Promotional Strategy Playbooks

This overview page introduces the Promotional Strategy Playbooks series.

Promotions are a major driver of retail sales, but they require strategy and analysis to get right. These playbooks will cover different types of promotional tactics, when to use each, and the key metrics to watch – setting the stage for the detailed playbook guides on each tactic.

Why Promotions Matter: In-store retail promotions (temporary discounts, in-store displays, features in retailer ads, coupons, etc.) can boost velocity in the short term and drive trial of your product . Retailers also expect brands to support their products with promos – it shows you are investing in the category and helps retailers drive traffic with deals . However, promotions come at a cost (trade spend or margin cut), so they must be used wisely . A short note will mention the concept of promotion ROI – the need to ensure that the lift in sales is worth the money spent on the promo. This is why analyzing promo performance is critical.

Primer on the Different Types of Retail Trade Promotions, and When to Use:

Below are some of the common types of promotional tactics in retail/CPG analytics:

Temporary Price Reduction (TPR): A straight price discount for a limited time (e.g., “$1 off” or “20% off” for two weeks). Usually, TPRs are reflected on shelf tags and possibly retailer systems, but not necessarily advertised in print – it’s a price promotion visible to any shopper who comes in. This is the simplest promo type, often funded by the brand to lower the retail price and encourage more sales.
Feature & Display (F&D): A promotion where the product is given special placement and featured in retailer advertising. “Feature” typically means the product is in a retailer’s flyer or digital ad, often at a discount, and “Display” means the product has off-shelf secondary placement in the store (like an endcap or standalone display) during the promo. These are usually combined because an advertised special gets a display to ensure stock and visibility. F&Ds are high-impact but also high-cost (since you often have to fund the discount and sometimes pay slotting or co-op for the ad and displays). We’ll explain that getting a Feature in a major retailer’s weekly ad can dramatically raise awareness and sales , especially for a newer brand – it’s effectively a mini product spotlight.
Display-Only (Secondary Display): In this case, the product gets additional placement in-store (like an off-shelf display, endcap, or special rack) but without being featured in the weekly ad. The display might or might not coincide with a price discount. Sometimes a product can be on display at full price (perhaps as part of a thematic or seasonal showcase), or with just in-store signage. Display-only promotions are great for visibility – they catch shoppers’ eyes and can drive impulse purchases or remind people about the product. They’re often used when the brand wants to push more product in high-traffic areas or when a retailer gives an opportunity for extra placement (even if not in print). The cost might involve free cases or display fees, but not as much as a full F&D.

(We might also briefly mention other types like Coupons or BOGO deals, but these can be considered variations of TPR for the purpose of this series. For example, “Buy One Get One 50% off” is essentially a 25% off per unit promotion for two units . Similarly, loyalty card discounts, etc., are forms of TPR. We won’t have separate playbooks for every variant, but the principles in TPR playbooks will largely apply.)

When to Use Each Strategy: Provide guidance on scenarios where each promo type is most suitable:

TPR: Best for driving a quick increase in volume and moving inventory. Use TPRs tactically to correct velocity issues – e.g., if a product’s velocity is below target in a certain retailer despite good distribution, a short-term price cut can entice more buyers and boost trial. TPRs are also common for price-sensitive categories or during seasonal events to stay competitive. They are easier to execute frequently than F&D since they don’t require retailer ad space, but if overused, consumers may get trained to wait for a sale . So, we caution: use TPRs strategically (e.g., one week every 4-6 weeks, or around holidays) to avoid eroding your brand’s premium image or margins.
Feature & Display: Use for big pushes – e.g., launching a new product, entering a new retailer, or a major seasonal promotion (like a Summer grilling feature if you sell condiments). Because F&D gives both awareness (feature) and impulse opportunity (display), it’s ideal for accelerating trial and penetration in the market. For mid-size brands, F&Ds might be limited to a few times a year due to cost – so plan them when you have a strong story (e.g., “#1 in category growth, now on sale!”) or when you need to meet a sales target for the quarter. Also, F&D can help secure retailer support – retailers love being able to advertise a good deal . We note to users: often, you have to negotiate these with the retailer’s buyer well in advance, and sometimes commit to funding a certain discount or providing display units. Save F&D for when you expect a significant payoff (and you have the inventory to support a big lift!).
Display-Only: Great for boosting visibility when you may not want to deeply discount. For instance, if your product is new and fairly premium, you might not want to cut the price immediately, but a secondary display can increase trial by simply being seen by more shoppers. Display-only can also be used in between major promotions as a way to keep momentum: e.g., in a month you’re not running a TPR, you negotiate a small display in a few high-volume stores to keep sales up. Another use case is slow-moving SKUs – if one flavor or item isn’t selling as fast, an off-shelf display can draw attention to it and clear out inventory. We’ll mention that studies often show an incremental sales lift from displays even without ads, because of impulse purchasing (shoppers can’t buy what they don’t see, after all).

