Advertising and Incrementality Analytics

Advertising and Incrementality Measurement: Understanding True ROI
Standard advertising dashboards show attributed sales and ROAS, but they can't answer the critical question: which sales would have happened anyway without advertising? A brand running Sponsored Products on their best-selling ASIN might see 5X ROAS, but if 80% of those sales would have occurred organically, the true incremental ROAS is only 1X - unprofitable after fees and COGS.
This attribution problem leads to systematic misallocation of advertising budgets. Brands over-invest in branded keywords (high attributed ROAS but low incrementality, since those shoppers were coming anyway) and under-invest in category/competitor keywords (lower attributed ROAS but high incrementality, since you're capturing new customers). The result: hundreds of thousands in wasted ad spend annually.
iDerive's Advertising and Incrementality module solves this by combining attribution modeling with incrementality testing. We track the complete customer journey using Amazon Marketing Cloud (AMC), platform APIs, and holdout experiments to isolate advertising's true causal impact. You see not just which ads generate attributed sales, but which ads drive incremental sales that wouldn't have happened otherwise - the only metric that matters for profitability.
Advertising & Incrementality Features
Measurement Methodology: Attribution, Incrementality, and Profitability
Attribution modeling provides the foundation. iDerive ingests data from all advertising platforms - Amazon Advertising, Walmart Connect, Target Roundel, Meta/TikTok - and uses algorithmic attribution to map customer journeys. We reveal which ad types drive awareness, consideration, and conversion at different funnel stages. This multi-touch attribution is vastly superior to platforms' last-click models.
Incrementality testing isolates causality. We design structured experiments (geographic holdouts, audience splits, time-based testing) that measure what happens when advertising is paused or increased. These tests reveal uncomfortable truths: branded search delivers 10-15% incrementality (most buyers were coming anyway), while category keywords deliver 60-80% incrementality (you're capturing genuinely new demand). This data transforms bidding strategies and budget allocation.
Profitability analysis is the final layer. Incremental sales only matter if they're profitable after all costs. iDerive calculates incremental contribution margin by deducting advertising spend, referral fees, fulfillment costs, and COGS from incremental revenue. You see which campaigns drive profitable growth (high incremental margin relative to spend) versus which drive unprofitable volume (incremental sales but negative margins). This prevents the common trap of growing revenue while shrinking profit.
Optimization Strategies: From Campaign Tactics to Portfolio Strategy
At the campaign level, incrementality data guides tactical optimization. High-ROAS branded keywords get de-prioritized (incrementality is low), while lower-ROAS category keywords get increased budgets (incrementality is high). DSP campaigns that show weak attributed ROAS but strong incrementality (introducing new customers to your brand) receive continued investment. These optimizations typically improve overall advertising profitability by 20-30% without reducing revenue.
At the product level, incrementality reveals which ASINs benefit most from advertising support. Some products (unique, premium-priced, high consideration) show strong incrementality because advertising genuinely drives demand. Others (commodity, price-competitive, low consideration) show weak incrementality because purchase decisions happen regardless of advertising. This insight guides where to concentrate advertising budgets for maximum ROI.
At the portfolio level, incrementality informs strategic budget allocation across marketplaces and channels. If Amazon Advertising shows 4X incremental ROAS while Walmart Connect shows 6X, shift budgets to Walmart. If DSP shows weaker short-term incrementality but stronger long-term brand building (measured through brand search lift and organic sales increases), maintain DSP investment as a strategic tool rather than evaluating it solely on immediate ROAS.
FAQ
Holdout-market testing is the only clean answer — pause Amazon ad spend in a geographically controlled holdout market for 3–4 weeks while running normally elsewhere, and measure DTC lift in the control versus holdout. AMC's cross-channel overlap reports help but require DSP plus a non-trivial identity stitch between Amazon shoppers and DTC CRM. Anything short of holdout testing is correlation, not causation — and correlation gets you a 70% overattributed halo number that justifies any budget decision you want. Most brands we work with discover actual halo is 15–25% of what their previous agency was claiming.
When you’ve outgrown pre-built reports — meaning your questions no longer match any canned dashboard. Helium 10 and similar tools are rules-based: they tell you what’s happening against known benchmarks. AMC is query-based: it answers questions no one’s pre-built for you ("what’s my path-to-purchase overlap between Sponsored Products and DSP for new-to-brand customers who came from competitor-conquest keywords?"). The trigger for graduating is usually (a) DSP spend above $50K/month, (b) cross-ad-type path analysis Sponsored Ads reports can’t produce, or (c) stitching Amazon behavior into a larger data model across DTC, retail-media networks, and CRM. Under those conditions, AMC typically cuts 15–25% of ad waste inside two quarters. Outside them — a $2M brand running Sponsored Products only — AMC is overkill and a dashboard tool tells you what you need.
Holdout testing is the cleanest method: pause a specific campaign or ad type for 2–4 weeks on a controlled subset of products, hold all other variables constant, and measure the delta in total units sold against a matched control set. AMC's built-in incrementality reports automate the audience-holdout logic for DSP campaigns. Most brands that claim to "test incrementality" are actually measuring attribution shift — true incrementality requires a real holdout and patience to weather the short-term ranking dip.
Considered-purchase categories (mattresses, high-end electronics, professional tools, beauty devices) show path-to-purchase windows of 14–45 days — shoppers touch 4–8 marketing events before buying. Impulse or replenishment categories (grocery, household, pet food) run 1–7 days with 1–3 touches. Setting the window wrong in AMC pulls in stale or incomplete data. For path-to-purchase analysis in AMC, default windows need customization by category — the template queries use a 14-day window that under-attributes for considered purchases and over-attributes for impulse.
AMC applies a privacy-preserving aggregation threshold — you can't see results from any subset with fewer than ~100 unique users. Holdout tests need to design around this: either pick holdout markets large enough that both test and control groups clear the threshold, or structure your test as a pre/post analysis at the account level (which loses experimental clean-ness). For most mid-size brands, the cleanest approach is a geo holdout at DMA level — US is divided into 210 DMAs, and most DMAs have enough activity to clear the floor for DSP campaigns. Sponsored Products-only campaigns rarely have enough DMA-level granularity to run a clean holdout in AMC — consider a market-matched attribution model outside AMC instead.
Three approaches in order of accuracy. (1) Holdout testing — pause ad spend in controlled markets/products for 2–4 weeks, measure delta vs continuously-advertised control. Cleanest method but operationally complex. (2) AMC's incrementality reports for DSP campaigns. (3) Pre/post analysis — measure performance before and after spend changes. Most brands claim "incrementality" without holdout — those numbers are usually 30–50% inflated.
Complementary. AMC measures within-Amazon attribution and cross-Amazon-ad-type interaction with high fidelity. MMM measures cross-channel impact (Amazon vs Meta vs Google vs DTC vs offline) with lower per-channel granularity but cross-channel context AMC can't see. For brands running multi-channel marketing, you need both. Brands that try to use AMC as a substitute for MMM systematically over-credit Amazon for what other channels actually drove.