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MARKETING AND BIG DATA 
Big Data, Better 
Decisions 
Tim McGuire, McKinsey & Co. 
NCDM Conference, Florida, Dec. 3 – 5, 2012
Big Data and advanced analytics
295 exabytes 
If you stacked a pile of CD-ROMs 
on top of one another 
until you’d reached the current 
global storage capacity 
for digital information …
… it would stretch 
80,000 km 
beyond the moon
Every hour, 
enough information is consumed 
by internet traffic to fill 
7 million DVDs
84o,56o m – side by side, 
they’d scale 
Mount Everest 
95 times
The world’s 
5oo,ooo+ 
data centers are large enough ...
… to fill 5,955 football fields 
The world’s 
5oo,ooo+ 
data centers are large enough ...
The Big Data market 
is set to increase from 
USD 3,2oo,ooo,ooo 
to 
USD 16,9oo,ooo,ooo 
2010 2015
By 2020 one third of all 
data will be stored or have 
passed through the cloud 
and we will have created 
35,ooo,ooo,ooo,ooo,ooo,ooo,ooo 
bytes of data
10x more servers 
75x more files 
50x more data 
By 2020 
IT departments 
will be looking after
We have produced more data 
in the last two years … 
3,5oo,ooo,ooo,ooo,ooo,ooo,ooo 
2010 2012
Bang 
… than in all of history 
prior to that
Advanced Analytics
1986 
Data volume 
3 exabytes of data 
Data variety 
99% analog 
Computing capacity 
0.001 bn of MIPS
2007 
Data volume 
295 exabytes of data 
Data variety 
94% digital 
Computing capacity 
6,380 bn of MIPS
Sophisticated analytics 
Predictive modeling 
Neural networks 
Social network mapping 
Visualization tools
Tremendous opportunities 
Creating transparency 
Enabling experimentation 
Segmenting populations 
Replacing/supporting human 
decision making 
Innovating new business 
models, products, and 
services
Capturing value 
from Advanced Analytics … 
Predictive and 
optimization 
models 
Big data 
Organizational 
transformation 
… is based on 
3 guiding principles 
Decision backwards 
Step by step 
Test and learn
example Direct mailing campaign
The traditional approach 
Direct mailing based on 
generic segmentation
Regression + clustering 
based on transaction 
history 
Predictive modeling based 
on customer preferences 
> 100 campaigns per year 
> 2 million customers 
> 10 million transactions
Customer value modeling 
Intelligent segmentation 
for direct mail campaigns
Average profit 
from promoted products 
Average profit 
from whole basket 
+ 288% 
Impact 
+ 147% 
Control group 
Test group 
EUR 0.31 
EUR 0.79 
EUR 0.08 
EUR 0.32
example Next product to buy
80 million consumers 
100 million transactions
Multivariate statistics 
Association rule analysis
Are being offered the most 
probable recommendation 
Get recommendations 
to generate maximum margin 
Receive recommendations 
from other categories to broaden 
their purchase behavior 
"infrequent shoppers" 
"frequent shoppers" 
"site lovers"
USD 1 billion identified 
USD 300 million already realized within 6 months 
Impact
example Assortment optimization
The traditional approach
The traditional approach 
Generic allocation 
of limited shelf space 
SKUs ranked by sales 
No substitution 
of SKUs considered 
Limited granularity
The Big Data approach
Terabytes of data 
Multi-year transaction data 
Consumer panel data 
Loyalty card data
Advanced statistical 
methods 
Stochastic switching model 
(entropy calculations)
Advanced statistical 
methods 
Stochastic switching model 
(entropy calculations) 
Hierarchical clustering 
(dendograms)
Advanced statistical 
