# Master PPC Audit & Optimization Guide > Comprehensive playbook for conducting thorough Amazon PPC account audits and troubleshooting performance issues Source: https://adlabs.app/guides/amazon-ppc-audit-guide/ (updated 2026-10-19) 1Core Philosophy "There are 'best practices,' and then there are 'practices that get the best results.' We only share the latter." ### The Golden Rule: "It Depends" In Amazon PPC, **context matters**. Everything is nuanced. Before applying any tactic, ask: "why does this make sense for THIS account?" The AdLabs Approach Understand the underlying logic/reasoning behind every tactic. Ask "why does this make sense here?" before applying any strategy. Maintain curiosity over certainty. When something seems counter-intuitive, investigate. Don't blindly accept or reject. ### First Principles Thinking Break down problems into fundamental truths, then rebuild solutions from the ground up. Don't follow rigid SOPs blindly. Don't apply "rules" without context. Don't assume what worked for Account A works for Account B. ### The ACOS Formula (Foundation of Everything) Core Formula ACOS = Spend ÷ Sales = CPC ÷ (CVR × AOV) = CPC ÷ RPC Where **CPC** \= Cost Per Click (controlled by bids; see our deep dive on bid optimization), **CVR** \= Conversion Rate (influenced by targeting, listings, placements), **AOV** \= Average Order Value (product price), and **RPC** \= Revenue Per Click = CVR × AOV. Critical Insight Rising ACOS is caused by CPC increasing, CVR decreasing, AOV decreasing, or some combination. Identify which lever moved, then optimize that lever. 2The 10-Step Audit Framework Time Investment **30-60 minutes** for experienced users | **2-3 hours** for comprehensive first audit. **Frequency:** Full audit quarterly; key steps weekly. ### Step 1: Total Sales Performance **Goal:** Understand product-level performance and budget distribution across catalog. #### A. Product-Level Analysis Combine data from Business Reports (total sales) and Advertised Product Report (ad sales/spend). Look for: 1. ASINs with high total ACOS → Scale back spend 2. ASINs under-advertised but generating sales → Increase ad support 3. ASINs with no ads but meaningful sales → New advertising opportunities 4. ASINs draining budget with poor ROAS → Reduce or pause #### B. Catalog Budget Distribution **Ideal State:** Ad spend share aligns with each product's sales contribution. | ASIN | Total Sales % | Ad Spend % | Status | | ---- | ------------- | ---------- | ---------------- | | A123 | 50% | 20% | Underspending | | B456 | 20% | 60% | Overspending | | C789 | 30% | 20% | Balanced | ### Step 2: Campaign Placements **Goal:** Identify placement inefficiencies and rebalance spend. Since August 10, 2026, Sponsored Products also serve off Amazon, including creator content, where top of search and product page adjustments don't apply, so check the placement report for that spend too. **The Three Placements** 1. **Top of Search (ToS)** – Typically highest CVR, best ROAS 2. **Rest of Search (RoS)** – Medium CVR 3. **Product Pages (PP)** – Often lowest CVR, highest ACOS Red Flags Product Pages consuming too much spend with low CVR or high ACOS ACOS varies wildly across placements Top of Search getting insufficient spend despite best performance Quick Wins ✓ Reduce Product Pages modifier by 25-50% if ACOS is 2x+ target ✓ Increase Top of Search modifier if CVR is strong ### Step 3: Bid Management **Goal:** Categorize keywords and apply appropriate bid optimization formulas. Every keyword falls into ONE of four categories: | Category | Definition | Formula | | ------------------------ | ------------------------------ | -------------------------------------- | | **High ACOS** | ACOS > Target ACOS | New Bid = RPC × Target ACOS | | **High Spend, No Sales** | Spend > Target CPA, Orders = 0 | New Bid = (AOV ÷ Clicks) × Target ACOS | | **Low ACOS** | ACOS < (Target - 20% buffer) | New Bid = Current Bid × 1.05 to 1.10 | | **Low Visibility** | Clicks < aCTC | New Bid = Current Bid × 1.05 | ### Step 4: Search Term Reports **Goal:** Mine search terms for 4 key insights: tactic allocations, branded spend leaks, irrelevant search terms, and harvesting opportunities. Use n-gram analysis to uncover hidden patterns in your search term data. ### Steps 5-10: Tactic Allocations, Structure, Targeting & More #### Step 5: Tactic Allocations **Goal:** Ensure spend is distributed for optimal growth. 