Mayank Patel
Apr 7, 2025
4 min read
Last updated Apr 7, 2025
When launching a new product—whether it’s a fresh seasonal drop, a limited-time collaboration, or a completely new SKU—you often face the same core problem: no historical data. No prior sales patterns. No customer behavior data. No previous forecasts to lean on.
But decisions still need to be made—about inventory, pricing, marketing, and fulfillment. This guide breaks down how to approach these zero-data SKUs using a blend of structured thinking, smart proxies, early signals, and adaptive systems.
Even without historical data, you can’t operate in a vacuum. Begin with assumptions built around:
These aren’t perfect—but they’re working hypotheses, and that’s better than flying blind.
Tip: Create a lightweight “SKU Assumption Template” where you log the category, price tier, expected marketing push, launch channel, and fulfillment method. Use this to compare similar past launches—even if the product is technically “new.”
When historical data for the SKU doesn’t exist, use similarity models. Look for analogs:
If your last collaboration with Artist X sold 500 hoodies in 3 days, your new drop might follow a similar trajectory—adjusted for changes like price point or season.
Also Read: Do Shoppers Love or Fear Hyper-Personalization?
The goal is to find patterns of performance from similar contexts, not identical products.
Use drops with similar:
If internal analogs don’t exist, tap external ones. It’s not perfect, but it's better than guesswork. Look at:
Pre-launch data can be a goldmine. Use it to adjust expectations before inventory locks in:
If you’re seeing stronger signals than previous launches, that’s your cue to up inventory. Weak signals? Dial it back or hold some units in reserve.
The first 24–72 hours of a new SKU’s lifecycle provide real-time learning. Monitor:
Push this data into your ops + marketing teams daily. Don’t wait for the week to end. React fast. Example: If size M sells out in 12 hours but others linger, trigger a "Notify Me" form or restock email, and consider a limited pre-order run.
Also Read: Why Retail Tech Needs to Think in Probability, Not Certainty
For truly unpredictable SKUs, consider:
A tiered inventory strategy works well:
Backfilling also works—especially if you have agile manufacturing or local production relationships.
As you gather more launch data, your team should build a “zero-data SKU” forecasting toolkit. It should include:
This lets you run “what-if” scenarios. E.g., “If this new collab gets a 30k email push and 5 influencer posts, and performs like our last 2 hoodie drops, what should inventory look like?”
Keep refining these models with every launch.
After every new drop, run a retrospective. Document:
Save this in a “Drop Debrief” database. Over time, it becomes a playbook for handling future unknowns.
Handling SKUs with no historical data is hard—but not impossible. You can make smart, proactive decisions by combining structured assumptions, proxy insights, early signals, and fast feedback loops.
The biggest mistake is treating these launches as “unpredictable.” They’re less predictable, yes—but with the right process, they can still be measurable, learnable, and improvable.
If you treat each new drop as both a launch and a test, your system will get sharper over time—and so will your outcomes.
What’s Really Slowing Down Your Product Pages
Your product pages are where buyers make up their minds but if they load a second too slow, you’ve already lost them. Behind every sluggish page lies a mix of hidden culprits: server delays, bloated scripts, oversized images, or tangled middleware calls.
In this guide, we’ll break down the real reasons your PDPs crawl instead of sprint, the key metrics that expose performance pain points, and the technical playbook to make every click feel instant. Whether you’re running on Shopify, Magento, or a headless stack, these insights will help you find and fix what’s really slowing you down.
Before digging into problems, let’s define the metrics that matter on a PDP:
These metrics are part of Google’s Core Web Vitals. They directly impact SEO ranking (Google now boosts faster sites) and correlate with user satisfaction: fast sites have higher engagement and conversions.
You can measure all of these with Google Lighthouse or PageSpeed Insights (which runs Lighthouse under the hood), with WebPageTest (detailed waterwalls and timing), or with Chrome DevTools. Each tool highlights bottlenecks: devs use the Network panel for TTFB and resource load timing, and the Performance tab to see the rendering lifecycle.
Also Read: How to Handle Tiered Pricing and Custom Quotes in a B2B Marketplace
The server-side response time heavily influences page speed. When a browser requests a page, TTFB is the delay before any content starts arriving. Slow TTFB means your server (or network) is lagging. Reasons include:
Why it matters: A high TTFB means all rendering is delayed. Even if your front-end is lean, the browser is waiting. While TTFB isn’t directly user-visible, a slow TTFB usually signals that the origin is taking too long to start sending data. Every extra 0.5–1 second on the server side can translate into visible lag and lost sales.
