Segmentation -The Shadowy Side of Persona Development


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Segmentation -The Shadowy Side of Persona Development

  1. 1.   Segmenta(on:    The  Shadowy  Side  of  Persona  Development   UPA  2012     David  A.  Siegel  Ph.D.   Dray  &  Associates,  Inc.   Minneapolis,  MN    USA        +1  612  377  1980       u  Copyright,  Dray  &  Associates,  Inc.,  2012   Copyright 2012
  2. 2.   Segmentation         Market  Segmentation                  User  Classification     2 Copyright 2012
  3. 3. Interlocking  Challenges:   Who?   What?   3 Copyright 2012
  4. 4. Shadowy?   4 Copyright 2012
  5. 5. •  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
  6. 6. 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
  7. 7. Unusually  Clean  Clusters   8 Copyright 2012
  8. 8. Coherence  Within  Clusters   9 Copyright 2012
  9. 9. Differen<a<on  Among  Clusters   9 Copyright 2012
  10. 10. Personas-­‐-­‐Landmarks  Within  Clusters   11 Copyright 2012
  11. 11. Dimensions  of  Difference  Are  Not  Givens   -­‐-­‐Even  when  they  describe  seemingly  obvious  differences   What  is  this?   12 Copyright 2012
  12. 12. Now  what  is  it?  13 Copyright 2012
  13. 13. Now?  14 Copyright 2012
  14. 14. Now?  15 Copyright 2012
  15. 15. 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
  16. 16. Informa<on  is  a  difference  that   makes  a  difference.                    -­‐-­‐Gregory  Bateson  Source: /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
  17. 17. Different  differences  make  a  difference,  depending  on  what  different  ac<ons  we  are  focusing  on.     18 Copyright 2012
  18. 18. 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
  19. 19. 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
  20. 20. Case  in  point:  Let’s  zoom  in  here   Non   Customers   customers         Opportunity   At  Risk   Aachment   21 Copyright 2012
  21. 21. 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
  22. 22. With  drill-­‐down,  subgroups  can  cut  across  segments   Seg.  A   Seg.  B   Seg.  C   Seg.  D   23 Copyright 2012
  23. 23. 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   paern   24 Copyright 2012
  24. 24. w  rapidly  ns  mul<ply,  n  simple  rip<ons   ‹#›
  25. 25. Overall  SESS  w  rapidly  ns  mul<ply,  n  simple  rip<ons   ‹#›
  26. 26. Overall  SESS  w  rapidly   Age  ns  mul<ply,  n  simple  rip<ons   ‹#›
  27. 27. Overall  SESS  w  rapidly   Age  ns  mul<ply,  n  simple  rip<ons   Ethnicity   ‹#›
  28. 28. Overall  SESS  w  rapidly   Age  ns  mul<ply,  n  simple  rip<ons   Ethnicity   ‹#›
  29. 29. 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   ‹#›
  30. 30. 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
  31. 31. 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
  32. 32. 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:  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
  33. 33. Striving  for  Precision  on  Mul<ple  Dimensions  Two  views,    same  points  in  space  à         34 Copyright 2012
  34. 34. Back  to  our  unnaturally  clean  clusters:   35 Copyright 2012
  35. 35. Imagine  they  are  really  in  3  dimensions,  but  we  have  only  viewed  them  from  one  angle  (i.e.,  only  focusing  on  2  dimensions)   36 Copyright 2012
  36. 36. Now  we  rotate.  Same  points  different  views—clusters  smear  out.       37 Copyright 2012
  37. 37. 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   38 Copyright 2012
  38. 38. 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
  39. 39. 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.     40 Copyright 2012
  40. 40. 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
  41. 41. 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
  42. 42. Exaggera<ng  Dis<nc<veness   These  look  dis<nct,  but  most  of   Cluster  2embers  have  a  lot  in   their  m   common  on  one  or  both   dimensions    Y   Cluster  1   X 43 Copyright 2012
  43. 43. 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  
  44. 44. Demographic  Segmenta<on   45 Copyright 2012
  45. 45. 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
  46. 46. Occupa<onal  Segmenta<on
  47. 47.  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
  48. 48. 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
  49. 49. 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
  50. 50. 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
  51. 51. Behavioral  Self-­‐Iden<fica<on      What  can  you  say  about  psychographics  (e.g.,  preferences)  of   people  who  gather  In  these  venues?   52 Copyright 2012
  52. 52. People  Who  Choose  These  Periodicals?   53 Copyright 2012
  53. 53. 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     
  54. 54.     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
  55. 55.     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
  56. 56. 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
  57. 57. 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
  58. 58. David  A.  Siegel  Ph.D.  Dray  &  Associates,  Inc.  Minneapolis,  MN    USA        +1  612  377  1980