70-80% Non-Brand New customer acquisition: the growth engine 10-20% Competitor Market share theft from competing brands 5-10% Brand Defense Protect your brand presence on Amazon | Tactic | % of Spend | Purpose | | -------------------- | ---------- | ------------------------ | | Non-Brand Keywords | 70-80% | New customer acquisition | | Competitor Targeting | 10-20% | Market share theft | | Brand Defense | 5-10% | Protect brand presence | #### Step 6: Campaign Structure --- **Goal:** Assess organization, control mechanisms, and scalability **Key Audit Points** * **[naming convention](https://adlabs.app/guides/amazon-campaign-structure-guide/)s** – Consistent format? Easily identifiable goals? * **Segmentation by Goal** – Research vs. Scaling vs. Brand defense campaigns * **Structure by Catalog Size** – Match structure to SKU count #### Step 7: Targeting Mix --- **Goal:** Balance discovery with control **Ideal Match-Type Distribution (Mature Accounts)** * **50%+ spend on Exact Match** (scaling engines) * **30-40% on Broad/Phrase** (discovery) * **10-20% on Auto** (continuous research) #### Step 8: Budget Management --- **Goal:** Ensure high-performers aren't constrained Red Flags Campaign at 25% ACOS (target: 30%) capping out at noon Top performer losing evening traffic due to budget Quick Win ✓ **Open up budgets on high-performing campaigns** – Immediate sales growth with minimal risk #### Step 9: Ad Type Utilization --- **Goal:** Maximize coverage across Amazon's ad formats | Ad Type | % of Budget | Purpose | | ---------------------------------------------------------------------------- | ----------- | ------------------------------- | | Sponsored Products | 85-95% | Core revenue driver | | [Sponsored Brands](https://adlabs.app/guides/amazon-sponsored-brands-guide/) | 5-15% | Brand awareness + ToS dominance | | Sponsored Display | 0-10% | Retargeting + product targeting | #### Step 10: Dayparting Opportunities --- **Goal:** Identify high-performance hours and optimize bids accordingly The Philosophy **Optimize by RPC, NOT sales volume.** High sales volume ≠ high profitability. Revenue Per Click reveals true efficiency. 3Troubleshooting Performance Issues ### The Diagnostic Framework When ACOS rises or sales decline, follow this structured approach: #### Step 1: Check Attribution Delays First Is the issue only in the last 1-2 days? If yes, it's likely attribution noise. Monitor but don't panic. If no, proceed with investigation. Important Amazon conversion data can lag 2-14 days. Always use 7+ day windows for reliable analysis. #### Step 2: Identify the Primary Issue * Did **spend increase** more than sales? → ACOS being "pulled up" * Did **sales drop** while spend stayed flat? → CVR problem * Did **CPC spike**? → Bid or competition problem #### Step 3: Find the Biggest Movers **Sort by DELTA** (change vs prior period): Spend DELTA, ACOS DELTA, Sales DELTA. Critical Don't sort by ACOS blindly. A campaign with 1,000% ACOS but $100 spend is inconsequential in a $50k account. ### ACOS Troubleshooting: The Three Levers Key Formula ACOS = CPC ÷ (CVR × AOV) **If CPC Increased:** Check bids raised recently, dynamic bidding set to "up and down," placement modifiers inflated, or increased competition. **Fix:** Reduce bids using the RPC formula. **If CVR Decreased:** Check product changes (pricing, images, reviews, Buy Box loss), macro/seasonality factors, targeting (irrelevant search terms), and placements (shift away from Top of Search). A decline in [organic ranking](https://adlabs.app/guides/amazon-organic-ranking-guide/) can compound the problem. ### The Negative Spiral (And How to Break It) The Problem Lower CVR → Worse placement → Lower CVR → Worse placement → **Death spiral** **The Solution:** Run deals/promotions → Boost short-term to regain Top of Search placement → Higher placement = higher CVR → Amazon recognizes product as better converter → Better [organic placement](https://adlabs.app/guides/amazon-organic-ranking-guide/) going forward. 4Bid Optimization: The RPC Method Foundation **RPC Bidding is the foundation of the AdLabs methodology.