munich, lmu – big data conference – march 13, 2015 · sales person said, “it makes us try to...

Big Data strategy beyond technology

Munich, LMU – Big Data Conference – March 13, 2015

CONFIDENTIAL AND PROPRIETARYAny use of this material without specific permission of McKinsey & Company is strictly prohibited

Big Data

7.2 billion

140 million

48 hours

1 billion

Contact to 350 universities in 35 countries

to recruit data analysts

page views per day

active users

of videos uploaded per minute

tweets every 72 hours

35%

Facebook decreased degree of separation

of all pictures taken are posted on Facebook

from 6 to 4

2

Big Data is only as relevant for a company as the business problem that it can solve

Big Data is about

getting insights

to solve high impact

business problems

out of data

Big Data projectsare primarily business initiatives andonly secondary technology efforts

3

PredictionInsights from historic datateach us what is likely tohappen

Example: Telecom provider"next product to buy"

MonitoringWe see what is happeningnow and can deriveimplications/insights

Example: Sentiment analysis of Twitter datafor Box office results

Backward looking Present Forward looking

Data MiningDo we find unusual„outliers“?

Example:Misuse of drugs

Evaluation We know we do thingsdifferently, but: is therean effect?

Example:

Cancer Center quality

Big Data use cases can be categorized by their "backward" or "forward" looking nature

Data Data Data

Analytics Analytics

Process

Increasing level of automation

Automated decision makingEstablish a „no touch“ backoffice process

Example: Automated handlingof mortage applications

Example Retail: What can they do with this?

How?

1 Created an algorithm to identify about 25 products that, when analyzed together, assign each shopper a “pregnancy prediction” score

Estimate her due date to within a small window, so Target could send coupons timed to very specific stages of her pregnancy

2

Use coupons, advertising and direct mail to entice those women or their husbands to visit Target and buy baby-related products

3

Then apply cue-routine-reward calculators to start pushing them to buy groceries, bathing suits, toys and clothing, as well

4

Identify pregnant women early to get them into the store and change shopping behavior

Example Retail: What customer data does Target have access to?

▪ Individual purchases

▪ Basket size

▪ What you looked at online

▪ Ethnicity

▪ Job history

▪ Magazines you read

▪ Bankruptcy or divorces

▪ Year you bought (or lost) your house

▪ Where you went to college

▪ What kinds of topics you talk about online

▪ Whether you prefer certain brands of coffee, paper towels, cereal or applesauce

▪ Political leanings

▪ Reading habits

▪ Charitable giving

▪ Number of cars you own

Purchase information Target can buy data about your

Demographic information including

▪ Age

▪ Marital status

▪ Kids?

▪ Which part of town you live in

▪ How long it takes you to drive to the store

▪ Estimated salary

▪ Whether you’ve moved recently

▪ Credit cards you carry in your wallet

▪ Web sites you visit

SOURCE: Press reports

Example Telecoms: The NPTB recommendation engine ranks all relevant products down to the VAS level for each customer

Customer

Product

Basic Mobile plan

iPhone mobile plan

Music VAS1 AutoRoam

IPTV Sports Package

IPTV News Package

Broadband

12% Owns Owns 87% Owns39% Owns

Owns 89% 12% 64% 97%22% 10%

15% Owns 95% 21% 31% 32% Owns

Product Probability

IPTV News Package 87%

Broadband 97%

Music VAS 95%

Recommendation – Customer View

Customer Probability

95%

39%

22%

Recommendation – Music VAS

1 VAS = value added service

Example Telecoms: “Can it really be this good…help me figure out how to operationalize this” – CEO of telco

Overview Results

Retail shop

▪ Partnered with sales staff at store to recommend products based on NPTB engine

▪ Collected feedback and uptake rates

▪ 15.3% conversion rate

▪ Salesman said, “I will definitelymake full use of it.” [referring to the NPTB engine]

Inbound telesales

▪ Partnered with sales staff at call centre to recommend products based on NPTB algorithm

