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iot,  my  small  ai  VERSION,  scope  of  analytics  2015    learning,  thinking,  practicing  

Any  other  thoughts  please  feel  free  to  contact,  luxiaoteng0  (at)  gmail  (dot)  com    

   Data  has  no  shadow.  Whatever  it’s  sculptured,  it’s.    As  long  as  it  has  landed  to  the  completion  of  model-­‐100,  TENG’s  self-­‐assignment  on  modeling  techniques  self-­‐learning,  predictive  analytics  related,  TENG  is  able  to  decipher  hybrid  data  solution,  which  is  crossing  of  generic  model  techniques  such  as  familiar  hierarchical  cluster  and  a  lot,  plus  domain  knowledge  i.e.  bank,  retail,  mobile  etc.  as  well  as  it  empowers  advanced  data  mining  applied  with  machine  learning.    It  has  built  into  three  dimensional  data  analysis  matrix.    Inside  the  genres,  TENG  is  unique  to  design  data  analysis  models,  not  only  learning  from  traditional  analysis  modules,  but  also  consolidate  multiple  techniques  to  address  realistic  business  needs.  It  rolls  out  a  positive  loop  that  algorithms  could  be  best  fit  into  where  it’s  needed  for  performance  improving  related  to  well  recognized  business  situation.  <data  UNME>  converge  the  usages  of  text  mining,  semantic  analysis  and  recommendation  system.  Each  of  these  analytics  modules  have  been  evidently  extracted  from  high  profile  source.  It  consolidates  movie/drama/program  viewingship  pattern  through  multiple  platforms  x  devices.  Individual  could  be  classified  by  tag  through  the  output  scoring  counts.  This  process  is  consistently  applied  with  supervised  learning  per  Google’s  tagged  word  embedding  methodology.  Brand  could  leverage  the  viewing  score  to  plan  optimum  brand  awareness  as  customer-­‐centric  driven.  Furthermore,  it  renders  in  the  range  of  multiple  channel  intelligences  because  it  

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makes  the  variables  standardization  available  across  online  video,  time-­‐shifted  TV,  social  media  plus  influences  from  movie  on-­‐air.      

 Case  Study,  NLP  adoption,    A  famous  early  example  of  the  use  of  cognitive  technology  to  improve  a  product  offering  is  the  recommendation  feature  of  the  Netflix  online  movie  rental  service,  which  uses  machine  learning  to  predict  which  movies  a  customer  will  like.  This  feature  has  had  a  significant  impact  on  customers’  use  of  the  service;  it  accounts  for  as  much  as  75  percent  of  Netflix  usage.  

To  improve  marketing  and  customer  service,  BBVA  Compass  bank  uses  a  social  media  sentiment  monitoring  tool  to  track  and  understand  what  consumers  are  saying  about  the  bank  and  its  competitors.  The  tool,  which  incorporates  natural  language  processing  technology,  automatically  identifies  salient  topics  of  consumer  chatter  and  the  sentiments  surrounding  those  topics.  These  insights  influence  the  bank’s  decisions  on  setting  fees  and  offering  consumer  perks,  and  how  customer  service  representatives  should  respond  to  certain  customer  inquiries  about  services  and  fees.22  