** This is "white box" bidding: transparent, mathematically-based, human-controlled. For a complete breakdown, see the Amazon PPC Bid Optimization Guide. ### The Core Formula & 4 Keyword Categories #### The Core Formula Master Formula Target CPC = RPC × Target ACOS Where **RPC** \= Revenue Per Click = Sales ÷ Clicks, **Target ACOS** \= Your profitability goal (as decimal), and **Target CPC** \= What you should bid. #### The 4 Keyword Categories High ACOS Keyword ACOS exceeds your target ACOS New Bid = RPC × Target ACOS High Spend, No Sales Spend exceeds Target CPA with zero orders New Bid = (AOV ÷ Clicks) × Target ACOS Low ACOS ACOS below target minus 20% buffer: room to grow New Bid = Current Bid × 1.05 to 1.10 Low Visibility Clicks below aCTC: not enough data yet New Bid = Current Bid × 1.05 ### Detailed Examples for Each Category #### High ACOS Example Target ACOS: 30% · Keyword RPC: $5.00 (Sales $50 ÷ Clicks 10) · **New Bid = $5.00 × 0.30 = $1.50** Important This formula usually reduces bids, but not always. If current bid is $0.02 but calculated bid is $1.50, bid will INCREASE. The current bid is just a status, not a data point. #### High Spend, Non-Converting (Anticipated RPC) AOV: $20 · Keyword Clicks: 10 (no conversions) · Target ACOS: 30% · Anticipated RPC = $20 ÷ 10 = $2.00 · **New Bid = $2.00 × 0.30 = $0.60** #### Low ACOS: The 20% Buffer Rule Target ACOS: 30% · Low ACOS threshold: 30% - (30% × 0.20) = **24%** · Only increase bids if keyword ACOS ≤ 24%. #### Low Visibility: aCTC Calculation Conversion Rate: 10% · aCTC = 1 ÷ 0.10 = **10 clicks** · Keywords with fewer than 10 clicks = Low Visibility. Special Case If keyword has BOTH Low ACOS and Low Visibility, **Low ACOS takes priority**. ### Anticipated RPC (For New Keywords) When launching new keywords with no historical data: New Keyword Starting Bid Starting Bid = (AOV × CVR) × Target ACOS Example: Product price $10 · CVR 10% · Target ACOS 30% → Anticipated RPC = $10 × 0.10 = $1.00 → **Starting Bid = $1.00 × 0.30 = $0.30** Why NOT Use Amazon's Suggested Bid? Amazon's suggested bid is based on auction market price, not YOUR profitability. 5Placement Optimization ### The Common Mistake ✕ Wrong Approach ✕ Look at placement ACOS ✕ Increase adjustments on LOW ACOS placements ✕ Decrease adjustments on HIGH ACOS placements ✕ Use target ACOS in placement calculations Why This Is Wrong Placements don't have independent CPCs. Placement adjustments are **modifiers on keyword bids**, not standalone bids. ACOS is a result of bid strategy, not an input. ### The Correct Approach ✓ Correct Approach ✓ Optimize by CONVERSION RATE, not ACOS ✓ Set lowest-converting placement to 0% ✓ Calculate relative CR lift for adjustments ✓ Control ACOS through keyword bids instead #### The Correct Calculation Placement Adjustment Formula Adjustment % = ((Higher CR - Lowest CR) ÷ Lowest CR) × 100 #### Example Calculation | Placement | Conversion Rate | Calculation | Adjustment | | -------------- | --------------- | --------------------- | ---------- | | Product Pages | 5% | Baseline (lowest) | **0%** | | Rest of Search | 7% | (7-5)/5 = 40% lift | **+40%** | | Top of Search | 9.45% | (9.45-5)/5 = 89% lift | **+89%** | ### Critical Rules for Placement Optimization Rule 1: Target ACOS Is NOT Part of the Calculation Target ACOS = 15% → Adjustments: 0%, 40%, 89%. Target ACOS = 30% → Adjustments: 0%, 40%, 89%. _They're the same!_ Rule 2: Control ACOS Through Keyword Bids Placements = WHERE to allocate budget. Keyword bids = HOW MUCH to spend. Rule 3: Higher Adjustment on High-ACOS Placement Can Be Correct If CR justifies it, increase the modifier. Top of Search at +89% may have highest ACOS but also highest efficiency per conversion. 6Search Term Analysis ### Keyword Harvesting Criteria Important **Don't harvest everything that converts!