Key Metrics to Watch: No matter the promo type, certain metrics determine success. The overview will list these metrics, which will also be referenced in each playbook:

Lift (%) and Incremental Sales: How much of a bump did the promotion generate over baseline? We define % Lift as (Promo sales – Baseline sales) / Baseline * 100% . Incremental sales is the extra units or dollars attributable to the promo . A promo that yields +50% lift is strong, but we also need to see the absolute incremental volume (selling 100 extra units vs 10,000 extra units are different stories). High lift with low absolute might mean a small base.
Incrementality (Promo Efficiency): What % of the promo sales were truly incremental? If you sold 1000 units during promo and 700 would have sold anyway, incrementality is 30%. Higher is better – it means the promo mostly attracted new purchases, not just subsidized existing ones. This metric helps evaluate if the promo was worth it or if it just gave a discount to people who would buy regardless.
Baseline Sales Post-Promo: Did velocity (baseline) improve, stay flat, or decline after the promo? For example, if a promotion brings new customers, ideally some will keep buying in subsequent weeks at full price, raising your baseline. If baseline stays the same or drops back, the promo might have had only temporary effects (or pulled forward sales). This is a bit longer-term, but important for evaluating if a promo had a lasting impact or just a blip.
Promo Frequency, Duration, Depth: A reminder of these attributes – how often you promote, for how long, and how deep the discount is – as context for results. For example, a 50% off (deep depth) will usually yield more lift than a 10% off, but costs more margin. We encourage tracking these: Frequency (e.g., weeks per quarter on deal) and Average Discount. It helps ensure you’re not over-promoting (if every other week you’re on sale, that’s a red flag ).
Trade ROI / Cost per Incremental Unit: If data is available, brands should compare the cost of the promotion (lost margin, trade funds spent) to the incremental sales gained. Even if we don’t have exact trade spend in Daasity, we can approximate: e.g., “We gave $2 off per unit and sold 300 incremental units, so roughly $600 cost to generate those extra sales”. That can be converted to $2 per incremental unit. Over time, tracking this helps decide which promos are most efficient.
Competitive Metrics: If the brand has syndicated data, watch market share during the promo period (did you steal share while on promo?), and note competitors’ promo activity. For instance, a playbook might suggest checking in URMS if a competitor was also on deal in that period – maybe that dampened your results. Or if your promo lifted you to #1 share for the week, that’s a win to mention to retailers. This overview will remind users that using syndicated data to benchmark promo performance is powerful (e.g., your 30% lift might sound good, but if category average promo lift is 50%, there’s room to improve).

In the following guides, we’ll walk through specific tactics for each promotion type – from identifying when you should use it, to executing the promotion, and measuring its success. Each guide will provide a step-by-step scenario to illustrate how to make data-driven promotional decisions. We encourage you to read the one relevant to your current needs, but also to understand all three because an optimal promo plan often uses a mix of tactics

Temporary Price Reduction (TPR) Playbooks

TPRs are versatile and commonly used promotions. These playbooks help brands decide when and how to implement a temporary price cut, and what to look for in the data before and after. Each guide is framed as a scenario.

TPR Playbook 1: “Boosting Trial in New Retailers with a TPR”

Scenario: Your brand just gained placement in a new retailer (or a set of new stores). Congratulations – you’ve expanded distribution! However, a look at the initial sales (sell-out) in these stores shows that velocity is modest – as expected, since consumers may not know your product yet. You want to accelerate trial and demonstrate strong sales quickly, to impress the retailer and ensure the product doesn’t get dropped after the category review. A Temporary Price Reduction can help jump-start volume.

