Big Data and advanced analytics

MARKETING AND BIG DATABig Data, BetterDecisionsTim McGuire, McKinsey & Co.NCDM Conference, Florida, Dec. 3 – 5, 2012

If you stacked a pile of CD-ROMson top of one anotheruntil you’d reached the currentglobal storage capacityfor digital information … 295 exabytes

… it would stretch 80,000 km beyond the moon

Every hour,enough information is consumedby internet traffic to fill7 million DVDs

84o,56o m – side by side, they’d scale Mount Everest 95 times

The world’s5oo,ooo+data centers are large enough ...

The world’s5oo,ooo+data centers are large enough ... … to fill 5,955 football fields

to USD 16,9oo,ooo,oooThe Big Data marketis set to increase fromUSD 3,2oo,ooo,ooo 2010 2015

By 2020 one third of alldata will be stored or havepassed through the cloudand we will have created35,ooo,ooo,ooo,ooo,ooo,ooo,ooobytes of data

10x more servers By 2020 IT departments will be looking after75x more files 50x more data

We have produced more datain the last two years …3,5oo,ooo,ooo,ooo,ooo,ooo,ooo2010 2012

Bang… than in all of history prior to that

Advanced Analytics

1986Data volume3 exabytes of dataData variety99% analogComputing capacity0.001 bn of MIPS

2007Data volume295 exabytes of dataData variety94% digitalComputing capacity6,380 bn of MIPS

Sophisticated analyticsPredictive modelingNeural networksSocial network mappingVisualization tools

Tremendous opportunitiesCreating transparencyEnabling experimentationSegmenting populationsReplacing/supporting humandecision makingInnovating new businessmodels, products, andservices

Net profitIndexed675 All promotions post-evaluated All range reviews use substitutability analysis Shopper panel launch "Value index" integrates price and promotion indices Rollout to international markets £1billion given back to customers Macro space optimization Standard reporting of customer insight KPIs and analytics Coupons@Till Promotions data Tesco personal finance launch Clubcard relaunched with key fobs Tesco baby club launch, followed by wine club Promotions cut by 1/4, customer perception improves Revise price strategy to price- sensitive customers Assortment tool 100% data Lifestyles segmentation Price sensitivity segmentation First statement mailing Tesco.com launched 10% data Finest launched Clubcard launched Identify "gaps" in baskets100 Pilot with basket data from 14 stores 1991 1995 2000 2005 2010

Capturing value from Advanced Analytics … Big data … is based on 3 guiding principles Decision backwards Predictive and Step by step optimization Test and learn modelsOrganizationaltransformation

example Direct mailing campaign

The traditional approachDirect mailing based ongeneric segmentation

Regression + clusteringbased on transactionhistoryPredictive modeling basedon customer preferences> 100 campaigns per year> 2 million customers> 10 million transactions

Customer value modelingIntelligent segmentationfor direct mail campaigns

Control group Test group Average profitfrom promoted products EUR 0.08 EUR 0.31 + 288% Impact Average profit from whole basket EUR 0.32 EUR 0.79 + 147%

example Next product to buy

80 million consumers100 million transactions

Multivariate statisticsAssociation rule analysis

"infrequent shoppers" Are being offered the most probable recommendation Get recommendations "frequent shoppers" to generate maximum margin Receive recommendations from other categories to broaden "site lovers" their purchase behavior

USD 1 billion identified Impact USD 300 million already realized within 6 months

example Assortment optimization

The traditional approach

The traditional approachGeneric allocationof limited shelf spaceSKUs ranked by salesNo substitutionof SKUs consideredLimited granularity

The Big Data approach

Terabytes of dataMulti-year transaction dataConsumer panel dataLoyalty card data

Advanced statisticalmethodsStochastic switching model(entropy calculations)

Advanced statisticalmethodsStochastic switching model(entropy calculations)Hierarchical clustering(dendograms)

Market Segment 1 Segment 2 Advanced statistical methods Brand A Brand B Multidimensional scaling (consumer decision tree)Type 1 Type 2 Type 1 Type 2 ▪ Actual behavior (switching, walk rates) Flavor 1 Flavor 2 ▪ Statistically relevant ▪ Optimal SKU selection per store Size 1 Size 2 ▪ Predictive sales forecast

Revenue growth more than Impactdouble the category growth in the market

example Optimizing branch networks

Segment customersbased on channelbehaviour and 0.08 FTEwillingness to travel Determine required capacity by customer and plot capacity within micromarket using geo- marketing methodsDefine branch con-cepts (e.g., advicebranch) in line withmulti-channel strategy

0.08FTE 4.3 14.1 9.2 Assess footprint risk profile 6.4 and adjust if risks are too high 9.4 2.4

