1. A Trading Partner Approach to Data
Centered Collaboration

2. • Background
• Panelists
• The Foundation
• Typical Scenario
• Stories
• Q&A
Agenda

3. Mindtree at a Glance
Basel, Switzerland
Brussels, Belgium
Cologne, Germany
London, UK
Paris, France
Solna, Sweden
Vianen, Netherlands
Europe Asia
Beijing, China
Dubai, UAE
Singapore
Sydney, Australia
Tokyo, Japan
IndiaNorth America
Company HQs Delivery Centers
Bangalore
Pune
Chennai
Hyderabad
Warren, NJ
Cleveland, OH
Dallas, TX
Gainesville, FL
Phoenix, AZ
Redmond, WA
San Jose, CA
Schaumburg, IL
Minneapolis, MN
Chicago, IL
Los Angeles, CA
New York, NY
Global Coverage
26% Revenue
Retail, CPG and
Manufacturing

4. Relational Solutions acquired by Mindtree
 Specialized provider
of analytics for CPG
retail execution
 Pioneer in demand
signal repository
technology
Relational Solutions
POSmart
BlueSky
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:

5. 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

6. Mike Marzano
Solutions Process
Expert, Retail
Execution
Mondelez
International
Donna Tellam
Vice President,
Customer &
Partner Solutions
Spring Mobile
Mark Horner
Director, Trade
Marketing
Eagle Family
Foods Group
Meet the Panelists

7. Managing Data
EDM, 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 Generation
Predictive 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 Science
Prescriptive
Turning actionable insights into
measurable outcomes and
improving the speed and quality of
decision making
ValuetotheEnterprise
Data Driven Organization Maturity
Data & Analytics Continuum
The power of an integrated data and analytics framework

8. Enables Many Business Driving Insights to Bubble Up
Building a Solid Foundation

9. A Typical Promotion Analysis
Scenario

10. Typical Scenario
High level Promotion Plan and Sales Facts
$2.70
$2.80
$2.90
$3.00
$3.10
$3.20
$3.30
$3.40
$3.50
$3.60
0
50
100
150
200
250
300
350
400
450
Sales Units
Retail Price
Retailer X 13 Week Price vs. Volume Trend

11. Where’s the Needle?
Syndicated
Data

12. Additional Information
Shipment Facts
0
100
200
300
400
500
600
700
800
900
1000
1100
Sales Units
Shipped Units
Retailer X Shipment vs. Consumption Trend

13. Now Where’s the Needle?
Syndicated
Data
Shipment
Data

14. More Information
Retail Execution Facts
Retailer X Store Sales by Day
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Store # City
9/11/2016 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 0
2 Los Angeles 2 1 1 2 1 2 1 10 50 40
3 East Los Angeles 0 0 0 0 0 1 4 5 50 45
4 Commerce 1 1 1 1 3 3 5 15 50 35
5 Ladera Heights 6 5 11 15 12 1 0 50 50 0
6 Vernon 0 1 1 1 2 1 3 9 50 41
7 Willowbrook 1 0 1 0 2 2 5 11 50 39
8 Bell Gardens 0 0 1 1 1 3 7 13 50 37
9 Beverly Hills 1 1 1 1 1 3 8 16 50 34
10 Compton 0 0 0 0 0 0 0 0 50 50
11 Downey 0 0 0 0 0 1 4 5 50 45
12 Gardena 2 1 0 1 0 1 3 8 50 42
13 Hawthorn 10 8 7 10 15 0 0 50 50 0
14 Hermosa Beach 1 1 1 1 1 4 3 12 50 38
15 Huntington Park 0 0 0 0 0 0 0 0 50 50
16 Lawndale 1 1 2 1 2 3 6 16 50 34
17 Lynwood 10 12 15 13 0 0 0 50 50 0
18 Malibu 15 15 15 3 1 1 0 50 50 0
19 El Segundo 1 1 1 1 1 3 7 15 50 35
20 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

15. Now Where’s the Needle?
Syndicated
Data
Shipment
Data
Retailer
Store
Master
Data
Retailer
Store Level
POS
Data

16. Is More Information
Useful?
If so, why isn’t it used
more often?

17. What You Said at POI Last Year
The POI 2015 TPx and Retail Execution Survey
Only 10% of CPG Companies felt they had an
Automated and Easy way to analyze trade