▪ Collected feedback and uptake rates

▪ 13% conversion rate for up/x-sell

▪ Sales person said, “It makes us try to cross sell and up sell more”[referring to the NPTB engine]

Out-bound telesales

▪ Recommended Chinese or Complete Pack to customers selected by NPTB algorithm

▪ Collected uptake rates

▪ 238% greater sales conversion than control sample for Complete Pack

▪ 100% greater sales conversion than control sample for Chinese Pack

E-mail DM

▪ E-mailed BBOM offer to customers selected by NPTB algorithm and client heuristics


▪ 154% greater sales conversion than client selection

▪ Uncovered white space by selling to stand alone mobile customers

Out-bound SMS

▪ SMSed VAS offers for VAS 1 and VAS 2 to customers selected by NPTB engine, client heuristics, and client logreg model


▪ 95% greater sales conversion than client selection for VAS 1

▪ 33% greater sales conversion than client selection for VAS 2

AIB reduced cost of mortgage application process by 70%through digitalization

70% reduced cost for mortgage application

99% reduction in average processing time for applications (down to 5 minutes)

75% service take-up rate regarding customer satisfaction

80% of loan applications are fully handled by program; decision quality greater than for manual processing

2001 - 11 CAGR

Peer group 5%

12%

Companies that use big data effectively are more successful than their competitors

Pilot with basketdata from 14 stores

First statement mailing

Revise price strategy to price sensitive customers

GBP 1 billion given back to customers

Promotions cut by 1/4 –customer perception

improves

Assortmenttool

"Value index" integratesprice and promotion indices

Macro space optimization

Promotions data

Clubcardlaunched

Tesco baby clublaunch, followed by wine club

Tesco personal finance launch

Tesco.com launched

Roll out to international markets

Price sensitivitysegmentation

100% data

10% dataLifestyles

segmentation

Identify "gaps" in baskets

Finest launched

Standard reporting of customer insight KPIs and analytics

Clubcardrelaunchedwith key fobs

Coupons@Till

All rangereviews use

substitutability analysis

All promotions post-evaluated

Shopper panel launch

Pioneers like Tesco …Net profit (indexed)

1991 1995 2000 2005 2010

600

200

300

400

500

700

100

800

McKinsey’s perspective on an insight-driven business

Insight value chain Key principles

Strategy

Decision backwards

Build the capability by starting with the business decisions you want to drive and working backwards

Step-by-step

Focus on specific topics and set each element in place – a chain is only as strong as its weakest link

Change journey

Implementing data driven processes is against the DNA of many successful companies. Mastering change – on multiple levels – is at least as important as technology

Stra

tegy

Each insight value chain can be defined across industry application areas ...

Specific use cases per application area to be defined to detail the insight journey

Application areasExamples

Retail

PricePro-

motionRanging MediaSpace

Purchas-ing

Avail-ability

InsightFoundation

Insurance

Product mgmt.

Marketing & Sales

Policy ad-ministration

Claims mgmt.

Support functions

Asset mgmt.

InsightFoundation

Insight value chain

Healthcare

InsightFoundation

Contracting.Revenue collection

Claims/ Fraud

Provider steering

EvaluationPatient

steering.

… and translated into an insight heat map ...2 3 4 51

Basic Insight-driven

ILLUSTRATIVE HEALTHCARE PAYOR EXAMPLE

ContractingRevenue Collection

Claims/ Fraud

InsightFoundation

Application areas

Churn MgmtProvider Steering

EvaluationPatient

Steering

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Use case

Data

Analytics

Technology

People

Process

Decision

Insig

ht



Data

Analytics

Technology

People

Process

Insig

ht

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering


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TechnologyOverall, created a good foundation with common ERP and single DWH; can be expanded to other areas, where not yet leveraged

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Data

Analytics

Technology

People

Process

Insig

ht

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering


AnalyticsOverall rated weak, since with rare exceptions application of “rule based expert systems” instead of advanced analytics

