Unlocking Powerful Insights with Amazon Marketing Cloud (AMC) in India

Here’s What This Covers

This blog explains how Amazon Marketing Cloud is changing ad optimisation for Indian brands. Many teams still rely only on ROAS, which gives a limited view of performance. AMC brings deeper insight into how customers move before purchase decisions happen.

It looks at signals like purchase paths, audience overlap, and new-to-brand data. These help connect campaigns instead of treating them in isolation. The blog also explains how AMC works in real setups and what global data indicates.

It ties this into the marketplace strategy as well. Brands working across platforms need better data alignment to improve outcomes. The takeaway stays simple. Better analysis with faster execution improves results over time.

Indian brands are increasing spend on Amazon Ads across most categories. Budgets are rising, and competition is getting tighter each quarter. But many teams still rely only on ROAS to make decisions. That view is incomplete.

Amazon Marketing Cloud changes that equation by giving brands their first real look at what is actually driving purchases, not just what the attribution model decided to credit. That distinction is worth real money when your ad budgets are scaling, and your ROAS is flattening. 

Most Indian brands competing on Amazon are optimising the same signals, bidding on the same keywords, and reading the same console reports. AMC is the first tool that actually separates what you know about your buyers from what your competitors can see. That informational gap is the real competitive advantage here, and right now, very few Indian brands are using it.

What Is Amazon Marketing Cloud and Why India Needs It Right Now 

Amazon Marketing Cloud (AMC) is a secure, privacy-safe, cloud-based clean room solution that lets advertisers run SQL-based analytics across pseudonymised Amazon Ads signals, combining Sponsored Ads data, DSP campaign events, shopping behaviour, and first-party brand data within a single privacy-controlled environment. Unlike standard Amazon advertising analytics dashboards that return aggregated surface-level metrics, AMC gives you event-level query capability without ever exposing individual user data to either side of the equation.

This is not a future-state tool or a pilot programme. It is live and operational in India today. AMC became available in India in 2024 with significantly expanded access through 2025, carrying lower minimum spend thresholds than the US or EU markets, a well-developed Amazon Ads Partner ecosystem already active in the country, and approximately 85% feature parity with the US version of the platform. Indian brands that structure their Amazon campaign management approach around AMC in the next 12 months will build data advantages and audience precision that later entrants simply cannot close by adding more rupees to their media budgets.

Your Amazon Ads Reports Are Lying to You

Most brand managers across India are making Amazon ad strategy decisions on metrics that answer the wrong questions entirely. ROAS tells you whether a campaign is spent efficiently when viewed in isolation. It does not tell you whether that campaign brought in genuinely new customers, what exact role it played in a longer multi-touchpoint purchase path, or whether it was quietly cannibalising another campaign that was running simultaneously against the same audience pool.

This is how it plays out in real brand accounts. A Sponsored Brands campaign shows weak ROAS in the console. Teams usually cut the budget quickly to improve efficiency numbers. But deeper analysis often tells a different story. That campaign may be driving early awareness. It supports conversions that happen later through other campaigns.

When it gets paused, reported ROAS might look better. But overall sales can drop over time without clear visibility. The connection is missed because standard reports do not show it. This pattern shows up often across categories. The data exists, but it is not always surfaced clearly.

India’s Amazon advertising strategy challenge compounds this structural issue in a category-specific way. With over 218,000 active sellers on Amazon India offering 168 million products across the marketplace, competitive intensity is not just high in premium categories. It is relentless across almost every product segment. Brands running undifferentiated ad stacks with the 

• same keywords, 
• same bidding logic, and 
• same frequency approaches 

They are fighting expensive auction battles without any real informational advantage over their competitors. The brands starting to pull ahead in India’s most competitive categories share one behaviour. They understand the actual path their customers travel before converting. Amazon Marketing Cloud is the tool that makes that full path visible and actionable for the first time.

The core problem is not that Indian brands are advertising badly on Amazon. The problem is that they are optimising against a partial, distorted picture of what their advertising is actually doing across the full purchase funnel.

How Amazon Marketing Cloud Actually Works

Understanding the mechanism here matters more than most marketers initially realise. Misunderstanding it leads brands to underinvest in the setup phase or arrive with expectations for analytical capabilities that AMC was never designed to deliver.

