Saama-POI Webinar Slides FINAL 04.27.2016 dm
- 1. 0 Copyright © 2016, Saama Technologies | Confidential Data Can Be Simple… Right? Capture Ingest Extract Aggregate Cleanse Visualize Automate Migrate Audit Optimize
- 2. 1 Copyright © 2016, Saama Technologies | Confidential CPG data sources – Wealth of Potential … & Challenges Traditional Data Sources • Syndicated • POS • Shipments • Spending Emerging • Crowd-sourced o Panel o Retail Conditions • Digital Promo Test • Social Listening • Many Others Re-purposed Data Sources • Panel • COGs • Weather
- 3. 2 Copyright © 2016, Saama Technologies | Confidential Data Stages Acquisition Integration Storage Analytics Decisions
- 4. INTEGRATION ALIGNMENT ANALYSIS Visualization SecurityandAccessControl CompleteLoadAutomation DataMart BI Cube ExternalDataSourcesInternalDataSources POS Data Sell-Thru, Sell-Out Syndicated Social & sentiment Data ERP SAP; Shipment, Pricing, Sell-in TPMS EDW/BW Master Data, Hierarchy, Price brackets PEA DAL Security and Access Control Automatic Mapping Engine Data Harmonization Load Validate Cleanse Transform APIs Saama Fluid Analytics Cloud Data Integration Engine Configurable Confidence % Thresholds Rules Engine Configurable Business Rules Audit & Error Handling RunETLJobs Accelerators PEA Admin Workbench Customer & Product Mapping (override auto mapping) Input Data Adjustment Event Dates, Shipped Volume, Price & COGS Updates to PEA Merged: Outlier Removal, Reported/ Non- Reported , Shopper Marketing Spend Mapped & Un-Mapped Products, Customers Missing and Misplaced Events Harmonizer Merging Exceptions Cleansing and Business Exceptions AdminFunctions Manual Override Ad-hoc Self Service BI PEA Canned Reports, Executive & Analyst Dashboards , and Foundation for Predictive & Prescriptive analytics Data Feed Data Architecture
- 5. 4 Copyright © 2016, Saama Technologies | Confidential Data Storage and Access Lost at Sea or Calm in a “Data Lake”?
- 6. 5 Copyright © 2016, Saama Technologies | Confidential Descriptive Analytics • Effectiveness and efficiency of promotional events • Effectiveness and efficiency of EDLP spend • Drill-down based on customer, product and event hierarchies Diagnostic Analytics • Under performing and over performing customers, products, deal structures, promotional tactics, times of year etc. • Link between Pricing and Promotional Strategy • Financial Driver Analysis Predictive Analytics / Test and Learn • Structured variety of Data • Different price levels, confounding factors • What-if Analysis based on predictive Models Advanced Analytics • Cannibalization of sales of other products vs. truly incremental sales • Retailer forward buy / Pantry Loading • The right baselines (“What would have been”, “business as usual forecast”, etc.) Analytical Methodology
- 7. 6 Copyright © 2016, Saama Technologies | Confidential Drive Strategic Agreement on Business Objective(s) Incremental Revenue/Turnover Incremental Profit / ROI Volume / % Lift Market Share Series 1 Series 2 Series 3
- 8. 7 Copyright © 2016, Saama Technologies | Confidential How will each use the system, and maintain consistency of interpretation? Joint Business Planning – which data to share with retailer Unified planning process Field awareness/adoption/incentive to provide accurate data Study and act upon results, provide diagnostic interpretations Stakeholder Management / Roles Account Managers Brand Managers Category Directors VP Finance Analyst
- 9. 8 Copyright © 2016, Saama Technologies | Confidential Change Promotional Tactics Shift spend among Products, Categories & Brands Reduce / Eliminate unprofitable Spend Increase Retailer Alignment Quarterly / Annual Planning Process Decisions Supported Shift spend among Retailers Identify & Expand best Practices Quarterly / Annual Planning Budgets
- 10. 9 Copyright © 2016, Saama Technologies | Confidential New POI Whitepaper
- 11. 10 Copyright © 2016, Saama Technologies | Confidential Inability to Effectively Manage Promotions, and Benefit from them, Stems from Four Key Factors 1. Complexity – Amount of resources/time required to analyze volume of trade promotions, given current systems, is unsustainable 2. Fidelity: – The fidelity of financial metrics within trade promotion analytics are highly suspect; end users trust output 3. Data utilization: – Much of the data that might help better inform trade analytics does not end up being used for analytics due to the difficulty in collecting, normalizing, and analyzing it 4. Data overload: – Increasingly more data is being collected each day, but most of it is not being utilized. – If anything, it tends to further cloak the problem because of the lack of resources and inability to get to the data that is most relevant.
- 12. 11 Copyright © 2016, Saama Technologies | Confidential 4 Key Capabilities Required for CPG Data & Analytics Excellence 1) Pre-built Analytics 2) Utilizing Advanced Modeling and Data Science 3) Merging Disparate Data 4) Expertise for Data Enrichment and Cleansing
- 13. 12 Copyright © 2016, Saama Technologies | Confidential About Saama 12 • Data & advanced analytics solutions company since 1997 • Multi-vertical solutions – High Tech, Insurance, Life Science/Pharma, CPG • Data scientists, “Big Data” engineers, consultants drive advanced analytics with business insights … Transitioned from Services to Unique, Hybrid Solution • Global – offices in San Jose, Phoenix, Columbus, London, Basel, & Pune