Segmentation - The Shadowy Side of Persona Development

Segmenta(on: The Shadowy Side of Persona Development

UPA 2012

David A. Siegel Ph.D. Dray & Associates, Inc. Minneapolis, MN USA

[email protected]

www.dray.com +1 612 377 1980

u  Copyright, Dray & Associates, Inc., 2012

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Segmentation

Market Segmentation User Classification

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Who? What?

Interlocking Challenges:

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Shadowy?

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•  Colors = dimensions •  Can you align them all? •  The most successful are

those willing to break a partial alignment and start from scratch

Goal: Ø  Make explicit choices and tradeoffs, whether working with and exis<ng

segmenta<on, or proposing a classifica<on scheme of your own Themes: Ø  Segmenta<on as a subtype of classifica<on Ø  Classifica<on = selec<ng, defining, priori<zing, and combining dimensions to

usefully divide up a mul<-‐dimensional space Ø  Influenced by subjec<ve choices and prone to distor<ons, whether done

casually or through the most high-‐powered sta<s<cal analysis

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Ø  What makes a useful classifica<on? Ø  Tensions between marke<ng and UX segments

Ø  The paradox of “precision”

Ø  Pros and cons of

•  Demographics

•  Occupa<onal Roles

•  Psychographics

•  Behavior

Ø  Tensions between marke<ng and UX segments

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Unusually Clean Clusters

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Coherence Within Clusters

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Differen<a<on Among Clusters

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Personas-‐-‐Landmarks Within Clusters

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Dimensions of Difference Are Not Givens -‐-‐Even when they describe seemingly obvious differences

What is this?

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Now what is it?

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Now?

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Now?

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The dimensions we perceive and identify depend on Ø  Context of comparison Ø  What have we sampled Ø  What distinctions we perceive or assume to be

relevant

E.g., if our purpose was to evaluate agricultural products in terms of potential for industrialized production, we might have classified differently

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Informa<on is a difference that makes a difference.

-‐-‐Gregory Bateson

Segmenta<on needs to point to different ac<ons that are available to us, on the basis of predicted differences in response from different audiences or users.

Source: http://www.nndb.com/people/169/000100866/

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Different differences make a difference, depending on what different ac<ons we are focusing on.

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Classifica<on Variable 1

Classifica<on Variable 2

Descriptor Dimensions

Ac<on

Implica<ons


Ac<on

Implica<ons Descriptor Dimensions

Ac<on

Implica<ons


Ac<on

Implica<ons Ø  Not necessarily 2 x 2, or even factorial Ø  Choice of classifica<on variables usually based on what we think makes cleanest

split, is easiest to detect, or summarizes the profile of descriptors Ø  But descriptors could be turned into classifiers, depending on what ma\ers

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Paradox of Precision: The “Zoom In” Problem

Ø  Zoom in = more detailed, granular description •  More dimensions •  More distinctions •  More subgroups

Ø  Perceived as more precise, more convincing

Ø  But (all things else being equal) finer grained dis<nc<ons become more fuzzy, boundaries blur

Ø  A law of nature!

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Case in point: Let’s zoom in here

Non customers

At Risk Opportunity

Customers

A\achment

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Anything we do to improve the ra<o of people in our sample that we are interested in will exclude some of them, and reduce our ability to know how they relate to the popula<on as a whole

A\achment

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With drill-‐down, subgroups can cut across segments

Seg. A Seg. B Seg. C Seg. D

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Case Example: Segments based on abtudes did differ in composi<on. But….

Seg. A Seg. B Seg. C Seg. D

…the groupings across segments were more coherent and dis<nct re: usage pa\ern

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See how rapidly dimensions mul<ply,

even in simple descrip<ons

‹#›

Overall SESS



‹#›

Overall SESS

Age See how rapidly dimensions mul<ply,


‹#›

Overall SESS

Age

Ethnicity

‹#›



Overall SESS

Age

Ethnicity

Net Worth Disposable Income

Orienta<on to self-‐service

Source of influence

Importance of Iden<ty



‹#›

Dimensions apply to all, but are called out only where most dis<nc<ve, heightening percep<on of difference

Affluent Others

Hispanics

• Highly Influenced by family? • High SES? • Manage own finances on line?

• Highly Influenced by family

Others • Manage own

finances on line

?