AMC is built on AWS Clean Rooms, and each advertiser gets their own dedicated, isolated AMC instance. Within that instance, Amazon Ads events, including impressions, clicks, purchases, branded searches, and product detail page views, are stored as pseudonymized signals. Advertisers can layer their own first-party data on top of this setup. CRM records, hashed emails, offline sales data, and web events all get included. But raw data is never shared between parties. That is important.

You cannot access Amazon’s user-level data directly. Amazon also cannot see your internal records. What you get is aggregated output from queries across both datasets.

Each output follows a minimum user threshold for privacy. This is not a limitation, it is a designed safeguard. It keeps the system usable and compliant in regulated markets like India.

The query layer is where the actual strategic power sits. AMC now stores event-level data for up to 25 months, extended from 13 months following a significant platform update in November 2025. This means Indian brands can now compare two full calendar years of campaign performance, track seasonal cohort purchase behaviour across consecutive years, and model long-term ROAS trends that were genuinely impossible to calculate before this change was implemented.

The bigger point for brand teams is that the questions AMC can answer no longer require a data scientist to ask them. In September 2025, Amazon opened direct, self-service AMC access to all Sponsored Ads advertisers globally, eliminating the previous requirement for a certified partner relationship or Amazon sales rep coordination just to get an instance activated. The updated interface includes no-code analysis templates, pre-built result visualisations, and Ads Agent, an AI-powered analytical assistant built on Amazon Bedrock that generates complete analytics SQL queries through plain-language conversations. Teams that previously spent hours writing and debugging SQL are now reaching usable insights in a fraction of the time.

Here is a practical breakdown of what you can actually query inside a properly configured AMC instance:

• Path-to-conversion analysis: Which ad touchpoints appear in the purchase journey before a conversion event, in what sequence they typically appear, and how often multi-touchpoint paths outperform single-exposure ones across converting customer segments
• New-to-brand vs. repeat buyer rates: Segmented by campaign type, advertising channel, and audience cohort, so you can identify which campaigns are genuinely growing your customer base versus which ones are recycling buyers who were already going to purchase
• Frequency and overlap analysis: Whether the same audience segment is being reached by both your Sponsored Products and DSP campaigns simultaneously, and whether that overlap is driving incremental purchase behaviour or producing ad fatigue and budget waste
• Custom audience building: Create and directly export precise behavioural audience segments from AMC into Sponsored Ads, Sponsored Display, or Amazon DSP for immediate campaign activation
• Cross-channel signal matching: Import hashed first-party data from outside Amazon to understand how your Amazon advertising activity influences off-platform behaviour, and how non-Amazon behavioural signals can sharpen your Amazon targeting precision

Amazon Marketing Cloud does not replace your campaign manager’s judgment or accumulated category experience. What it replaces is the guesswork they have been using as a substitute for real behavioural data.

What AMC Data Is Telling Brands Globally?

The global case for Amazon marketing analytics powered by AMC is well past the emerging stage at this point. The results are documented, they are consistent across product categories and market contexts, and they are becoming increasingly difficult for India-focused brand teams to rationalise setting aside.

A 49% monthly sales revenue lift without increasing the ad spend budget. An art supply brand working with their agency SparkX used AMC to discover that a significant portion of their Amazon sales were originating from B2B buyers, specifically schools and small-to-medium businesses purchasing products in bulk quantities. Building custom AMC audience segments around this behavioural pattern delivered 169% higher ROAS compared to the brand purchaser control group, and a 49% lift in monthly sales revenue without adding a single dollar to the media budget. The customer insight was sitting inside the platform the entire time. AMC made it queryable and actionable for the first time.

A 71% ROAS improvement year-over-year. Goal Zero, a portable power station brand, partnered with Xnurta and used AMC’s audience segmentation capabilities and path-to-purchase query layer to fundamentally restructure how they approached Amazon Ads optimisation at a campaign architecture level. By Q2 2024, total ROAS had improved 71% year over year, sales growth accelerated from 16% to 55% in a single quarter, and cost per new-to-brand purchase dropped by 34%. That gap represents the difference between a brand managing individual campaigns and a brand managing a data-informed customer acquisition engine at scale.