Identify the Opportunity: Using Daasity, segment your sales data to the specific retailer or stores that are new for your brand (for example, filter the URS Sales or Retailer POS dashboard to those store IDs or the retailer’s name). Check the velocity metrics – e.g., Units per Store/Week in those stores. Compare it to your velocity in similar retailers or the category’s velocity (if you have URMS data for that region/channel) . If velocity is significantly below benchmark or your overall average, that’s an opportunity. Also look at % ACV in that retailer – if you’re in 100% of their stores (full distribution in that chain), all the more reason to ensure velocity climbs; if you’re in say 50% of stores, maybe focus the promo on those and plan to pitch expansion once you prove success in them.
TPR Execution: Plan a Temporary Price Reduction for a sensible period (commonly 1-2 weeks, perhaps aligned with a payday or a relevant event). Determine the depth: e.g., 20% off might be a starting point. The depth might depend on your margin and how much incentive consumers need. If possible, coordinate with the retailer – they may need to authorize the discount or could even fund part of it if they see value. Make sure to set up the promotion in the retailer’s system so that the discounted price is active during that period. (This is outside Daasity’s scope, but a note for the user’s coordination.) If the retailer allows, you could also put up shelf talkers or small signs to highlight the sale – since this isn’t a feature ad, shoppers will only know if they see the new lower price on shelf.
Evaluate Performance: During and after the promo, use Daasity to measure the impact. On the Sales Performance dashboard (URS), look at weekly sales for that retailer: you should see a spike during the promo weeks. Specifically calculate the lift: compare the promo week(s) to the prior weeks. If Daasity has a promotion reporting feature (like a promo calendar or if you input promo events in BSD), use that to get % lift and incremental units . Let’s say you observe a +100% lift (sales doubled) in those stores during the promo – that’s a positive sign. Also check incrementality if data allows: maybe your baseline in those stores was 5 units/week, and during promo it went to 10; if baseline would’ve stayed 5 without promo, then you got +5 incremental units per store, incrementality 50%. If, however, you only got +1 or +2 units extra (like baseline 5 → promo 6 or 7), incrementality is low (~17-33%), meaning the promo didn’t pull as many new buyers as hoped.
Adjust & Next Steps: If the promo was a success (solid lift and good incremental sales), document that and consider sharing results with the retailer’s buyer: “We ran a 2-week $1-off promo and saw a 80% sales lift, bringing velocity to 9 units/store/week, which is on par with the category average – showing strong trial uptake.” This builds trust that you’re investing to make the product work in their stores. Now plan how to maintain momentum: monitor the post-promo velocity in those stores for the next 4-6 weeks. Does it settle higher than it was pre-promo (indicating some new customers stuck around)? Use Daasity to track this. You can also plan the next promo – maybe another TPR in a couple of months if velocity dips again, or if a seasonal period is coming (like a summer promo if this was in spring). If the promo underperformed (low lift), consider: Was the discount too small to entice? Was the product still hard to find in store (maybe needed a display)? Or did a competitor have a promo at the same time overshadowing yours? Adjust strategy accordingly: you might try a slightly deeper discount next time, or pair it with a display or local marketing to drive awareness. The playbook would note these common adjustments. The goal remains to get velocity up to a sustainable level so the retailer continues to reorder and ideally expands your product to more stores or SKUs.

TPR Playbook 2: “Clearing Excess Inventory with Strategic Discounts”

Scenario: One of your SKUs is overstocked at a retailer. Perhaps a forecasting error or slower sales led to a build-up of inventory both at the retailer’s warehouse and maybe on your balance sheet. The product is approaching a date (e.g., seasonal flavor, or shelf-life concerns). You need to move volume quickly to avoid returns or markdowns by the retailer later. A Temporary Price Reduction can stimulate a spike in sales to draw down the inventory.