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Segments Summarizing Overall Difference in Profile on Mul<ple Dimensions

Ø  Some dimensions differen<ate more strongly than others. Ø  Smaller differences should be weighted less Ø  But ofen all the differences become equal parts of the descrip<on

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Some<mes, it may look like we can make precise dis<nc<ons based on small differences, only because large samples make them sta<s<cally significant. But do those differences ma\er?

Source: http://www.wanoah.co.uk/?p=37

The larger the sample it takes to find a sta<s<cally significant difference, the less likely it is to have a prac<cal significance!

Math scores: Yes, the distribu<ons are different (assuming large N). But if you made dichotomous decisions based on gender, (e.g., pubng girls in low math group and boys in high math group) you could be wrong large % of cases.

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Two views, same points in space à

Striving for Precision on Mul<ple Dimensions

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Back to our unnaturally clean clusters:

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Imagine they are really in 3 dimensions, but we have only viewed them from one angle (i.e., only focusing on 2 dimensions)

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Now we rotate. Same points different views—clusters smear out.

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In these dimensions, clusters are broader, have different members, personas not as “well placed” to represent them.

Op<mizing groupings on some dimensions, tends to “smear” them on

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Now imagine if we had started off with a more realis<c set of clusters—just slight varia<ons in density – because on many of the dimensions we care about, people don’t fall into such discreet groups.

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Is vanilla ice cream more like chocolate milk

or banana yogurt?

?

Needed: a way of combining differences on mul<ple dimensions into a judgment of overall similarity and difference.

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Y

X = number of features used

Which group is this one most like?

Cluster 1

Cluster 2

Y = overall usage (me

Group A = Heavy users of many

features

Group B = Light to medium users of few features

Heavy user of very few features

Ø  Sta<s<cal approaches to building clusters usually try to manage problem of over-‐op<mizing on some dimensions and smearing on others

Ø  Use “distance” to represent “difference”

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But how do we measure distance? You get to choose, e.g.:

Euclidean distance ≈ 4.6 ΔX + ΔY distance ≈ 6.5

Euclidean distance ≈ 5.94 ΔX + ΔY distance ≈ 6

Centroid of Group B

Centroid of Group A

Also, the dimensions should be weighted differently based on: Ø  Are they scaled the

same? Ø  Are they measured

equally reliably? Ø  Are they equally

good predictors of something we care about?

Euclidean: Hypotenuse of difference on x and difference on y ΔX + ΔY: Sum of the differences on each separate dimension Both make intui<ve sense, but give different results!

The two methods assign the point to different clusters.

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Exaggera<ng Dis<nc<veness

Y

X

Cluster 1

Cluster 2 These look dis<nct, but most of

their members have a lot in common on one or both

dimensions

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Prac<cal Criteria for Priori<zing, Weigh<ng and Combining Dimensions

Ø  How efficiently they let you divide the sample into categories Ø  Whether there is a clear breakpoint or threshold effect on other variables Ø  Ease of defini<on, measurement Ø  Ease of loca<ng real representa<ves when you want to study group in

more depth Ø  Amount of varia<on on the dimension Ø  Amount of independent informa<on added, how much heterogeneity

the dimension accounts for Ø  Usefulness as proxy for harder to measure variables Ø  Availability of external informa<on sources for es<ma<ng prevalence Ø  Power as a predictor of differen<al response Ø  Prac<cal ability to act differen<ally depending on where on the

dimension people fall

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Ø  Ofen cri<cized as selec<on criteria for usability studies Ø  But demographic variables have some advantages

•  Rela<vely easily defined, measured, detected, sized •  Easy to locate real representa<ves when •  Amount of varia<on can be great •  Informa<on added at li\le cost, makes them good proxies •  Many products designed for targeted demographics •  Many aspects of life may correlate with demographic dis<nc<ons, so

can have power as a predictor of differen<al response, needs •  Prac<cal ability to act differen<ally toward them for messaging, sales

channels, etc. •  First level filter when you don’t yet know enough to be more nuanced

Demographic Segmenta<on

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Occupa<onal Segmenta<on

Occupa<onal Segmenta<on

Ø  Profession

Ø  Abstract, higher order category (e.g., “knowledge worker,” “entrepreneur”

Ø  General functional area: operations, customer service, finance, IT

Ø  Specific roles

Ø  Hierarchy: “Executive,” “Manager,” “Supervisor,” “Front line worker”

Ø  Context focused: Industry or industry type, company size, business model, organizational structure

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Occupa<onal Segmenta<on: Issues

Ø  Varying degrees of standardization in nomenclature, function, and job design

Ø  Can your domain knowledge, focus, and sample size compensate for the “zoom in” problem?