Serve as much as possible from the edge rather than your origin. For SaaS platforms like Shopify, this is automatic. For Magento or headless, put Cloudflare, Fastly, or Vercel’s CDN in front. The CDN can cache static HTML or API responses, drastically cutting TTFB for repeat visits. (Shopify’s own CDN automatically optimizes images and assets, improving both TTFB and LCP.)
If on Magento or custom host, enable full page caching (e.g. Varnish/Redis for Magento, Redis cache for DB) so that pages or data get served from memory. Magento 2 and Adobe Commerce emphasize Full Page Cache to slash response times.
Profile and streamline your Liquid/Magento/Node code. For example, Shopify recommends limiting complex loops or sorts in Liquid templates: do filtering once per loop, not inside each iteration. Similarly, in Magento or custom backends, avoid N+1 database queries on product pages.
If you use serverless (AWS Lambda, Cloud Functions) or containers, keep your functions warm (avoid cold-start) and trim dependencies. Consider running SSR on platforms optimized for speed (like Next.js on Vercel or Remix, which caches server-side renders automatically).
Host in regions closer to your customers. Multi-region deployments or geo-routing can reduce first-byte delays.
Also Read: When Your B2B Ecommerce Site Doesn’t Talk to Your ERP
Modern eCommerce stacks often rely on APIs (headless architectures, composable CDNs, microservices). This can inadvertently slow down PDPs if not managed. A common pattern is issuing many synchronous API calls when a user lands on a product page.
For example, your frontend might fetch separate endpoints for product details, stock, pricing, recommendations, reviews, personalization, marketing banners, etc all “at once”. Each of these calls adds network latency and parsing time.
Why it matters: Even if individual API responses are fast, dozens of parallel calls clog up the browser’s connection pool and delay when any one piece of critical content arrives. This can severely hurt LCP and FID because the browser has to wait for those payloads.
Fetch only the data needed for initial render. For example, on a PDP, the product image, title, and price should load first. Defer lower-priority calls (e.g. reviews, cross-sells) until after initial paint or when in view. This may mean loading recommendations or personalization after the page is usable.
Use GraphQL or backend-for-frontend services to bundle multiple data needs in one call. Instead of 5 separate REST calls, a well-designed GraphQL query can return product + inventory + variants + images in a single round-trip.
Pre-fetch data on the server so the client gets a fully rendered HTML immediately. For example, Next.js getStaticProps or getServerSideProps can fetch product info at build/runtime, delivering HTML with data already inserted.
Employ “stale-while-revalidate” caching on API responses. For content that changes infrequently (like product details, inventory that updates every few minutes), cache it on edge or in browser.
Architect for failures. Don’t let a slow analytics or ad script block product load. If an API call fails, display skeleton content or ignore it rather than stalling. The user doesn’t care if a recommendation widget doesn’t load immediately, but they do care if the “Add to Cart” button doesn’t show up.
Each middleware/API gateway adds overhead. Use lean proxies or edge functions. For example, avoid routing a request through multiple services if you can hit the data store directly (e.g. direct DB query vs. going through 2+ layers).
Third-party widgets and scripts (chatbots, analytics, ads, personalization tools, review badges, tracking pixels, etc.) can easily cripple page performance, especially on PDPs where trust-building scripts are common. Each third-party snippet often loads additional JavaScript, images, or iframes from external domains. Every one of these can block rendering, consume CPU, and introduce unpredictability.
Why it matters: These scripts can fire dozens of extra network requests to various servers, each adding latency. Even one extra analytics script adds overhead; Queue-it data shows each third-party script adds on average ~34ms to load time. And because third-party code is hosted on their servers, any slowness or failure on their end can stall your page (in worst cases, a buggy ad script can hang the browser, leaving customers staring at a blank page).
First, inventory all third-party tags on your PDP. Use Chrome DevTools’ coverage and network panel to list scripts and time spent. Remove any that aren’t mission-critical. For example, do you really need a chat widget on every product page, or only on high-intent pages? Every script should justify its cost.
For scripts you must use (analytics, chat), ensure they load asynchronously or defer execution. Place <script async> or <script defer> to prevent blocking the HTML parser. (Be cautious: some chat widgets don’t work with async; test them.)
If a widget isn’t needed immediately, load it after page load or on scroll. For example, don’t load a heavy recommendation engine until the user scrolls past the fold or after the main content is visible.
