Trade Promotion Performance

Trade Promotion Performance Dashboard (ROI and Planning)

💡 It’s important to distinguish between Wholesale/Retail POS analysis and Syndicated Data (URMS) analysis, as each is powered by its own Trade Promotion data models (Explores) and dashboards.

• Wholesale + Retail POS: Delivered as two Explores but combined into a single dashboard, these models integrate Promotional Events/TPM data with promotional “actuals.” They are designed to support both general planning/analysis and trade-specific analysis by including a Revenue Plan vs. Actuals comparison for each event.

• Syndicated Data (URMS): Delivered as a distinct Explore and dashboard, this model also integrates TPM event data with promotional “actuals.” However, instead of plan comparisons, it emphasizes ROI analysis in the context of syndicated metrics (e.g., merchandising conditions) and enables competitive promotional analysis: Which tactics are most effective in the market? Which would be most profitable for your brand or product line?

Overview

The suite of Trade Promotion Performance Dashboards are an advanced tool that integrates promotional outcomes with trade spend, TPM integrations for deal-level costs in order to get to a true profitability analysis. It’s similar to the retail promotional analysis dashboards but goes a step further by answering: “Did our promotion pay off financially? What was the ROI of that trade promotion? How did the actual results compare to our forecast or expectations?” This dashboard is typically used internally by sales finance or trade marketing managers, especially when evaluating Trade Promotion Management (TPM) data. It combines syndicated sell-through results (or your own sales data) with your internal promotion events, costs, and profit margins. In essence, it helps you not just measure incremental sales, but also understand incremental profit and return on investment, and even plan future promotions with forecast vs actual comparisons.

(Note: Daasity’s implementation may have two versions of this – one using syndicated retail sales data as the baseline, and one using your own shipment/wholesale data (URS.Wholesale) if retail scan data isn’t available. The concepts are similar, but the data source differs.)

Key Components

• Promotion Event Table: A list of individual promotion events (or a summary by product/period) with details and metrics. Each event might be a row with columns such as: Promotion Dates, Product/Brand, Market (or Retailer), Type of Deal (e.g., “20% off with display”), Baseline Sales (forecast), Actual Sales, Incremental Sales, Trade Spend (cost), Incremental Profit, ROI. This table provides a comprehensive post-event breakdown. For example, an event entry could read: “Memorial Day Promo – Brand X – Retailer Y – Base forecast $100k, Actual $140k, Incremental $40k, Cost $15k, Incremental Profit $8k, ROI = 0.5 (or 50%)”. ROI here typically is defined as Incremental Profit / Trade Spendcpgvision.com. If ROI < 1.0, the promo didn’t pay back in profit; if >1.0, it generated more profit than cost (a success financially).

• Summary KPIs: At the top, you might see aggregate figures like Total Trade Spend in period, Total Incremental Sales, Average ROI, etc. This gives a big-picture health check of your promotional efficiency across many events. For instance, “Trade ROI = 150%” would mean overall you got $1.50 back for every $1 spent on promotions in whatever timeframe is selected – a strong outcome. If it’s below 100%, that indicates net loss on promotions in aggregate (which might be a deliberate investment or a problem to address).

• Forecast vs Actual Charts: The mention of a “state model inclusive of forecasting comparison” suggests the dashboard also helps in planning. There might be a chart that shows planned sales vs actual sales for promotions. Perhaps a line graph with expected baseline, expected incremental (plan) vs actual baseline and actual incremental. This allows you to see how reality matched your projections. For example, you planned a promo to add $50k incremental but only got $30k – the chart would visualize that shortfall. Or vice versa, maybe you over-achieved.

• ROI or Efficiency Chart: A visualization (perhaps a scatter or bar chart) could plot each event’s ROI or Cost per Incremental Dollar (CID)cpgvision.com. This quickly flags which promotions were most efficient and which were underperformers. You might use a threshold line at ROI=1.0 to see which events fell below (cost more than they returned). Sometimes events are color-coded by type or retailer to spot patterns (e.g., “feature+display events are green, TPR-only are blue” – and you notice green dots mostly above 1.0, meaning feature+display tend to pay off).

• Filters for Scenario Planning: Likely, you can filter by brand, retailer, timeframe, etc., and perhaps simulate adjustments. For instance, some systems let you input a hypothetical trade spend or lift to project ROI. The prompt mentions using it to plan promotional events and then compare results vs expected – so it’s both a planning and post-evaluation tool. Before an event, you might input an expected lift (from historical learnings) to forecast ROI; after, you plug in actuals to see what happened.