18. What You Said at POI Last Year
The POI 2015 TPx and Retail Execution Survey
96 % of Companies Have Trouble Analyzing Trade

19. What You Said at POI Last Year
The POI 2015 TPx and Retail Execution Survey
76% of CPG Companies
Believe they have ongoing Data Quality Issues

20. • 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

21. Shipments Sales
Do You Speak the Same Language?
Case, Pallet, Loaded Display UPC / SKU

22. Item Information
Tab It Brand Item List
Multiple Items Can Represent 1 UPC
Item Number Description Brand UPC Business Unit UOM Units per Case
1234 Blue Vnyl Tab 12 pk TabIt 12345678901 Folders Case 12
1234TG Target Bl Vinyl Folder Tab It 12345678901 School Supplies Case 12
1234CV 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 12
11158 Yllw Vinyl Tab 12 pk Mass TabIt 12345987965 Folders Case 12
11160 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 Number
1234 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 4321
1234CV 6 pk Blue Fldr CVS Tab It 12345678901 Office Supplies Case 6 Blue Vinyl Tab Folder 4321

23. Whose Calendar do you use?
Sunday Monday Tuesday Wednesday Thursday Friday Saturday
Week Ending 9/11/2016 9/12/2016 9/13/2016 9/14/2016 9/15/2016 9/16/2016 9/17/2016
Syndicated X
Retailer X Promo X
Shipments X

24. To Move the Needle
Gather ALL the Facts, Integrate, Harmonize  Insights
Master
Data
Shipment
Data
Consumption
Data
Forecast
Data
3rd Party
Distributor
Data
Merchandiser
Feedback Data
Weather
Trend Data
Promotion
Data

25. TPM Data Challenge Example
Mark Horner

26. 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

27. Step #1:
Gain financial controls over your trade funds
Implement 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

28. Step #1: Implementing a Trade Promotion Management System
Requires a lot of data alignment!
Customer: Plan-to, Bill-to, Ship-to, Indirect and Direct
Product: Promotion Group, UPC, Cases, Shippers/Display Pallets
Time: Order dates, Ship dates, Requested Delivery Dates
Metrics: Off-Invoice, Deduction, Check, Shipment Allowances,
Warehouse Withdraw Allowances, Scans, Lump Sums,
Expected Spend, Actual Spend
TPM

29. Step #2: Incorporate POS data into TPM
Merchandising executed, incremental sales, forward buy, ROI
More data alignment!
Customer: Plan-to vs Banner definitions
Product: Promotion Group vs UPC’s
Time: Ship weeks vs Syndicated Weeks vs Promotion Weeks
Metrics: Case Shipments vs Unit Sell-through

30. Step #3: Post-Promotion Automation
Create a library of promotion events
Even more data alignment…
Aligning shipment dates and performance dates that match actuals
Planned
Performance
Dates
Missed
Sales

31. 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!

32. Leveraging Data to Activate Retail
Sales/Merchandising Teams
Donna Tellam

33. Start with a long term approach
and take small steps
Automate the process, enrich the data
being collected & begin to leverage data1
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

34. “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.

35. Data and insights have been enhanced
down to the SKU level, so analysts have the insight needed
to proactively avoid out-of-stock situations.

36. Managers can now access pre-
configured reports from within the HQ Portal, so
data is easy to find and understand.

37. Journey to data-driven collaboration
Achieving retail visibility through data analytics

38. Challenge: Can data help to assure
Mondelez products are on the shelf at
retail outlets and available for purchase?
Upstream
Causes,
28%
Store
Ordering
&
Forecastin
g, 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.

39. 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

40. 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

41. 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!

42. 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

43. Managing Data
EDM, 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 Generation
Predictive 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 Science
Prescriptive
Turning actionable insights into
measurable outcomes and
improving the speed and quality of
decision making
ValuetotheEnterprise
Data Driven Organization Maturity
Data & Analytics Continuum
The power of an integrated data and analytics framework