Source:  Deloitte  Review      Seed  Program  (1)#  <data  UNME>  follows  the  working  principle,    Since  content  consumption  dominates  online  video  viewing,  it  is  possible  to  reinvent  viewingship  measurement  according  to  preferences  of  the  programs.  With  the  chosen  variables,  targeting  market  could  be  defined  whereas  brands  attribute  according  to  the  methods  of  content  connection  instead  of  digital  metrics  only.  It  makes  feasible  translation  with  data  works  out  virtually  channel  mix  strategy.  So  does  it  fulfill  end-­‐to-­‐end  data  solution  about  performance,  segmentation,  engagement.    .  Track  the  influences  generated  from  each  viewingship;  .  It  carries  in  defined  framework;  .  Viewingship  is  measured  as  a  link  to  the  contents  counted  by  chosen  variables;  .  Besides  monitoring  actions  on  programs,  it  also  has  advantage  to  decipher  similar  program  influences  differentiated  in  various  platforms;  .  In  other  words,  the  platform  performance  could  be  taken  as  the  adoption  of  variety  of  programs  which  associate  to  the  observing  variables;    .  The  preferences  on  programs  reflect  the  quality  of  users  and  it’s  conveniently  to  quantify  users  attributions.    More  releases  that  social  network  analysis  links  to  utilize  tangible  conditional  probability  predictive  method.    In  the  mean  time,  it  has  the  other  ascendencies.    First,  it  could  be  synergized  with  well-­‐established  segmentation.  According  to  log  linear  model,  target  groups’  viewing  habit  could  be  drawn  simultaneously  with  pre-­‐option  affinity  variables,  besides  viewingship  data,  reviews,  favors,  there  are  more  like  program  type,  host,  director,  actor,  geo,  timing,  devices.  With  time  being,  it’s  obvious  to  maximize  the  significance  bonding  between  brand  

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consumer  features  and  viewing  indexation.  Consequently,  the  value  of  data  insight  could  be  further  solicited  on  brand  awareness  optimization.  How  much  it  correlates  to  the  revenues?  Only  when  this  solid  awareness  combination  could  be  visualized,  the  equation  about  brand,  consumer  interaction,  learned  from  data  analysis  guru  Dawn  Iacobucci  [fig.1]  can  be  measured  in  a  learning  path  wherever  it’s  under  the  bigger  background  as  forming  intelligent  enterprise  [fig.2].    [fig.1]    Ad  exposure  !  Brand  awareness  !  Attitude  ~  ad  !  Buying  intention  !  Purchase  Ad  exposure  !  Brand  awareness  !  Attitude  ~  brand  !  Buying  intention  !  Purchase  Price  !  Buying  intention    [fig.2]      

     Have  to  thank  for  references  being  with  theoretical  proofs,    

1. Natural  Language  Processing  (almost)  from  Scratch,  Journal  of  Machine  Learning  Research  1  (2000)  1-­‐48,  Ronan  Collobert,  Jason  Weston,  L  ́eon  Bottou,  Michael  Karlen,  Koray  Kavukcuoglu,  Pavel  Kuksa.  

2. Deep  learning  &  NLP  -­‐  Graphs  to  the  Rescue,  (or  not  yet!),  Stockholm,  Sics,  October  21  2014,  Roelof  Pieters,  KTH/CSC,  Graph  Technologies  R&D  -­‐    

3. Deep  learning  for  NLP,  An  Introduction  to  Neural  Word  Embeddings*  and  some  more  fun  stuff…,  KTH,  December  4,  2014,  Roelof  Pieters    PhD  candidate  KTH/CSC  CIO/CTO  Feeda  AB  

4. Three  New  Graphical  Models  for  Statistical  Language  Modelling,  Andriy  Mnih,  Geoffrey  Hinton,  Department  of  Computer  Science,  University  of  Toronto,  Canada  

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5. Statistical  Language  Models  Based  on  Neural  Networks,  Google,  Moutain  View,  2nd  April  2012,  Tomas  Mikolov,  Strategies  for  training  large  scale  neural  network  language  models,  Microsoft  Research,  Redmond,  WA,  USA,  Toma  ́sˇ  Mikolov  #1,  Anoop  Deoras  ∗2,  Daniel  Povey  †3,  Luka  ́sˇ  Burget  #4,  Jan  “Honza”  Cˇ  ernocky  ́  #5  #  Brno  University  of  Technology,  Speech@FIT,  Brno,  Czech  Republic  

6. DeViSE:  A  Deep  Visual-­‐Semantic  Embedding  Model,  Google,  Inc.  Mountain  View,  CA,  USA,  Andrea  Frome*,  Greg  S.  Corrado*,  Jonathon  Shlens*,  Samy  Bengio  Jeffrey  Dean,  Marc’Aurelio  Ranzato,  Tomas  Mikolov  

Whatever  it’s  probably  not  a  game-­‐changer,  it  still  transforms  to  model  designer  and  pursues  my  own  best  practices.  Thank  you.  