Ø  Functional labels can be very difficult to define: •  What is a “knowledge worker”? •  What is a “power user”?

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u  What is a “knowledge worker?”

Controller (finance) Logis<cs manager u  •  Focus: high-‐level processes to

manage financial risk •  Priority: Preven(on of low

probability events •  Decisions based on

professional judgment, knowledge of best prac(ce

•  Sets policy for long term

•  Focus: tac(cal, opera(onal

•  Priority: Increase efficiency,

ensure smooth opera(on •  Needs quan(ta(ve data to

manage processes, look for improvement opportuni(es

•  Manages processes in real (me

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Psychographic Segmenta<on

Ø  Abtudes, preferences, values Ø  Intended to predict “resonance” for messaging Ø  Also ofen emphasized in personas for broad,

generalizable implica<ons Ø  How strongly do they relate to or predict usage

pa\ern or other behaviors? Ø  Are they really more “stable” than behaviors? Ø  How hard are they to measure reliably and validly Ø  Self report versus behavioral self-‐iden<fica<on

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Behavioral Self-‐Iden<fica<on

What can you say about psychographics (e.g., preferences) of people who gather In these venues?

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Behavioral Segmenta<on

Ø  Self-‐reported versus observed Ø  Purchasing behaviors Ø  Usage behaviors: Amount? Variety? Qualita<ve

pa\ern? Ø  Expressed behavioral inten<ons: •  How immediate? •  Evidence of preliminary steps to confirm?

Ø  Evaluate degree of demonstrated associa<on with behavior of ul<mate interest

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Marke<ng Segments & UX Categories: The Ideal

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Time

Targeted value messaging increases concentra<on of

poten<al buyers

Purchase decision process filters out most of non-‐target popula<on

UX delivers promised value (and more) à sa<sfac<on,

reten<on

≈ ≈

Target Non-‐Target

Marke<ng Segments & UX Categories: The Ideal

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Time

Targeted value messaging increases concentra<on of

poten<al buyers

Purchase decision process filters out most of non-‐target

popula<on

UX delivers promised value (and more) à sa<sfac<on,

reten<on

≈ ≈

Warning: Ø  This is most likely when Market Segmenta<on and UX

categoriza<on map to each other Ø  But market segmenta<on guides strategies for ini<al

filtering, rather than ongoing experience, so relevant and available dis<nc<ons in ac<on may be different

Ø  UX has to provide extended sa<sfac<on over a range of encounters for each user

Ø  UX has more at stake in each touch point, because goal is engagement for an already-‐filtered audience

Ø  Therefore, UX may introduce deeper and/or transverse dis<nc<ons

Tips Ø  Method triangula<on:

Ø  Start with criterion groups (differences you really care about) and then look for differen<ators.

Ø  Start with possible differen<ators and test to see if they do predict differences you really care about.

Ø  Test dis<nc<ons among segments that people already believe in to validate that they really do predict something important and ac<onable

Ø  Par<al alignment on a few variables of different types may be more robust and useful than than op<mizing for “clean” dis<nc<ons

Ø  Priori<ze dimensions based on both prac<cal and conceptual tradeoffs

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More Tips Ø  Test dis<nc<ons across mul<ple studies, or do cross-‐

valida<on within your sample by splibng it. Ø  Consider impact of variables one at a <me rather than only in

combina<ons, to reduce risk of illusory precision Ø  Try to work within exis<ng segments, but be prepared to

show how different contexts may make transverse segments more or less relevant

Ø  Studying pre-‐defined segments one at a may blind you to subgroups that are similar across segments-‐include contras<ng hypothesized segments into samples within or across studies

Ø  Don’t expect the “average” differences of segments to show up in small samples.

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David A. Siegel Ph.D. Dray & Associates, Inc. Minneapolis, MN USA

[email protected]

www.dray.com +1 612 377 1980

Segmentation - The Shadowy Side of Persona Development

Technology

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