Promotion Optimization Institute Spring 2013 Summit

Driving Business Insights and Resultsthrough DSR-Sourced Analytics

Speaker: Bob Hannah – Managing Director

Data Ventures, Inc.

Valuable (But Overwhelming) Data Streams For CPGs

Delivery DataSupply Chain

DataRetailer POS

Data(100s)

Retailer LoyaltyData

SyndicatedData Sources

Proprietary CPGShopper Data

DIFFICULTY TO MANAGE & UTILIZE ALL AVAILABLEINFORMATION

Consumer Data

1. Consistent and Robust DSR2. Consistent and Advanced Analytics

True Insights

Targeted In-Market Action

Improved Business Results

SolutionMust BeScalable

CPG Needs:

The Current Environment

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Promotion Optimization Institute Spring 2013 Summit

DRIVING THE COLLABORATIVE AGENDA!

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Spun out from the U.S. National Labs in LosAlamos, NM in 1994‒ Big Data processing with Massive Scale Pattern

Recognition Algorithms

Principal Clients (not full list)‒ The Global Coca-Cola System: DV has worked

together with Coke and Major Retailers in 36countries (to date)

‒ US and International Retailers• Supermarket, C&C and Convenience Retail

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Analytic Possibilities @ Full Category/SKU Level Data Granularity

SignificantImprovements in

Targeted Marketing& Effectiveness

The Current Environment

First Steps On The Analytic Action PathValue Generation vs. Difficulty

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RelativelyDifficult

RelativelyEasy

Ease/AbilityToImplement

Level 1 Level 2 Level 3 Level 4 Level 5

Starting Point:Shared CollaborativePlanning Vision ForThe Category

- Manufacturer- Retailer

Remove Inefficiencies:Out-of-ShelfAssortment OptimizationSpace Optimization

Revenue IncreaseWorking Capital Reduction

Store Segmentation

Even Better- OOS Reduction- Assortmt Opt’n- Space Optim’n

Improved TradePromotion Practices- “Effective Retail

Pricing” only- Utilize Brand &

Package Switching- TPM

Advanced Demand-Based ForecastingLinked to TPOOn Time, In FullImproved Merch

SchedulingSupply Chain

Efficiencies

PromotionDecomposition- Understanding

Time Shifting &Cannibalization(Self & Category)

Value Generation

CATEGORY/DAILY LEVEL DATA

POS OR T-LOG LEVEL DATA

POS OR LOYALTY CARD DATA

CATEGORY/DAILY LEVEL DATA

CATEGORY/DAILY LEVEL DATA

Utilizing Shopper Intelligence to Drive the Collaborative Agenda

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Previous Discussion (November 2012) Shared real-world examples of two high-impact Shopper Analytics applications:

1. Shopper Behavior-based Assortment Optimization + Inventory Optimization

→ Reduced Out-of-Shelf conditions

→ Reduced Working Capital

→ Increased Sales growth

2. Promotion Decomposition

→ Understanding of True Promotion Incrementality

• Net of Time Shifting and Self/Category Cannibalization

→ Optimized Promotion planning

Today’s Discussion One updated real-world example, plus one new high-impact Shopper Analytics application

1. Shopper Behavior-based Assortment Optimization + Inventory Optimization

→ Reduced Out-of-Shelf conditions

→ Reduced Working Capital

→ Increased Sales growth

2. Pricing Strategy Change Impact

→ From “Extreme High-Low”

→ To “ED Very Low Price”

→ Incorporating Promotion Decomposition

Driving Business Insights and Results through DSR-Sourced Analytics

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Romania

Retailer: Chain “A” Data Type: Transaction Log data (i.e., Basket Level)

Objectives: • Understand Out-of-Shelf Conditions on Branded products By Store and By Hour

• Conduct Assortment Optimization Eliminate all Volume Transferable/Non-Productive SKUs that are choking the

Supply Chain and causing OOS

• Incorporate Retailer & Manufacturer Supply Chain Data

Actions: • Reset stores based on Optimized Assortment Reallocate inventory toward productive SKUs with high OOS

• Utilize Optimized Supply Chain Practices Efficient Replenishment and Receiving

Major Brand Manufacturer Action – Updated Example

Romania: OOS Event Report – Details (by Store, by Item)

OOS-Duration

Average Duration (days) ofNon-Intraday OOS-Events: Days without Sales for Top Items