Is  it  formidable?  What  if  it’s  just  thy  truth  too  much.  Who  just  plays  autocorrelation?  Unsuspected  social  site  easily  becomes  assistive.  How  about  time-­‐shifted  TV?  It  gears  up  the  follower  in  ahead.  Is  it  awesome?  Whenever  think  about  brand  tailors  your  own  broadcasting  station,  secret  recipe  is  about  virtual  channel  *  type  *  program.  Please  contact  Teng,  it  will  have  hypothesis,  experiments,  data  analysis  methodology  decipher,  insight  report  and  your  brand’s  quotient,  everything  in  the  case  looking  at  viewhingship/clicks/comments  as  the  series  of  actions  caused  by  users.  There  is  the  correlation  between  the  certain  group  of  viewers  and  their  preferences.  Preferences  have  been  discerned  by  the  program  viewingshing  habit.  How  about  brand  overarches  the  findings  in  veins,  thus  exert  the  inherent  influences  into  viewers  who  has  been  identified  as  worth  of  targeting  according  to  previous  program  classifications.  [fig.3].  

As  long  as  observing  on  conditional  probability  merged  with  social  mining,  unstructured  data  utilization  could  be  standardized  firstly  in  the  sequence  to  apply  hierarchy  bayesian.  It  is  consistent  to  Seed  Program  (2)#  e-­‐chainTM  that  the  extreme  focus  on  top  three  conversions  in  data  stream,  e-­‐commerce,  engine,  email.  In  tradition,  it  veins  into  impression,  click,  acquisition,  transaction.  After  developing  word  bag,  2  algorithms  Gibbs  Sampling  and  Metropolis  Hasting  are  particularly  useful  to  draw  posterior  distribution.  This  is  very  crucial  conclusion  drawn  from  my  some  researches  and  theoretical  learning.  Probably  it’s  familiar  by  others,  but  for  me  it  takes  some  efforts.  It  has  4  strengths  by  adopting  of  this  data  solution  from  my  point  of  view,  1.  It  has  the  completed  achievement  of  data  tracking  in  each  outcome  layer;  2.  Follows  contextual  measurement  without  losing  focus  90%  conversions  regarding  of  e-­‐business  backbone;  3.  It  works  under  hive  philosophy.  It  involves  the  touch  points  as  beneficial  extensions;  4.  It’s  effective  to  merge  with  other  analysis  module  i.e.  brand  simulation  in  advance.  It’s  able  to  utilize  Deep  Learning  as  far  as  there  are  feasibly  55  layers  applied  in  Google’s  analysis  that  I  read  from  one  article  before  in  somewhere.      

 [fig.3]  

. about  Spark  Internet  Button  /  SIB*,  {if  this,  then  that.}  data  analysis  integration  inside  <data  UNME>  applied  with

myself  some  reliable  proofs  in  data  analysis  theories,  [fig.3]

 

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 other  scenario  soonest  specifically  for  CIO  references.    

What  is  differentiations  between  supervised  learning  and  unsupervised  learning  in  terms  of  social  mining.  

• A  new  study  has  revealed  a  way  to  do  sentiment  analysis  on  a  large  number  of  social  media  images  using  unsupervised  learning.  

