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 david.siegel@dray.com www.dray.com +1 612 377 1980 u  Copyright, Dray & Associates, Inc., 2012 Copyright 2012

•  Colors = dimensions •  Can you align them all? •  The most successful are those willing to break a partial alignment and start from scratchGoal: 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 6 Copyright 2012

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 7 Copyright 2012

The dimensions we perceive and identify dependonØ  Context of comparisonØ  What have we sampledØ  What distinctions we perceive or assume to be relevantE.g., if our purpose was to evaluate agriculturalproducts in terms of potential for industrializedproduction, we might have classified differently 16 Copyright 2012

Informa<on is a difference that makes a difference. -‐-‐Gregory Bateson Source: http://www.nndb.com/people/169/000100866 /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. 17 Copyright 2012

Classifica<on Variable 1 Descriptor Descriptor Dimensions Dimensions Classifica<on Ac<on Ac<on Variable 2 Implica<ons Implica<ons Descriptor Descriptor Dimensions Dimensions Ac<on Ac<on Implica<ons 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 maers 19 Copyright 2012

Paradox of Precision: The “Zoom In” Problem Ø  Zoom in = more detailed, granular description •  More dimensions •  More distinctions •  More subgroupsØ  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! 20 Copyright 2012

Aachment 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 22 Copyright 2012

w rapidly ns mul<ply, n simple rip<ons ‹#›

Overall SESS w rapidly ns mul<ply, n simple rip<ons ‹#›

Overall SESS w rapidly Age ns mul<ply, n simple rip<ons ‹#›

Overall SESS w rapidly Age ns mul<ply, n simple rip<ons Ethnicity ‹#›

Overall SESS Orienta<on to self-‐service w rapidly Age ns mul<ply, Net Worth n simple Disposable Income rip<ons Ethnicity 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 • Highly Influenced by family? • Highly Influenced Hispanics • High SES? by family • Manage own finances on line? • Manage own ? Others finances on line 31 Copyright 2012

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 32 Copyright 212 Copyright 2012

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 maer? 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. 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! 33 Copyright 2012

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. 39 Copyright 2012

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” Heavy user of very few features Cluster 2 Group A = Heavy Y = overall users of many usage (me features Which group is this one most like? Group B =1 ight to medium Cluster L users of few features X = number of features used 41 Copyright 2012

But how do we measure distance? You get to choose, e.g.: 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. Euclidean distance ≈ 4.6 ΔX + ΔY distance ≈ 6.5 Also, the dimensions should be weighted Centroid of Group A differently based on: Ø  Are they scaled the same? Ø  Are they measured Euclidean distance ≈ 5.94 ΔX + Δ Y distance ≈ 6 equally reliably? Ø  Are they equally good predictors of Centroid of Group B something we care about? 42 Copyright 2012

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 44 Copyright 2012

Demographic Segmenta<on 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 lile 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 46 Copyright 2012

Occupa<onal Segmenta<on

Occupa<onal Segmenta<on Ø  ProfessionØ  Abstract, higher order category (e.g., “knowledge worker,” “entrepreneur”Ø  General functional area: operations, customer service, finance, ITØ  Specific rolesØ  Hierarchy: “Executive,” “Manager,” “Supervisor,” “Front line worker”Ø  Context focused: Industry or industry type, company size, business model, organizational structure 48 Copyright 2012

Occupa<onal Segmenta<on: Issues Ø  Varying degrees of standardization in nomenclature, function, and job designØ  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”? 49 Copyright 2012

u  What is a “knowledge worker?” Controller (finance) Logis<cs manager u  •  Focus: high-‐level processes to •  Focus: tac(cal, opera(onal manage financial risk •  Priority: Preven(on of low •  Priority: Increase efficiency, probability events ensure smooth opera(on •  Decisions based on •  Needs quan(ta(ve data to professional judgment, manage processes, look for knowledge of best prac(ce improvement opportuni(es •  Sets policy for long term •  Manages processes in real (me 50 Copyright 2012

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 paern 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 51 Copyright 2012

Behavioral Segmenta<on Ø  Self-‐reported versus observed Ø  Purchasing behaviors Ø  Usage behaviors: Amount? Variety? Qualita<ve paern? 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 54 Copyright 2012u 

Marke<ng Segments & UX Categories: The Ideal Time Non-‐ Target Target UX delivers promised value (and more) à sa<sfac<on, reten<on Targeted value messaging Purchase decision increases concentra<on of process ﬁlters out most poten<al buyers of non-‐target popula<on 55 Copyright 2012

Marke<ng Segments & UX Categories: The Ideal Warning: Time 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 essaging filters out most of non-‐target Targeted value m increases concentra<on of Purchase decision process UX delivers promised value (and more) à sa<sfac<on, poten<al buyers popula<on reten<on 56 Copyright 2012

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 57 Copyright 2012

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. 58 Copyright 2012

David A. Siegel Ph.D. Dray & Associates, Inc. Minneapolis, MN USA david.siegel@dray.com www.dray.com +1 612 377 1980

Segmentation -The Shadowy Side of Persona Development

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