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Data

Analytics

Technology

People

Process

Insig

ht

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering


Churn-ManagementProven process in place, executed with high diligence. However: focus not on value-adding segments (e.g., preventing high margin individuals and their families from churning)

4 34 2 4 2 2 2

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Data

Analytics

Technology

People

Process

Insig

ht

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering


Contracting and EvaluationWeakest overall rating, due to lack of analytics and people skill in applying it, e.g., in hospital price negotiations; starting in “Contracting” more promising, as established process already in place

4 5 2

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Data

Analytics

Technology

People

Process

Insig

ht

1

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering


PeopleVery large variation in people skills. In the focus areas of last 18 months good skills were built; biggest lever for sustainability

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Data

Analytics

Technology

People

Process


Basic Insight-drivenIn

sig

ht

Expected impact:

Improving “Contracting”

Analytics for Churn Management

Additional potential in claims

EUR 500 million

-40% Churn

EUR 150 million

Use case

Decision


Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering



Claims/ Fraud

InsightFoundation

Application areas


EvaluationPatient

Steering

4 5 2

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4 3

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Use case

Data

Analytics

Technology

People

Process

Decision

Insig

ht

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… which is the starting point for the Insight Journey

Sequencing of initiatives is

driven by ease of implemen-

tation and financial impact

1

1

2

2

3

4

4

5

5

3

2013 2014 2015 20172016

2013 2014 2015 20172016

Most common challenge is the interface between people and software

Put in place a Build insightIntegrate insight

dedicated insight capabilities and trainteam into business

team with strong cross-functionallyunits through

analytical talent throughout insightformal processes

journey

The category accelerator: build capabilities to leverage insights through "navigated self-discovery" in a dedicated facility

Plan integration

▪ Category strategy

▪ Customer decision trees

▪ Private label strategy

▪ Promotion optimizer

▪ Visual merchandising

▪ Optimal store clustering

▪ Inventory management

▪ COGS playbook

▪ Merchandising strategy

▪ Supply Chain Management

▪ Negotiation playbook

SKU health check

Node

NumberNode Name SKU Ref # SKU Description

# Name # Description

1 7¼ General Purpose (Value)1983280 SAW CIRC.7 1/4"13AM SK548001

1 7¼ General Purpose (Value)1197016 SAW CIRCULAR 7 1/4" 15AM

1 7¼ General Purpose (Value)00275540 SAW CIRCULAR SAW 7 1/4" 15AM

2 7¼ General Purpose (Premium)00275497 SAW CIRCULAR 7 1/4" 15AM

1 7¼ General Purpose (Value)1983224 SAW CIRC. 7 1/4" 15AM CS10

1 7¼ General Purpose (Value)00275552 SAW CIRCULAR 6 1/2" 18VT(BARE)

2 7¼ General Purpose (Premium)1197142 SAW CIRC. 7 1/4" 15AM 5007FAK

2 7¼ General Purpose (Premium)1197280 SAW CIRC. 71/4+LAMP 5007MG

2 7¼ General Purpose (Premium)8400048 SAW CIRC. 7 1/4" 14AM HCS002

1 7¼ General Purpose (Value)19835530 SAW CIRC.LASER 7 1/4" 15AM

1 7¼ General Purpose (Value)1983247 SAW CIRC. 7 1/4" 15AM CS5

5 6½ General Purpose (Value)19835163 SAW CIRC. 7 1/4" 15AM SHD77

Total Node

5.6 6.0 5 .8 5.5 5.7 6.0 6.0 Prox. Plus

4.8 4.3 4 .5 4.7 4.5 4.5 4.4 B-Box Plus

Is my range profitable? Should I include this product in my core or core plus assorment?Should I include this product in my core or core plus assorment?

