General mills presentation

Big Analytics for Big Data: CPG Perspectives on the Journey Brandon L. Paris Methods Principal Global Consumer Insights General Mills

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A Quick Synopsis of Yesterday (Paraphrased)

• Alex Tosolinin – “The new challenges are Big Data, Attribution, New Systems added to old ones, and Defining/Training the new marketing organization.”

• Hal Brierley (and Don Smith) – “Loyalty is the most misused word in Marketing.”

– “The best loyalty programs offer Rewards for Engagement® -reward behavior AND providing information.”

• Edward Malthouse – “The only way to get good at something is to practice it . . .”

• What Else?

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What is A Methods Principal?

• Part of Consumer Insights Function @ GMI • Central Resource, Supports Entire Function • Primary Responsibilities – – – –

Oversee the Efficacy of the “Toolkit” Scan for Emerging Methods/Approaches Implement the Research Development Roadmap Elevate Research & Statistical Expertise of the Function

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Analytics & Consumer Research at GMI • A Rich History of Proprietary Consumer Research – Product Testing, Concept Research, STMs, etc.

– Behavioral Analytics, Marketing Mix

• Centered on a Core “Central CI” Function

• Organizational Culture Focused on Best Practices

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Proprietary MR Analytics @ GMI circa 2003 Reporting & Descriptive Analysis

Marketing Mix Analytics

Survey Analytics

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What is Analytics? “Analytics is the discovery and communication of meaningful patterns in data.�1

Descriptive

Predictive

Prescriptive

Using Data to Understand & Analyze Performance

Modeling Data to Anticipate What May Come Next

Using Models to Find & Take the Best Course of Action

Standardized Reports

Data Mining

Schedule Optimization

Ad-hoc Reporting

Machine Learning

Real-Time Marketing

Data Visualization

Probability Assessment

Resource Allocation

Database Queries

Regression & Modeling

Spreadsheet Tables

Simulation Techniques 1http://en.wikipedia.org/wiki/Analytics

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How Analytics Is Applied Analytics Utilization by Activity 200

25% 25% Prescriptive Predictive Descriptive

100

20% 80%

50%

Circa 2003

Future

0

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CPG Co. Challenges to Expanding Big Analytics Access to Raw Information

Access to Talent

Meeting Today’s Needs While Building for Tomorrow 9

Data Access Challenges • Consumer Marketers, B2B Sales Organizations – Limited access to the transactional relationship – Historically, “Push” communications – Tell vs. Converse

• Need to Leverage Partnerships – Can be high-cost – Type of Access – summaries or raw data?

• Big Data starts to rectify this challenge

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Talent Management • Data Democratization = Analytics Democratization – Analytics no longer the purview of a select few

• Cultivating Current Talent – Revising Training & Development

• Sourcing New Analytical Talent – Identifying the mix of business vs. analytic talent – Recruiting Sources – Training, Development & Career Pathing

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Embracing Change In the Moment • Seeing past the existing platform can be a challenge – Considerable focus on the sell-in – Usually starting with same analysis, via automation

• Existing processes are “optimized” – The hardest part of change is in the transition, when both systems are active

• Ability to predict is appealing, but creates new challenges on the ability to react

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Resolving These Challenges Via Big Data & Analytics Access to Raw Information

• Social; Social; Social • Better Internal Data Connections • Partnerships When Needed

• Expanded Training and Development • New Roles & Talent Sourcing • Partnerships with Other Analytic Depts.

• Corporate-wide Reporting Platforms • Automation & Desktop Access • Creating the ability to React to Analytics

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Proprietary MR Analytics @ GMI circa 2014

Infrastructure & Reporting

Marketing Mix

Strategic Analytics

Social/Web Analytics

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Areas We Need Academic Help • Sound Theoretical Frameworks – Consumer Behavior & Social, Attribution Issues, etc.

• Making Sense of Big Data

• Building Talent with Business & Analytic Intuition • (Continue) Development of Practical Research Approaches

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Some GMI Examples

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Examples of Our Progress: Improved Forecasting for Consumer Demand The Issue Organization is spending too many man-hours on demand planning – Logistics, Sales, Marketing. Numerous forecasts with significant time spent reconciling between them.

Can a single forecast system be built that minimizes time and improves forecasting accuracy?

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Examples of Our Progress: Improved Forecasting for Consumer Demand

Utility

Goals

The Goal

The Utility

Prediction

Broad User Base

Create an accurate estimate of future sales

Simple, transparent models are needed for utilization

Inputs

The Inputs Simple Access, Few Inputs “Causals� that can be reasonably anticipated 18

Examples of Our Progress: Improved Forecasting for Consumer Demand What We Did   

Partnership – Data Infrastructure, Strategic Analytics, Logistics, External Assessed the available data and identified the candidate model classes Built hundreds of models, learning the technology along the way.

How We Did 



Models varied from parity to +50% improvement in accuracy Simple – Five or fewer inputs, predicting up to 12 months out

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Examples of Our Progress: (Insert Project Gwendolyn) • What we learned . . .

• The value of refocusing on the loss function – Forecast MAPE vs. In-Sample MSE • Simple, Quality models can be built with “causals” inputs • The importance, and challenge, of transparancy and organizational training

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Examples of Our Progress: Predicting Trends That Will “Pop” Within 72 Hours • Predicting = What will happen next?

• What’s going “to pop” next • Social patterns used to develop predictive models

Activate Against

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Examples of Our Progress: Predicting Trends That Will “Pop” Within 72 Hours The Issue 

Can we predict which trending consumer interest patterns across multiple social channels will “pop” in the next 72 hours?

The Approach   

Baseline holiday commentary from Pillsbury.com & BettyCrocker.com Collect real-time social conversations around baking/cooking/holiday topics Develop & apply predictive algorithm to predict trending topics over next 72 hours

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Examples of Our Progress: Predicting Trends That Will “Pop” Within 72 Hours • What we learned . . .

• Everyone wants to see the future, most don’t know how to do it • External partner options are immature, limited

• Predicting vs. Responding

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Thank You

Brandon L. Paris Methods Principal Global Consumer Insights General Mills

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