Using the Trade Promotion Dashboard

• Measure Profitability: This is the primary purpose – ensure that promotions aren’t just driving volume, but profitable volume. Let’s say one event shows Incremental Sales $100k, Trade Cost $80k, ROI = 0.5. That’s a signal that although you sold a lot, the promo was too expensive (maybe a combo of margin giveaway and spend on displays, etc.) – you effectively paid $80k to get $50k worth of gross profit (assuming certain margins), a loss-making deal. On the other hand, another event might have Incremental $50k at cost $10k, ROI = 3.0 – very efficient. By identifying these, you can decide what kinds of promotions to repeat. It could be that smaller, local promos yield great ROI, while national ones are too costly – or vice versa. Without this analysis, you might chase sales blindly; with it, you focus on profitable growth.

• Learn from Forecast Accuracy: If the dashboard shows a big gap between planned and actual outcomes, investigate why. Were your lift assumptions too high? Did execution falter (e.g., displays not in all stores as planned)? Or was there an external factor (competitor dropped price at same time, etc.)? Consistent variance patterns can help recalibrate your planning model. For example, maybe you always overestimate lift for a certain retailer – next time, plan more conservatively for them. Improving forecast accuracy helps in allocation of budgets and setting realistic expectations for stakeholders (like finance or senior management). It’s better to promise a 1.5 ROI and deliver 1.6 than promise 2.5 and deliver 1.6.

• Optimize Trade Spend Allocation: With ROI data, you can make a strong case to reallocate funds. If certain promotions or retailers yield subpar ROI, you might cut back there and put resources where ROI is strong. For instance, “Our analysis shows promotions in Retailer A average 120% ROI, whereas in Retailer B they average 60%. We should reconsider the depth or frequency of promotions in B, or find a way to improve them, and maybe shift some budget to A where promotions are paying off.” That said, you’ll also consider strategic factors (maybe Retailer B is new and you accept short-term losses to build presence). But at least decisions are informed by data, not gut feel.

• Plan Future Promotions (Pre-event): Use the tool to simulate outcomes. If you’re planning a big holiday promotion, input expected variables: e.g., baseline, anticipated lift (based on similar past events), and trade spend requirements. The dashboard (or an associated planning mode) can then spit out a projected ROI or profit. If it looks negative, you can tweak parameters: “What if we reduce the discount from 25% to 15%? How much lift might we lose and what happens to ROI?” Essentially, you can iterate to design a more effective promo before it happens. This is the "optimization" part of Trade Promotions & Promotional Strategy Optimization (as the outline section name suggests). Over time, you build a library of “best practices” – e.g., “Feature + Display at 15% off for one week yields ~25% lift and ROI >1, whereas 30% off for two weeks yields 40% lift but ROI <1. Let's go with the former structure.”

• Align Sales and Finance: This dashboard fosters collaboration between sales (who care about volume) and finance (who care about profit). It provides a single version of truth where both aspects are visible. When reviewing promotions, the team can discuss both incremental sales and incremental margin. For example, “That back-to-school promo gave us great shelf presence (and we hit our volume target) but we lost money on it. Next year, can we negotiate a smaller discount with the retailer or get vendor funding to offset the cost?” These are the kinds of insights that drive better negotiations and smarter deals.

• Wholesale Version vs Syndicated Version: If using internal shipment data (Wholesale), the base vs incremental might be calculated differently (possibly using internal baseline forecasts). The principles remain: baseline is what you’d have sold anyway (could be a shipments forecast), incremental is what you sold above that during the promo. The ROI would incorporate perhaps different metrics (like deduction spend, etc.). But fundamentally, both versions aim to quantify lift and ROI – one from the perspective of retail off-take, one from shipping/inventory perspective. The syndicated (retail) version is great for understanding consumer offtake; the wholesale version is useful if you don’t have full retail data or want to tie it to your shipments and revenue directly. Both should ideally tell a consistent story – if a promo increased retail sales, you likely shipped more too (unless pipeline filled).

Conclusion & Best Practices

The Trade Promotion Performance Dashboard is the culmination of the analytics journey: starting from raw sales (Sales Dashboard), drilling into causes (Key Drivers), analyzing execution (Promotional analysis), and finally evaluating financial return (this dashboard). Best practices when using it include:

• Always pair volume results with cost to judge success.

• Build an internal ROI benchmark – e.g., maybe your company expects at least 1.2 ROI on promotions; use that as a barometer.

• Use insights to refine promotion guidelines: you might develop rules like “We will not run promos deeper than 20% unless we expect at least 50% lift” or “No off-shelf displays without feature ad, because data shows display-only yields lower ROI.”

• Continuously update the model: as you get new results, feed them in to improve future forecasts (a virtuous cycle of learning).

By consistently applying this disciplined approach, you can significantly optimize trade spend, ensuring your promotional dollars are driving not just top-line growth, but healthy bottom-line contribution as well. This is how brands turn promotions from a cost center into a strategic investment with measurable returns, aligning with the ultimate goal of promotional strategy optimization.