Competitor

price tie r

3Net

margin $

m illion

3Net

margin $

rank

3Net

margin %

Growth in

1st margin

%

CO

RE

Clu

ste

r 1

Clu

ste

r 2

Clu

ste

r 3

Re

gio

n 1

Re

gio

n 2

Re

gio

n 3

CORE Clus ter 1 Clus ter 2 Clus ter 3 Region 1 Region 2 Region 3

$0.5 28% 3%

Better $0.06 1 24% 1% 2 1 3 1 3 2 2 Pot. Delete

Best $0.06 2 26% 3% 5 5 6 6 5 2 5 B-Box Core B-Box Plus Prox. Plus Prox. Plus B-Box Plus B-Box Plus B-Box Plus

Best $0.02 9 20% 5% 5 6 5 5 6 6 5 B-Box Core B-Box Plus B-Box Plus B-Box Plus Prox. Plus Prox. Plus B-Box Plus

Better $0.04 5 31% 3% 6 4 7 6 5 8 7 Prox. Core B-Box Plus Prox. Plus Prox. Plus B-Box Plus Prox. Plus Prox. Plus

Best $0.01 11 24% 4% 4 4 5 5 5 4 3 Pot. Delete

Best $0.01 13 23% 5% 5 6 3 3 5 6 6 B-Box Core B-Box Plus B-Box Plus B-Box Plus B-Box Plus Prox. Plus Prox. Plus

Better $0.05 4 33% 3% 5 4 6 4 6 7 6 B-Box Core B-Box Plus B-Box Plus B-Box Plus Prox. Plus Prox. Plus B-Box Plus

Best $0.05 3 36% 5% 6 6 8 6 7 7 7 Prox. Core B-Box Plus Prox. Plus Prox. Plus Prox. Plus Prox. Plus Prox. Plus

Better $0.03 7 39% 3% 5 3 5 6 6 6 5 B-Box Core B-Box Plus Prox. Plus Prox. Plus B-Box Plus

Better $0.01 12 29% 5 5 5 6 5 5 3 Pot. Delete B-Box Plus Prox. Plus

Best $0.02 8 29% 5 6 6 5 5 5 7 B-Box Core B-Box Plus B-Box Plus B-Box Plus Prox. Plus

Best $0.01 14 23% 5 4 6 4 5 4 6 Pot. Delete B-Box Plus B-Box Plus B-Box Plus

Is it priced well? Is it driving profit and/or profit growth? What is the SKU score? Should I include this in CORE PLUS?

Score

Average o f decile performanceRecommendationProfit

Profit

growth

GMROI tree

From-To category strategy

Standardized category plansCoffee - Executive Summary Category Role: Core

From To

Customer

* Under- indexing in Budget customers compared to the Supermarket as a whole

* Two clusters (Café Culture and Instant), with no range differences and minimal planogram flexing

Customer

*Winning in all customer segments including Budget Customers, ENGAGING customers!

* Moving to 3 clusters (Café Culture, Everyday, Value) with tailored range and space allocation

* Targeted customer offers developed e.g.. 20 Greek Stores focusing on "Turkish & Greek Coffee"

Own Brand

* Own Brand Penetration 2.40% (Aztec 03/07/2011)

* Homebrand 1.10% (-8.1% on LY)

* Select 1.30% (+86.8% on LY)

* Macro n/a

Own Brand

* Own Brand penetration 4% by end of FY12 focussing on Macro and Select growth

* Homebrand: target 1.25%

* Select: target 2.50%

* Macro: target 0.25% FY12 (Launch 3 skus Q3)

Financials

* Market share of 43.50% by end of FY12

* F2012 - Sales +7.19% on LY ($398,597,284) Budget, inflation anticipated at 3-4% due to price increases

* Developing a plan to invest in key pack groups to drive sales and make budget

* F2012 - Profit +12.47% on LY, Rate 24.35%

Value

* Reduce Churners and remove Losers, reinvest funds in more effective promotions

* Reduce number of promotions by 20% and move towards FCP in 3 nodes and additional 'loser' lines

* Only invest in All Star and Traffic Builder activity

* Tailored promotional program for Premium and Turkish/Greek stores

Value

* 2% All Stars, 0.43% Traffic Builder, 45.72% GP Builders, 45.2% Churners, 16.5% Losers

