Day 2–Unlocking the power of advanced quantitative methods

— Hi there. I’m here to talk about the following quantitative methods and how they can enhance your UX practice. They are:

  1. Cluster analyses to replace personas
  2. Perception maps to show qualitative relationships betwen concepts visually
  3. Binary logistic regression to predict human behavior
  4. Conjoint analysis to uncover human preferences without asking them

— So let’s start talking about it

— For method one, whether dealing with users or customers, a cluster analysis can beused to classify different profiels of undergrad students tor drive marketing messages

  • All customers are not equal and found out having at least three groups

— Helped marketing team figure out claims to understand marketing material and doing right group of users

— Combine with qual research to come up with 11 statements and ask for each person how much they agree with each statement. Grouping users and mathematics to group them together

— Seeking similar answers to different questions and grouping users to figure out best way to segment features, products and so one

  • Which questions can we answer?
    • How can we segment customers based on consumer preferences?
    • How do different patients cluster on disease symptoms and medical history?

— Next, what are perception maps?

— They are strange, but easy to understand

— Before people decide to use it, let’s talk about example of co-branding effort where we would see what would happen if we mix different brands together

— See brand associations above and compare whether good for certain features like photos at night and different aspects are correlated with each other

  • One aspect can be reliability or good for work
  • If points close to each other, relationship based on space revealed

— Attributes can cluster with each other along a particular brand like Apple

  • Each brand fits into different space, and attributes clustered with each one

— Example of search for shopping malls and seeing how mall was losing customers and connection between outdated mall facilities and links to competitors

— Another example of music

  • Client playing EDM music in restaurants, but what would happen if they played another genre, what should they play
    • Blue represented restaurants
    • Red represented customers

— Next, for binary logistic regressions, we predict the probability of a binary outcome (e.g. success vs. failure, purchase vs. no purchase) based on predictor variables

— And example of this: How many factors do people consider when choosing a college 1? 5? 10?

  • Answer was 61 factors and taking into consideration each one

— So qual could show 61 factors, but of course qual research can’t cover this

— Showing all 61 reasons to stakeholders, and having them pick would be crazy

— Different aspect of choosing college and based on diagram distance in students and college show 11% of decision

— But using weights it was 70% of decision, and comparing two groups at local college, and competitor college

  • Found statistical results that were significant

— Not shown via survey and typical

— Other questions can include:

  • What characteristics predict a fraudulent online transaction?
  • What variables increase the likelihood of a consumer purchasing a product?

— Helping figure out value of features, and how conjoint analysis works to measure

— Focus is on trade-offs to make

  • Choosing anything is hard, as trade-offs require compensation of X

— Customers tell researcher what they want to hear, and surveys measure attitude not behavior, and attitude is just 28% of real behavior

  • Conjoint analysis shows choices

— Taking basket of goods for how attributes have different weights based on different clusters and here we have

— Different attributes within each one to have levels, and those that you can understand which ones have highest preference. If you combine levers you will have perfect combination

— Another example of ice cream for flavor, price, and size

—To summarize, here is what these quantitative tools can do

  • People’s behavior based on common characteristics, instead of using fictional speculative personas
  • Understand relationships among qualitative concepts in a visual manner
  • Understanding what differentiates behavior among 2 groups in a binary situation (cancel/not cancel, keep/return items, choose brand A/B)
  • Understanding what drives preferences for products and services

— QuestionPro is recommended tool linear regression

— AYTM for perception maps

— DisplayR as another perception map tool

— Conjointly as tool to show collective data and simulate scenarios to show preference share

— Python for all kinds of analysis but demands study from your part

  • To learn more follow-me on LinkedIn or subscribe

Q&A

  1. Time used in designing a service and situation and method applied?
    1. Favorite question and using all techniques and cluster analysis userful to analysis if creating online school, different segment of customers and cluster analysis can create different prices,
    2. Regression to grasp why customer losing customers and compare two offers from branches of restaurant and see how behaviors differ
    3. Conjoint analysis key tool for start-ups and perfect price
    4. Perception maps to understand user needs and order in qual data and list is infinite