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as a company grows, the number of users increasesthis often results in a decline in the clarity of personas
Time
UsersPersonas
DataStartup
Enterprisex10 interviews!
x1M transactions!
Impact of Scale
A persona might look like this...
LauraQuote: “I want to stay connected to my friends back home.”About: A sophomore at an out-of-state college where she is studying molecular biology on a football scholarship.
Likes: Starbucks, pre-med classes, the smell of fresh grass
Pain Points: Constantly juggling her schedule, friends are too far for a bus and a plane is too expensive.
...while the data looks like this.
SELECT first_name, last_nameFROM people_massachusettsWHERE hair_color = "red" OR hair_color = “brown”AND birth_date BETWEEN '2003-01-01' AND '2014-12-31'ORDER BY last_nameLIMIT 100;
id First Last Color $ Birth Date
40100 Zach Aaronson Red $139 2004-07-21
40101 Melody Bronson Red $47 2005-08-01
40102 Ankur Cole Brown $52 2003-11-17
40114 Simon Dreer Red $201 2006-04-18
40115 Chelsea Effingham Brown $25 2003-10-09
40116 Xavier Gondor Brown $76 2007-05-29
40203 Jean Jones Brown $143 2004-03-20
40204 Paul Jones Red $12 2008-01-02
40205 Peter Smith Brown NO 2005-06-12
40206 Mary Smith Red NO 2003-05-17
How do we scale insight from personas
and act upon data from users?
Persona Informed Segmentation
A persona informed data feedback loop
Research → Personas → Features → Launch → Data → Segmentation
The gap between research and reality prompts the questions that seed the next iteration of user centered design.
Δ
Product
LET’S DO A CASE STUDY!
Personas
Lynda - New Chef
Cook times < 30minSimple recipesHealthy ingredients
Jamie - Family Chef
Prefers crock potLarge dishes (pasta, etc)Simple ingredients
Tyson - Hobby Chef
Quality > TimeComplex recipesExotic ingredients
Product Launch!……
click…
click, click…
click, click, click, click, click, click, click, click, click, click, click, click,
QUESTION
Who is using our website?How do they compare to our personas?
What do we build next???
ANSWER
Check the data.
Birthday, saved recipes, viewed recipes, log-in frequency, # of comments, subscriptions, social media shares, etc…
1. Correlation
Recipes < 30 min
Chicken
Age 35-55
Post to Facebook
>5 saved
Blue = Positively CorrelatedOrange = Negatively CorrelatedThickness = Strength of Correlation
Select a set of factors from the data that are important to understanding the users
Data S1 S2 S3
Recipe Cook Time 0.26 0.24 -0.33
Veggie <--> Meat -0.28 -0.08 -0.06
Posting 0.09 0.57 0.39
Saved Recipes -0.15 -0.45 -0.23
2. Modeling
Important factors● Difficult● Posting● Age 35 to 55
...segments represented by all data points.Only consider the most significant correlations
Via math transformation we can obtain...
Data Model
S1 S2...S11 S12
Cook Time < 30 min
Very Few Log-ins
Kale LOTS of Comments
Shares on Facebook
Easy Recipes
3. SignificanceNot all segments are meaningful
WTF?!?Might be Lynda???
Representative Segments
Persona-User Gap Analysis
compare attributes of personas gathered during design
with factors of segments gathered from usage data
Lynda - New Chef
Cook times < 30minSimple recipesHealthy ingredients
Jamie - Family Chef
Prefers crock potLarge dishes (pasta, etc)Simple ingredients
Tyson - Hobby Chef
Quality > TimeComplex recipesExotic ingredients
S1 - I <3 Salad
Cook times < 30minKale in most dishesShares on Facebook
S2 - On the Go
Only views on mobileNever saves recipesDoubles # of servings
S3 - Aspirational
Views difficult recipes but doesn’t save themHighly active on forums
Looks good! Let’s add more salads and increase our Facebook advertising budget.
Is this engagement healthy? Maybe we need better instructions or to add videos?
What is going on here???
Personas Segments Insights
Results
Gaps provide a conversation starter for future research
This is NOT reflect on the quality of personas
This IS a tool for testing, feedback, and constant iteration
RECIPE
PERSONAS INSTRUMENTATIONREGISTRATION MODELING
SYNTHESIS & GAP ANALYSIS
TAKEAWAY
Questions to ask during an engagement:
What data is important?What data are we collecting?How do we analyze the data?What do we do with the data?
TAKEAWAY - DETAILS
P DE DS
Understand and communicate the value of targeting for the product
Leverage the roadmap to create design / data feedback loops
Inform the team early about what is important to track
Leverage the team to help you find users and interesting behaviors
Instrument all the things!
Incorporate the concept of “user type” for routing where appropriate for testing and user flow
Recommend models for performing segmentation
Think ahead about design for real-time prediction
How do we identify personas when we don’t even know where to start?
Segment first to narrow the field.
K-FACTOR & DENDROGRAMSDon’t try this at home!Ask a friendly, neighborhood Data Scientist for help =)