* Knock-down has been limited to Nescafe Blend 43 150g (coming off in September)

* 55% sales on promotion, $51.2 Deal subsidy, $17.4 unfunded

*Promotional program consistent across all stores

Financials

* TY MAT market share is 43.08% (Aztec 03/07/2011)

* F2011 - Sales +1.42% on LY ($371,843,686)

* F2011 - Units +0.76% on LY

* F2011 - Profit +22.40% on LY, Rate 24.95%

* F2011 - Swell Investment $747K

COGS

* $18.6m eCOGS opportunity identified

* Nestle Represent 70% of the total eCOGS opportunity

* High shrinkage in Roast and Ground due to damage at the store level

COGS

* 3% ($8.6m) COGS reduction by end of FY12

* Work with Vendors to adjust packaging to reduce shrinkage to 0.7% and re-coup losses in Roast and

Ground

* Negotiate waste and markdown agreements with Vendors to capture Shrink losses

Merchandising

* Range tailored to 2 Clusters: Cafe Culture and Instant

* 684 stores have base (7 bays) or above layouts

* SFP not currently used in the category

- 145/229 in SFP's in base layout 63%

- 63/229 being used as 2 ctn fill 27%

Merchandising

* Range is tailored to 3 Clusters, Cafe Culture, Everyday and Value

* Specific Turkish/Greek stores offered a tailored range

* Specific small range products remain to tailor to communities (Byron Bay)

* Category dominated by SFP

Range

* Recent significant range reduction in 2010/11

* Trial Range Reduction of 20% in Roast and Ground Café Cluster: sales -5.30%, volume -4.50% and profit -6.4%

on control stores (breadth and depth of range important to these customers)

Competition

* Coles: Maintain competitiveness particularly through promotions (enforce clash policy)

* Aldi: HB used to match everyday, with Select Knockdown to provide improved perception

* Costco: Value Cluster to focus on Bulk packs and Value

Range

* Medium Term sku count to remain stable, due to recent reductions in range, however greater range

variation between clusters, maintain overall SKU count, with reductions by cluster.

* Clearly defined price tier strategy in each segment (particularly Roast and Ground)

Competition

* Coles: category is being used as a traffic driver, OB offer growing (but penetration similar to WOW)

* Aldi: maintain a small range which includes Nescafe Blend 43 100g & Gold 80g (local)

* Costco: Moccona 2x400g, Blend43 500g, Illy 250g and range of Kirkland Roast & Ground

Pricing

Competitive ranging Brand-price ladder

…

8 - 10 tailored modules, e.g.,

Loyalty card data Click-stream dataInternal transaction data Sales and profit data Nielsen/IRI/GfK, etc data

MODULES

Analysts act as

navigators in the category accelerator

10Ranging1 2

Key points and summary

Always start with the use case for Big Data from a business perspective

Implement Big Data "business back", not "technology forward"

Big Data is a business initiative

More data is better

Start with internal data – value is in linking different sources

Learn to accept messiness

Big Data@scale requires automation

Transformational impact from Big Data by enabling data-driven processes

However, not all use cases need automation across all alyers

Don’t be afraid of analytics

Nobel prize level analytics is not required

There are very innovative ways of sourcing good solutions

thank you!

[email protected]

@sbiesdorf

From ToSmall,i.e., averages andsamples are"good enough"

Comprehensive,i.e.,all available data is analyzed on the most granular level

Data volumes

Exact,i.e., zero-fault tolerance of input data

Messy,i.e., incorrect single data points are acceptable, since overall number of data points is huge

Data quality

Causality,i.e., cause-effectconfirmend by data

Correlation,i.e., significant correlation between input-output data good enough, even if causality is not understood

Management

conclusion

From ToSmall ComprehensiveData volumes

Exact MessyData quality

Causality CorrelationManagement

conclusion

Why?

munich, lmu – big data conference – march 13, 2015 · sales person said, “it makes us try to...

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