A Trading Partner Approach to Data Centered Collaboration

• Background• Panelists• The Foundation• Typical Scenario• Stories• Q&A

Agenda

Mindtree at a Glance

Basel, SwitzerlandBrussels, BelgiumCologne, GermanyLondon, UKParis, FranceSolna, SwedenVianen, Netherlands

Europe AsiaBeijing, ChinaDubai, UAESingaporeSydney, AustraliaTokyo, Japan

IndiaNorth America

Company HQs Delivery Centers

BangalorePuneChennaiHyderabad

Warren, NJCleveland, OHDallas, TXGainesville, FLPhoenix, AZRedmond, WASan Jose, CASchaumburg, ILMinneapolis, MNChicago, ILLos Angeles, CANew York, NY

Global Coverage

26% RevenueRetail, CPG and Manufacturing

Relational Solutions acquired by Mindtree

Specialized provider of analytics for CPG retail execution

Pioneer in demand signal repository technology

Relational Solutions

POSmartBlueSky Analytics TradeSmart PromoPro

Integrates, Validates and

Analyzes Point-of-Sale Data

Business Intelligence and Reporting Tool

CPG sales and supply chain improvement

Grow U.S. Data and Analytics Centre out of

Relational Solutions’ Cleveland office

Advanced data-driven solutions for supply

chain optimization and trade promotions

analytics

Enhance digital transformation journey

of CPG clients

Accurately Measure CPG Trade Spend

ROI, Use Predictive

Models to Plan New Promotions

Align CPG Trade

Promotions and Shopper

Marketing for Improved Trade

Spend ROI

Solution Offerings:

Moderator

Kristy Weiss

Director CPG Analytics

Relational Solutions a Mindtree Company

• 19+ years in CPG industry

• Bachelors degree in Direct Response Retail from Johnson & Wales University

• Masters degree in I/O Psychology, focus in Consumer Psychology from The Chicago School of Professional Psychology

• Extensive background in CPG/retail business analysis with Fortune 100 manufacturers

• Expert in integrating and analyzing complex data points to identify actionable insights

• Able to translate efficiently between business users and technical teams

• Develop and manage Business Analyst teams in-house and on-site

Mike MarzanoSolutions Process

Expert, Retail Execution Mondelez

International

Donna TellamVice President,

Customer & Partner Solutions

Spring Mobile

Mark HornerDirector, Trade

Marketing Eagle Family Foods Group

Meet the Panelists

Managing DataEDM, DI, MDM, DW, Big Data

Provide a comprehensive data management framework, architecture and governance to achieve a “single version” of truth

Business Intelligence

Descriptive Analytics

Provide a comprehensive data reporting/dashboards framework, architecture and governance to deliver appropriate, timely and actionable information

Insight GenerationPredictive Analytics

Through an integrated analytics framework and by applying business rules, statistical models, visualizations, and industry specific context derive actionable insights from disparate data

Decision SciencePrescriptive

Turning actionable insights into measurable outcomes and improving the speed and quality of decision making

Valu

e to

the

Ente

rpris

e

Data Driven Organization Maturity

Data & Analytics ContinuumThe power of an integrated data and analytics framework

Enables Many Business Driving Insights to Bubble Up

Building a Solid Foundation

A Typical Promotion Analysis Scenario

Typical ScenarioHigh level Promotion Plan and Sales Facts

8/6/2016

8/9/2016

8/12/2

016

8/15/2016

8/18/2

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8/21/2

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8/24/2016

8/27/2

016

8/30/2

016

9/2/2016

9/5/2016

9/8/2016

9/11/2

016

9/14/2016

9/17/2

016

9/20/2

016

9/23/2

016

9/26/2

016

9/29/2

016

10/2/2016

10/5/2016

10/8/2016

10/11/2016

10/14/2016

10/17/2016

10/20/2016

0

50

100

150

200

250

300

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450

$2.70

$2.80

$2.90

$3.00

$3.10

$3.20

$3.30

$3.40

$3.50

$3.60

Sales UnitsRetail Price

Retailer X 13 Week Price vs. Volume Trend

Where’s the Needle?

SyndicatedData

Additional InformationShipment Facts

8/6/2016

8/13/2

016

8/20/2016

8/27/2

016

9/3/2016

9/10/2

016

9/17/2016

9/24/2016

10/1/2016

10/8/2016

10/15/2016

10/22/20160

100

200

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1100

Sales UnitsShipped Units

Retailer X Shipment vs. Consumption Trend

Now Where’s the Needle?