Tools like Chrome DevTools and WebPageTest can show which third-party domains are taking time. WebPageTest even has a “domain breakdown” chart to see bytes from first-party vs third-party. If one third-party is slow (for example, a tag manager or personalization API), consider a lighter alternative.
Where possible, proxy third-party calls through your own CDN. For instance, self-host common libraries or fonts (analytics code from Google can be hosted on your domain via tag manager). Some CDNs (Cloudflare’s “Zaraz”) can also load third-party scripts on a different thread.
Group non-critical JS together. For example, delay loading social sharing buttons or rich media until after initial load. If using Google Tag Manager, put rarely used tags in one container and trigger it later.
Every new app or marketing pixel can degrade performance. Set a policy that every new third-party inclusion must pass a performance audit (e.g. see if Lighthouse performance score drops) before going live.
Also Read: How to Determine the Right Type of Marketplace to Scale Your B2B Ecommerce
Product pages are image-heavy by nature, but unoptimized media can turn a fast page slow. Large, high-resolution images (without compression or responsive sizes) bloat the page. If your PDP loads 5–10 images at full desktop resolutions, you could easily send megabytes of data, leading to massive LCP delays.
Why it matters: Large images mean longer download and decode times. Users often see blank space or spinners for the hero image until it arrives, inflating LCP. Slow images also push out FID and CLS: a late-loading banner might shift text, hurting layout stability.
Always compress product photos. Use tools or CDNs that convert to modern formats (WebP or AVIF) which significantly reduce file size at comparable quality. For instance, Shopify’s CDN auto-selects WebP/AVIF when possible. Vercel’s image optimizer likewise serves WebP/AVIF to improve Core Web Vitals. (LazyTools can use Shopify’s image tags or Next.js next/image for this.)
Serve different image sizes to different devices. Use <img srcset> or framework helpers. Shopify’s image_tag filter can generate appropriate srcset sizes automatically, so mobile devices download smaller images. This avoids, say, sending a 2000px-wide photo to a phone.
Always include width/height attributes or CSS aspect ratios on images. This reserves space and prevents layout shifts (improving CLS). If dimensions aren’t set, the layout jumps when the image loads.
Do not load images (or videos) that are below the fold on initial render. HTML’s loading="lazy" or IntersectionObserver can defer below-the-fold images. Shopify specifically recommends lazy-loading non-critical images so the page appears to load quicker
A specialized image CDN (Shopify’s, Cloudinary, Cloudflare Images, Imgix, etc.) can auto-resize and cache images at edge. This means you upload one high-res image, and the CDN does on-the-fly resizing/compression for each request. The benefit: users only download what’s needed.
Sometimes themes load extra images (e.g. hidden slides in a carousel). Remove or lazy-load any image not immediately visible. Also, trim metadata (EXIF) and unnecessary channels from image files.
Lighthouse will flag oversized images (the “Efficient images” audit). WebPageTest’s filmstrip shows when images appear. If LCP is a hero image, check its download time in DevTools Network. Even a 500KB saving on that image can knock ~100ms off LCP.
Remember: every image counts. Queue-it stats suggest 25% of pages could save >250KB just by compressing images/text. On product pages, optimizing imagery is the low-hanging fruit. It not only speeds loading but also reduces mobile data use, which customers appreciate.
Browsers have to build the DOM and CSSOM before painting the page. By default, CSS in the <head> and synchronous JS can block rendering. If your PDP’s HTML references large CSS or JS files in the head, the browser will wait to parse them before showing anything on-screen.
Why it matters: Render-blocking resources delay both the LCP and Time To Interactive. For example, if you load a 200KB CSS file at the top without splitting, the browser spends time
downloading it instead of painting. Similarly, a large JS bundle (including many libraries) can stall rendering or delay interactivity, increasing FID.
Extract the minimal CSS needed for above-the-fold content and inline it in the <head>. This reduces the initial CSSOM construction time. Load the rest of the stylesheet asynchronously (e.g. with media="print" trick or rel=preload on CSS).
Minimize your CSS and JS (remove whitespace, comments) and concatenate files to cut HTTP requests. (Note: HTTP/2 lessens request costs, but reducing file size always helps.)
For scripts that aren’t needed immediately (e.g. UI widgets, analytics), add the defer or async attribute. defer tells the browser to download the JS without blocking and execute it after HTML parsing, which prevents blocking the initial render. For example: <script src="gallery.js" defer></script>.
If using a JS framework, eliminate unused code. Tools like webpack or Rollup can remove unused exports. Also break your code into bundles: load only the JS needed for this page. For instance, product gallery code should only load on PDPs, not on every page.