4.3 14.1 9.2 Optimize locations to set6.4 up branch for success 9.4 2.4

Carefully plan and execute micromarket transition

Multiple scenarios based on 90 processes 7 million customers 1,000 branches

40% cost reduction Impact< 1% revenue at risk

A new approach

Data Data consultanciesModels IT software vendorsTransformation Management consultants

Data Center infrastructure to supportData Tb+ datasets Analytics Center of Competence DeliveringModels with state-of the-art statistics end-to-end Solutions Office (MSO) selects, modifies, builds and implements software capabilities IT – BTO with experience in implementing sustain- able solutions in existing IT landscape Process – broad expertise in ops, technology, merchandizing, etc.Transformation People – capability building, transformational change, organizational design Strategy – decades of experience in developing and implementing strategies

Big Data & Advanced Analytics: Success Storiesfrom the Front Lineshttp://www.forbes.com/sites/mckinsey/2012/12/03/big-data-advanced-analytics-success-stories-from-the-front-lines/Making Advanced Analytics Work For Youhttp://cmsoforum.mckinsey.com/article/making-advanced-analytics-work-for-you Additional ReadingThe One Tool You Need to Make Big Data Work: from McKinseyThe Pencilhttp://www.forbes.com/sites/mckinsey/2012/10/09/the-one-tool- Advanced Datayou-need-to-make-big-data-work-the-pencil/ and AnalyticsGetting Beyond the Buzz: Is Your Social MediaWorking?http://cmsoforum.mckinsey.com/article/getting-beyond-the-buzz-is-your-social-media-workingSimplify Big Data - Or Itll Be Useless for Saleshttp://cmsoforum.mckinsey.com/article/simplify-big-data-or-itll-be-useless-for-sales

tim_mcguire@mckinsey.comMcKinsey Consumer Marketing Analytics Center thank you Vicky & Tanja inspired by Gerhard Richter

http://www.linkedin.com	21955
https://www.linkedin.com	8260
http://www.scoop.it	1177
http://www.maddyness.com	170
http://www.pridea.sk	162
http://futurecmo.org	116
http://scoop.mheir.com	89
http://huguesrey.wordpress.com	83
https://twitter.com	75
http://athamim.blogspot.co.uk	50
http://www.facebook.com	45
http://pipelinemarketingalliance.blogspot.com	33
https://www.facebook.com	32
http://crowdflower.com	22
http://blog.jonathanfrei.com	9
http://pinterest.com	3
https://www.rebelmouse.com	3
https://content-preview.socialcast.com	2
https://fr.twitter.com	2
http://blackhaturricane.collected.info	2
http://athamim.blogspot.com	2
http://safe.txmblr.com	2
https://content-preview-philips.socialcast.com	2
http://www.diffbot.com&_=1361631289676 HTTP	1
http://www.diffbot.com&_=1361631289487 HTTP	1
http://www.diffbot.com&_=1361898361469 HTTP	1
http://www.americanaraballiance.com	1
http://www.yatedo.fr	1
https://m.facebook.com&_=1363441278924 HTTP	1
http://athamim.blogspot.de	1
http://www.diffbot.com&_=1361898361786 HTTP	1
http://athamim.blogspot.be	1
http://www.diffbot.com&_=1361631274820 HTTP	1
http://www.diffbot.com&_=1361631274554 HTTP	1
http://www.diffbot.com&_=1361589589633 HTTP	1
http://www.diffbot.com&_=1360850219686 HTTP	1
http://www.diffbot.com&_=1360853545493 HTTP	1
http://www.diffbot.com&_=1360853560606 HTTP	1
http://www.diffbot.com&_=1360853576264 HTTP	1
http://www.diffbot.com&_=1360894568798 HTTP	1
http://www.diffbot.com&_=1360894586796 HTTP	1
http://www.diffbot.com&_=1360932981736 HTTP	1
http://www.diffbot.com&_=1360936234388 HTTP	1
http://www.diffbot.com&_=1360947271769 HTTP	1
http://www.diffbot.com&_=1360947281234 HTTP	1
http://www.diffbot.com&_=1360953985856 HTTP	1
http://www.diffbot.com&_=1360955687687 HTTP	1
https://dub002.mail.live.com	1
http://www.diffbot.com&_=1361239000055 HTTP	1
http://www.diffbot.com&_=1361523060122 HTTP	1
http://www.diffbot.com&_=1361589589504 HTTP	1

Big Data and advanced analytics

by McKinsey Chief Marketing & Sales Officer Forum on Dec 19, 2012

Accessibilité

Détails de l'import

Droits d'utilisation

52 Ajouts 32,325

Statistiques

Big Data and advanced analytics Presentation Transcript