LIVE WEBINAR

Top Trends in Category Management:

Accelerating Maturity & Sophistication

Audio Dial In: (877) 273-4202

Passcode: 6360544#

Speaker Introductions

Gordon Wade,

Category Management Association

– Managing Partner and Director of Best Practices

Vaughn Roller, Revionics

– Vice President of Assortment and Space

2

Implications of Change in the Marketing Ecosystem

The Category Management Association

3

• CatMan & The CMA

• Major Trends

– Retailer Power Growth

– Shopper Empowerment

– Big Data Impact

• Implications/Recommendations

– CatMan Commitment & Maturity

– Retailer/Supplier Collaboration

– Data & Analytics

Agenda

Category Management

• Demand-side Process

• Purpose: Optimize Shopper Experience

• Drives Supply-side & Replenishment

• Data Intensive & Increasingly Complex

Category Definition

Category Role Category

Assessment Category

Scorecard Category Strategy

Category Tactics

Plan Implementation

Category Review

• Mission: Increase CatMan ROI by Improving Shopper Experience via:

– Thought Leadership

– Sharing Best Practices

– Facilitating Member Interaction

– Professional Competence Certification

Category Management Association

• Global Professional Community

Three Major Trends Driving Change

Growing Power of the Retailer

Digital Empowerment of the Shopper

Impact of the “Big Data” Big Bang

Power Shift

• Weakened Brands

– Brand Equity: All Time Low

– TV Efficiency: Down

• Retailer Controls Shelf & MOT

– In-store Display: Most Effective

– Retailer Flyers: Most Influential

• Retailer-owned POS & Loyalty Data

– Enhanced Predictive Analytic Capabilities

– Optimized Recommendations Target Loyal Shoppers

Threats to Conventional Food Retailers

• Changed Shopper Attitudes

– New Value Mentality Triggered by Recession

– Increased Shopper Individuation

– Increased Category Cherry Picking by Format

• Growth in Value Format Alternatives

– Dollar Stores

– Deep Discounters

– Walmart

– Amazon

• Margin Compression

Digitally Empowered Shoppers

• Armed with New Weapon

– Digital Item Level Info

– Ubiquitous/Immediate/Individual Data

– Two-way Info Flow

• Enables Format Leakage by Need State

• Explosive Growth in Digital Usage

– 50% using Digital

– 25% use Technology in 2 or more ways at Grocery Store

– 24% check Multiple Sources before Grocery Shopping

Digitally Empowered Access & Innovation

• Smartphone Shopping Lists

• Online Comparison:

– Testimonials

– Inventory

– Pricing

– Promotions

Digitally Empowered Shopper Data

Retailer Shopper

Shopper Data

Targeted Offers (traditional channels)

• Basket History • Loyalty Card • Demographic Data

Digital Platform

Intent Driven Targeted Offers

• In-Store Actions & Habits • Responsiveness to Offers • Lists & List History • In-Store Searches & Scans • Shopper Location & Shopping Patterns

• Product Details • Price • Inventory • Product Location • Store Map

Premium Data • Nutrition • Eco-index • Recipes • Ratings & Reviews • Comparisons • Related Products

Product Data

What is the Value of Big Data?

• Enables:

– Granular Understanding of Shopper Attitudes & Behaviors

– Customized Merchandise & Offers at Store/SKU Level

– More Efficient & Effective Management of Demand & Supply

Organizing Big Data for Insights

Household Behavior Filter

Need States Filter

Insight Generation

Need States Insights

Category Insights

Price Optimization

Forecasting

Inputs Retail Loyalty

Card

Other Retail Behavior

Social Media

Media Consumption

Lifestyle Behaviors

Filters

Analytics

Outputs

• Better Understanding of Shopper Behavior

• Enhances Targeting Capabilities

– Better understanding of response efficiency by element

• Builds Direct Shopper Relationships

• Uncovers Unarticulated Needs

• More Effective Allocation of Retail Marketing $’s

Big Data Helps Manufacturers

• Household Level Merchandise/Offer Customization

• Localize/Customize Pricing

• Individualize Communication

• Target Private Brand Conversion

• Create Loyalty Strengthening Offers

• Market to Need States

• True Shopper Management

Big Data Helps Retailers

• Total Store Optimization/Customization:

