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Event	
  sponsored	
  by	
  Affinnova	
  
All	
  copyright	
  owned	
  by	
  The	
  Future	
  Place	
  and	
  the	
  presenters	
  of	
  the	
  material	
  
For	
  more	
  informa=on	
  about	
  Affinnova	
  visit	
  h>p://www.	
  affinnova.com/	
  
For	
  more	
  informa=on	
  about	
  NewMR	
  events	
  visit	
  newmr.org	
  
Advanced	
  Quant	
  Techniques	
  
July	
  14,	
  2011	
  
Product	
  Por4olio	
  and	
  Revenue	
  
Op9miza9on	
  for	
  CPG	
  
Juan	
  Andrés	
  Tello,	
  SKIM	
  	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Juan Andrés Tello
SKIM US Director
Product Portfolio and Revenue
Optimization for CPG	
  
expect	
  great	
  answers	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Outline	
  
1.  Mo=va=on	
  for	
  Revenue	
  Op=miza=on	
  (RO)	
  
2.  Concept	
  of	
  RO	
  
3.  RO	
  requires	
  a	
  MR	
  shiS	
  from	
  insight	
  to	
  foresight	
  	
  
4.  Building	
  blocks	
  of	
  a	
  RO	
  system	
  
a.  Consumer	
  behavior	
  models	
  
b.  Demand	
  forecas=ng	
  
c.  Constrained	
  op=miza=on	
  tools	
  
5.  Some	
  RO	
  strategies	
  
6.  Delivering	
  op=miza=on	
  results	
  to	
  clients	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Revenue	
  Op=miza=on	
  -­‐	
  Mo=va=on	
  
•  Maximize:	
  	
  
	
  	
  Profits	
  =	
  f(Pricing,	
  Product	
  por]olio	
  composi=on	
  |	
  Selling	
  channel)	
  
•  Turns	
  data	
  into	
  ac=onable	
  foresight	
  tools	
  for	
  clients	
  
•  Determine	
  op=mal	
  pricing/por]olio	
  strategy	
  within	
  given	
  constraints	
  
•  RO	
  pioneers:	
  fixed	
  capacity	
  industries	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
•  Solu=on:	
  price	
  differen=a=on	
  	
  
(some=mes	
  controversial	
  –	
  Smart	
  vending	
  machines)	
  
B
How	
  to	
  charge	
  the	
  max	
  willingness	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
to	
  pay	
  to	
  each	
  customer?	
  
Demand
0 $5 $10 $15
Price
1,000
d(p)
ß marginal cost
A
C
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Traditional
MR function
Business
contribution
team
Strategic
insight
organization
Strategic
foresight
organization
RO	
  requires	
  a	
  MR	
  shiS	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
from	
  insight	
  to	
  foresight	
  
90%	
  of	
  consumer-­‐facing	
  companies	
  have	
  a	
  Consumer	
  Insights	
  (CI)	
  func=on	
  in	
  
early	
  stages	
  of	
  development	
  (1)	
  or	
  (2)	
  
1
2
3
4
Source: BCG Consumer Insight Benchmarking (May 2009)
Consumer insight
as a source of
competitive
advantage
MR as an
order-taking
function
BCG’s	
  CI	
  stages	
  of	
  development	
  	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Building	
  blocks	
  of	
  a	
  RO	
  system	
  
1.  Quan=ta=ve	
  models	
  of	
  consumer	
  behavior	
  à	
  Choice	
  based	
  Conjoint	
  (CBC)	
  
2.  Demand	
  forecasts	
  à	
  Market	
  simulator	
  
3.  Constrained	
  op=miza=on	
  tools	
  à	
  Search	
  algorithm	
  of	
  op>mal	
  solu>on	
  
within	
  market	
  constraints	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
1.	
  Choice	
  based	
  Conjoint	
  
•  Proven	
  and	
  unbiased	
  research	
  technique	
  to	
  model	
  consumer	
  preferences	
  
and	
  market	
  heterogeneity	
  
•  Rooted	
  in	
  U=lity	
  Theory	
  (Von	
  Neumann–Morgenstern)	
  
•  Preferences	
  es=ma=on	
  process	
  has	
  evolved	
  over	
  =me:	
  	