Identify the Opportunity (or rather, the Issue): The data trigger here might come from the Inventory reports and Weeks of Supply metrics. In Daasity’s URS schema, look at urs.inventory_report or an Inventory dashboard for that SKU at the retailer . If you see, for example, 12 weeks of supply on hand (and normal is 4-6 weeks), that’s a red flag. Also, check if sell-in (shipments) have slowed or stopped because the retailer is sitting on too much stock. If the retailer uses scan-based trading or just will return product that doesn’t sell, you have a strong incentive to help sell it through. Moreover, look at the sales trend: has velocity been declining, or did it never take off for this SKU? Confirm that the issue is specific – e.g., it might be a flavor that isn’t as popular.
TPR Execution: Arrange a steep but short-term discount to encourage a quick volume lift. In these scenarios, brands often do a heavier TPR (say 30-40% off or a “Buy 2 for $X” deal that effectively cuts price) because the priority is to move units. Coordinate with the retailer – they’ll likely be on board since they also want to clear stock. Ensure the timing is right: do it sooner rather than later, before the product gets too old. Perhaps run it for 2 weeks and be prepared to extend one more week if needed (but generally avoid very long discounts as it might train consumers or annoy the retailer if margins are hit too long). If you have field teams or broker partners, have them ensure the stores execute the discounts correctly (sometimes tags don’t get updated, etc. – execution at store level is key for this to work).
Evaluate Performance: Track the sell-out data daily or weekly in Daasity to see if the discount is actually moving the needle. A successful clearance TPR should show a sharp increase in units sold for that SKU in that retailer – possibly several-fold increase if the discount was big. You might not care as much about incrementality percentage (because even subsidizing some sales is okay when your goal is clearance), but you still want to ensure the inventory is dropping. Use the inventory table or any WOS (weeks of supply) metric: did weeks of supply fall from 12 to, say, 6 after the promo? If so, progress! If not, it might mean the issue was bigger (maybe consumers just don’t want it even at a discount – at which point deeper tactics are needed, like maybe discontinuing that SKU or bundling it). Also, keep an eye on retailer feedback – sometimes they might tell you if stores still have a lot or if they had to mark it down further.
Adjust & Next Steps: If the TPR significantly reduced the excess stock, great. Document the sales lift (e.g., “Moved 500 units in 2 weeks vs 150 units in the prior 2 weeks, clearing ~8 weeks of inventory”). This not only solves a short-term problem but can be a learning: maybe that SKU only sells well at a lower price point – is it overpriced normally? Or maybe it needs more marketing support. If the TPR did not achieve the clearance (say lift was mediocre), you might need to take more drastic measures: perhaps a deeper discount (even at break-even or loss) or a different promo mechanic like BOGO to really entice consumers. In worst case, you might plan to withdraw that SKU and focus on others. The playbook would advise to analyze why it didn’t move – was it a product-market fit issue or just insufficient promo awareness? For future, it might be wise to forecast better or produce less of that variant. On the retailer relationship side, by taking action with a TPR, you show them you’re being proactive in managing inventory – which can earn goodwill and avoid penalties. Always communicate the plan and result to the retailer (they hate surprises like a huge batch of unsold goods, so being on the front foot is key).

Coming soon! TPR Playbook 4: "New product launch with TPR in multiple retailers to drive trial"

Feature & Display (F&D) Playbooks

Feature & Display promotions are big moves – often part of a broader marketing push. These playbooks focus on making the most of those high-visibility promotions and ensuring they truly pay off. Each scenario deals with planning, executing, and assessing F&D events.

Feature & Display (F&D) Playbooks 1-3

F&D Playbook 1: “Maximizing a Featured Promotion for a Product Launch”

Scenario: You are launching a new product (say a new flavor or line extension). You secured an F&D promotion at a key retailer during the launch month – this means your new product will be in the weekly circular (feature) and on a special endcap display for a week or two. This is a huge opportunity to gain trial. The goal is not only to drive sales during the promo but to establish a high baseline sales rate after the promo (so the product continues to sell and stays on shelf).

Pre-Promo Planning (Identify Opportunity): Long before the promotion hits, set your success criteria. Using analogues (past launches or similar products), estimate what a “good” velocity would be post-launch. For example, you might say: we want at least 5 units/store/week after launch, and the F&D week should hit perhaps 15 units/store (3x the baseline) given the extra visibility and discount. Ensure your data tracking is set up: in Daasity, confirm that the new product is in the product master, and that initial shipments (sell-in) to the retailer are being captured in URS wholesale data (so you know stores got the product). The feature in the ad likely comes with a discount, so decide on that price (maybe 25% off for the launch week to incentivize trial). Also, plan any supplementary efforts: e.g., sending your field team for demos during that week, or running social media geotargeted ads to drive people to the store – these aren’t Daasity’s realm, but mention them as part of a comprehensive launch plan.
Execution and Monitoring: When the F&D week arrives, closely monitor sales data as it comes in. Daasity might have daily POS data from that retailer (if not, at least weekly). Check mid-week if possible: are stores selling through the inventory? Because this is a new item, watch % stores selling – did all stores actually get it and put it on shelf? If you see only 80% stores selling in the first few days, there may be distribution voids or execution issues (some stores didn’t set up the display). That’s where having broker teams check compliance is key. But data-wise, by the week’s end, you’ll measure how many units sold vs how many were in stock (if you have inventory data). A successful feature might clean out most of the stock on display. Also, gauge category share for that week if syndicated data is available – maybe your brand jumps to, say, 10% share of category during that week due to the feature, which is a strong result for a new item.
Post-Promo Evaluation: After the F&D period, see how sales settle. Compare the 4 weeks post-launch (without the feature) to the launch week. Did you achieve a sustained lift? For example, if baseline velocity in the weeks after is 5 units/store/week, and you planned for that, great. If it’s only 2, then despite a big launch promo, repeat sales are low – perhaps indicating trial didn’t convert to repeats or awareness didn’t stick. Look at repeat purchase indicators if you have them (though that might come from DTC or panel data, not Daasity directly). Also examine inventory restocks: did the retailer reorder promptly after the launch week? If not, that’s a bad sign (they might have been unimpressed or overstocked initially). You might need to follow up with them, armed with some positives – e.g., “We achieved 8 units/store in launch week, which was 120% of their expectation, and we gained new customers – now we plan marketing to keep growth.”
Adjust & Learn: Document what worked: was the discount adequate? Perhaps next time, a combo of feature + demo could push even more. Or if something underwhelmed (maybe the display wasn’t in a great location, or the ad was on page 10 not page 1, etc.), take note. For the next retailer or next product launch, you might adjust tactics (e.g., ensure more build-up marketing, or try to coincide with a relevant season to get better traction). The data from this launch should also inform your category story: for instance, if your new product drove incremental category sales (maybe your category share bump didn’t cannibalize others too much), that’s a story to tell other retailers to get them to carry the product. Essentially, use the success metrics from one F&D to build credibility for expansion. The playbook emphasizes tying results back to business questions like “Did this F&D set my product up for long-term success or just a flash in the pan?” and using the data to answer that.