** One sale doesn't mean you should promote to manual targeting. #### The 6 Criteria | # | Criterion | Rule | Why It Matters | | ----- | ------------------------------------ | ------------------------------------------------- | ---------------------------------- | | **1** | **Orders > 1** | At least 2 sales; weeds out one-off flukes | Most important criterion | | **2** | **CVR ≥ Benchmark** | Product's overall CVR or tactic CVR | Only harvest strong performers | | **3** | **Clicks ≥ aCTC** | Average Clicks to Conversion = 1 ÷ CVR | Ensures sufficient traffic signal | | **4** | **ACOS (Optional)** | Can optimize after harvesting | Don't use as hard filter | | **5** | **Query Text Matches Campaign Type** | Keeps segmentation clean | Ensures structural integrity | | **6** | **Same SKU Orders** | Orders came from the same product, not cross-ASIN | Validates true keyword-product fit | Once you've harvested keywords, place them into the right campaign structure to maintain clean segmentation and data clarity. ### Negative Keyword Strategy **Add negatives when:** Zero sales with significant spend, high ACOS (>100%), irrelevant to product category, product variation you don't sell, wrong product type entirely. Better Approach **Irrelevant** non-converters → Negate immediately. **Relevant** non-converters → Harvest to exact match for more control and negate from research campaigns to control bids on product-keyword relationships and manage spend allocation. Don't negate relevant terms too fast: seasonal patterns may be at play. For a more systematic approach to finding wasteful search terms at scale, n-gram analysis can surface hidden patterns that individual search term review misses. 7N-Grams Analysis ### What Are N-Grams? An n-gram is a sequence of N words from a phrase: * **Monogram (1-gram):** Single words * **Bigram (2-gram):** Two-word sequences * **Trigram (3-gram):** Three-word sequences For a complete walkthrough with step-by-step examples, see the Amazon N-Gram Analysis Guide. ### Why N-Grams Matter The Hidden Waste Problem Individual search terms look harmless ($0.50-$1.00 each). But if a word like "prescription" appears in 200 search terms at 400% ACOS, that's **$150+ wasted**. Individual analysis reveals nothing. N-gram analysis reveals the pattern. #### N-Gram Workflow Pull Search Term Report (6-12 months) Break into Monograms: Single Words Filter: Sales = 0, Sort by Spend ↓ Is the word relevant to your products? If irrelevant Add as Negative Phrase (campaign or account-wide) Then repeat for bigrams Check Bigrams: Two-Word Combinations Validate: Search Amazon for the term. Do competitors appear? ### Analysis Frequency | Timing | When | | ------------------- | ---------------------------------------- | | Account onboarding | **MUST DO**: biggest cleanup opportunity | | First few months | Monthly | | Mature accounts | Quarterly | | After major changes | Ad hoc | 8Campaign Structure & Match Types ### The Granularity Spectrum From aggregated to segmented: the right level depends on your catalog size and management capacity. For a complete framework, read the Amazon Campaign Structure Guide. * **MPAGs** (Multiple Product Ad Groups): Easiest to manage * **SPAGs** (Single Product Ad Groups): Balanced (best for large catalogs 500+ SKUs) * **SPCs** (Single Product Campaigns): Maximum control _Recommended_ * **SKWs** (Single Keyword Campaigns): Most granular SPC Benefits **Single Product Campaigns (SPCs) are the gold standard for most accounts.** Dedicated budgets per product, complete bid isolation, and full visibility into exactly what's driving performance for each ASIN. You control spend allocation at the product level, with no shared budgets cannibalizing your winners. For large catalogs (500–1,000+ SKUs or high variation counts), **graduate to SPAGs** (Single Product Ad Groups) for a balance of granular control with more manageable organization. SPAGs give you product-level keyword control and clear product-keyword relationships while keeping campaign counts practical at scale. ### The Launch Campaign Stack Every product should launch with a standard campaign stack that covers all traffic sources and match types: | Campaign | Type | Purpose | | -------------------------------------- | --------- | ------------------------------------------------------------------------------- | | **Auto** | SP Auto | Keyword and product targeting discovery (all 4 targeting types in one campaign) | | **Research: Broad/Phrase** | SP Manual | Keyword research and expansion using broad and phrase match | | **Performance: Exact** | SP Manual | Proven converting keywords at exact match for maximum control | | **Product Targeting: Expanded** | SP Manual | Category-level and broad product targeting for discovery | | **Product Targeting: Individual ASIN** | SP Manual | Specific competitor and complementary ASIN targeting for precision | This stack ensures complete coverage: autos