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How R Transformed the Analytics Paradigm at Millward BrownJ U A N M A N U E L H E R N Á N D E Z

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help(Millward Brown)W H AT W E D O F O R O U R C L I E N T S

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library(Millward Brown)

For mor information, go to www.millwardbrown.com

40 years collecting consumer attitudinal data - demographics, social, economic, and cultural habits & opinions.

Marketing communications, media, digital and brand equity research. A lot of touch points <- attitudinal and behavioural data

We work with 90% of the world’s leading brands. Categories and consumer profiles <- data of all types

Offices in 56 countries Global/Regional <- data from almost anywhere.

Brand Strategy

Creative Development

Channel Optimization

Brand Performance

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data(BrandZ™)

BrandZ™ is just one example of the type and magnitude of studies carried out by MB around the world.

BrandZ™ is the world's largest brand equity database. Created in 1998 and continually updated. It contains data on brands gathered from interviews with over 150,000 people every year in up to 400 studies around the world.

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MB_2014 <- sum(seq_along(MB))

# T H E J O U R N E Y T O A N E W A N A LY T I C S PA R A D I G M

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library(BrandDynamics)

Analytics originally written in SPSS!

Voltage2.0 Brand Map

Measuring brand equity with BrandDynamics™

PresenceV

olt

age 2

.0

High

High

Strong Equity(Large strong brands)

Declining Equity(Large weaker brands)

Little Equity(Small weaker brands)

Growing Equity(Small strong brands)

LowLow

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ts(BrandDynamics™)

20 years provide a lot of learning - conceptual, analytical, and operational. What, why, where, when, and how to analyse brand equity?

1996 1998

2010

2010

20092003

2003

20051992 2012

Launch of BrandDynamics

Launch of BRANDZ

Launch of the ‘Paw Print’ analysis

Bonding Factor analysis

Launch

of D&A

Meaningfully Different Framework

Development of Voltage2.0

Development of the Brand

Strength Score

Development of the Value Driver workshops

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The new framework required a new calculation engine.

Traditional software development teams aren’t usually skilled in high-level statistics.

Real, enterprise software is much more than making sure calculations are correct.

Error in library(MDf) : no package called ‘MDf'

Original development estimate timeframe of 2 years!

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Wikipedia :: define(“enterprise software”)

Traditional statisticians have never even heard of enterprise software!

“Enterprise software, also known as enterprise software application (ESA), is purposed-designed computer software used to satisfy the needs of an organization rather than individual users […] Enterprise software is an integral part of a (computer based) Information System, and as such includes web site software production.” - http://en.wikipedia.org/wiki/Enterprise_software

Global, enterprise systems have to consider:• Systems architecture• Support• Deployment• Source control• General software dev. best practices• Development programs (e.g. versioning, agile vs. waterfall development, etc…)

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Open Source – We could build a free prototype

Statistical Power – Endless, dynamic array of statistical and data processing capabilities

There were hidden demons - an R script/package is not the same as an enterprise analytic system

install.package(MB_R) # the New Calculation Engine

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UnsupportedLibrary quality

Resource Management“Unknown language”

“R encountered a fatal error”

Open-source software, like R, can be difficult to manage for the enterprise.

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installed.packages() # Developers & R Statisticians

R was an appealing solution, but very little expertise was available.

An immediate lack of balance in new requirements for analytics systems vs. skillsets available became apparent.

A lot of our solutions would require a level of high-level automation of statistical analyses our developers could not deliver fast enough.

Requirement Skills

Developers

R

Stats

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load(R Leap of Faith)# M B B O L D LY G O E S F O R R

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A Universe of Data <- 40 Years

Our analyses are cleverly designed to be applied at a global, generic level, maximizing insight and minimizing noise through dynamic learning.

<-Messy

“Pathological” Data

Survey data is always challenging. Over time, big improvements in data collection have been made, but many, unexpected inconsistencies and biases are a constant presence we need to control.

A difficult mixture of effects on data quality and tidiness make harnessing so much data a major challenge – Millward Brown has a lot of data.

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DESCRIPTION

We went for it and came up with a plan. An R based enterprise system would require a wrapper to deliver and supply services to the calculation engine. R would have to cooperate with other programming languages.

Agile

Dev

elop

men

t

Calculation Engine

Input Loading

Validation

Analytics

Output

Wrapper

Distribution to Users

Internal Infrastructure

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sapply(Validation)

Business Logic

Data Validation

Validation routines allowed us to control and enhance the level of flexibility in our analyses and systems.

Validating inputs guarantees required input consistency.

Provides (near immediate) feedback to the user if sufficient conditions aren’t met for successful processing.

Happens promptly to avoid wasting time.

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• Quickly embraced R’s excellent data manipulation functionality.

• Implementing any sort of statistical analysis/model was possible.

• Vibrant open-source community provides the best possible support, if you know how to harness it!

• Leaning process is difficult but rewarding.

MB_Analytics <- function(BrandEquity)

We quickly learned we could do everything we required in R.

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read/write.output(MB)

We could plug R into a system/architecture that would make the most of its analytical capabilities.

R can read in data from almost any data source.

R can generate pretty much any type of output.

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require(Beast)

The Beast is MB’s R-based analytics service.

“’Brand Equity Analytics…’, get the name to spell ‘Beast’ and you’re onto something.” – Dale Smith, Global Head of Analytical Innovations, MB

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SPSSCSVSAS

str(Beast)

R

Outputs

Enriched respondent level data

(Tabs, dashboards)

Summary reports(XLS, PPT, PDF)

Normative database

Wrapper

Inputs

What started out as a calculation engine quickly evolved into a grand vision that empowered MB’s statistical minds.

Analytics contributed by MB’s statistical community

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print(MB_Beast)# L E S S O N S L E A R N E D , B L O W S TA K E N , R E W A R D S R E A P E D

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Warning: Planning returned NA

As statisticians, we had to learn a lot about a lot of standard software best practices that are alien to us by nature!

• R, CRAN, & Custom Library versions• As the team grows, how will several programmers contribute code?

Versioning & Source Control

• Processing Time, Memory Usage• Hardware vs. Code Optimization

Single Analysis vs. Process

• Unit, Regression Testing• Error Handling

Testing & Exception Handling

• Code re-usability• Documentation• Classes, Methods, Functions, Services

Code/Package Structure & Quality

sessionInfo() # R & the Beast over time

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Phase 1Phase 2

January 2013

March 2013 May 2013 August 2013 August 2014

Phase 4

Beast Community

Prototype built.Single, lonely R programmer wrote one epic script run locally!

T h e B e a s t

The Beast is born! An app-based .Net wrapper is created to deliver functionality.Beast team: 1 R, & 1 .Net Developers

Phase 32 developers for each language.

The Beast is packaged!

The Beast code is structured in line with a Service-oriented architecture.

3 R Developers!

MB now has 6 R Developers in the Global Analytics team.

MB Global R Community is born: 70 active members world-wide.

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summary(Beast)

Today, the Beast is at the heart of much of what we deliver to our clients, with around 1000 different jobs being processed over the last 12 months around the world.

10%

5%

13%

6%

31%

35%

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Statisticians aren’t software developers, but they can learn and harness the best of both worlds

Building enterprise software with R is challenging: open source languages require you to consider things you wouldn’t otherwise have to worry about.

R can communicate with all sorts of platforms, enabling efficient gateways for analytics system success. Statisticians and developers make powerful allies!

return(Beast)

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How R Transformed the Analytics Paradigm at Millward BrownJ u a n M a n u e l H e r n á n d e z