  
1.  Aggregate	
  Logit	
  model	
  (one	
  size	
  fits	
  all)	
  
2.  Latent	
  class	
  (segmenta=on)	
  
3.  Hierarchical	
  Bayes	
  (individual	
  level)	
  
•  Choice	
  task	
  resembles	
  purchase	
  behavior	
  process	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
1.	
  Choice	
  tasks	
  within	
  a	
  compe==ve	
  context	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
2.	
  Market	
  Simulator:	
  from	
  consumer	
  
preferences	
  to	
  market	
  shares,	
  to	
  revenue	
  
forecas=ng	
  
•  Volumetric	
  adjustments	
  
and	
  calibra=ons	
  
•  Ability	
  to	
  test	
  unlimited	
  
pricing	
  /por]olio	
  
strategies	
  and	
  poten=al	
  
compe==ve	
  reac=ons	
  
•  In	
  its	
  simplest	
  form,	
  the	
  
simulator	
  is	
  a	
  “show	
  of	
  
hands”	
  from	
  respondents	
  
given	
  a	
  number	
  of	
  choice	
  
op=ons	
  
Input	
  prices	
  	
  
Market	
  share	
  
output	
  
Revenue	
  
output	
  
Change	
  
por]olio	
  
composi=on	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
3.	
  Searching	
  for	
  the	
  op=mal:	
  	
  
define	
  the	
  feasible	
  space	
  first	
  
Total space
of possible
solutions
Constrained space of
feasible solutions
Sample of solutions
within constraints
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
3.	
  Searching	
  for	
  the	
  op=mal:	
  define	
  	
  	
  	
  	
  	
  	
  	
  
objec=ve	
  func=on	
  &	
  apply	
  search	
  algorithm	
  
Max
Revenue
(Optimal
solution)
Revenue
Surface
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
3.	
  It’s	
  not	
  only	
  about	
  finding	
  the	
  winning	
  
solu=on,	
  but	
  about	
  the	
  pa>erns	
  observed	
  
•  While	
  the	
  main	
  goal	
  is	
  to	
  uncover	
  
the	
  strategy	
  that	
  maximizes	
  
revenue,	
  ask	
  yourself:	
  
•  What	
  makes	
  it	
  the	
  op=mal	
  solu=on?	
  
•  Are	
  there	
  alternate	
  strategies	
  with	
  
different	
  tradeoffs	
  yielding	
  posi=ve	
  
results?	
  
•  In	
  this	
  example:	
  
•  80,000	
  scenarios	
  generated	
  
•  40%	
  yield	
  gains	
  in	
  both	
  revenue	
  and	
  
share.	
  Cluster	
  analysis	
  is	
  used	
  to	
  
further	
  group	
  and	
  interpret	
  
Focus	
  on	
  
upper	
  right	
  
quadrant	
  
Max	
  Rev	
  gain	
  =	
  8%	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Some	
  RO	
  strategies	
  
1.  Maximize	
  volume	
  share	
  profitably	
  	
  (capping	
  revenue	
  loss)	
  
	
  Balanced	
  “investment”	
  strategy	
  to	
  grow	
  customer	
  base	
  
2.  Maximize	
  revenue	
  while	
  capping	
  volume	
  loss	
  
	
  Ideal	
  situa=on,	
  not	
  always	
  feasible;	
  will	
  depend	
  on	
  price	
  elas=city	
  
3.  Game	
  theory	
  strategies:	
  compe==ve	
  reac=ons	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Delivering	
  op=miza=on	
  results	
  to	
  clients	
  
A	
  few	
  insights	
  for	
  a	
  successful	
  deployment:	
  
•  Involve	
  key	
  stakeholders	
  from	
  different	
  func=ons	
  early	
  in	
  the	
  game	
  
•  Plan	
  accordingly	
  
•  Kick-­‐off:	
  constraints	
  from	
  every	
  func=on	
  are	
  expressed	
  and	
  discussed	
  
•  Delivery:	
  results	
  are	
  discussed	
  in	
  a	
  workshop	
  style	
  
•  Dynamic	
  session	
  
•  Create	
  tools	
  that	
  allow	
  clients	
  to	
  interact	
  with	
  the	
  data	
  (e.g.	
  ability	
  to	
  ac=vate/
deac=vate	
  constraints,	
  rank	
  and	
  select	
  scenarios)	
  
•  Don’t	
  be	
  afraid	
  to	
  show	
  the	
  “raw”	
  data;	
  involve	
  stakeholders	
  in	
  the	
  analysis	
  