• Unsupervised  learning  in  AI  is  a  step  above  supervised  learning  where  machines  have  to  work  with  unlabelled  data,  observe  and  make  sense  of  it,  and  provide  an  outcome.  Supervised  learning,  on  the  other  hand,  gives  machines  labelled  data  or  examples  to  learn  from  when  carrying  out  certain  tasks  such  as  classifying  an  object  or  predicting  future  outcomes.  The  study,  Unsupervised  Sentiment  Analysis  for  Social  Media  Images,  was  released  as  part  of  the  International  Joint  Conference  on  Artificial  Intelligence  in  Argentina  this  week.  It  reveals  a  novel  framework,  called  Unsupervised  Sentiment  Analysis  (USEA),  that  uses  both  textual  and  visual  data  in  a  single  model  for  learning.  

• Images  from  social  media  sites  offer  rich  data  to  work  with  when  doing  sentiment  analysis.  However,  manually  labelling  millions  of  images  is  too  labour-­‐  and  time-­‐intensive,  meaning  this  data  often  goes  untapped.  This  is  why  the  study's  authors  focused  their  efforts  on  unsupervised  learning.  

• “In  order  to  utilise  the  vast  amount  of  unlabelled  social  media  images,  an  unsupervised  approach  would  be  much  more  desirable,”  researchers  from  Arizona  State  University  wrote  in  their  paper.  

• “As  of  2013,  87  millions  of  users  have  registered  with  Flickr.  Also,  it  was  estimated  that  about  20  billion  Instagram  photos  are  shared  to  2014.  

• “To  our  best  knowledge,  USEA  is  the  first  unsupervised  sentiment  analysis  framework  for  social  media  images.”  

• The  framework  infers  sentiments  by  combining  visual  data  with  accompanying  textual  data.  As  textual  data  is  often  incomplete  with  hardly  any  tags  or  noisy  with  irrelevant  comments,  relying  on  it  alone  is  difficult  when  doing  sentiment  analysis.    

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• Therefore,  the  researchers  used  the  supporting  textual  data  to  provide  semantic  information  on  the  images  to  enable  unsupervised  learning.  

• “Textual  information  bridges  the  semantic  gap  between  visual  features  and  sentiment  labels.”  

• The  researchers  crawled  images  from  Flickr  and  Instagram  users,  collecting  140,221  images  from  Flickr  and  131,224  from  Instagram.    

• They  built  a  framework  to  classify  images  into  three  categories  or  class  labels  –  positive,  negative  and  neutral,  looking  at  image  captions  and  comments  associated  with  the  images.    

• “Some  words  may  contain  sentiment  polarities.  For  example,  some  words  are  positive  such  as  ‘happy’  and  ‘terrific’;  while  others  are  negative  such  as  ‘gloomy’  and  ‘disappointed’.  

•  “The  sentiment  polarities  of  words  can  be  obtained  via  some  public  sentiment  lexicons.  For  example,  the  sentiment  lexicon  MPQA  [Multiple  Perspective  Question  Answering]  contains  7,504  human  labeled  words  which  are  commonly  used  in  the  daily  life  with  2,721  positive  words  and  4,783  negative  words.  

• “Second,  some  abbreviations  and  emoticons  are  strong  sentiment  indicators.  For  example,  ‘lol’  [laugh  out  loud]  is  a  positive  indicator  while  ‘:(‘  is  a  negative  indicator.”  

• Visual  features  from  the  images  were  extracted  by  large-­‐scale  visual  attribute  detectors,  with  term  frequency  and  stop  words  (removing  words  like  ‘a’  and  ‘the’)  used  to  form  text-­‐based  features.  

• The  framework  was  compared  to  other  sentiment  analysis  algorithms  such  as  Senti  API  for  unsupervised  sentiment  prediction  and  a  variant  of  the  framework,  USEA-­‐T,  which  only  takes  textual  data  into  account  when  doing  sentiment  analysis.    

• Other  methods  that  were  also  compared  with  the  USEA  framework  were  Sentibank  with  K-­‐means  clustering,  which  uses  large  scale  visual  attribute  detectors,  and  adjective  and  nouns  visual  sentiment  description  pairs;  EL  with  K-­‐means  clustering,  which  is  a  topical  graphical  model  for  sentiment  analysis;  and  Random,  which  randomly  guesses  to  predict  sentiment  labels  of  images.  