SyndicatedData

ShipmentData

More InformationRetail Execution Facts

Retailer X Store Sales by DaySunday Monday Tuesday Wednesday Thursday Friday Saturday

Store # City9/11/2016 9/12/2016 9/13/2016 9/14/2016 9/15/2016 9/16/2016 9/17/2016

Total Sales Units

Shipped Units

Remaining On Hand

1 Florence-Graham 7 6 5 8 7 12 5 50 50 02 Los Angeles 2 1 1 2 1 2 1 10 50 403 East Los Angeles 0 0 0 0 0 1 4 5 50 454 Commerce 1 1 1 1 3 3 5 15 50 355 Ladera Heights 6 5 11 15 12 1 0 50 50 06 Vernon 0 1 1 1 2 1 3 9 50 417 Willowbrook 1 0 1 0 2 2 5 11 50 398 Bell Gardens 0 0 1 1 1 3 7 13 50 379 Beverly Hills 1 1 1 1 1 3 8 16 50 34

10 Compton 0 0 0 0 0 0 0 0 50 5011 Downey 0 0 0 0 0 1 4 5 50 4512 Gardena 2 1 0 1 0 1 3 8 50 4213 Hawthorn 10 8 7 10 15 0 0 50 50 014 Hermosa Beach 1 1 1 1 1 4 3 12 50 3815 Huntington Park 0 0 0 0 0 0 0 0 50 5016 Lawndale 1 1 2 1 2 3 6 16 50 3417 Lynwood 10 12 15 13 0 0 0 50 50 018 Malibu 15 15 15 3 1 1 0 50 50 019 El Segundo 1 1 1 1 1 3 7 15 50 3520 Maywood 0 1 1 1 1 5 6 15 50 35

58 55 64 60 50 46 67 400 1000 600

Sales Units

Retailer X

Now Where’s the Needle?

SyndicatedData

ShipmentData

Retailer Store

Master Data

Retailer Store Level

POSData

Is More Information Useful?

If so, why isn’t it used more often?

What You Said at POI Last YearThe POI 2015 TPx and Retail Execution Survey

Only 10% of CPG Companies felt they had an Automated and Easy way to analyze trade

What You Said at POI Last YearThe POI 2015 TPx and Retail Execution Survey

96 % of Companies Have Trouble Analyzing Trade

What You Said at POI Last YearThe POI 2015 TPx and Retail Execution Survey

76% of CPG Companies Believe they have ongoing Data Quality Issues

• Prevailing belief that data is available and smart people will stitch it together meaningfully.

– Time– Resources– Leverage Data Investment– Prioritization– Repeatable

• Validation – is this analysis correct? • How do we impact execution activity?

Industry Challenge

ShipmentsSales

Do You Speak the Same Language?

Case, Pallet, Loaded Display UPC / SKU

Item InformationTab It Brand Item List

Multiple Items Can Represent 1 UPC

Item Number Description Brand UPC Business Unit UOM Units per Case1234 Blue Vnyl Tab 12 pk TabIt 12345678901 Folders Case 12

1234TG Target Bl Vinyl Folder Tab It 12345678901 School Supplies Case 121234CV 6 pk Blue Fldr CVS Tab It 12345678901 Office Supplies Case 6

11157 Grn Bl Yllw Mixed Tab Fldr Costco 144 Tab It 12345786092 Office Supplies Pallet 1211158 Yllw Vinyl Tab 12 pk Mass TabIt 12345987965 Folders Case 1211160 Tab It Green Tab Folder Vinyl TabIt 12345876775 School Supplies Case 8

Item Number Description Brand UPC Business Unit UOM Units per Case Distinct Description Distinct Item Number1234 Blue Vnyl Tab 12 pk TabIt 12345678901 Folders Case 12 Blue Vinyl Tab Folder 4321

1234TG Target Bl Vinyl Folder Tab It 12345678901 School Supplies Case 12 Blue Vinyl Tab Folder 43211234CV 6 pk Blue Fldr CVS Tab It 12345678901 Office Supplies Case 6 Blue Vinyl Tab Folder 4321

Whose Calendar do you use?

