Retail Competitive Analysis
Overview: The Retail Competitive Analysis explore integrates multiple syndicated and retailer data sources (such as Kroger POS, SPINS, Nielsen, Circana, Stackline, etc.) to provide a comprehensive view of competitive retail market performance【26†Description】. Its goal is to allow you to evaluate your brand’s performance in the context of the broader market and competitors, all in one place. Essentially, this explore is your one-stop for market share and competitor analysis across retail channels.
Key Features of Retail Competitive Analysis
Combined Syndicated Data: The explore aggregates data from various market tracking services:
SPINS, NielsenIQ, Circana (IRI) – which offer category and market level sales for broad retail channels (Natural, Grocery, xAOC, etc.).
Retailer Portals (Kroger, etc.) – direct POS data from specific retailers.
Stackline – possibly e-commerce marketplace analytics if included, but likely mostly for Amazon or online retail metrics.
Any other competitive intel sources.
Market-Level Metrics: Expect metrics like:
Total category sales, Your brand sales, Competitor brands sales (if available).
Distribution metrics such as % ACV (All Commodity Volume distribution) or “stores selling” etc., which syndicated data often includes to measure distribution breadth .
Velocity metrics like sales per point of distribution (a measure of how well product sells where it is stocked).
Market Share % for your brand and key competitors.
One Explore, Multiple Tables: Because it spans different schemas (URMS – Unified Retail Market Schema【24†Name/Old Name】, etc.), under the hood it likely joins or unions various data. This explore was previously known as “URMS” (Unified Retail Market Schema)【26†Old Name】 but now given a user-friendly name “Retail Competitive Analysis”, indicating it surfaces the unified view.
Competitive Performance Over Time: You can trend how the total market or specific competitors are performing over weeks or months, just as you would your own sales, enabling identification of market trends or share shifts:
E.g., see total category growth, and how your brand vs competitor A contributed to that growth.
Use Cases for Retail Competitive Analysis
Market Share Tracking: Monitor your brand’s share in specific markets or categories. For instance, if you had 10% share last quarter and now 12%, you’re gaining share – this explore gives those numbers.
Competitor Benchmarking: Analyze how competitors’ sales are trending. Identify top competitors’ growth rates vs yours. If a competitor surged, see if it correlates with new distribution or promotions.
Category Insights: Look at the category as a whole:
Is the category growing or declining?
Are there sub-segments (if data has category breakdowns) that are driving that (e.g., Natural channel growing faster than Grocery, etc.)?
Distribution Gaps: Using distribution metrics, find opportunities:
Maybe a competitor has distribution in 80% of stores in a region and you have 50% – indicating a distribution gap. Or if you both have similar distribution, but their sales per store are higher, indicating either better velocity or execution.
Prepare Sales Pitches: This data is useful for retail buyer meetings – proving your brand’s performance relative to category. E.g., “We’re #2 in the category in the Natural channel and growing 20% while category is 5% – so we deserve more shelf space.”
Investigation of Anomalies: If your sales dip but category grows, it flags an issue (lost share). If category dips and you dip less, you gained share in a down market (outperforming category).
Data Structure and Fields
Time Dimension: likely weekly or monthly (depending on source granularity, often syndicated data is weekly). Might roll up to quarter or year if needed.
Market/Channel Dimension: e.g., “Total US – MULO” vs “Natural” vs retailer-specific. So you can filter or select specific market definitions.
Product/Brand Dimension: This is key – being able to select your brand vs others.
Possibly a dimension like “Brand” (with values including your brand and competitors).
Or “Manufacturer” if focusing higher level.
If very granular, maybe down to item, but competitive analysis is usually brand-level.
Metrics:
Sales (Units and Dollars) for each Brand in the market.
Market Total Sales (category total).
Share % (maybe calculated or you do it by dividing brand sales by category total).
Distribution (% ACV or Stores Selling) : showing availability in market.
Velocity (Sales per store or per ACV point).
Growth Rates: This might require year-ago data present to compute YoY growth for each brand and the market overall.
Filters to isolate:
Category or sub-category if data includes multiple categories.
Region if data can be segmented by region (some syndicated data can separate regions).
Given multiple sources, some sources cover certain retailers, some cover others. The description says “all competitive data sources, at the RMA/Market-level”【26†Sources】:
So likely it’s aggregated where possible to create a full view. RMA might refer to “Retail Market Area” or something similar, but probably means they unify on the concept of market.
Important Notes:
Data Lag: Syndicated data often has a lag (e.g., Nielsen might be 4-6 weeks behind current). So if your internal is real-time, don’t be alarmed that category data might not show the latest couple weeks.
Confidentiality: Only aggregate competitor data is shown (which is fine, you subscribe to it).
Accuracy: Ensure that when comparing share, you are comparing same basis. E.g., if your internal data might show slightly different numbers than syndicated because of differences in what’s captured (maybe syndicated doesn’t include a certain channel). Ideally, rely on the syndicated numbers for share calculations.
References:
CSV Description: “Integrates multiple syndicated and retailer data sources… comprehensive competitive retail market analysis. Ideal for retail teams analyzing market metrics and trends.”【26†Description】.
Old Name (URMS) hints this is basically making the Unified Retail Market Schema user-friendly【26†Old Name】.
Unified Retail Market Schema documentation likely outlines tables for category sales, distribution etc. .
Possibly in knowledge base, there might be mention of “Market Share” analysis.
Questions for Daasity Team (Retail Competitive Analysis Explore):
Data Coverage: Which syndicated data streams are included exactly (SPINS, NielsenIQ, Circana?), and do they cover specific channels or retailers? (Understanding which markets we have data for helps explain what can be analyzed.)
Brand/Competitor Identification: How are competitors identified in the data? Is there a “Brand” dimension listing all brand names, including ours and competitors? And do we need to filter our brand specifically by name (assuming data might list our brand as well)?
Metrics Availability: Do we have distribution metrics (like ACV% or stores selling) and velocity in this explore, or is it mostly sales values? If yes, which distribution metrics are present?
Granularity & Filtering: Can we filter by category or segment within the explore (for brands that play in multiple categories)? Or is it focused on one category/industry? Essentially, is this explore segmented by product category, or is it assumed single category?
Time Granularity: Are the figures weekly, monthly, or quarterly? And is year-over-year data readily accessible (so we can compute YoY growth and share changes)?
Integration of Sources: Are the various source data unified on the backend such that they avoid double counting or gaps? For example, if Nielsen and SPINS overlap on Natural channel data, are we using one source as primary for that channel?
Example Scenario: To illustrate usage, if I want to see our market share in Total US Grocery for the past 4 weeks – is it as simple as filtering Market = “Total US – xAOC” and selecting our Brand’s sales vs total? (This is to ensure I describe the usage correctly and any specific steps needed.)
Old vs New Naming: Since this was fka URMS, should we mention that name (for context of existing users), or just stick to “Retail Competitive Analysis”? (This might influence if I note “previously known as URMS” in passing)
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