•  As	
  always,	
  be	
  clear	
  about	
  the	
  model’s	
  assump=ons	
  and	
  limita=ons	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
Q & A
Andrew	
  Jeavons	
  
Survey	
  Analy=cs	
  
Juan	
  Andrés	
  Tello	
  	
  
SKIM	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
contact us or follow us online!
SKIM | Consumer Goods
Juan Andrés Tello | Director US
j.tello@skimgroup.com | +1 201 963 8430
twi>er.com/	
  
skimgroup	
  
facebook.com/	
  
skimgroup	
  
linkedin.com/	
  
company/skim	
  
youtube.com/	
  
skimvideos	
  
skimgroup.com	
  
Juan Andrés Tello, SKIM, US
NewMR Advanced Quant Techniques, July 14, 2011
contact us or follow us online!
SKIM | Locations
New York, USA
Juan Andrés Tello
j.tello@skimgroup.com
+1 201 963 8430
Rotterdam, NL
Mini Kalivianakis
m.kalivianakis@skimgroup.com
+31 10 282 3535
twi>er.com/	
  
skimgroup	
  
facebook.com/	
  
skimgroup	
  
linkedin.com/	
  
company/skim	
  
youtube.com/	
  
skimvideos	
  
skimgroup.com	
  
Geneva, Switzerland
Vicky Nef
v.nef@skimgroup.com
+41 22 747 7519
London, UK
Debora Corfield
d.corfield@skimgroup.com
+44 203 178 6910

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Juan tello advanced quant - 2011