– Dramatic >/< of Category Space

– Right Sizing of Categories by Cluster

– Assortment Customization by Cluster

– Shopper Segment Profit Optimization

– Antidote to Leakage

Big Data Enables Total Store Optimization

CMA 2013 Thought Leadership

© 2013 The Partnering Group, Inc.

Category Management Association

CatMan Maturity Curve

Data

Analytics

&

Software

Org.

Skills

Process

&

Culture

Maturity Curve Components

Analytics & Software Maturity

• Excelling Stage Requirements:

– Advanced space & assortment analytics by total store & aisle

– Activity based costing for category, brand & items

– Shopper behavior analytics by multiple shopper segments

– Advanced loyalty card & 3rd party analytics by need state

– Individual store level supply chain analytics to reduce OOS

– Success models for assortment, shelf merchandising, pricing & promotion by format & banner

Manufacturer/Retailer Partnership

Collaboration & Commitment = Success

• Collaborators get better business results!!

– Better Value Chain Transparency

– Better Shopper Understanding via Jointly Developed VOS

– Better Planning of What is Needed from Each Partner

– Better Forecasting

– Less Format Leakage

– Better ROI

2013 State of the Industry Survey

Results: Supplier – What’s Working

23

Supplier – Retailer Collaboration

24

Implications for 2018 Ecosystem

• Growth of Internet as Marketing Platform

• Shopper Individualism Drives Complexity

• Big Data is a Problem & Opportunity

• Requires Superior Data, Tools & 3rd Party Analytics

• Leakage Conquered via Need State Marketing

• Total Store Optimization Becomes Competitive Necessity

Optimization Case Studies

Leveraging Science, Data & Analytics to

Right-Size Space & Localize Assortments

Vaughn Roller

VP Assortment & Space

Revionics

What’s Happening & Why?

Pressure from All Sides:

– Format Encroachment

– Channel Blur

– Hyper-Competition

– Empowered Shopper

– Weak Economy

Hardest Hit: Traditional Grocery!!

Supermarket Example:

Share of food/drug/mass

available dollars is shrinking

44%

28%

1995 2008

25%

2010

Categories Increasing

Categories Decreasing

No Change

24%

29%

47%

Strategic Implications for Retailers

53% of Categories Impacted

Leveraging Science, Tools & Data

29

Science ● Simulation ● Predictive Analytics

zzzzzzzzzzzzzzzzzzz Business Rules

Operational Constraints

Strategic Objectives

Category Roles

Category Strategies

Financial Objectives

Right Sized Localized Space & Assortments

Basket

Drivers

Loyalty

Drivers

Competitive

Gaps Affinities Elasticity

Floor Plans

Planograms

Market Data

P.O.S./T-Log

Inventory

Loyalty Data

“Right” Sizing Space Diminishing Returns

2200 2000 1800 1600

Linear Base Space

Sale

s Ch

ange

- 5000

0

5000

Current Space Allocation Unproductive Space Removal Diminishing Improvement Loss From Current Sales

The Shrinking Center Store

Store A Results

Impacted 110 Categories

Increased YOY Sales +1.8%

Removed 100 ft for New Store Features

YOY Sales

Space

Increasing Sales while Shrinking Center Store

Making Room for Growing Categories

Store B Results

45 Categories Reduced by 250 ft

Performance for Reduced Categories

Increased YOY Sales +1.0%

Optimization Recommendations

• Made Room for Growing Categories

• Increased Sales in Reduced Categories

YOY Sales

Space

Reallocating Center Store Space to New or

Growing Categories & Line Extensions

The Importance of Store Specificity

Category Increase

Space

Decrease

Space

No

Change

Cheese 75% 25%

Yogurt 42% 58%

Juice/Drinks 42% 17% 41%

Cottage Cheese 17% 83%

Butter/Margarine 25% 75%

RTE Desserts 33% 67%

Bread 42% 58%

Localize Space to:

• Shopper Demand

• Market Conditions

•Store Format/Layout

Number of Stores Requiring Category Size Changes

% of Stores Requiring Category Size Changes

Total Inventory (Cost)

Pre Optimized Change % Change

Store

Average $ 1,090,000 $ 980,000 $ (110,000) -10.1%

Excess Inventory (>60 Days of Supply)

Pre Optimized Change % Change

Store

Average $ 288,000 $ 228,000 $ (60,000) -20.8%

Days of Supply

Pre Optimized Change % Change

Store

Average 80 74 -6 -7.5%

Enforcing Pack-Out

Standards Decreased:

• Out-of-Stocks

• Excess Inventory

• Labor Costs

Optimizing Inventory Across The Store Reducing Out-of-Stocks & Excess Inventory

Optimization Reduced:

• Total Inventory

• Excess inventory

Market Performance

Optimization Cut Baby Diaper Space in Half

• Space Reduced by 24ft

• Space Performance Increased by 76%

• $35/foot (48ft) Increased to $62/foot (24ft)

• Stock Target Minimums Covered

At Half Space, Baby Diaper Category outperformed Market at -2.6%

Evolving Category Roles Changing Market Dynamics

Competitive Strategy Impact:

• 100 ft Re-Allocated Space

• 9.4% Sales Increase

Market Data Impact

Optimization Without Considering

Competitive Strategy

Applying

Competitive Strategy Change

Annual Sales Impact $101,900 $111,500 9.4%

Responsive Categories:

• Chips & Snacks

• Frozen Entrees

• Frozen Pizza

• Coffee

• Household Cleaners

• Baby Diapers

• Juice & Drinks

Identify Competitive Gaps & Opportunities

Market Basket Impact

Basket Impact:

• On Category Space

• On Assortment

Category

Change With

Sales Only

Optimization

Change With

Sales &

Basket

Impact

Net

Difference

Dinners/Side Dishes - 1 0 + 1

Natural Snacks +3 0 - 3

Cat Litter - 2 -1 + 1

Rice + 3 +2 - 1

Item

Objective

Score

(Sales

Only)

Rank Objective

Score

(Sales &

Basket)

Rank

Kraft Velveeta Shells & Cheese 12 oz 0.95 1 1.05 2

Kraft Mac & Cheese Dinner 7.25 oz 0.77 2 1.60 1

Kraft Deluxe Dinner 14 oz 0.69 3 0.73 3

Kraft 3 Cheese Mac & Cheese 7.25 oz 0.35 12 0.44 8

Loyalty Data Impact

Understanding Loyalty Impact:

• On Category Space

• On Underlying Assortment

Category

Change

Without

Loyalty

(ft)

Change

With

Loyalty

(ft)

Net

Difference

(ft)

Condiments - 2 0 + 2

Cough & Cold - 3 0 + 3

Household Cleaners - 4 0 + 4

Laundry Detergent - 4 0 + 4

Item

Objective

Score

Without

Loyalty

Rank Objective

Score

With

Loyalty

Rank

Best Foods Real Mayonnaise 9.96 1 11.86 1

Best Foods Mayonnaise Light 2.98 2 3.55 3

Heinz Ketchup Squeeze Bottle 2.88 3 3.64 2

Heinz Ketchup Easy Squeeze 2.58 4 3.24 4

What Revionics Does

39

Q & A Gordon Wade, Category Management Association

– Managing Partner and Director of Best Practices

Vaughn Roller, Revionics

– Vice President of Assortment and Space