Organizing for ecomm. The New Disruptive Retail Partner: Commerce Captain

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Cynthia Mathews
5 years ago
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1 Organizing for ecomm The New Disruptive Retail Partner: Commerce Captain

3 Source: Predicting the Turn (Dave Knox)

4 Source: Predicting the Turn (Dave Knox)

5 ecomm Capabilities Pyramid

6 ecomm Capabilities Pyramid DISTRIBUTION/ DIGITAL SHELF

7 ecomm Capabilities Pyramid

8 ecomm Capabilities Pyramid TRAFFIC

9 ecomm Capabilities Pyramid

10 ecomm Capabilities Pyramid CONVERSION/REPEAT

11 ecomm Capabilities Pyramid

12 ecomm Capabilities Pyramid INCREASE BASKET

13 ecomm Capabilities Pyramid

14 ecomm Capabilities Pyramid "NEW"

15 Locked Data vs. Unlocked Data Retailer Portal Data Sales Forecasting Inventory Advertising Supply Chain Accounting Operations EDI/API Feeds Public-Facing Reviews Buy Box Third Party Sellers Out of Stocks Descriptions Imagery Pricing Rich Content

16 Locked Data vs. Unlocked Data

17 Locked Data vs. Unlocked Data

18 All About the Robots All 3 Million of Them General Fetching Geolocation Self-Healing Scheduling

19 New Category Manager Challenges How Will This New Data Help Your Business? Analyzing and reporting of physical and digital commerce Identification of assortment gaps (in-store and online) Tracking key category players Cross-channel pricing discrepancies and opportunities Demand/development gapping Online Grocery Pick-Up (OGP) in-store availability Cross-retailer stitching to see a product or category s entire distribution ecosystem

20 Harnessing Data as a Commerce Captain PHASE 1: HUNTING & GATHERING

21 HUNTING & GATHERING Manual gathering of the Wild West ecomm data No reconciling or data validation Pick & choose what to address Digital marketing (pixel tracking) by force

22 Harnessing Data as a Commerce Captain PHASE 2: ORGANIZING & ANALYZING

dot-com and brick & mortar retailers one at a time Self-reflection

23 ORGANIZING & ANALYZING Historical archiving or ecomm trends Vertical blending of data for single SKUs or brands manually by marrying dot-com and brick & mortar retailers one at a time Self-reflection of company items only over time SEO generally limited to top search terms

24 Harnessing Data as a Commerce Captain PHASE 3: BLENDING & EXPLAINING

25 BLENDING & EXPLAINING Horizontal blending of data for company and category SKUs/brands automatically across retailers by marrying dotcom and brick & mortar retailers systematically Digital marketing (pixel tracking) by choice SEO expands to advanced content tagging

26 Harnessing Data as a Commerce Captain PHASE 4: AUTOMATING & INFLUENCING

27 AUTOMATING & INFLUENCING Automated alerts flag KPIs like MAP compliance, OOS, negative reviews, etc. so that prescriptive measures are taken Machine learning is utilized to predict/adapt shopping patterns Visual assortment planning is utilized to become the digital equivalent of modular planning

28 Harnessing Data as a Commerce Captain PHASE 5: LEADING

29 LEADING Mastery of agnostic source data reporting. All strategic versus tactical analytics Commerce Captain is fully recognized by flagship retail partners Full ecosystem: know when to pull levers on Pure Play, B2C, B2B

Solid Foundation: Omnichannel Data Specialists ecommerce Data Amazon, Walmart, Target, Best Buy, Peapod, Home Depot, Lowes, and many more Brick and Mortar Data (Integration with ATLAS, and others)

31 Solid Foundation: Omnichannel Data Specialists ecommerce Data Amazon, Walmart, Target, Best Buy, Peapod, Home Depot, Lowes, and many more Brick and Mortar Data (Integration with ATLAS, and others) Retail Link, MerchIQ, 1010, etc. IRI, Nielsen, NPD Shopper Data CRM, In-Store Events, FSIs, Coupons, Loyalty Digital Data Banners, Campaigns, Google, Flix, Coupons (ibotta) Social & Influencer Data Twitter, Facebook, YouTube, Instagram, Pinterest, SNAP, Twitch, Collective Bias

32 Canopy Analytics & Tracking Tracking and Analysis of: Pricing MAP/MSRP (your products and competitors) Content Quality Reviews Third Party Sellers Customer Engagement Product Affinity Digital Advertising Online Grocery Pickup

33 Canopy Analytics & Tracking Category Share and Analysis Estimated sales for all competitors above and below rank New SKU alerts and tracking Category item movement Cross-retailer category tracking Above/below the fold Desktop, mobile, tablet

38 AMP Automated Assortment Modular Planning

39 Modular Automation The New Paradigm Completely Customized Software & Process Every Category and/or Relay is different. Full Transparency Plug-in and Module based Platform Immediate Response Time to Changes Problem Solving with Engineering/Programmers End-to-End complete relay solutions Assortment Drawing Validation Reporting - Virtualization

Assortment Decisions AMP provides the End User with

Nielsen/IRI) Days of Supply Tolerance Levels

40 Assortment Decisions AMP provides the End User with the ability to adjust settings and variables on the fly to affect the assortment. Custom Rules Item Metric Weights (POS, Units, Nielsen/IRI) Days of Supply Tolerance Levels Core/Protect Lists Item/Store Assortments Completely Customized per Relay/Category/Customer

41 Custom Rules Plastic Bottle Rule Only merchandise in plastic product where stores are traited for bodies of water. Segmentation Rule Flex/Keep Segment Space based on custom parameters Item Placement Conditions If Item A exists, then Item B must go next to it. If the Modular is > 98ft, then all Items of supplier Y must go on 3 rd shelf regardless of item flow

42 Custom Plugins/Modules Custom Workflows for your Category Mini-Mods Trait Surveys Generate new mod for a store based on existing stores

43 Assortment Decision Matrix

44 Merchandizing Steps Above the Fold Step by Step Visual Validation of Automated Process 1. Identify Delete Items 2. Adjust Facings 3. Identify ADD Items 4. Auto Merchandize Items 5. Completed Final Modular Full Transparency to easily understand how decisions influence the Modular layout.

45 Reporting Completely Customized Space to Sales DC Matrix OSA Scores Before/After Performance

46 3D Above the Fold is the New Eye-Level

Highlights Planogram Differences Live Daily Data Feeds Current Store

47 3D Above the Fold is the New Eye-Level Macro Data Top/Low Performing Items Chronic Out of Stocks Predictive Out of Stocks Brand Block Highlights Planogram Differences Live Daily Data Feeds Current Store Conditions On Hands Predictive Out of Stocks POS$ Expectations Markdowns

48 3D Heat Mapping

49 3D Product Information

50 Time Savings

51 Reactive Defense Proactive Offense

52 Long Reaction Time Short Reaction Time

53 Insights Without Action are Nothing But Overhead. Write the Book on Defining What a Commerce Captain is to Your Retail Partners. Bill Akins bill.akins@canopy.net

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