Place <script> tags just before </body> so that they load after the content. This way the browser can render the visible content before parsing the script.
For heavy computations (image sliders, 3D viewers), consider offloading to a Web Worker so the main thread isn’t blocked, improving FID.
Some third-party scripts (e.g. certain chat widgets) can execute big JS on page load. Audit their impact and use deferred loading if possible.
Custom web fonts can block text rendering. Use font-display: swap or preload critical fonts to minimize FOUT/FOIT (flash of invisible text). Or use system fonts to avoid downloads.
Headless commerce (React/Vue/Svelte frontends) can create snappy dev experiences, but without care, user-facing performance suffers. In pure client-rendered pages, the browser may receive an almost-empty HTML shell and then fetch all data and templates via JavaScript.
Why it matters: Shifting rendering entirely to the client means more round-trip time and more work on the user’s device. Mobile shoppers or older devices suffer. A slow mobile can’t quickly parse a 500KB JS bundle and fetch data. The result: a longer LCP and higher FID (unresponsive while JS initializes). Also, many single-page apps render all components (including non-visible ones) on the client, causing unnecessary work.
Pre-render the PDP on the server so that HTML arrives filled with content. For example, Next.js’s getServerSideProps or Nuxt’s SSR mode can generate product HTML with data, so the user sees content immediately and the JS can hydrate later. Or use Static Site Generation (SSG) for products that don’t change often (with ISR to update).
Instead of booting an entire SPA, load just pieces. For instance, frameworks like Astro or React Server Components allow only the dynamic parts (e.g. interactive review widget) to be hydrated, while static parts remain pure HTML.
Stream the HTML to the client as soon as chunks are ready (some frameworks and streaming SSR allow this). The idea is to show content progressively rather than waiting for full bundle load.
Consider lighter-weight libraries or compiled frameworks (Svelte, Preact) that produce smaller bundles than React/Vue. Or use Alpine.js for small interactions instead of full SPA in some parts.
If using React, ensure components use React.memo, avoid re-rendering heavy subtrees, and hydrate as soon as possible. Lazy-load components that aren’t critical on first paint.
Show a minimal layout (gray boxes or spinners) quickly so the page feels responsive, then fill in content. This helps perceived performance even if actual data takes longer.
Use Lighthouse or webpack bundle analyzers to cut down your JS. Every library you add (lodash, moment, analytics) inflates the bundle.
Some 3rd-party PDP features (3D viewers, AR) might only need to load on user action (e.g. “View in AR” button click) rather than on initial load.
No matter how fast your code and assets, a lack of caching can make every visit slow. Conversely, smart caching can make repeat PDP views near-instant. Inefficient caching is often a silent culprit: devs think “we’re using cache” without checking what or how.
Why it matters: Without caching, every product page load requires full origin work: DB reads, template renders, API calls. This not only slows that single load (high TTFB/TTLB), but also compounds under traffic. On the other hand, misused caching (e.g. caching nothing dynamic, or using very short TTLs) yields little benefit.
If your platform supports it (Magento 2, Adobe Commerce have built-in FPC; Next.js/Vercel can cache pages; Shopify caches themes), enable it. FPC stores the rendered HTML so repeat views don’t hit the server again.
Ensure HTML or API responses are cached at the edge. For Shopify sites, this happens automatically. For custom sites, configure your CDN to cache HTML pages, at least for anonymous visitors. Use appropriate Cache-Control headers (e.g. max-age=60, stale-while-revalidate) so that if one user loads a page, the next user benefits immediately.
Set far-future caching for static assets (CSS/JS/images/fonts) with versioned filenames. For dynamic APIs, use stale-if-error and stale-while-revalidate to let the browser or CDN serve a slightly out-of-date version while fetching a fresh one in background.
In some cases, “preloading” critical assets (by sending Link: preload headers) can speed things up. (Shopify Liquid has preload filters for CSS/JS.) Also, enabling HTTP/2 or HTTP/3 on your server allows multiplexed requests, reducing overhead.
Make sure repeat visits don’t re-download unchanged assets. Check browser devtools to verify cache hits on CSS/JS images for reloads. If they always re-download, increase max-age.
For dynamic data (e.g. product details that don’t change mid-day), use in-memory caches (Redis, Memcached). For example, cache popular product queries so the DB isn’t hit every time.
When a product update happens (price change, etc.), invalidate or update the cache just for that resource, rather than purging everything. This keeps most pages cached while ensuring freshness.