A 400% ROAS improvement within a single quarter. bareMinerals, working with agency Envision Horizons, used AMC to build custom audience segments derived from specific shopping behaviour signals, improved their repurchase rate from 12% to 28%, and delivered a 400% improvement in overall campaign ROAS across three months. They exceeded their quarterly revenue target by more than 50%.

NTB purchase rates are running 20% above the category benchmark. The Honest Company worked with Tinuiti to test AMC Flexible Shopping Insights for discovering incremental customer audience segments that standard DSP reporting tools could not surface or identify. That 90-day test campaign generated 36% of total new-to-brand purchases during the campaign period while consuming only 17% of the total ad budget allocated, and it cut the cost per NTB acquisition by 52% relative to the previous comparable period.

The Lyxel&Flamingo AMC Intelligence Stack: Our Framework for Indian Brands 

At Lyxel&Flamingo’s Commerce Strategy Practice, we have spent time understanding how Indian brands should approach AMC differently. Market conditions here are not the same as those in the US or EU. Competition patterns and adoption levels vary widely.

Purchase journeys are also longer in many categories. Buyers take more time before converting.

We structure this through the AMC Intelligence Stack. It is a three-layer model designed for Indian e-commerce realities. The goal is to move from scattered signals to clearer campaign decisions.

Layer 1: Signal Foundation

What changes after this layer: your queries stop returning misleading outputs.

Most Indian brands set up AMC and skip straight to running queries. The problem is that uncalibrated signals produce results that look plausible but point you in the wrong direction. Getting this layer right means linking all active ad accounts to your AMC instance, uploading your hashed first-party email list, and aligning your attribution window to your actual category repurchase cycle rather than Amazon’s default 14-day setting. A mattress brand running a 14-day attribution window is measuring the wrong thing entirely. A consumable FMCG brand might be fine with that default. The calibration is specific to your category, and skipping it contaminates every query output and audience segment that follows downstream.

Layer 2: Insight Generation

What changes after this layer: you stop allocating budget based on assumptions.

This is where most Indian brand teams treat AMC as the finish line rather than the starting point. The four queries we consider non-negotiable before modifying any Amazon campaign management structure are:

1. Path-to-purchase by channel: Which ad types appear most frequently in converting journeys, in what sequence, and how often multi-touchpoint paths outperform single-exposure ones
2. New-to-brand rate by campaign type: Whether Sponsored Products is actually acquiring new customers or primarily converting shoppers already moving toward an organic purchase
3. Frequency distribution by audience: At what exposure level does your conversion rate peak for key segments, and exactly where it starts declining from saturation
4. Audience overlap analysis: What share of your DSP audience has already been reached by Sponsored Ads within the same 30-day window, and whether that overlap is adding lift or burning budget

These four queries have surfaced actionable findings in every Indian brand account we have worked with. Without exception across categories or spend levels.

Layer 3: Activation Loop

What changes after this layer: AMC stops being a reporting exercise and starts compounding.

A query output that sits in a deck for three weeks is not an insight. It is a missed window. Every AMC finding needs to close into one of three specific actions: a bid modifier adjustment, a budget reallocation between campaign types, or a new audience segment pushed live into Sponsored Ads or DSP. When brands build and maintain this loop consistently, the results compound. Audience signals get richer each cycle, queries get more precise, and campaigns gradually stop behaving like generic category bids and start functioning like targeted acquisition tools built around real buyer behaviour.

AMC in Real Campaigns

Across the Amazon brand accounts we manage and advise at Lyxel&Flamingo, spanning consumer electronics, FMCG, personal care, and fashion, consistent and repeatable patterns emerge when AMC is deployed with a proper signal foundation and a maintained activation discipline.

1. Sponsored Brands is often undervalued when teams rely only on console ROAS. The numbers look weak on the surface, so budgets get reduced quickly. That decision usually misses the bigger picture.
   AMC data shows Sponsored Brands working as a mid-funnel layer. It supports conversions that happen later through Sponsored Products. This link is not visible in standard reports. When spending is reduced, downstream efficiency drops over time. Cost per conversion increases, but the cause is not clearly tracked. That dependency stays hidden in most accounts.
2. Frequency mismanagement is more common than most teams realise in Indian accounts. Brands often hit the same audience too many times each week. AMC data shows optimal exposure is much lower than current levels. This creates wasted spend and reduces efficiency over time. Once identified, fixes are usually simple and quick to implement. Adjustments can be done within a short window. The recovered budget is often significant and improves overall performance.
3. New-to-brand rate is the single most strategically important metric that Indian brands are the most underinvested in actively monitoring and acting on. Most brand managers optimise primarily for aggregate account-level ROAS. But a brand running at 90% repeat buyers with a progressively declining NTB rate is a brand that is slowly losing category share, even when its monthly revenue totals look healthy on the surface. AMC surfaces the NTB rate at the individual campaign level, which is precisely where the structural intervention needs to happen for it to change the trajectory.