F&D Playbook 2: “Driving Seasonal Sales with Feature & Display Events”

Scenario: You have an established product line, and you want to capitalize on a seasonal uptick. For example, a beverage or snack brand doing a big summer promotion around July 4th, or a baking ingredient doing a holiday push in Q4. You negotiate an F&D at a top retailer during that season. The aim is to maximize sales during the high season and maybe gain new customers when category traffic is high.

Identify the Opportunity (Seasonal Analysis): Before the season, analyze last year’s data (if available) in Daasity. Look at the seasonal period – e.g., June-July last year – and see how your sales trended. Did you see a natural lift from baseline due to increased foot traffic or consumption? Also note competitor activities: perhaps last year a competitor ran a big promo during the holiday and grabbed share. This year, you secured the feature, so you have the offensive position. Set goals: e.g., “We want to double our sales during the holiday week vs a normal week.” If you have syndicated category data for that season, note the total category lift typically – this helps set realistic expectations (if the category usually +50% at holidays, your brand being +100% would be stellar).
Execution: Execute the F&D with excellence. Since it’s seasonal, your display might be themed (e.g., a Fourth of July endcap with patriotic decoration, etc.). Ensure supply: seasonal promos can go wrong if stock runs out, so load in extra inventory beforehand (sell-in should be checked – URS wholesale data – to make sure shipments to the retailer increased before the promo). As the promo runs, watch sales. If mid-promo data suggests some regions are selling out, try to restock quickly (if possible). Also watch attachment – if your product is often bought with something else (e.g., chips with salsa), sometimes features can have cross-product effects. Not directly visible in Daasity unless you do basket analysis (which might be beyond current scope), but anecdotally or via retailer feedback you might glean if the promo drove bigger baskets.
Evaluate Performance: After the season, use Daasity to compare year-over-year performance. Did your brand’s seasonal sales grow more than the category’s? For instance, maybe the category was up 30% YOY in that week and you were up 80% – meaning you took share. Check market share in URMS if possible: your share might have spiked thanks to the feature, which is a win . Evaluate the efficiency too: how much volume was moved and at what cost? Seasonal F&Ds often involve lots of spend (co-op, ads, extra manufacturing overtime, etc.), so gauge if it paid off in volume. Also check if the seasonal bump had any carry-over (some new customers keep buying you post-season).
Adjust & Next Time: If the seasonal event was very successful, consider making it an annual play. You might even expand it – maybe next year, aim for two retailers instead of one. If something underperformed (e.g., despite the feature, a competitor without a feature still did better – how?), analyze contributing factors. It could be pricing (maybe your discount wasn’t as attractive), or display execution (your display got set up late or in a low-traffic area). Use any data or retailer feedback to adjust. This playbook would also mention planning for seasonal inventory – don’t get stuck with leftover seasonal stock, so time the tail of the promo to wind down inventory. Possibly segue into an everyday price check: sometimes after heavy seasonal promos, brands consider whether their everyday pricing is right (if you only see volume when on sale, maybe base price is too high). That might lead into Pricing strategy considerations (to be covered elsewhere).

F&D Playbook 3: “Measuring and Improving F&D ROI Across Retailers”

Scenario: You’ve run multiple F&D promotions at different retailers over the past year. Some seem to perform better than others. This guide is more of an analytical retrospective: comparing promotions to understand what drives success, and creating a framework to improve ROI for future big promotions.