Use tools to monitor cache hit rates. Some teams “warm” caches by pre-requesting key pages (e.g. homepage, top 100 PDPs) after a deploy, so the first real user doesn’t face a cold cache.
Platform-specific:
Performance isn’t a one-time project. You’ll continually add new features (apps, scripts, UI improvements) to your store. Each change is a potential speed regression. A 1–2 second gain in load time can be worth thousands in revenue. Use the metrics and tools above to pinpoint the root causes (be it slow backend, heavy scripts, or bloated images) and apply the solutions suggested. This systematic approach will help your eCommerce site deliver the speedy user experience that modern shoppers demand.
Mayank Patel
Oct 15, 20255 min read
How to Handle Tiered Pricing and Custom Quotes in a B2B Marketplace
Unlike traditional retail, where prices are mostly fixed, B2B buyers expect dynamic pricing that reflects order size, long-term value, and negotiation potential. That’s where tiered pricing and custom quotes (RFQs) come in.
Together, these two models let you cater to very different buyer needs. When implemented thoughtfully, they can help your marketplace attract larger clients, improve conversion rates, and boost overall sales volume, without sacrificing margins.
In this guide, we’ll break down how to set up tiered pricing from scratch, build a smooth custom quote (RFQ) workflow, and combine both approaches for a pricing strategy that scales with your business.
Tiered pricing (also called volume pricing or quantity break pricing) is the practice of offering better per-unit prices at higher quantities. In simple terms, “the more a customer buys, the lower the unit price becomes”. Setting up tiered pricing from scratch involves strategic configuration:
If you have any prior sales data (or industry benchmarks), identify common bulk order sizes. This helps determine logical breakpoints. For example, perhaps many buyers order in quantities of 50, 200, and 1,000; these could become your tier thresholds.
Create clear quantity tiers for each product (or category) and assign a discount or special price to each tier. Always ensure the discounts make business sense (maintain profit margins). For example, a product sold individually might have a 5% discount starting at 50 units, then a 10% discount at 200 units. The key is to carefully structure discount tiers to remain profitable while still being appealing to buyers.
Your marketplace software should allow multiple price points per product. If you’re coding this yourself, you’ll need a pricing engine that checks the order quantity and applies the appropriate unit price. Many B2B eCommerce platforms offer built-in support for quantity-based pricing (sometimes called price lists or quantity breaks).
Make sure buyers can easily see the tiered pricing structure on the product page or catalog. For example, show a table or note: “Buy 100+, get 5% off; 500+ get 10% off,” etc.
In B2B, different customer segments might have different pricing. Consider if you need login-based tiered pricing for certain buyer groups. For instance, perhaps “Gold” tier customers get an extra discount or have their own price tiers. Some marketplace solutions let you show different prices to different customer tiers (regular vs. premium members) when they log in.
Also Read: When Your B2B Ecommerce Site Doesn’t Talk to Your ERP
In B2B marketplaces, it’s not always practical to list a price for every possible order scenario. This is where Request for Quote (RFQ) or custom quote functionality comes in. An RFQ system lets buyers ask, “Here’s what I need, what price can you offer?” and receive a tailored quote from the seller.
It essentially brings the negotiation process online, within your marketplace, rather than over countless emails or phone calls. Here are key reasons an RFQ (custom quote) system is a must-have:
Perhaps a buyer needs a huge quantity beyond your normal tiers, or they have custom specifications (e.g. special packaging or product modifications). With an RFQ, the buyer can specify these needs and get a price that factors in volume, customization, or unique logistics.
Even if you set up tiered pricing, a client with an order much larger than your highest tier will want an even better deal. RFQ makes it easy for vendors to offer further tiered or volume-based discounts dynamically for such large requests.
In a marketplace with multiple sellers, a buyer’s quote request could go out to several vendors. Those vendors then compete to offer the best price. Buyers can compare multiple offers side by side, which creates a healthy competition and pushes sellers to give their most favorable pricing and terms.
Large or high-value B2B deals might involve negotiation on not just price, but payment terms, delivery schedules, or product bundles. An RFQ system aids these discussions in a structured way. All communication and terms can be documented within the marketplace platform. It also speeds up deal closure by keeping the process organized and trackable.
Also Read: How to Determine the Right Type of Marketplace to Scale Your B2B Ecommerce
Implementing custom quotes in your marketplace from scratch requires careful planning on both the user interface and the backend process. Here’s how to handle it:
Allow buyers to initiate a quote request wherever it makes sense. For example, on a product page you might have a “Request a Quote” button (especially for high-volume items), or in the shopping cart offer an option like “Request special pricing” for large orders. The process should be intuitive: the buyer selects the product(s) and quantities and can add any special
requirements or notes. Make the RFQ option visible especially when the purchase volume exceeds normal online checkout limits.