discover, research campaigns test, and performance campaigns scale your winners. Keywords flow **up** from auto → research → performance as they prove themselves through the harvesting criteria above. What About Segmenting Further? Some advertisers segment auto campaigns by targeting type (close match, loose match, substitutes, complements) or split broad and phrase into separate campaigns. We don't think it makes a meaningful difference in performance; it just creates more campaigns to manage. Keep it simple unless you have a specific reason to split. Don't Segment by Placement Another common approach is creating separate campaigns per placement (Top of Search, Rest of Search, Product Pages). This is unnecessary. Amazon's placement multipliers let you adjust bids by placement _within_ a single campaign, giving you the same control without the campaign bloat. ### Match Types: Ideal Distribution Ideal Match Type Distribution (Mature Accounts) Exact 50%+ 50%+ Broad/Phrase 30-40% 30-40% Auto 10-20% 10-20% | Match Type | Behavior | Best For | | ------------------ | ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------- | | **Exact** | Your keyword + close variants: misspellings, plurals, other languages, abbreviations, acronyms, and reordered words. _Exact does NOT mean literal exact match._ | Scaling proven performers | | **Phrase** | Your phrase appears intact in order, with words allowed before or after. Also includes close variants (misspellings, plurals). Cannot be split or reordered. | Controlled discovery | | **Broad** | Synonyms, misspellings, related terms, reordered words, and loosely related queries. Amazon interprets intent broadly and can match terms you didn't anticipate. | Wide research net | | **Modified Broad** | Add a + before words that MUST appear in the search term (e.g., +protein +powder organic). Locks in critical words while allowing broad matching on the rest. Gives more control than broad without being as restrictive as phrase. | Targeted research with guardrails | | **Auto** | Amazon decides targeting across 4 types: **Close Match** (related keywords), **Loose Match** (broadly related), **Substitutes** (competitor products), **Complements** (frequently bought together). | Initial discovery | "Exact" Match Is NOT Exact Amazon's exact match includes: misspellings ("protien" → "protein"), plurals/singulars, other languages (Spanish search terms matching English keywords), abbreviations, acronyms, and reordered words ("whey protein" = "protein whey"). This is fundamentally different from Google Ads exact match. Always check your search term reports: you may be matching queries you didn't expect even on exact. 9Day-Parting Strategy ### Core Principle & Implementation Don't chase traffic volume. Chase HIGH-CONVERTING traffic volume. * **Daytime (9am-5pm):** 20-30% HIGHER conversion rates * Time-constrained shoppers * Purposeful buying * Mobile-heavy, quick decisions * **Evening (6pm-12am):** More traffic, lower conversion rates * Leisure browsing * Comparison shopping * Desktop multi-tabbing For a detailed hour-by-hour implementation strategy, see the Amazon Dayparting Guide. #### Wrong vs. Correct Day-Parting Don't Do These 1. Chasing total sales volume: high volume with low CVR = wasted spend. 2. Pausing/unpausing campaigns: zero chance of sales. 3. Adjusting budgets hourly: budgets are daily settings. Correct Implementation 1. Calculate RPC by hour (last 30 days). 2. Identify peak RPC hours (not peak sales hours). 3. Increase bids 20-40% during high-CR hours. 4. Decrease bids 20-40% during low-CR hours. 5. Give 2-4 weeks to stabilize. Expected: Sales increase 10-25% with ACOS same or better. 10Budget Management ### Key Principles & Red Flags **Key Principles:** 1. **Open up budgets on high-performing campaigns**: if ACOS is below target and budget caps out, increase immediately for instant growth. 2. **Review daily during high-traffic periods**: Prime Day, Black Friday, holidays. Top performers shouldn't run out of budget. 3. **Set budgets 20-30% above expected daily spend**: headroom for growth, prevents mid-day exhaustion. 