Gather Data: List all the F&D events (maybe 3-5 events) you did, with retailer name, timing, discount depth, and any qualitative notes (e.g., “Walmart – Memorial Day – 50% extra display locations”, “Kroger – Fall feature – ad only, small display”). Use Daasity to pull the sales results for each event:
- Absolute incremental volume (units and dollars).
- % lift and incrementality.
- Post-promo baseline change (did velocity permanently increase? or back to old levels?).
- Cost: if possible, estimate trade spend (maybe you know the billback amount or you can approximate from discount * units).
- ROI: incremental sales dollars vs trade cost.
- Market share gain: if URMS data, did your share jump during promo and did you retain any share after?
  Put this into a comparison table.
Identify Patterns: Perhaps you find, for example, that short, high-impact promos (like a one-week blast with big display) delivered higher efficiency than longer, milder promos. Or maybe Retailer A gave you an eye-level endcap in a key aisle and that drove more lift than Retailer B who gave a smaller display – even if discount was same. If one retailer’s customer base is more responsive (e.g., natural food shoppers vs mass shoppers might react differently), note that. Also, if the timing was different (back-to-school vs holiday), did that matter? Essentially, figure out which factors correlate with success.
Actionable Insights: Based on the above, draw conclusions. For example: “Our best F&D ROI came when we offered ~20% off for 1 week with secondary displays in >50% of stores. When we tried a 4-week long feature at 10% off, it had lower lift and cost more in total discounts – suggesting we should go big but short.” Or, “Retailer X promotions underperformed because execution at store level was spotty – next time we must ensure 90%+ store compliance on displays or it’s not worth it.” If some promotions didn’t boost post-period baseline at all, maybe they were attracting mostly deal-seekers who didn’t stick – next time, maybe target a different demographic or pair promo with a loyalty program to capture customer info for re-marketing.
Implement Changes: With these insights, outline what you’ll do differently. Possibly: negotiate for better display placement or more stores (show the retailer the data: “We lifted sales 5x with an endcap in 50% of stores, imagine if we had 100% store displays!”). Or adjust discount levels (maybe you realize a moderate discount worked as well as a deep one in one case, so you can save money by not over-discounting). Also, plan how to measure future promos even more rigorously – perhaps set up a promo tracking dashboard in Daasity if not already (this could pull in planned promo periods and automatically compute lift).
Continuous Improvement: Conclude that F&D promos are costly but can be game-changing when done right. By systematically comparing outcomes, you turn each promo into a learning experiment. Over time, this will increase the efficiency of trade spend and sales impact. Encourage the user to maintain a “Promotion Playbook” document internally with these results, and to share big wins with internal stakeholders (like “our Labor Day promo made us the #2 brand that week in the category, we should replicate that playbook in other regions”). Also note, if something like a display-only or TPR-only event sometimes nearly matched an F&D, that’s worth noting too (maybe you don’t always need to pay for the feature ad if a well-executed display can do 80% of the lift at less cost ). Such trade-offs are gold for future planning.

(These F&D playbooks cover launch, seasonal, and retrospective optimization scenarios. There could be others, e.g., one for “Defending shelf space with an F&D when a competitor launches” – basically preemptive promo to prevent losing share – but aspects of that are touched in Distribution defense in Part 4 perhaps.)

Display-Only Playbooks

Display-only tactics often complement other promotions but can also stand on their own. These playbooks focus on using secondary placements effectively without necessarily leaning on advertised discounts.

Display Playbook 1-3

Display Playbook 1: “Improving Velocity for Underperforming Stores via Secondary Displays”

Scenario: You have a set of stores (or a whole region) where your product is authorized (on shelf) but selling poorly. The rest of the chain might be doing fine, but these stores lag – maybe due to lower awareness or suboptimal shelf placement in those locations. Instead of a blanket price cut, you decide to try a merchandising solution: give those stores a secondary display for a period to see if visibility boosts sales.