• The  results  show  that  USEA  performed  better  than  all  the  other  algorithms  tested,  receiving  56.18  per  cent  accuracy  with  the  Flickr  dataset  compared  to  Senti  API  at  34.15  per  cent  and  USEA-­‐T  at  40.22  per  cent.  With  the  Instagram  dataset,  it  received  59.94  per  cent  accuracy  compared  to  Senti  API  at  37.80  per  cent  and  USEA-­‐T  at  36.41  per  cent.  

• “The  proposed  framework  often  obtains  better  performance  than  baseline  methods.  There  are  two  major  reasons.  First,  textual  information  provides  semantic  meanings  and  sentiment  signals  for  images.  Second  we  combine  visual  and  textual  information  for  sentiment  analysis.”  

• The  research  pointed  out  that  deep  learning  approaches  (many  hidden  layers  in  artificial  neural  networks)  to  this  have  shown  to  be  effective,  but  still  are  mostly  used  in  a  supervised  learning  way,  which  depends  on  the  availability  of  a  good  training  dataset  with  labels.  

• “In  the  future,  we  will  exploit  more  social  media  sources,  such  as  link  information,  user  history,  geo-­‐location,  etc.,  for  sentiment  analysis.”    

• Source:  http://www.cio.com.au/article/580602/study-­‐uncovers-­‐unsupervised-­‐learning-­‐framework-­‐image-­‐sentiment-­‐analysis/?fp=16&fpid=1  

   There  are  some  opinions  from  Professor  Miller  about  text  mining  

supervised  vs  unsupervised:    Unsupervised  text  analytics  problems  are  those  for  which  there  is  no  response  or  class  to  be  predicted.  Rather,  as  we  showed  with  the  movie  taglines,  the  task  is  to  identify  common  patterns  or  trends  in  the  data.  As  part  of  the  task,  we  may  define  text  measures  describing  the  documents  in  the  corpus.      For  supervised  text  analytics  problems  there  is  a  response  or  class  of  documents  to  be  predicted.  We  build  a  model  on  a  training  set  and  test  it  on  a  test  set.  Text  

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classification  problems  are  common.  Span  filtering  has  long  been  a  subject  of  interest  as  a  classification  problem,  and  many  e-­‐mail  users  have  benefitted  from  the  efficient  algorithm  that  have  evolved  in  this  area.  In  the  context  of  information  retrieval,  search  engines  classify  documents  as  being  relevant  to  the  search  or  not.  Useful  modeling  techniques  for  text  classification  include  logistic  regression,  linear  discriminant  function  analysis,  classification  trees,  and  support  vector  machines.  Various  ensemble  or  committee  methods  may  be  employed.      Automatic  text  summarization  is  an  area  of  research  and  development  that  can  help  with  information  management.  Imagine  a  text  processing  program  with  the  ability  to  read  each  document  in  a  collection  and  summarize  it  in  a  sentence  or  two,  perhaps  quoting  from  the  document  itself.  Today’s  search  engines  are  providing  partial  analysis  of  documents  prior  to  their  being  displayed.  They  create  automated  summaries  for  fast  information  retrieval.  They  recognize  common  text  strings  associated  with  user  requests.  These  applications  of  text  analysis  comprise  tool  of  information  search  that  we  take  of  granted  as  part  of  our  daily  lives.    Seed  Program  (3)#  Data  Analysis  in  general  +  Bank  in  particular  (just  name  it  symphony  analysis)  >>>  unpublished  my  first  book  <data  analysis  is  a  symphony  in  big  data  jungle>  