When a buyer submits an RFQ, collect all details needed for sellers to respond. This often includes the list of desired products, quantities, target delivery date, and any customization requests. A structured form helps here (e.g. fields for quantity, custom specs, comments) so the seller gets a clear picture.
Provide a dedicated interface for your sellers to manage quotes. For instance, a seller dashboard might have an “Requests for Quote” section where they can view each incoming request, then respond with their pricing. The seller should be able to input a custom price (per unit or total), set an expiration date for the quote, and include any terms (like shipping costs or volume breakpoints).
If your marketplace involves multiple roles (sales reps, managers, etc.), set up an approval process for special quotes. For example, if a vendor offers an unusually large discount or a unique deal, the system can automatically route that quote to a manager for approval before it’s sent to the customer.
Time is money in B2B sales. Implement notifications so that when a buyer submits a quote request, the relevant seller is instantly alerted (via email or an in-platform alert). Likewise, when the seller responds with a quote, the buyer should be notified immediately. To avoid delays, you can also send automatic reminders if a quote request hasn’t been answered within a certain timeframe.
A custom quote system shouldn’t be one-and-done. Often there’s back-and-forth negotiation. Your platform can support this by allowing buyers to ask questions or request changes, and sellers to adjust their quotes. Essentially, it becomes a secure communication thread tied to the quote, keeping all discussions in one place. (Some marketplace solutions even provide an inbuilt buyer-seller chat to bring about faster negotiations.) Make sure sellers can easily modify their offer, for e.x. if the buyer wants to increase the order quantity during negotiation, the seller should be able to update the pricing on the quote dynamically.
Once the buyer is satisfied with a quote, the system should let them accept it and seamlessly convert that quote into an order. This “quote-to-cash” step might involve generating a special
checkout link or adding the agreed items to the buyer’s cart at the quoted price. The buyer can then pay through the usual methods, and the order proceeds with the negotiated terms. By contrast, if the buyer declines the quote, have a mechanism to capture that outcome (which can provide insight, for e.x. did they find a better price elsewhere?).
Just as with tiered pricing, monitor your RFQ process and performance. How many quote requests convert to sales? What is the average turnaround time for quotes? Are certain products frequently triggering RFQs? Tracking these metrics helps you pinpoint where to improve. For instance, if you see that quotes with very long response times seldom convert, it’s a sign to streamline your quote workflow. Or if a particular seller consistently wins quotes with very low prices, it might inform pricing strategy for others.
Also Read: What is a B2B Marketplace?
Tiered pricing and custom quotes aren’t mutually exclusive; in fact, the most effective B2B marketplaces use both together to cover all bases. Think of tiered pricing as handling the straightforward, self-service scenarios and RFQ handling the exceptions or very large deals. Here’s how to make them work in harmony:
Display tiered prices on product listings so buyers can self-serve for typical order sizes. This upfront clarity (e.g. “Price: $100 each, or $90 each if you buy 100+”) helps buyers make quick decisions without involving a sales rep for common orders. However, always provide an easy path to request a quote if the buyer’s needs fall outside those tiers. Many companies do exactly this: they show standard volume discounts, but if an order exceeds the largest published tier, they invite the buyer to get a custom quote for an even better rate.
As a rule of thumb, when a buyer requests a special quote for a large volume, the offer they get should be equal or better than what they’d get just by looking at the tiered pricing. Custom quotes are your chance to reward very large orders or strategic customers with something extra. This not only closes the deal, it also signals to the buyer that requesting a quote is worth their time.
Data from tiered pricing can inform your RFQ strategy and vice versa. For example, if you notice many buyers maxing out the highest tier (e.g. constantly buying just under the cutoff for the next discount), that might be an opportunity to have sales reach out proactively or to adjust your tiers. Conversely, if RFQ negotiations for a certain product often settle at similar quantities or discounts, you might introduce a new tier in the standard pricing to streamline future deals.
The real opportunity lies in how you use data to make both systems smarter over time. Every RFQ, every bulk order, every price tier hit or missed tells a story about demand elasticity, buyer behavior, and negotiation trends. The marketplaces that learn from this data and keep refining their pricing logic will steadily pull ahead.
Mayank Patel
Oct 13, 20255 min read
