Promotional “Bump Chart” (URMS)

Promotional “Bump Chart” Dashboard (Promotion Timeline Analysis)

Overview

The Promotional Bump Chart Dashboard is a deep-dive analysis tool for examining promotional impact over time for a specific product (or brand) in a specific market. It gets its name from the visual “bump” you see in sales during promotional weeks. The dashboard combines a time series view of sales (segmented into base vs incremental volume) with pricing information, allowing you to analyze individual promotion events or time periods in detail. It’s the go-to dashboard for a “post-event analysis” of promotions: “What happened during the promotion last month? How much incremental volume did we get, and what was the pricing and discount? What was the baseline vs actual sales each week?” This helps trade marketing and sales teams refine promotion tactics by seeing which promotions delivered and which didn’t.

Dashboard Components

• Weekly Sales Chart (Stacked Base & Incremental + Price Line): The main chart typically shows each week’s sales as a stacked bar, split into Base Sales and Incremental Sales. Base is the estimated sales you would have had with no promo (often a baseline calculated from non-promo periods), and Incremental is the extra sales attributed to promotions that week. Overlaying this is a line for Average Retail Price (ARP) each week. When a promotion occurs, you’ll usually see ARP drop (e.g., from $5 to $4 if a 20% off sale happened) and correspondingly see a green (incremental) portion appear on the bar, indicating a lift in sales. This visual immediately shows the “bump” – for instance, in week 35 you see a price drop and a big incremental chunk, meaning a promo was executed and it drove additional sales. In weeks with no promo, the bar is essentially all base (with maybe a tiny incremental if baseline modeling isn’t perfect).

  This chart lets you correlate price and volume: you can observe how sensitive the product is to price changes. A shallow price cut that yields a small incremental bar indicates maybe low responsiveness, whereas a deep cut that yields a huge incremental bar indicates high responsiveness. It also highlights baseline trends – if base sales are gradually rising week over week aside from promos, that’s good underlying growth; if base is declining, that’s a concern being masked by promos.

• Key Metrics Summary (for selected period): To the side or below, there’s usually a summary table that calculates promotional performance metrics for whatever date range or filter you have applied. For example, if you filter the dashboard to show the last 4 weeks that had a promotion, the summary might show: % of Volume on Promo, Avg % Price Discount, % Lift, Incremental Sales ($ and units), and sometimes Promo Efficiency or ROI (if costs are known, though in syndicated data likely just efficiency metrics). This table basically crunches the numbers of the visual above. For instance: “In the selected 8 weeks of promotion, 30% of volume was sold on promo at an average 15% discount, achieving an average 25% sales lift vs baseline.” It’s like a report card for the period.

• Filters for Focus: The dashboard will allow filtering by product, market, and time. If you want a surgical analysis, you might filter to one product in one retailer for the exact weeks around a promotion. You could also compare different periods by adjusting the filter (some users might screenshot one period vs another). Usually, you’ll look at one product-market at a time for clarity.

• Annotations: Sometimes, specific promotions might be annotated (e.g., “Labor Day promo” or “Feature ad here”) if that info is integrated. If you have a promotions calendar loaded, the chart might have markers or shading for promo weeks. If not, the price line drop is the giveaway of a promo.

How to Use the Bump Chart Dashboard

• Evaluate Individual Promotions: After a promotional event, use this dashboard to quantify its impact. For example, suppose you ran a 2-week 20% off promo in March with a display. By selecting that timeframe, you can see exactly how much incremental volume was generated above the baseline. The summary might say “Incremental Units: 5,000, Lift: +40%”. This means you sold 5,000 more units than you would have without the promo, and sales were 40% higher than baseline during those weeks. If you have multiple promotions, you can compare them: perhaps a July promotion with only a price cut but no display gave you 15% lift, whereas March with display gave 40%. Now you have evidence that displays significantly improve promo effectiveness in this category.

• Assess Promotion Efficiency: Look at the % Lift relative to the % Discount. If you gave a 10% discount and got a 30% lift in sales, that’s a 3x lift — not bad. If you gave 30% off and got only a 10% lift, that’s problematic — you heavily subsidized volume that mostly would have sold anyway (low efficiency). The dashboard’s summary metrics make this clear by showing depth of discount vs lift. Efficient promotions typically have lift percentages greater than the discount percentage (though that also depends on baseline elasticity and such). Inefficient ones might have a smaller lift than the discount, implying margin was lost without commensurate volume. For example, if ARP dropped 20% but units sold only rose 10%, you lost revenue in that period (a negative outcome unless it had other strategic aims like inventory reduction).

• Optimize Timing and Duration: The week-by-week view might show that the first week of a 2-week promo did great (big bump), but the second week added little additional incremental (maybe everyone who wanted the deal bought in week 1). This could suggest shorter promos are sufficient. Or you might see a post-promo dip (sometimes after a promo, sales fall below baseline as some consumers stocked up; the chart would show base dropping slightly or even negative incremental in weeks after). That indicates pantry-loading effects. Understanding these patterns helps in scheduling: e.g., a deep promo might be best run infrequently to avoid constantly borrowing sales from the future. If you see that when promotions end, sales don’t dip much, then the promo likely brought in new or extra consumption rather than pulling from future weeks – that’s a good sign of incremental reach.

• Plan Future Promotions: Learn from each event. Perhaps you test a feature ad in one promo and see a higher lift compared to a similar promo without feature. The bump chart would evidence that incremental difference. Next time, you can justify the cost of feature ads or special displays. Or maybe you try a slightly shallower discount and notice the lift was almost the same as a deeper discount you did earlier – implying you’re over-discounting and could save margin next time. These insights feed into a playbook: what’s the optimal discount to maximize profit? What support elements are most effective? The fine-grained data here is what you’d use to refine those tactics.

• Cross-Functional Communication: This dashboard is very intuitive for discussions with executives or partners because it visually shows what happened. Instead of just saying “the promo was successful,” you can show the chart: “See this big green spike? That’s the incremental sales we got when price dropped. Our baseline was $100k/week and with the promo we hit $150k – a 50% jump.” It’s easy to digest. It also fosters a culture of data-driven post-mortems – after every major promotion, the team can review this together and document the results, building a historical record of what works.

• Foundation for Trade Promotion Management: The user prompt notes this will be the basis for a more advanced Trade Promotion dashboard with profitability and planning (coming next). That makes sense: this bump chart view in syndicated data gives the sales uplift piece. When you layer in internal data like trade spend and profit, you’ll get ROI. So, think of the bump chart as phase 1: understanding volume impact. Phase 2 (trade dashboard) will add cost/ROI. If you already want to approximate efficiency, you can pair the incremental volume from here with your internal cost info offline to calculate rough ROI (e.g., “we moved +5,000 units with a 20% discount, what did that cost us in margin and did the extra volume make up for it?”). But the upcoming Trade Promotion dashboard will do that systematically.

In short, the Promotional Bump Chart Dashboard is your microscope for promotion analysis. It zeroes in on when and how much promotions moved the needle, week by week. Use it to build a fact base of promotion performance so that over time, you’re not guessing what a “good” promo looks like – you’ll know, because you’ve seen it in the data. The next and final step is to tie this volume lift to profitability, which is where the Trade Promotion ROI Dashboard comes into play.