methods 
Multidimensional scaling 
(consumer decision tree) 
Market 
Segment 1 Segment 2 
Brand A Brand B 
Type 1 Type 2 Type 1 Type 2 
Flavor 1 Flavor 2 
Size 1 Size 2 
▪ Actual behavior 
(switching, walk rates) 
▪ Statistically relevant 
▪ Optimal SKU selection 
per store 
▪ Predictive sales forecast
Revenue growth more than 
double the category growth 
in the market 
Impact
example Optimizing branch networks
Segment customers 
based on channel 
behaviour and 
willingness to travel 
Define branch con-cepts 
(e.g., advice 
branch) in line with 
multi-channel strategy 
0.08 
FTE 
Determine required 
capacity by customer 
and plot capacity within 
micromarket using geo-marketing 
methods
0.08 
FTE 
9.2 
9.4 
4.3 
14.1 
6.4 
2.4 
Assess footprint risk profile 
and adjust if risks are too high
9.2 
9.4 
4.3 
14.1 
6.4 
2.4 
Optimize locations to set 
up branch for success
Carefully plan and execute 
micromarket transition 

 
Multiple scenarios 
based on 
90 processes 
7 million customers 
1,000 branches
40% cost reduction 
< 1% revenue at risk 
Impact
A new approach
Data 
Models 
Transformation 
Data 
consultancies 
IT software 
vendors 
Management 
consultants
Delivering 
end-to-end 
capabilities 
Data 
Models 
Transformation 
Data Center infrastructure to support 
Tb+ datasets 
Analytics Center of Competence 
with state-of the-art statistics 
Solutions Office (MSO) selects, modifies, builds 
and implements software 
IT – BTO with experience in implementing sustain-able 
solutions in existing IT landscape 
Process – broad expertise in ops, technology, 
merchandizing, etc. 
People – capability building, transformational 
change, organizational design 
Strategy – decades of experience in developing 
and implementing strategies
Additional Reading 
from McKinsey 
Advanced Data 
and Analytics 
Big Data & Advanced Analytics: Success Stories 
from the Front Lines 
http://www.forbes.com/sites/mckinsey/2012/12/03/big-data-advanced- 
analytics-success-stories-from-the-front-lines/ 
Making Advanced Analytics Work For You 
http://cmsoforum.mckinsey.com/article/making-advanced-analytics- 
work-for-you 
The One Tool You Need to Make Big Data Work: 
The Pencil 
http://www.forbes.com/sites/mckinsey/2012/10/09/the-one-tool-you- 
need-to-make-big-data-work-the-pencil/ 
Getting Beyond the Buzz: Is Your Social Media 
Working? 
http://cmsoforum.mckinsey.com/article/getting-beyond-the-buzz- 
is-your-social-media-working 
Simplify Big Data - Or It'll Be Useless for Sales 
http://cmsoforum.mckinsey.com/article/simplify-big-data-or-itll-be- 
useless-for-sales
tim_mcguire@mckinsey.com 
McKinsey Consumer Marketing Analytics Center 
thank you 
Vicky & Tanja inspired by Gerhard Richter

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Big Data and advanced analytics

  • 1. MARKETING AND BIG DATA Big Data, Better Decisions Tim McGuire, McKinsey & Co. NCDM Conference, Florida, Dec. 3 – 5, 2012
  • 3. 295 exabytes If you stacked a pile of CD-ROMs on top of one another until you’d reached the current global storage capacity for digital information …
  • 4. … it would stretch 80,000 km beyond the moon
  • 5. Every hour, enough information is consumed by internet traffic to fill 7 million DVDs
  • 6. 84o,56o m – side by side, they’d scale Mount Everest 95 times
  • 7. The world’s 5oo,ooo+ data centers are large enough ...
  • 8. … to fill 5,955 football fields The world’s 5oo,ooo+ data centers are large enough ...