 In  an  analysis  list  about  banking  data,  may  bring  my  version  listed*  live.  My  little  behemoth,  it’s  with  all  my  nurturing  from  learning.  Whereas  it’s  fully  understandable  my  deepest  respects  to  the  behemoths  who  has  been  authoritative  over  50  years  in  bank  data  analysis  relates,  especially  approached  with  seeable  continuous  advances  e.g.  machine  learning,  predictive  analytics.      .  Customer  portfolio  management  .  Customer  segmentation  .  RFM  models  &  Migration  .  Market  basket  analysis  .  Recommendation  tool  .  Existing  customer  analysis  .  Customer  acquisition  .  Customer  retention  including  churn  analysis,  and  the  side  of  risk  management  (over  50  years’  professions  in  FICO  &  Others)  .  Cross-­‐selling  &  Up-­‐selling  .  Multiple  channel  planning  .  ROI  modeler  .  Customer  lifetime  value  system  .  Techniques  in  predictive  analytics,  machine  learning  &  Neural  Network  .  Risk  analysis  (over  50  years’  professions  in  FICO  &  SAS  &  Others)  .  Fraud  analysis  (over  50  years’  professions  in  FICO  &  SAS  &  Others)  .  Credit  score  (over  50  years’  professions  in  FICO  &  Others)  (&  more  a  lot  about  financial  data  areas  that  probably  I  don’t  know,  related  to  over  50  years’  professions  in  FICO  &  SAS  &  Others)  .  Hypothesis  *  Experiments  

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 • *about  the  list,  • It’s  suggested  to  remove  the  name  limitation  here  for  convenient  reading,  

despite  name  system  in  list  is  simply  consistent  to  e-­‐business.  My  learning  has  been  through  data  mining  methodology,  so  that,  within  my  available  data  capabilities,  it  enables  to  switch  verified  data  situation  with  specific  data  analysis  techniques  behind  the  name.  It  also  includes  some  instances  that  there  are  data  analysis  essences  I  have  learned  about  e.g.  logit,  conjunct  analysis  and  a  lot  etc.  despite  it  can’t  tell  from  the  listed  names.        

And there are more small modelers related to modeling techniques.

Need to highlight Seed  Program  (4)#  the  innovation analytics would consist to holistic analytics list and resonating to industry shift on both technology and bank network including bank urbanization phenomena, why IoT is much relevant to bank business, influences caused from millennials, mobile bank, e-wallet etc. This part will be more involved into continuous industry insight decipher. The similar analytics could be expanded into other sectors Seed  Program  (5)#, like retail, telecom, travel/hotel, restaurant. Nonetheless, it’s still necessary to tackle the equation**.

From TENG:“Data is new currency. Banking it.” Roadmap as one of the topics [fig.4] data in mock-up for category survey, for instance, Newer/Driver/Challenger.

My 1 case connects to bank analysis.

A few years ago, in my chat with my one ex-colleague, he talked about one hurdle

0"1"2"3"4"5"

Newer" Driver" Challenger"

•  Data$Analy)cs$Matrix$consolidates$Bank$dynamic$insights$(Newer/Driver/Challenger)$throughout$comprehensions$of$shiBing$fric)on$which$is$caused$by$millennials’$dis)nc)on.$$

TENG"data"unme"FRAMEWROK"~"9th"pillar"

Industry"Insight"

EGBusiness"

Consumer"+/G"Channel"

Social"Network"""

Brand"+/G"Consumer"

Movie/Drama"

IndexaPon"""

Modeling$Techniques$

Machine$Learning/Deep$

Learning$

Business$Savvy$

Biz"Model"

my$contacts:$$erinteng$(at)$hotmail$(dot)$com$139$1862$0956$

  9  

happened in performance marketing during his working in insurance services. In the challenges to decide how much scaling is most appropriate during dealing with targeting, there is a phenomena there is absolute loss of quality acquisitions in any case when enlarging recruitment base.

Case Study, it’s all about logistic regression to fix targeting paradox.

All other things being equal, the customers with the highest predicted sales should be the ones the sales team will approach first. Alternatively, we could set a cutoff for predicted sales. Customers above the cutoff are the customers who get sales calls—these are the targets. Customers below the cutoff are not given calls.

