Retail Promotional Analysis
Overview: The Retail Promotional Analysis explore is designed to analyze the effectiveness and return on investment of trade promotions in retail channels. It achieves this by combining retail sales performance data with promotional event information. Essentially, it links when and where you ran trade promotions (such as in-store discounts, features, displays funded by you) with the sales outcomes during those periods, allowing an in-depth evaluation of how promotions lifted sales and whether they were profitable.
Key Components
Retail Sales Data: The explore integrates data from all retail channels (likely weekly retail sales figures by product, perhaps from syndicated or retailer-specific sources). This provides the baseline and promotional period sales metrics.
Promotional Events Data: Information on trade promotions (from a TPM – Trade Promotion Management system like Adesso, or spreadsheets) is included. For each promotion event, data such as:
Timeframe of promotion (start and end dates, which would align with certain weeks).
Products involved.
Type of promotion (e.g., price discount, BOGO, display, etc.).
Cost of promotion (trade spend, like allowances, slotting, etc. or total investment for that promo).
Linking Mechanism: The explore likely has a way to tie the promotions to the sales:
Possibly a flag or identifier that a given product-week was “On Promotion”.
Or an event dimension like Promotion ID that can be used to filter sales during that promotion’s timeframe at specific retailers or markets where it ran.
Metrics:
Incremental Sales: A crucial calculation is incremental lift – how much extra sales were generated due to the promotion. This might be derived if baseline sales are known or assumed.
ROI (%): Return on investment = incremental gross profit or revenue / cost of promotion. If the data includes the cost and incremental sales, ROI can be computed. The explore itself might not directly calculate ROI %, but it sets up all pieces (sales, costs) so you can.
Promotion Effectiveness: Measures like lift in % (promo vs non-promo period sales) for each event.
Overlap: If multiple promos overlap, analysis can become complex; ideally events are distinct or labeled.
Use Cases
Promotion Post-Mortem Analysis: After a promotional campaign, assess how it performed:
Did the promotion significantly increase sales volume? By how much compared to periods without promotion?
Calculate the lift in sales and then see if the cost of the promotion (trade spend) was justified.
E.g., promotion cost $10k and yielded $50k extra sales, ROI = 5x (or if factoring margin, maybe $20k extra margin for $10k cost = 2x ROI).
Optimize Future Promotions: By examining a history of promotions:
Identify which types of promos yield highest ROI (maybe displays yield better ROI than simple price discounts, etc.).
See which retailers or markets respond best to promotions (perhaps some regions see big lift, others none).
Budget Allocation: Use ROI insights to allocate trade spend budget to the most effective events. If certain promotions had negative ROI (lost money), you might cut those in future.
Collaboration with Sales Teams: Provide data to sales/account managers to discuss with retailers: e.g., “Our promotion increased category sales by X%” which could help negotiation for better placements or shared costs.
Data Considerations:
Sources: Promotional events could come from systems like Adesso, Excel uploads, etc. and sales from all retail sources. Possibly any brand-supplied promo calendar is integrated.
Granularity: Likely weekly by product by market by promotion. But how is it structured?
Possibly each record might be a combination of (Week, Product, Market, PromoID) with fields for sales during promo vs baseline.
Or they might present it more aggregated by promotion event (like each promotion as one row with total results).
The description suggests linking events with sales outcomes, but as an explore, probably more granular (so you can break down by product or week).
Promotion Types and Overlap: If promotions overlap or are continuous, separating their effects might require statistical analysis (not just raw data). Daasity’s explore may not inherently solve that, but it provides raw figures.
Important Fields
Promotion Event ID/Name: to identify specific promos (maybe dimension).
Promotion Start/End (or a time dimension to filter which weeks had promo).
Product/Category involved (some promotions cover a category or brand, others a specific SKU).
Retailer/Market: Many trade promos are retailer-specific, so there might be a dimension for which retailer or market the promo applied to.
Promotional Lift Metrics:
Sales during promo period vs a prior period baseline (maybe previous 4 weeks avg or same period last year).
Possibly an explicit “Baseline Sales” and “Actual Sales” field.
Incremental Sales = Actual - Baseline (could be a calculated field).
Trade Spend: The cost of the promotion. Could be broken down or just total cost. E.g., if the promo was a discount, cost might equal units sold * discount per unit (which you could compute if discount info is there). Or a lump sum like $5k on an endcap display fee.
ROI Calculation: Possibly a field or require user to calculate. If present, perhaps ROI% or dollar ROI (incremental margin - cost).
Other KPIs: Like promotional lift % (incremental / baseline), incremental units, etc.
Analysis Techniques:
Compare multiple promotions: use Promotion ID as a pivot or grouping to see which had highest incremental sales or ROI.
Filter on promotions that lost money (ROI < 1) to learn why (maybe happened in low-traffic period or product had supply issues).
Filter by retailer to see e.g., all promotions run at Walmart vs at Target, see where ROI tends to be higher.
Look at a specific time (like Q4 promotions) to evaluate seasonal promo effectiveness.
Because trade promotions often correlate with seasonality (holidays etc.), ensure baseline calculation accounts for that (perhaps using year-ago as baseline).
References:
CSV Description: “Combines retail sales performance data with promotional events information… link promotional events with sales outcomes… ideal for evaluating trade promotions impact and optimizing strategies.”.
Potential external: If Adesso or other TPM integrated
Industry concept references: Knowledge that trade promotion analysis requires both internal spend and external sales, which this explore addresses by merging data sources.
Add additional context when possible:
Promotion Data Details: What fields do we have for each promotional event? (e.g., promo ID, name/description, start and end dates, type of promo, planned spend vs actual spend, etc.)
Baseline Sales Calculation: How is baseline (non-promotional) sales determined in this explore? Do we use year-over-year or prior period averages, or is it left to the user to define baseline?
ROI Computation: Is the explore already calculating ROI or incremental lift, or are users expected to calculate those? If pre-calculated, how exactly (e.g., incremental revenue or margin vs cost)?
Promotional Overlap: How are overlapping promotions handled? (For example, if two promos run on the same product in the same period, can the explore distinguish their effects, or would that scenario be combined under one event entry?)
Promotion Scope: Are promotions keyed to specific retailers/markets in the data? In other words, can we filter to see the effect of a promo in one retailer versus another if it ran nationally?
Trade Spend Granularity: Is the cost of promotion allocated down to week-level or product-level in the explore, or is it just total cost per promotion event? (E.g., if a promo lasts 4 weeks, do we split cost across those weeks for analysis?)
Example Use: Could you provide a simple example of how a promotion is represented in the data (e.g., “Promotion X on Product Y from Week 10-12 cost $Z and resulted in N incremental units, giving ROI of X%”)? This will help ensure the content aligns with actual fields.
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