  • 1. Event  sponsored  by  Affinnova   All  copyright  owned  by  The  Future  Place  and  the  presenters  of  the  material   For  more  informa=on  about  Affinnova  visit  h>p://www.  affinnova.com/   For  more  informa=on  about  NewMR  events  visit  newmr.org   Advanced  Quant  Techniques   July  14,  2011   Product  Por4olio  and  Revenue   Op9miza9on  for  CPG   Juan  Andrés  Tello,  SKIM    
  • 2. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Juan Andrés Tello SKIM US Director Product Portfolio and Revenue Optimization for CPG   expect  great  answers  
  • 3. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Outline   1.  Mo=va=on  for  Revenue  Op=miza=on  (RO)   2.  Concept  of  RO   3.  RO  requires  a  MR  shiS  from  insight  to  foresight     4.  Building  blocks  of  a  RO  system   a.  Consumer  behavior  models   b.  Demand  forecas=ng   c.  Constrained  op=miza=on  tools   5.  Some  RO  strategies   6.  Delivering  op=miza=on  results  to  clients  
  • 4. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Revenue  Op=miza=on  -­‐  Mo=va=on   •  Maximize:        Profits  =  f(Pricing,  Product  por]olio  composi=on  |  Selling  channel)   •  Turns  data  into  ac=onable  foresight  tools  for  clients   •  Determine  op=mal  pricing/por]olio  strategy  within  given  constraints   •  RO  pioneers:  fixed  capacity  industries  
  • 5. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 •  Solu=on:  price  differen=a=on     (some=mes  controversial  –  Smart  vending  machines)   B How  to  charge  the  max  willingness                                               to  pay  to  each  customer?   Demand 0 $5 $10 $15 Price 1,000 d(p) ß marginal cost A C
  • 6. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Traditional MR function Business contribution team Strategic insight organization Strategic foresight organization RO  requires  a  MR  shiS                                                                             from  insight  to  foresight   90%  of  consumer-­‐facing  companies  have  a  Consumer  Insights  (CI)  func=on  in   early  stages  of  development  (1)  or  (2)   1 2 3 4 Source: BCG Consumer Insight Benchmarking (May 2009) Consumer insight as a source of competitive advantage MR as an order-taking function BCG’s  CI  stages  of  development    
  • 7. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Building  blocks  of  a  RO  system   1.  Quan=ta=ve  models  of  consumer  behavior  à  Choice  based  Conjoint  (CBC)   2.  Demand  forecasts  à  Market  simulator   3.  Constrained  op=miza=on  tools  à  Search  algorithm  of  op>mal  solu>on   within  market  constraints  
  • 8. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 1.  Choice  based  Conjoint   •  Proven  and  unbiased  research  technique  to  model  consumer  preferences   and  market  heterogeneity   •  Rooted  in  U=lity  Theory  (Von  Neumann–Morgenstern)   •  Preferences  es=ma=on  process  has  evolved  over  =me:     1.  Aggregate  Logit  model  (one  size  fits  all)   2.  Latent  class  (segmenta=on)   3.  Hierarchical  Bayes  (individual  level)   •  Choice  task  resembles  purchase  behavior  process  
  • 9. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 1.  Choice  tasks  within  a  compe==ve  context  
  • 10. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 2.  Market  Simulator:  from  consumer   preferences  to  market  shares,  to  revenue   forecas=ng   •  Volumetric  adjustments   and  calibra=ons   •  Ability  to  test  unlimited   pricing  /por]olio   strategies  and  poten=al   compe==ve  reac=ons   •  In  its  simplest  form,  the   simulator  is  a  “show  of   hands”  from  respondents   given  a  number  of  choice   op=ons   Input  prices     Market  share   output   Revenue   output   Change   por]olio   composi=on  
  • 11. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 3.  Searching  for  the  op=mal:     define  the  feasible  space  first   Total space of possible solutions Constrained space of feasible solutions Sample of solutions within constraints
  • 12. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 3.  Searching  for  the  op=mal:  define                 objec=ve  func=on  &  apply  search  algorithm   Max Revenue (Optimal solution) Revenue Surface
  • 13. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 3.  It’s  not  only  about  finding  the  winning   solu=on,  but  about  the  pa>erns  observed   •  While  the  main  goal  is  to  uncover   the  strategy  that  maximizes   revenue,  ask  yourself:   •  What  makes  it  the  op=mal  solu=on?   •  Are  there  alternate  strategies  with   different  tradeoffs  yielding  posi=ve   results?   •  In  this  example:   •  80,000  scenarios  generated   •  40%  yield  gains  in  both  revenue  and   share.  Cluster  analysis  is  used  to   further  group  and  interpret   Focus  on   upper  right   quadrant   Max  Rev  gain  =  8%  
  • 14. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Some  RO  strategies   1.  Maximize  volume  share  profitably    (capping  revenue  loss)    Balanced  “investment”  strategy  to  grow  customer  base   2.  Maximize  revenue  while  capping  volume  loss    Ideal  situa=on,  not  always  feasible;  will  depend  on  price  elas=city   3.  Game  theory  strategies:  compe==ve  reac=ons  
  • 15. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Delivering  op=miza=on  results  to  clients   A  few  insights  for  a  successful  deployment:   •  Involve  key  stakeholders  from  different  func=ons  early  in  the  game   •  Plan  accordingly   •  Kick-­‐off:  constraints  from  every  func=on  are  expressed  and  discussed   •  Delivery:  results  are  discussed  in  a  workshop  style   •  Dynamic  session   •  Create  tools  that  allow  clients  to  interact  with  the  data  (e.g.  ability  to  ac=vate/ deac=vate  constraints,  rank  and  select  scenarios)   •  Don’t  be  afraid  to  show  the  “raw”  data;  involve  stakeholders  in  the  analysis   •  As  always,  be  clear  about  the  model’s  assump=ons  and  limita=ons  
  • 16. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 Q & A Andrew  Jeavons   Survey  Analy=cs   Juan  Andrés  Tello     SKIM  
  • 17. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 contact us or follow us online! SKIM | Consumer Goods Juan Andrés Tello | Director US j.tello@skimgroup.com | +1 201 963 8430 twi>er.com/   skimgroup   facebook.com/   skimgroup   linkedin.com/   company/skim   youtube.com/   skimvideos   skimgroup.com  
  • 18. Juan Andrés Tello, SKIM, US NewMR Advanced Quant Techniques, July 14, 2011 contact us or follow us online! SKIM | Locations New York, USA Juan Andrés Tello j.tello@skimgroup.com +1 201 963 8430 Rotterdam, NL Mini Kalivianakis m.kalivianakis@skimgroup.com +31 10 282 3535 twi>er.com/   skimgroup   facebook.com/   skimgroup   linkedin.com/   company/skim   youtube.com/   skimvideos   skimgroup.com   Geneva, Switzerland Vicky Nef v.nef@skimgroup.com +41 22 747 7519 London, UK Debora Corfield d.corfield@skimgroup.com +44 203 178 6910