A leading personal care brand we worked with on Amazon achieved a 34% improvement in new-to-brand acquisition cost within a single quarter after restructuring their Amazon advertising strategy based on AMC path-to-purchase analysis and NTB rate segmentation across campaign types. The total media budget remained completely unchanged throughout that restructuring period. Only the allocation between campaign types and audience segments changed.

AMC does not improve performance by finding new places to spend money. It improves performance by showing you exactly where your existing spend is working against itself.

Five Things Indian Brand Managers Should Do Before Q4 

• Access Your AMC Instance Today Because It Is Already Waiting For You

Since September 2025, every advertiser running Sponsored Ads campaigns can access AMC directly from within their Amazon Ads Console account by navigating to “Measurement and Reporting” in the left-side navigation panel. There is no external partner requirement to unlock it, no separate approval process, and no waiting period for access. Log in, open AMC, and start with the no-code analysis templates that Amazon built for advertisers who are newer to the platform environment. The most instructive first move is running the path-to-purchase report for your three highest-revenue ASINs and reading carefully what the actual pre-conversion sequence looks like across your real buyers.

• Run a New-to-Brand Audit Across Every Active Campaign Type

Before your next quarterly media planning session, pull the NTB purchase rate for every active campaign inside AMC and segment the results separately by Sponsored Products, Sponsored Brands, and DSP campaign types. The variance you find across those three segments will almost certainly surface something that your console reporting has been systematically obscuring. More critically, it will show you whether your current media budget allocation is structurally weighted toward growing your actual customer base or toward defending and cycling through the buyers you already have.

• Upload Your First-Party Customer Email List in Hashed Format

This step is often underused but has a strong impact in AMC setups. Matching your customer email list helps create suppression audiences quickly. It reduces wasted spend on users who have already purchased.

You can also build lookalike and cross-sell segments from real data. Match rates are usually enough to generate useful insights. This makes targeting more precise and efficient over time.

• Align Your Attribution Windows to Your Actual Category Repurchase Cycle

Amazon’s default 14-day attribution window works fine for fast-moving categories like FMCG. But it creates issues for products with longer decision cycles. Electronics and appliances need more time before the purchase happens.

If you use the same window, your data becomes misleading. Late funnel campaigns look stronger than they actually are.

Pull real purchase cycle data and adjust attribution settings accordingly. When windows match behaviour, insights become more accurate. This prevents wrong budget cuts on important upper funnel campaigns.

• Design the Activation Loop Before You Generate a Single Insight

AMC investments often fail because of process gaps, not technical issues. Teams generate insights but delay action on them. By the time decisions are made, the data has already lost some relevance.

This creates a disconnect between analysis and execution. Insights look useful, but they do not impact live campaigns in time.

Before running queries, roles should be clearly defined. Someone must own the output and act on it quickly. Each insight should link to a specific campaign change.

Turnaround time matters more than most teams expect. If action takes too long, the advantage fades. Fast execution is what makes the data valuable.

Conclusion

India’s e-commerce market is tracking toward $345 billion by 2030, with Amazon anchoring a substantial portion of that growth through over 150 million active users and a committed $35 billion platform investment running through the end of the decade. This is not a marketplace channel where guesswork is an acceptable operating posture for brands serious about compounding growth. The Amazon Marketing Cloud gives Indian advertisers something they have genuinely never had access to before: a clean, privacy-safe, query-driven view of the actual behavioural sequence customers move through before they purchase from you on Amazon.

The data is already available inside the platform for advertisers. Access is not the problem anymore. What most brands miss is how to use it correctly.

The focus should be on asking better questions and acting faster. A basic setup with key queries is enough to start. The real advantage comes from execution speed. Insights need to turn into campaign changes quickly. That is where results begin to compound.