AMC Cloud

Understanding AMC Cloud: New-to-Brand and Path-to-Purchase Insights

Amazon shows you that a campaign drove sales. What it does not show you is whether those sales came from new customers or the ones who already buy from you every month — and that difference decides whether your advertising is growing the brand or just harvesting it. AMC Cloud answers that. It uses Amazon Marketing Cloud data to show you new-to-brand performance, how long shoppers take to convert, which sequences of ads actually lead to a purchase, and which products pull new customers into your catalogue. TrackIQ runs the queries for you and presents them as seven ready-made dashboards, so you never touch SQL or the AMC console. Watch the AMC Cloud walkthrough Prefer to watch? A three-and-a-half minute tour of all seven dashboards. A tour of the seven AMC Cloud dashboards The seven dashboards at a glance — each one answers a different question about where your new customers come from. Step 1: Opening AMC Cloud On the left-hand sidebar, under ADVERTISING, click AMC Cloud. The AMC Cloud landing page showing all seven dashboards You will land on a page of dashboard cards. Each card describes what that dashboard answers and lists the topics it covers, and each has a View Dashboard → link that opens it. Step 2: Knowing your way around any AMC dashboard Every one of the seven dashboards follows the same layout, so once you can read one you can read them all. The NTB ASINs dashboard, showing the standard AMC layout - Breadcrumb — Dashboard / AMC / [dashboard name], in the top left. Click AMC to get back to the card list. - Month selector — top right. AMC data is reported by calendar month rather than a custom date range, so pick the month you want before reading anything else. - About this dashboard — the grey banner under the breadcrumb. Read this once per dashboard; it defines the terms precisely. On NTB ASINs, for example, it tells you a customer counts as new-to-brand if they bought from your brand for the first time in the previous 365 days. - Summary cards — the row of headline figures directly beneath. - Charts — usually two panels, most with a Top 10 selector so you can widen or narrow the ranking. - Data table — the full breakdown at the bottom of the page, which is what you export. Step 3: Choosing the right dashboard The seven dashboards answer quite different questions. This is the fastest way to pick: | If you want to know… | Open | |---|---| | Which products win me new customers | NTB ASINs Analysis | | Which campaigns bring new customers rather than repeat ones | NTB Purchases | | How my audience splits between new and returning buyers | NTB Customers | | How long people take to buy after seeing an ad | Time to Conversion | | Which sequence of ads leads to a sale | Path to Purchase | | What people type when they search for my brand | Brand Keywords | | What customers buy alongside my lead products | ASIN Purchase Upsell | NTB ASINs Analysis Identifies the products that bring first-time buyers into your brand — your gateway products. The NTB ASINs summary cards Tracked ASINs, Converted Users, NTB Users and NTB Share — the four figures every other number on the page breaks down. Top ASINs by new-to-brand users Summary cards cover Tracked ASINs, Converted Users, NTB Users and NTB Share, with the share card splitting new-to-brand against repeat. Two charts follow — Top ASINs with Highest NTB Users (a stacked split of each ASIN's purchasers) and NTB User Share Breakdown by ASIN. The per-product breakdown The table breaks it down per product: PRODUCT DETAILS, CONVERTED USERS, NTB USERS, NTB %, REPEAT %, PURCHASES, NTB PURCHASES, UNITS SOLD, NTB UNITS SOLD, SALES and NTB SALES. How to use it: sort by NTB %. A product with a high