  • 9. The Big Data market is set to increase from USD 3,2oo,ooo,ooo to USD 16,9oo,ooo,ooo 2010 2015
  • 10. By 2020 one third of all data will be stored or have passed through the cloud and we will have created 35,ooo,ooo,ooo,ooo,ooo,ooo,ooo bytes of data
  • 11. 10x more servers 75x more files 50x more data By 2020 IT departments will be looking after
  • 12. We have produced more data in the last two years … 3,5oo,ooo,ooo,ooo,ooo,ooo,ooo 2010 2012
  • 13. Bang … than in all of history prior to that
  • 15. 1986 Data volume 3 exabytes of data Data variety 99% analog Computing capacity 0.001 bn of MIPS
  • 16. 2007 Data volume 295 exabytes of data Data variety 94% digital Computing capacity 6,380 bn of MIPS
  • 17. Sophisticated analytics Predictive modeling Neural networks Social network mapping Visualization tools
  • 18. Tremendous opportunities Creating transparency Enabling experimentation Segmenting populations Replacing/supporting human decision making Innovating new business models, products, and services
  • 19. Capturing value from Advanced Analytics … Predictive and optimization models Big data Organizational transformation … is based on 3 guiding principles Decision backwards Step by step Test and learn
  • 21. The traditional approach Direct mailing based on generic segmentation
  • 22. Regression + clustering based on transaction history Predictive modeling based on customer preferences > 100 campaigns per year > 2 million customers > 10 million transactions
  • 23. Customer value modeling Intelligent segmentation for direct mail campaigns
  • 24. Average profit from promoted products Average profit from whole basket + 288% Impact + 147% Control group Test group EUR 0.31 EUR 0.79 EUR 0.08 EUR 0.32
  • 26. 80 million consumers 100 million transactions
  • 28. Are being offered the most probable recommendation Get recommendations to generate maximum margin Receive recommendations from other categories to broaden their purchase behavior "infrequent shoppers" "frequent shoppers" "site lovers"
  • 29. USD 1 billion identified USD 300 million already realized within 6 months Impact
  • 32. The traditional approach Generic allocation of limited shelf space SKUs ranked by sales No substitution of SKUs considered Limited granularity
  • 33. The Big Data approach
  • 34. Terabytes of data Multi-year transaction data Consumer panel data Loyalty card data
  • 35. Advanced statistical methods Stochastic switching model (entropy calculations)
  • 36. Advanced statistical methods Stochastic switching model (entropy calculations) Hierarchical clustering (dendograms)
  • 37. Advanced statistical methods Multidimensional scaling (consumer decision tree) Market Segment 1 Segment 2 Brand A Brand B Type 1 Type 2 Type 1 Type 2 Flavor 1 Flavor 2 Size 1 Size 2 ▪ Actual behavior (switching, walk rates) ▪ Statistically relevant ▪ Optimal SKU selection per store ▪ Predictive sales forecast
  • 38. Revenue growth more than double the category growth in the market Impact
  • 40. Segment customers based on channel behaviour and willingness to travel Define branch con-cepts (e.g., advice branch) in line with multi-channel strategy 0.08 FTE Determine required capacity by customer and plot capacity within micromarket using geo-marketing methods
  • 41. 0.08 FTE 9.2 9.4 4.3 14.1 6.4 2.4 Assess footprint risk profile and adjust if risks are too high
  • 42. 9.2 9.4 4.3 14.1 6.4 2.4 Optimize locations to set up branch for success
  • 43. Carefully plan and execute micromarket transition 
  • 44.  Multiple scenarios based on 90 processes 7 million customers 1,000 branches
  • 45. 40% cost reduction < 1% revenue at risk Impact
  • 47. Data Models Transformation Data consultancies IT software vendors Management consultants
  • 48. Delivering end-to-end capabilities Data Models Transformation Data Center infrastructure to support Tb+ datasets Analytics Center of Competence with state-of the-art statistics Solutions Office (MSO) selects, modifies, builds and implements software IT – BTO with experience in implementing sustain-able solutions in existing IT landscape Process – broad expertise in ops, technology, merchandizing, etc. People – capability building, transformational change, organizational design Strategy – decades of experience in developing and implementing strategies
  • 49. Additional Reading from McKinsey Advanced Data and Analytics Big Data & Advanced Analytics: Success Stories from the Front Lines http://www.forbes.com/sites/mckinsey/2012/12/03/big-data-advanced- analytics-success-stories-from-the-front-lines/ Making Advanced Analytics Work For You http://cmsoforum.mckinsey.com/article/making-advanced-analytics- work-for-you The One Tool You Need to Make Big Data Work: The Pencil http://www.forbes.com/sites/mckinsey/2012/10/09/the-one-tool-you- need-to-make-big-data-work-the-pencil/ Getting Beyond the Buzz: Is Your Social Media Working? http://cmsoforum.mckinsey.com/article/getting-beyond-the-buzz- is-your-social-media-working Simplify Big Data - Or It'll Be Useless for Sales http://cmsoforum.mckinsey.com/article/simplify-big-data-or-itll-be- useless-for-sales