Check Supply Chain Issues

Average Duration(Hours) of IntradayOOS-Events:

Hour/Day Sun Mon Tue Wed Thu Fri Sat

50 % 0 % 0 % 0 % 0 % 0 % 0 %

61 % 1 % 1 % 1 % 1 % 1 % 1 %

71 % 2 % 2 % 2 % 2 % 2 % 3 %

83 % 3 % 3 % 4 % 3 % 3 % 4 %

94 % 5 % 5 % 5 % 5 % 5 % 4 %

107 % 7 % 6 % 7 % 7 % 8 % 7 %

111 0 % 8 % 9 % 1 0 % 9 % 8 % 8 %

121 2 % 9 % 1 0 % 1 1 % 1 1 % 1 0 % 1 0 %

131 2 % 1 0 % 1 1 % 1 1 % 1 1 % 1 1 % 1 0 %

141 2 % 1 1 % 1 1 % 1 1 % 1 1 % 1 1 % 1 1 %

151 1 % 1 0 % 9 % 9 % 9 % 1 0 % 1 1 %

168 % 7 % 7 % 8 % 8 % 8 % 8 %

176 % 8 % 7 % 7 % 7 % 7 % 8 %

186 % 7 % 6 % 5 % 6 % 6 % 5 %

193 % 5 % 5 % 4 % 4 % 5 % 4 %

203 % 4 % 3 % 3 % 3 % 3 % 3 %

212 % 2 % 2 % 2 % 2 % 2 % 2 %

220 % 1 % 1 % 0 % 1 % 0 % 1 %

Most OOS Eventsstart at particulartime of day

Check Inventory,Supply Chain andoptimizeMerchandisingHours towardsthese hours

Stage #1: T-Log data enabled understanding of Intra-Day OOS, plus OOS duration

Store #___, Product ___, Week ___

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Romania Stage #2: Expanded OOS Analysis

With Manufacturer and Retailer collaboration, key Supply Chain data wasincorporated into the analysis, which enabled understanding into root causes: Due to Delivery/Supply Chain issues? Caused by in-store conditions (wrong SKUs, wrong space, etc)?

KPI’s to support the processFill Rate (%)

Fill Rate (%) - promo

On-time deliveries (%)

Lead time (days)

Stock availability (%)

Stock coverage (days)

Out of Shelf Rate (%) Data Ventures

False Inventory detection

Late Orders / Emergency Orders

Impact on Lost Sales is measured to support prioritization

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Romania – Actionable Output Summary

1. Exception Reporting– Identify SKUs with highest OOS

2. Supply Chain Analyses– Identify Root Causes by Store

3. Assortment Optimization– Identify SKUs without “Shopper Rationale”

→ Redundant & Volume Transferable→ Clogging the Supply Chain Delist

‒ Reallocate inventory to Productive SKUs

4. Prioritized Action Plan

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IMPROVEMENTInitial OOS

Country Channel Retailer # Stores OOS Rate Reduction Sales Increase

Romania C & C Metro 26 13.8% (8.5) pts / 3 yrs +5.7 pts per yearUSA Petro Pantry 75 4.8% (1.0) pt / 1 yr +2.7 ptsGermany Hyper real,- 90 15.7% (1.9) pts / 1 yr +4.7 ptsMexico Petro OXXO 20 10.4% (4.4) pts / 1 yr +9.9 ptsItaly Super Carrefour 20 18.3% (4.2) pts / 1 yr +5.7 pts

Romania: Exceptional Results – Updated The value of store clustering, assortment optimization, and demand-based out-of-stock analytics using shopper

data has been strong in this example, and proven in many other countries and trade channels around the world

Other Examples

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USA Retailer: Chain “A” Product: Frozen Brand “Z” Data Type: Loyalty Card data (i.e., Household Level)

Situation: • Change in Pricing and promotion strategy from High-Low toED Very Low Price

Objectives: • Understand the impact of these strategy changes on: Sales and Units Effectiveness / Incrementality Household Franchise

Actions: • Monitor Sales results trends Household trends

Total HHs and Exclusive HHs• Utilize Promotion Decomposition True Incremental, net of Time Shifting and Cannibalization

Major Brand Manufacturer Action – Example

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Across 2012, Brand “Z” employed 4 different Pricing and Promotion strategiesUSA Frozen Product Example

High-Low

Q1: Notworking well

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Across 2012, Brand “Z” employed 4 different Pricing and Promotion strategiesUSA Frozen Product Example

