a trading partner approach to data centered collaboration
TRANSCRIPT
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
016
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
350
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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
300
400
500
600
700
800
900
1000
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