Identify Target Stores: Use Daasity’s data at the store level (if available in URS). The URS.locations table paired with sales can help rank stores by velocity . Identify, say, the bottom 20% stores in terms of Units per Store/week for your product. Ensure these stores indeed have your product (i.e., distribution gaps aren’t the issue – if some have zero sales because they never got inventory, that’s a different problem). Let’s assume they all carry it but sell way below average. Check if there are any commonalities – are they smaller format stores, or certain geographies? This might inform display strategy (e.g., maybe those stores are in areas where your product is less known – so display can educate).
Execute Displays: Work with the retailer or field teams to arrange secondary displays in those target stores for a few weeks. This could be a side stack near a complementary aisle or checkout, an endcap if possible, or even just a bigger off-shelf placement near related products. No additional price discount is given (or maybe a very small one like “Special Display: 2 for $X” that’s basically the same as regular price but packaged as a deal – optional). The key is visibility. Ensure that the displays are set up properly and kept stocked. Sometimes underperforming stores also have inattentive staff; you might need reps to visit and maintain the display.
Measure Lift: Compare sales in those stores before vs during the display period. You might use a control group if possible – e.g., similar underperforming stores where you didn’t do a display – to isolate the effect. But even a pre-post in the same stores can be indicative. Ideally, you see a clear uptick in the display stores. Perhaps units/store/week were 2, and with display they went to 4 – a 100% lift without any price cut. That would demonstrate success. Calculate incremental units similarly as other promos. Also check % stores selling for those stores – if some stores weren’t consistently selling any units week-to-week (sometimes a product might not sell every week in a low volume store), a display might generate sales in weeks that previously had zero, which is a win (increases % stores selling in that cohort from, say, 70% to 100% during the period).
Result and Next Steps: If the displays significantly improved velocity in those stores, consider a few actions:
- Share with retailer: “In the 20 trial stores where we added displays, velocity doubled, narrowing the gap vs chain average. We’d like to expand this program.” This might convince them to allow displays in more stores or for longer, or to not cut those low-performing stores since you found a way to improve them.
- Evaluate longevity: Did sales stay up during the whole display period or wane after initial curiosity? And what happened after the display was removed? If sales sunk back, it might mean you need to periodically re-merchandise or also add some local marketing to sustain awareness.
- Consider other tactics: If some stores barely improved even with a display, maybe those locations just aren’t right for the product (demographics wrong, etc.), which could feed into distribution decisions (perhaps focus elsewhere).
  This playbook highlights that sometimes the barrier is not price but visibility – especially for impulse or new categories. A good takeaway might be: “If product education or awareness is the issue, displays can be as effective as discounts in driving trial – and without hurting your price integrity.”

Display Playbook 2: “Maintaining Shelf Presence During a Competitor’s Promotion”

Scenario: A competitor is running a big promotion (feature and discount) at a retailer, and you are not on deal at the same time. You fear your sales might dip as shoppers flock to the competitor’s promo. While you didn’t schedule a price cut, you arrange with the retailer to set up a secondary display for your product during the same period. The idea is to remind shoppers you’re still there, potentially capturing some attention even while the rival is on deal – essentially a defensive move to retain share.

Preparation: Once you learn of the competitor’s promo (sometimes via retailer or syndicated data intel), identify key stores or regions where that competitor significantly overlaps with you. Focus your display resources there. For example, if the competitor has a strong presence in chain A and is doing a feature week 40, plan to have displays in as many of chain A’s stores as possible in week 40 as well.
During Execution: Monitor your sales in near real-time if possible. The expectation is your sales might still dip because the competitor is cheaper that week, but the displays could soften the blow. Use URS sales data by day if available to watch the trend. If you normally sell 100 units a week chainwide and expected a 30% drop due to competitor promo (based on past patterns or market data), see if maybe you only dropped 10% with the displays out – that difference can be attributed to your display presence. You might also check URMS data to see if category grew that week due to the promo – if so, maybe everyone’s sales went up, including yours.
Post-Event Analysis: Compare the week where competitor promoted (and you put up displays) to similar past scenarios where competitor promoted and you did nothing. If you have historical data of a similar competitor deal where you lacked any presence, perhaps your share fell to, say, 10%. This time with displays, maybe you held 12% share. It might seem small, but in large volume that could be substantial revenue saved. Also note absolute sales – maybe you even grew slightly year-over-year where previously you would have declined.
Insights: While it’s hard to precisely quantify, if the data suggests your defensive displays helped, you have a strategy for the future: “When competitor X runs a major promo, invest in displays.” It’s like playing defense by increasing visibility when you can’t or don’t want to play the price game at that moment. A side benefit: some shoppers drawn by competitor’s ad might still notice your product on display and try it – so you could gain new customers even without discounting. We’ll remind to use syndicated data to validate this: e.g., maybe your competitor’s lift was slightly less than expected because you siphoned some volume – a win for you .
Follow-up: If this worked, plan it into your annual calendar – coordinate with sales team or brokers to know competitor promo schedules (sometimes industry calendars or the retailer can hint at big events) and allocate budget for displays accordingly. This playbook underscores creative use of merchandising as a competitive strategy, not just as a sales booster in isolation.