When evaluating a regression model using data from the previous year, we can determine how close the predicted sales are to the actual/observed sales. We can find out the sum of the absolute values of the residuals (observed minus predicted sales) or the sum of the squared residuals.

Another way to evaluate a regression model is to correlate the observed and predicted response values. Or, better still, we can compute the squared correlation of the observed and predicted response values. This last measure is called the coefficient of determination, and it shows the proportion of response variance accounted for by the linear regression model. This is a number that varies between zero and one, with one being perfect prediction.

If we plotted observed sales on the horizontal axis and predicted sales on the vertical axis, then the higher the squared correlation between observed sales and predicted sales, the closer the points in the plot will fall along a straight line. When the points fall along a straight line exactly, the squared correlation is equal to one, and the regression model is providing a perfect prediction of sales, which is to say that 100 percent of sales response is accounted for by the model. When we build a regression model, we try to obtain a high value for the proportion of response variance accounted for. All other things being equal, higher squared correlations are preferred.

The focus can be on predicting sales or on predicting cost of sales, cost of support, profitability, or overall customer lifetime value. There are many possible regression models to use in with regression methods.

To develop a classification model for targeting, we proceed in much the same way as with a regression, except the response variable is now a category or class. For each customer, a logistic regression model, for example, would provide a predicted probability of response. We employ a cut-off value for the probability of response and classify responses accordingly. If the cut-off were set at 0.50, for example, then we would target the customer if the predicted probability of response is greater than 0.50, and not target otherwise. Or we could target all customers who have a predicted probability of response of 0.40, or 0.30, and so on. The value of the cut-off will vary from one problem to the next.

When observed binary responses or choices are about equally split between yes and no, for example, we would use a cut-off probability of 0.50. That is, when the predicted probability of responding yes is greater than 0.50, we predict yes. Otherwise, we predict no.

Logistic regression provides a means for estimating the probability of a favorable (yes) response to the offer. The density lattice in figure 3.6 provides a pictorial representation of the model and a glimpse at model performance.

To evaluate the performance of this targeting model, we look at a two-by-two contingency table or confusion matrix showing the predicted and observed response values. A 50 percent cut-off does not work in the Bank Marketing Study, given the low base rate of responses to the offer.

A 50 percent cut-off will not work for the bank, but using a 10 percent cutoff for the response variable (accepting the term deposit offer or not), yields 65.9 percent accuracy in classification. The confusion matrix for the logistic regression and 10 percent cut-off is shown.

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The Bank Marketing Study is typical of target marketing problems. Response rates are low, much lower than 0.50, so a 50 percent cut-off performs poorly. In fact, if bank analysts were to use a 50 percent cut-off, they would predict that every client would respond no, and the bank would target no one. Too high a cut-off means the bank will miss out on many potential sales.

Too low a cut-off presents problems as well. Too low a cut-off means the bank will pursue sales with large numbers of clients, many of whom will never subscribe to the term deposit offer. It is wise to pick a cut-off that maximizes profit, given the unit revenues and costs associated with each cell of the confusion matrix. Target marketing, employed in the right situations and with the right cut-offs, yields higher profits for a company.

Source: <Modeling Techniques in Predictive Analytics>

Being through the enterprise & innovation equation**,

Business value = ecosystem x business model x category pacing x data skill x resources

End. Thank You. My ‘thank you’ has to be sent to, with my rough counts, around data analysis gurus x1,000 ppl, and a group of professions, authors, contributors x5,000 ppl, besides corporations, universities, institutions, organizations, and other team members. Mirroring infusive magnets, we can use data analysis capability to refine data into tangible data model, thus it’s enable to decode human being’s new information adoption pattern when it embeds into a shifting lifestyle movement. It’s ready to embark into a learning mode of the new experiences. It’s found multiple dimensional relationships between customer and brand, reciprocity since it’s along with disruptive technology revolution.