical performance \(\ne\) economic performance

  • We’ll start estimating logit models soon

  • Meet your study group. Create a group chat. Arrange a regular weekly time to discuss homework, preferably in person. Enter your schedule into Canvas Intermission 2.

How we segment

  • Data
  • Methods

  • Suppose we segment the smartphone market according to each customer’s desired brand.
  • Is this a good approach?

How to pick attributes?

  • We want to segment based on attributes that drive sales, profit, retention. But how?
  1. Theory
  2. Market research
  3. Customer database
  4. Consult customer experts (salespeople)
  5. Find out what other firms are doing
  6. Let sales data pick for us (het. logit)

How GBK segments

Cluster analysis

  • “Unsupervised learning” - techniques to segment/partition data


  • Simple, elegant approach to define \(k=1,\ldots,K\) segments
  • Main idea: Choose \(K\) centroids \(\{C_1, ..., C_K\}\) to minimize total within-segment variation:

\[ \min \sum_{k=1}^K W(C_k)\]

  • where \(W(C_k)\) measures variation among customers assigned to segment \(k\)


  • Most common \(W(C_k)\) function is Euclidean distance
  • Given a set of \(i \in I_k\) customers in segment \(k\), each with \(p=1,...,P\) measured attributes \(x_{ip}\),

\[ W(C_k)=\sqrt {\sum_{i \in I_k} \sum_{p} (x_{ip}-\bar{x}_{kp})^2}\]

where \(\bar{x}_{kp}\) is the average of \(\bar{x}_{ip}\) for all \(i \in I_k\), and the centroid is \(C_k=(\bar{x}_{k1}, ..., \bar{x}_{kP})\)

K-Means Algorithm

  • How do we assign customers to segments?
  • There are nearly \(K^n\) ways to partition \(n\) obs into \(K\) clusters
  • Happily, a simple algorithm finds a local optimum:
  1. Randomly choose \(K\) centroids
  2. Assign every customer to nearest centroid
  3. Compute new centroids based on customer assignments
  4. Iterate 2-3 until convergence
  5. (Optional) Repeat 1-4 for many random centroids

    - $W$ is not globally concave, so we can't guarantee a global minimum
    - Thus, we pick many starting points, and see which offers the lowest W
    - Note: Some algos promise to find global minimum, but this is usually impossible without stringent criteria. Can be a 'tell'

Script prelude: Data structures

  • Matrix or Table:
    2-dimensional data storage structure. Often inefficient
  • R data.frame: Compact, flexible way to store data
  • Tibble: Tidy’s version of data.frame. Similar
  • List: Set of disparate structures

Class script

  • Standardizing variables
  • Running canned kmeans
  • Selecting from a list
  • Iris example
  • Coding & graphing kmeans

Wrapping up


  • Let’s take a look


  • Customer attributes are similar within segments and differ between segments
  • Good segments are Measurable, Accessible, Substantial, Actionable
  • Customer behavior usually predicts behavior better than demographics

Going further