Display Playbook 3: “Evaluating Display-Only vs Discount Trade-offs”

Scenario: Your brand has a limited trade budget and you’re trying to decide: should we spend it on price discounts or on securing displays (when you often can’t afford both everywhere)? This guide helps analyze past cases where you had displays without discounts, discounts without displays, both, or neither – to inform the best ROI tactic.

Data Gathering: If possible, find examples in your data:
- A period/store where you ran a TPR without any special display.
- A period/store where you had a display (perhaps anecdotal – or infer it if sales spiked in one region where no ad was running) without a price cut.
- Cases where you had both (full promotion).
- Cases with neither (baseline periods).
  Use these to compare sales lifts. For instance, maybe a pure TPR gave +30% lift, a pure display gave +20%, and together gave +60%. If a display alone yields a good chunk of lift, it might be more cost-effective in some situations.
Survey Costs: Consider the costs of each tactic: a TPR costs you margin on every unit sold (and possibly some fixed fees), whereas a display might cost a fixed co-op fee or free cases (or just negotiation effort). If a display gives 20% lift at, say, cost of 5 free cases ($ cost X) and a TPR gives 30% lift but costs $Y in margin, compare X and Y. This will require some assumptions but we can guide how to do it.
Decision Framework: The playbook will then propose a framework: for a given objective (volume vs profit vs share), which tactic to favor. Example: “If your goal is to maximize volume regardless of short-term profit, a deep discount (TPR) with display will drive the most units. But if your goal is to increase sales with minimal margin loss, focusing on displays and smaller discounts might yield a better profit result. For instance, a 10% lift via display might net more profit than a 15% lift via a 15% discount.”
Case Study Example: We could present a hypothetical case from the data: “In Q2, Region West: display-only promo saw baseline units rise from 100 to 120 (+20) with essentially no margin loss (cost was $500 in racks). In Region East: a 15% TPR (no display) saw units from 100 to 130 (+30) but cost $1000 in discounted revenue. The lift per dollar cost was 0.04 units/$ for discount vs 0.04 units/$ for display – actually equal in this hypothetical, but if display had a slight edge, you’d favor displays.”
Recommendation: Encourage trying mixed approaches: maybe do a small TPR but invest in displays to amplify it rather than spending all on a huge discount. And highlight that in some cases, product category matters: for impulse-driven categories (candy, beverages), displays might have outsized impact, while for highly price-sensitive categories (paper towels, bulk items), price cuts might be more necessary. Use your data to tailor strategy by category.
Conclusion: This playbook’s purpose is to foster efficient trade spend. It will conclude with something like: “Trade marketing isn’t one-size-fits-all. By analyzing the ROI of displays versus discounts, you can allocate your budget to where it drives the most incremental sales. Often a balanced approach (a modest discount with a strong display) can yield the best of both – a sizable sales lift without fully sacrificing margins . Always test and learn, and adjust based on what the data tells you.”

(The above display playbooks cover raising velocity in low stores, defending during competitor promos, and general ROI analysis. Brands can adapt these to their context – the idea is to illustrate how to approach display tactics systematically.)

Linking to Metrics & Schemas: Throughout the playbooks, we’ll reference where to get the needed data in Daasity. For example, when we say “check % ACV or % stores selling,” we’ll note that those are found in URMS.SALES_REPORT for syndicated data (ACV% is a field there ) or in URS (if the retailer shares numeric distribution, etc.). When checking lift and baseline, we might point to a Promotional Efficiency dashboard which presumably is built on comparing promo vs non-promo periods . If such dashboards exist (like Promotional Comparison (URMS), Promotional Efficiency (URMS) ), we should mention them so users know to utilize those tools rather than manually calculating everything. Also, point to the Metrics reference for definitions of incrementality, lift, etc., to reinforce understanding.

Scalability to Enterprise: Lastly, although the playbooks are written with mid-size brands in mind, they scale to larger enterprises ($500M+). We may add notes like: larger companies might have more sophisticated tools (like predictive promo lift models) or more complex trade promotion management systems (TPM software). But the principles of using data to decide promotions and measure them remain the same. Daasity’s unified data can feed those systems or serve as a simpler way to crunch the numbers for any size organization. The playbooks encourage a data-driven culture regardless of company size.

That concludes the Promotion & Pricing Playbooks content for now (pricing-specific strategies like everyday pricing, pack pricing, etc., could be a separate series, but many pricing insights were embedded in the promo context – we will address any pure pricing playbooks in the next follow-up if needed

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