NTB percentage is doing acquisition work — worth protecting with budget even if its ACoS looks unremarkable, because it is buying you customers rather than orders. NTB Purchases Shows, campaign by campaign, how much of what you bought was genuinely new business. NTB Purchases Columns are CAMPAIGN, NTB PURCHASES, NON-NTB PURCHASES, NTB PURCHASES %, NTB SALES, NON-NTB SALES and NTB SALES %. How to use it: compare two campaigns with similar ROAS. If one has a far higher NTB SALES %, it is expanding the customer base while the other is being paid for sales you would likely have made anyway. NTB Customers The same new-versus-repeat split, counted as people rather than purchases. NTB Customers Columns: CAMPAIGN, TOTAL USERS, NTB USERS, NTB %, REPEAT USERS, REPEAT %. How to use it: read it alongside NTB Purchases. A campaign that reaches many new users but converts few new purchases has an offer or listing problem, not a targeting problem. Time to Conversion How long shoppers take to buy after being exposed to an ad. Time to Conversion Purchases are bucketed across the row: < 1 MIN, 1 - 10 MIN, 10 - 30 MIN, 30 - 60 MIN, 1 - 2 HRS, 2 - 12 HRS, 12 - 24 HRS, 1 - 7 DAYS and 7+ DAYS, alongside ADVERTISER and CAMPAIGN. The page also carries CAMPAIGN TYPES and METRIC filters at the top. How to use it: this is your attribution-window sanity check. If most conversions land in 1 - 7 DAYS or 7+ DAYS, short lookback windows are under-crediting your upper-funnel campaigns — and any decision to cut them based on same-day numbers is being made on bad information. Path to Purchase The actual sequences of ad exposures that ended in a sale. Path to Purchase Columns: PATH, OCCURENCES, IMPRESSIONS, TOTAL COST, CONVERTED USERS, SALES, PURCHASES, USER PURCHASE RATE, NTB CONVERTED USERS, NTB SALES, NTB PURCHASES and NTB PERCENTAGE. PATHS INCLUDE and RANKED BY controls sit above the table. How to use it: sort by USER PURCHASE RATE rather than OCCURENCES. The most common path is usually just your highest-spend channel; the most effective path is the combination worth funding. Brand Keywords The branded search terms your advertising generates. Brand Keywords Columns: KEYWORD, CAMPAIGN, BRANDED SEARCHES. How to use it: this is where upper-funnel spend shows its work. Display and video rarely convert on the click, but if branded searches climb after a campaign runs, it created demand that your Sponsored Brands campaigns then captured. ASIN Purchase Upsell Which products customers buy together. ASIN Purchase Upsell Columns: LEAD ASIN, OVERLAP ASIN, OVERLAP USERS, LEAD DISTINCT USERS, OVERLAP RATIO, LEAD PURCHASES. How to use it: a high OVERLAP RATIO is a ready-made remarketing audience — target buyers of the lead ASIN with the overlap ASIN, and build your bundles from pairs that already sell together. Step 4: Getting the data out Every AMC dashboard's table can be exported the same way as the rest of TrackIQ — use the Export control on the table, or manage recurring exports from Exports in the sidebar under TOOLS. A note on timing. AMC data arrives on Amazon's schedule and is reported by calendar month, so the current month will look incomplete until it closes. If a dashboard looks emptier than you expect, check the month selector before assuming something is broken. A note on requirements. AMC dashboards need an AMC instance connected to your account. If a dashboard is empty across every month, check Settings > Connections to confirm the AMC data source is linked for that brand. Related - What 'New to Brand' Actually Means — the metric every AMC dashboard turns on. - Why AMC Data Is Monthly and Sometimes Empty — why a dashboard can be legitimately empty. - NTB ASINs Analysis: Finding Your Gateway Products — the gateway-product dashboard.