High-Low

Q2: Slightlyworse

Reduced Everyday Price,Compressed High-Low

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Across 2012, Brand “Z” employed 4 different Pricing and Promotion strategiesUSA Frozen Product Example

High-Low

Q3: $ Salesdecline

continues

Reduced Everyday Price,Compressed High-Low

Low Everyday WithLower Promotions

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Across 2012, Brand “Z” employed 4 different Pricing and Promotion strategiesUSA Frozen Product Example

High-Low

Q4: Unitgrowth

returns, but$ declinesaccelerate

Reduced Everyday Price,Compressed High-Low

Low Everyday WithLower Promotions

Everyday VeryLow Price

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The overall Frozen subcategory in which Brand “Z” competes also did not benefit duringthis time.

‒ Units increased in Q4,

‒ But the Retail Price was reduced so much that Retail Sales declined by (11.7%)• Possibly driving more shopper traffic to store but no benefit Brand Z or the subcategory• Selling more subcategory units, but possibly without Profit

USA Frozen Product Example

Q4: Samepattern also

for theSubcategory

1.3%

-0.5%

0.8%

-11.7%-11.8% -12.8%

1.9%

7.0%

14.9% 14.2%

-1.0%

-17.5%

2012 Q1 2012 Q2 2012 Q3 2012 Q4

Sales % Change Units % Change Average Retail % Change

CHAIN A – Frozen SubcategorySales Value and Unit Sales Percent Change versus Last Year

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Promotion DecompositionAnalysis Approach Each product is evaluated using its own Shopper Purchase Behavior

– “Time Shifting – Post” will be based on one purchase cycle (3 weeks for the Subcategoryexample at this Retailer)

Question: When are promotions truly successful?

PROMOTED SUPPLIEROTHER

PRODUCT ---- TIME ---- SELF- ----PRODUCT

INCREMENTAL SHIFTING CANNIB’NCANNIB’N

– Supplier/CPG:

Positive Total Supplier Sales

– Retailer:

Positive Total Category Sales

Promotions usually fall into 3 possible “outcome classes”:1. “Win/Win” Both the Supplier and the Retailer have net positive results

2. “Win/Lose” Only the Supplier has net positive results

3. “Lose/Lose” Neither have net positive results

Sometimes “Lose/Win” can occur Usually linked to other promotions in the Category 20

Promotion Decomposition

=

Promotion Decomposition

‒ And in the ability to sustain the number of Brand “Z” Loyal HHs and Switchers

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Sample of Summary Findings: As retail pricing on Brand “Z” moved toward Every Day Very Low Pricing, the promotions

became less and less effective‒ Both in the ability to generate “Truly Positive” results for both parties

PromotionDecomposition

Promotion Types:

Mfr Retailer

Win Win

Win Lose

Lose Lose

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RelativelyDifficult

RelativelyEasy

Ease/AbilityToImplement

Level 1 Level 2 Level 3 Level 4 Level 5

Starting Point:Shared CollaborativePlanning Vision ForThe Category

- Manufacturer- Retailer

Remove InefficienciesOOS RevRed’n Incr

Pts Pts

Germany (1.9) +4.4

Romania (8.5) +5.7(3 yrs)

StoreSegmentation

OOS RevRed’n Incr

Pts PtsMexico (4.4) +9.9

Italy (4.2) +5.7

Advanced Demand-Based Forecasting

• 25+ pts increase inforecast accuracy

Improved TradePromotion Practices

Ex: Brazil Chain AMajor Brand 1.5L

Price Unit RevDisc Lift Lift(10%) 80% 61%

(15%) 84% 52%

(20%) 91% 52%

(35%) 191% 89%

Value Generation

First Steps On The Analytic Action PathProven Results

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Bob Hannah Managing Director‒ Email: bob.hannah@dataventures.com

‒ Phone: +1.704.887.1007 Mobile: +1.704.965.3167

Thank You!

Advanced | Effective | Actionable | Proven Analytics™

AWARDS: FMI/GMA OUT-OF-SHELF BEST PRACTICE (WITH P&G) EUROPEAN ECR BEST PRACTICE

COCA-COLA / WALMART INTERNATIONAL CATEGORY CAPTAIN

COCA-COLA HELLENIC – BEST PRACTICE AWARD

COCA-COLA REFRESHMENTS – NEURAL NET/TPO