  • 50. tim_mcguire@mckinsey.com McKinsey Consumer Marketing Analytics Center thank you Vicky & Tanja inspired by Gerhard Richter

Editor's Notes

  1. Tesco have undergone a 20 year journey contributing to a increase in net profit Becoming insights-driven is a multiyear journeyThe journey is phased to deliver value at early stageToday &quot;100% data&quot;, production of insights, continuous view across all aspects of the business&quot;[insight] is used all over the business, not just in marketing but in the property programme, decisions on which product ranges to stock and in under-standing how different types of customers are responding to what we do&quot;– Caroline Bradley, Marketing Director
  2. Big Data: Creatively source internal and external data / Upgrade IT architecture and infrastructure for easy merging of data (Distinguish irrelevant and relevant data)Optimization Models: Focus on the biggest drivers of performance / Build models that balance complexity with ease of use (Translate data into insights)Organizational Transformation: Create simple, understandable tools for people on the front lines / Update processes and develop capabilities to enable tool use (Turn insights into impactful frontline actions)Definition of insightThe discovery of something fundamental about a consumer’s need, which business strategies and tactics can leverage to create customer value and competitive advantage3 guidingprinciples: - Decisionbackwards- Stepbystep- Test andlearnBuild the capability by starting with the business decisions you want to drive and working backwardYou need each element in place … a chain is only as strong as its weakest link“Test and learn” moves from data to decision, and from decision back to the data with which to measure the outcomeBig Data is only as relevant for a company as the business problem that it can solveBig Data projectsareprimarilybusiness initiatives andonlysecondarilytechnologyefforts
  3. Client hadonly a rudimentarysegmentationofitscustomers, based on, forexample, genderorregion, despitesignificantdataavailabilityfromloyaltycard
  4. WIE: Kunden klueger segmentiert und selektiert an Hand von Kaufverhalten, Verbindung einer transaktions- und kundenwertbasierten mit einer einstellungsorientierten Segmentierung für noch vollständigere Kundenprofile, Selektion von Kunden mit der höchsten Kaufwahrscheinlichkeit für bestimmte Produkte und Kampagnen, Vorhersage und Ansprache von Kunden mit hohem AbwanderungsrisikoWARUM ADVANCEDANALYTICSAND BIG DATA: Über 2 Mill Kunden, mit über 10 Mill Transaktionen, über 100 Kampagnen pro Jahr, analysiert mit Hilfe relationaler Datenbanken (MS SQL Server, IBM DB/2) und Statistiksoftware (SAS, SPSS), einige der statistischen Methoden die wir verwendet haben: Regressionsanalyse, Clustering, Multidimensional Scaling, Kundenwert- und NPTB-Modelle
  5. WIE: Kunden klueger segmentiert und selektiert an Hand von Kaufverhalten, Verbindung einer transaktions- und kundenwertbasierten mit einer einstellungsorientierten Segmentierung für noch vollständigere Kundenprofile, Selektion von Kunden mit der höchsten Kaufwahrscheinlichkeit für bestimmte Produkte und Kampagnen, Vorhersage und Ansprache von Kunden mit hohem AbwanderungsrisikoWARUM ADVANCEDANALYTICSAND BIG DATA: Über 2 Mill Kunden, mit über 10 Mill Transaktionen, über 100 Kampagnen pro Jahr, analysiert mit Hilfe relationaler Datenbanken (MS SQL Server, IBM DB/2) und Statistiksoftware (SAS, SPSS), einige der statistischen Methoden die wir verwendet haben: Regressionsanalyse, Clustering, Multidimensional Scaling, Kundenwert- und NPTB-Modelle
  6. IMPACT: 10 - 15% Wachstum im Umsatz von Direct Mailing (zB durch 20% erhöhte Conversions und 25% größere Warenkörbe)TRANSFORMATIONTrained client staff from day 1Restructured CLM organizationHired CRM analysts and managerCoached analytics team to segment customers and build predictive modelsin 1 roomFully integrated *McK&amp;client( team with joint project plan and task listMcK Team used client IT systems in order to ease the implementation Rollout into additional countries done independently by client team members (McKinsey only supports)
  7. WHATThe number of consumer products is increasing every day. The shelf space of retailers is limited.
  8. Traditional approach would use excel sales ranking but this would not take into account substitution and uniqueness of specific skus.We found a new way to select those products that are relevant for the customer.