What 'New to Brand' Actually Means

New to Brand means the purchaser had not bought anything from your brand on Amazon in the previous twelve months. Not new to the product. Not new to the category. New to you, on a rolling one-year definition, measured by Amazon across your whole brand rather than the ASIN they bought. That definition drives every NTB figure in TrackIQ, and getting it slightly wrong leads to real budget mistakes. Watch the walkthrough Prefer to watch? Sixty-eight seconds on the definition, and what it changes about budget. What follows from the definition NTB figures across the AMC dashboards A returning customer buying a product they have never tried is not NTB. They already know your brand. The metric is about acquiring people, not about product discovery. A customer who last bought thirteen months ago counts as NTB again. The window rolls, so genuinely lapsed customers re-enter the count. That is reasonable — winning someone back after a year does cost acquisition effort. It is brand-wide. Buy your shampoo in March and your conditioner in June, and only March was new-to-brand. This is why NTB by product tells you which product does the acquiring, not which sells most. Only Amazon purchases count. Someone who buys from your own website every month is new-to-brand the first time they buy on Amazon. Why it is the number worth optimising ACoS asks was this sale efficient? NTB asks did this sale buy me a customer? Those come apart constantly. A campaign on your own brand name usually looks superb on ACoS and produces almost no new customers — it harvests people who were already coming. A broad campaign against a generic query often looks mediocre on ACoS and brings in people who have never heard of you. Judged on ACoS alone, you cut the second one. That is the mistake NTB exists to prevent, and it is why the metric matters more than its reputation as an upper-funnel vanity number. Where it shows up | Where | What it tells you | |---|---| | NTB ASINs | which products bring in new customers | | NTB Purchases | which campaigns bring in new customers | | DSP reporting | new-to-brand purchases from display | The first two are the pair worth reading together, because they answer different budget questions: which product to protect, and which campaign to fund. Gateway products Some products acquire; others are bought by people who already know you. NTB share by product tells you which is which. A gateway product — high NTB share — earns different treatment from a high-revenue product. It is the front door. Running it out of stock, or cutting its advertising because its own ACoS is unremarkable, costs you customers who would have gone on to buy other things. That is the practical use of the whole metric: it changes what you protect. Reading NTB share correctly Three cautions, each of which produces a wrong conclusion if missed. NTB share falls as a product matures, and that is fine. Once most buyers in your category know you, a lower proportion of purchases can be new. Falling share on rising volume is not a problem. Do not add months. AMC reports monthly, and each month is a distinct cohort of people. Somebody new in March may buy again in April — adding the months double-counts them. Compare months; never sum them. A high NTB share on tiny volume is noise. Three purchasers, all new, is 100% and means nothing. NTB purchasers, purchases and sales Three related figures that answer different questions: | Figure | Counts | |---|---| | NTB purchasers | people | | NTB purchases | orders | | NTB sales | money | For acquisition questions you want purchasers, because that is the count of humans you gained. Sales figures are skewed by basket size. Where it comes from NTB is calculated by Amazon and delivered through AMC, which publishes monthly. So a brand-new account has no NTB data until Amazon's next delivery — that is expected, not broken. See Why AMC Data Is Monthly and Sometimes Empty. Related - Understanding AMC Cloud — the dashboards NTB appears on. - DSP Metrics Explained — NTB purchases in display reporting. - Aria Prompt Library — questions 33 and 34 ask these directly.

Why AMC Data Is Monthly and Sometimes Empty

An empty AMC dashboard is the most-reported non-problem in TrackIQ. Almost always the answer is Amazon has not delivered yet, and the fix is to wait rather than to check anything. Worth understanding once, because it also changes how you read AMC when it does arrive. Watch the walkthrough Prefer to watch? Sixty-three seconds on the cadence, and how it changes the way you read AMC. AMC is monthly AMC reporting arrives in monthly cohorts Amazon Marketing Cloud publishes on a monthly cadence. Not daily, not weekly, and not on demand. So: - A new account has no AMC data until Amazon's next monthly delivery. - The current month is not there. You are always looking at completed months. - Refreshing changes nothing. There is no sync to trigger. Every other data source in TrackIQ updates far more often, which is exactly why this one surprises people. Sales are minutes old; AMC is a month behind by design. Why monthly, and why it matters for reading it AMC answers questions that need a whole cohort of people observed over time — who was new to your brand, how long they took to buy, which sequence of ads they saw before purchasing. Those questions cannot be answered from a day. Somebody who saw a display ad on the 2nd and bought on the 20th is one story, and you only have it once both ends exist. That has a direct consequence for how you use the numbers: each month is a distinct cohort of people, so months are compared, never added. Somebody new in March may buy again in April; summing the two double-counts them. Work down this list 1. Has Amazon delivered a month yet? On a new AMC connection, no. Wait for the next monthly publication. 2. Is AMC actually connected? It is a separate authorisation from Seller, Advertising and DSP. Having advertising does not give you AMC. Check Settings → Connections — the brand's card lists its connected sources. See How to Connect Your Amazon Accounts. 3. Is AMC on your plan? It starts at PRO. On Starter the section is not available at all, and a missing sidebar item is not the same as an empty screen. See What's Included in Each Plan. 4. Is the date range on a month that exists? Ranges covering only the current month will be empty. Widen to cover completed months. 5. Is the right brand selected? Every screen is scoped to the brand in the top bar. 6. Does your account have enough volume? AMC suppresses results below a minimum threshold to protect shopper privacy. A small brand may genuinely have sparse rows, and no amount of waiting changes that. That last one is the only case where the answer is not “wait” — and it is a deliberate privacy behaviour rather than a fault. Which screens this covers Every AMC dashboard shares the cadence: NTB ASINs, NTB Purchases, Time to Conversion, Path to Purchase, Brand Keywords, ASIN Purchase Upsell. If one is empty and the others have data, that is not the cadence — check your date range and brand. Not AMC, but often confused with it Search Query Performance is weekly, in Sunday-to-Saturday cohorts. Also published by Amazon on its own schedule, also never aggregated across periods, but a different cadence entirely. See SQP Is Weekly. The two together are the reason “how fresh is my data?” is a per-source question. See Where Your Data Comes From and How Fresh It Is. What to do while you wait AMC answers acquisition questions. Until it arrives, the closest available substitutes: - DSP reporting carries new-to-brand purchases on a 14-day window, without the cohort depth. - Search Query Performance shows where new demand is, if you are on PRO. - Sales Performance shows which products grow, though it cannot tell you who bought. None replaces AMC. They are what you have in the meantime. Related - Understanding AMC Cloud — the dashboards themselves. - What 'New to Brand' Actually Means — the definition behind most AMC figures. - My Dashboard Is Empty — the general version of this checklist.