4. **Use unlimited budgets for proven winners**: if cash flow allows, maximum capture of profitable traffic. Pairing smart budget management with dayparting ensures your budget goes furthest during the hours that convert best. Red Flags Campaign at 25% ACOS (target: 30%) capping out at noon Top performer losing evening traffic due to budget constraint 11Optimization Workflow & Frequency The Big Misconception **Most people optimize way too frequently.** AdLabs does ZERO regular daily optimizations. Over-optimization = analysis paralysis + wasted effort. ### Recommended Cadence D Daily: Monitor Only Check anomalies, respond to clients. No optimizations. W Weekly: Bid Optimizations Apply RPC formulas across all 4 keyword categories. Use 7-14 day data windows. M Monthly: Harvesting & Placements Keyword harvesting, placement optimizations, reporting. Use 30-day windows. Q Quarterly: Deep Analysis N-grams cleanup, day-parting review, market conditions assessment. Use 90-day+ data. Y Yearly: Full Account Review Comprehensive audit, seasonal analysis, strategic planning for the year ahead. ### Date Range Selection | Situation | Date Range | Reason | | ----------------------- | ------------------ | ------------------- | | First optimization | 30 days | More confidence | | Follow-up optimizations | 7-14 days | More relevance | | Post-holiday/event | Exclude event data | Avoid distortion | | Placement optimization | 30-60 days | Need stable CR data | | Day-parting | 30+ days | Stable patterns | Critical Don't include Prime Day, holiday, or promotional data in normal optimization windows. ### New Campaign Exception For new/immature campaigns (< 6 months), move everything one level more granular: | Mature Campaign | New Campaign | | --------------- | ----------------------- | | Monthly tasks → | Weekly | | Weekly tasks → | Daily (but not hourly!) | 12Common Mistakes to Avoid ### Bid Optimization Mistakes ✕ Avoid These ✕ Daily bid optimizations (not enough data change) ✕ Bulk bid changes without analysis ✕ Using Amazon's suggested bid (doesn't know YOUR margins) ✕ Negating relevant keywords instead of reducing bids ✕ Jumping to conclusions on 1-2 day data (attribution lag) ### Placement Mistakes ✕ Avoid These ✕ Optimizing placements based on ACOS (should use CVR) ✕ Using target ACOS in placement calculations ✕ Weekly placement adjustments (need 30-60 days) ✕ Jumping to +200% modifiers overnight (work up gradually) ### Search Term Mistakes ✕ Avoid These ✕ Harvesting every converting term (one sale doesn't mean good keyword) ✕ Instant negation of relevant non-converters ✕ Ignoring n-grams analysis (missing cumulative waste) ✕ Not filtering query text by campaign type ### Day-Parting Mistakes ✕ Avoid These ✕ Chasing sales volume instead of conversion rate ✕ Pausing campaigns instead of adjusting bids ✕ Adjusting budgets instead of bids ✕ Weekly day-parting adjustments (patterns are extremely stable) ### Structural Mistakes ✕ Avoid These ✕ Over-segmentation (too many campaigns with no data) ✕ Over-aggregation (can't see product-keyword relationships) ✕ Inconsistent naming conventions ✕ Not separating brand/non-brand/competitor campaigns ✦Quick Reference Formulas ### Core Calculations | Metric | Formula | | ------------------- | ---------------------------------- | | **ACOS** | Spend ÷ Sales | | **RPC** | Sales ÷ Clicks | | **CVR** | Orders ÷ Clicks | | **CPA** | Spend ÷ Orders | | **Target CPA** | Target ACOS × AOV | | **aCTC** | 1 ÷ CVR (or Total Clicks ÷ Orders) | | **Break-Even ACOS** | (Price - COGS - Fees) ÷ Price | ### Bid Formulas | Situation | Formula | | ------------------------ | ---------------------------------------- | | **High ACOS** | New Bid = Keyword RPC × Target ACOS | | **High Spend, No Sales** | New Bid = (AOV ÷ Clicks) × Target ACOS | | **Low ACOS** | New Bid = Current Bid × 1.05 to 1.10 | | **Low Visibility** | New Bid = Current Bid × 1.05 | | **New Keyword** | Starting Bid = (AOV × CVR) × Target ACOS | ### Placement Formula & Harvesting Thresholds #### Placement Formula Placement Adjustment Adjustment % = ((Placement CR - Lowest CR) ÷ Lowest CR) × 100 #### Harvesting Thresholds | Criterion | Threshold | | ------------------- | ---------------------------------------- | | **Orders** | \> 1 | | **CVR** | ≥ Product benchmark | | **Clicks** | ≥ aCTC | | **Same SKU Orders** | Orders from same product, not cross-ASIN | "We're not just trying to tell you the definitions. We're trying to show you how to think critically so that you can do this on your own."