Sunday Monday Tuesday Wednesday Thursday Friday SaturdayWeek Ending 9/11/2016 9/12/2016 9/13/2016 9/14/2016 9/15/2016 9/16/2016 9/17/2016Syndicated XRetailer X Promo XShipments X

To Move the NeedleGather ALL the Facts, Integrate, Harmonize Insights

Master Data

ShipmentData

Consumption Data

ForecastData

3rd Party Distributor

Data

Merchandiser Feedback Data

Weather Trend Data

PromotionData

TPM Data Challenge ExampleMark Horner

Post-Promotion Analysis• Gain insights around what is working and what is not• Share with sales organization and incorporate into planning• Maximize the ROI of trade dollars

Step #1:Gain financial controls over your trade fundsImplement a fully integrated TPM system

ERP

Connecting Customer Plans to Actual Shipments and Spending

What did we expect to Sell and Spend – What did we Sell and Spend

Step #1: Implementing a Trade Promotion Management SystemRequires a lot of data alignment!

Customer: Plan-to, Bill-to, Ship-to, Indirect and DirectProduct: Promotion Group, UPC, Cases, Shippers/Display PalletsTime: Order dates, Ship dates, Requested Delivery DatesMetrics: Off-Invoice, Deduction, Check, Shipment Allowances,

Warehouse Withdraw Allowances, Scans, Lump Sums, Expected Spend, Actual Spend

TPM ERP

Step #2: Incorporate POS data into TPMMerchandising executed, incremental sales, forward buy, ROIMore data alignment!

Customer: Plan-to vs Banner definitionsProduct: Promotion Group vs UPC’sTime: Ship weeks vs Syndicated Weeks vs Promotion WeeksMetrics: Case Shipments vs Unit Sell-through

Step #3: Post-Promotion AutomationCreate a library of promotion eventsEven more data alignment…

Aligning shipment dates and performance dates that match actualsPlanned

PerformanceDates

MissedSales

Do not be daunted by these steps

Get help from integration and data management experts

Post-promotion analysis can be done during the journey…and is worth it!

Leveraging Data to Activate Retail Sales/Merchandising Teams

Donna Tellam

Start with a long term approach and take small steps

Automate the process, enrich the data being collected & begin to leverage data 1

Actionable Insights - Automatically take action based on data3

Test & Learn - Use data to test, learn & improve4

Begin connecting retail execution data to external systems & expand field communications2

Predict issues and proactively take action5

“We gained visibility into data required to optimize operations and identify growth

opportunities.”When critical stores have performance issues, they can now shift resources so top-performing merchandisers are servicing those stores.

They can identify which merchandisers should be coaching low performers.

Data and insights have been enhanced down to the SKU level, so analysts have the insight needed

to proactively avoid out-of-stock situations.

Managers can now access pre-configured reports from within the HQ Portal, so

data is easy to find and understand.

Journey to data-driven collaborationAchieving retail visibility through data analytics

Challenge: Can data help to assure Mondelez products are on the shelf at

retail outlets and available for purchase?

Up-stream Causes;

28%

Store Ordering & Forecasting; 47%

In Store, Not On Shelf; 25%

OOS Root Causes*

* A Comprehensive Guide To Retail Out-Of-Stock Reduction In The Fast-Moving Consumer Goods Industry by T. W. Gruen and D. Corsten.

What we did

Shipment

Order

Store POS Data ConsumerWarehouse

Inventory

Combining Inventory, Order and Shipment data with POS data = Insights

Step 1: Pulling it all together

Data Visualization allows teams to assimilate data effectively and efficiently

Prescriptive Alerts deliver targeted tasks to Field Sales Reps

What we did

Step 2: Presenting insights and making it meaningful

Sales & Merchandisi

ng

Retail & Store

Operations

Supply Chain

Results: Data drives Collaboration

Mfg. Account Team

Retailer HQ

Mfg. Field Sales

Retailer Store Mgr.

Retail Shelf

Result: Stimulated internal and external collaboration to get the shelf right!

Conclusion

• Data can provide visibility at Retail and drive internal and external collaboration– But you have to work at it

• Pull it all together• Present it and make it meaningful• Change Management

• There is an evolution– Reporting, Descriptive, Predictive, Prescriptive

Managing DataEDM, DI, MDM, DW, Big Data

Provide a comprehensive data management framework, architecture and governance to achieve a “single version” of truth

Business Intelligence

Descriptive Analytics

Provide a comprehensive data reporting/dashboards framework, architecture and governance to deliver appropriate, timely and actionable information

Insight GenerationPredictive Analytics

Through an integrated analytics framework and by applying business rules, statistical models, visualizations, and industry specific context derive actionable insights from disparate data

Decision SciencePrescriptive

Turning actionable insights into measurable outcomes and improving the speed and quality of decision making

Valu

e to

the

Ente

rpris

e

Data Driven Organization Maturity

Data & Analytics ContinuumThe power of an integrated data and analytics framework