  9. Traditional approach would use excel sales ranking but this would not take into account substitution and uniqueness of specific skus.We found a new way to select those products that are relevant for the customer.
  10. Our methodology leverages advanced statistical methods like dendograms and entropy models to combine and analyze different data sets (like shopping baskets, loyalty card data, consumer panel data,...)
  11. Our methodology leverages advanced statistical methods like dendograms and entropy models to combine and analyze different data sets (like shopping baskets, loyalty card data, consumer panel data,...)
  12. McKinsey applies a methodology that allows us to calculate how a shoppers looks at the shelf and visualizes the decision making journey in a &apos;customer decision tree&apos; (eg brand first, price level second, pack size third, functionality fourth ...). By this we can create the &apos;optimal product selection&apos; and even calculate what happens if we take a product out of the shelf - how many customers walk away and how many switch to another SKU At a store level Ability to quantify business impact (based on expected volume transfers) for retailer (at store level) and manufacturerNOTE: We combine Art and Science, not just science, so there is still some Category expertise to bring to the mix for making the shelf looking good (e.g., with blocking of colors)
  13. WAS: Analyse und Optimierung von Retail-Netzwerken, insbesondere Retail-BankenWIE: Basierend auf aktuellen Kundenstamm, demographische Faktoren und Wettbewerbsintensität wird für jeden einzelnen Store des Klienten ein optimales Store-Format zugewiesen. Interdependenzen und Kannibalisierung zwischen den Stores werden in die Optimierung einbezogen. Je nach Fokus des Klienten kann der Schwerpunkt auf Netzwerkreduktion (weniger Standorte), White-Spot Analyse (neue Standorte) oder Potentialoptimierung (gleiche Standorte besser nutzen) gelegt werden.WARUM ADVANCEDANALYTICSAND BIG DATA: In Klientensituation mit Fokus auf Potentialoptimierung Analyse von 7 Millionen Kunden und mehr als 1000 Stores. Mittels Verfahren aus der ComputationalGeometry sowie Integer Programming Techniken wurde ein Netzwerk mit mehr als 7 Milliarden potentiellen Kanten analysiert und mit Hilfe von Parallelisierungs-Techniken auf sechs Computern innerhalb von zwei Wochen berechnet.
  14. WAS: Analyse und Optimierung von Retail-Netzwerken, insbesondere Retail-BankenWIE: Basierend auf aktuellen Kundenstamm, demographische Faktoren und Wettbewerbsintensität wird für jeden einzelnen Store des Klienten ein optimales Store-Format zugewiesen. Interdependenzen und Kannibalisierung zwischen den Stores werden in die Optimierung einbezogen. Je nach Fokus des Klienten kann der Schwerpunkt auf Netzwerkreduktion (weniger Standorte), White-Spot Analyse (neue Standorte) oder Potentialoptimierung (gleiche Standorte besser nutzen) gelegt werden.WARUM ADVANCEDANALYTICSAND BIG DATA: In Klientensituation mit Fokus auf Potentialoptimierung Analyse von 7 Millionen Kunden und mehr als 1000 Stores. Mittels Verfahren aus der ComputationalGeometry sowie Integer Programming Techniken wurde ein Netzwerk mit mehr als 7 Milliarden potentiellen Kanten analysiert und mit Hilfe von Parallelisierungs-Techniken auf sechs Computern innerhalb von zwei Wochen berechnet.
  15. WAS: Analyse und Optimierung von Retail-Netzwerken, insbesondere Retail-BankenWIE: Basierend auf aktuellen Kundenstamm, demographische Faktoren und Wettbewerbsintensität wird für jeden einzelnen Store des Klienten ein optimales Store-Format zugewiesen. Interdependenzen und Kannibalisierung zwischen den Stores werden in die Optimierung einbezogen. Je nach Fokus des Klienten kann der Schwerpunkt auf Netzwerkreduktion (weniger Standorte), White-Spot Analyse (neue Standorte) oder Potentialoptimierung (gleiche Standorte besser nutzen) gelegt werden.WARUM ADVANCEDANALYTICSAND BIG DATA: In Klientensituation mit Fokus auf Potentialoptimierung Analyse von 7 Millionen Kunden und mehr als 1000 Stores. Mittels Verfahren aus der ComputationalGeometry sowie Integer Programming Techniken wurde ein Netzwerk mit mehr als 7 Milliarden potentiellen Kanten analysiert und mit Hilfe von Parallelisierungs-Techniken auf sechs Computern innerhalb von zwei Wochen berechnet.
  16. WAS: Analyse und Optimierung von Retail-Netzwerken, insbesondere Retail-BankenWIE: Basierend auf aktuellen Kundenstamm, demographische Faktoren und Wettbewerbsintensität wird für jeden einzelnen Store des Klienten ein optimales Store-Format zugewiesen. Interdependenzen und Kannibalisierung zwischen den Stores werden in die Optimierung einbezogen. Je nach Fokus des Klienten kann der Schwerpunkt auf Netzwerkreduktion (weniger Standorte), White-Spot Analyse (neue Standorte) oder Potentialoptimierung (gleiche Standorte besser nutzen) gelegt werden.WARUM ADVANCEDANALYTICSAND BIG DATA: In Klientensituation mit Fokus auf Potentialoptimierung Analyse von 7 Millionen Kunden und mehr als 1000 Stores. Mittels Verfahren aus der ComputationalGeometry sowie Integer Programming Techniken wurde ein Netzwerk mit mehr als 7 Milliarden potentiellen Kanten analysiert und mit Hilfe von Parallelisierungs-Techniken auf sechs Computern innerhalb von zwei Wochen berechnet.
  17. WAS: Analyse und Optimierung von Retail-Netzwerken, insbesondere Retail-BankenWIE: Basierend auf aktuellen Kundenstamm, demographische Faktoren und Wettbewerbsintensität wird für jeden einzelnen Store des Klienten ein optimales Store-Format zugewiesen. Interdependenzen und Kannibalisierung zwischen den Stores werden in die Optimierung einbezogen. Je nach Fokus des Klienten kann der Schwerpunkt auf Netzwerkreduktion (weniger Standorte), White-Spot Analyse (neue Standorte) oder Potentialoptimierung (gleiche Standorte besser nutzen) gelegt werden.WARUM ADVANCEDANALYTICSAND BIG DATA: In Klientensituation mit Fokus auf Potentialoptimierung Analyse von 7 Millionen Kunden und mehr als 1000 Stores. Mittels Verfahren aus der ComputationalGeometry sowie Integer Programming Techniken wurde ein Netzwerk mit mehr als 7 Milliarden potentiellen Kanten analysiert und mit Hilfe von Parallelisierungs-Techniken auf sechs Computern innerhalb von zwei Wochen berechnet.
  18. Only McKinsey advancedanalyticscandeliveracrossentirevaluechain
  19. …andhired a broadrangeofanalyticalexpertsfromcompanies such asIpsos, Amazon, AT&amp;T, dunnhumby, Sprint, Citibank, eTrade~140 advanced analytics practitioners worldwideSecure data centers opening soon in AtlantaOffshoring capability in IndiaMulti-terabyte hosting environmentLogical data modelsETL, data cleansing and householdingHeuristics for matching disparate customer / household dataProprietary data sets (e.g., CLM Panel, eConsumer, etc.)
  20. …andhired a broadrangeofanalyticalexpertsfromcompanies such asIpsos, Amazon, AT&amp;T, dunnhumby, Sprint, Citibank, eTrade~140 advanced analytics practitioners worldwideSecure data centers opening soon in AtlantaOffshoring capability in IndiaMulti-terabyte hosting environmentLogical data modelsETL, data cleansing and householdingHeuristics for matching disparate customer / household dataProprietary data sets (e.g., CLM Panel, eConsumer, etc.)