NTB ASINs Analysis: Finding Your Gateway Products

Some of your products sell to people who already know you. Others bring in customers who have never bought from your brand before. NTB ASINs Analysis tells you which is which — and the answer is rarely the product you would guess. Those first-purchase products are your gateway ASINs, and they deserve to be funded differently from everything else in the catalog. Watch the walkthrough Prefer to watch? Seventy-nine seconds on gateway products, and why rate and volume disagree. Where it lives AMC Cloud opens on a grid of seven dashboards. This one is the first card. The AMC Cloud dashboards Like every AMC dashboard it reports one calendar month at a time — the date control top-right selects the month, not a free range. See Why AMC Data Is Monthly and Sometimes Empty. The definition it uses The dashboard states it plainly: Customers are considered new-to-brand (NTB) if they purchase from your brand for the first time during the previous 365-day period on Amazon. Brand-level, 365 days, and measured against all of Amazon — not just your ads. Somebody who bought from you organically eleven months ago is not new to brand, however they arrive this time. The headline numbers Tracked ASINs, converted users, NTB users and NTB share | Tile | Reads | |---|---| | Tracked ASINs | products in the NTB analysis | | Converted Users | customers who purchased after ad exposure | | NTB Users | first-time brand purchasers among them | | NTB Share | NTB as a percentage, with repeat beside it | NTB Share is the number to anchor on, and a figure around 28% is unremarkable for an established brand — roughly one buyer in four is new. A brand-new catalog would sit far higher; a mature, heavily-remarketed one lower. Top ASINs with Highest NTB Users Stacked split of each ASIN's purchasers into new-to-brand versus repeat customers — the longest NTB segments mark your best gateway products for acquiring fresh customers. Each bar is one ASIN, split into NTB and repeat. Check the legend before you read the bars. The three NTB dashboards do not agree with each other on which colour or which side means NTB. On this one, NTB Users is the lighter segment, on the left. On NTB Purchases the NTB segment is darker and on the right. Reading one dashboard's convention into another inverts your conclusion completely. A useful sanity check: the per-ASIN NTB percentages should average out to roughly the headline NTB Share tile. If your reading gives a set of bars averaging 70% against a headline of 28%, you have the segments backwards. NTB User Share Breakdown by ASIN Each slice shows the share of total new-to-brand users a single ASIN drives — surfaces the products that anchor most of your customer acquisition. This is the chart that changes budgets, because acquisition is almost always far more concentrated than people expect. A single ASIN accounting for 56% of every new customer the brand acquired that month is not unusual. The product table underneath Every tracked ASIN with its converted users, NTB users and NTB share Below the charts, every tracked product is listed with Converted Users · NTB Users · NTB % · Repeat % · Purchases, each with its change against the previous month, plus the usual Search, Filters, Manage Columns and Export. Each row carries the ASIN and seller SKU, and a Dashboard chip that jumps straight to that product's page — which is the quickest route from "this is a gateway product" to "so what is its stock cover?" Rate and volume are different questions The two charts answer different things, and confusing them is the main way this dashboard gets misread: | Chart | Question | Best product is | |---|---|---| | Stacked bars | what proportion of this ASIN's buyers are new? | the highest rate | | Pie | what share of all new customers does this ASIN bring? | the highest volume | A niche product can be 80% new-to-brand and still contribute 2% of your new customers, because almost nobody buys it. It is a genuine gateway with no traffic. The hero ASIN might be only 35% NTB and still deliver half your acquisition, simply because it sells. Fund the volume; protect the rate. Concentration is the risk worth naming: if one ASIN brings most of your new customers, a stock-out on that ASIN is not a sales problem for one product — it is an acquisition outage for the whole brand. See Catch a stockout before it costs you rank. What to do with it Never let a gateway ASIN go out of stock. Set its inventory cover threshold higher than the rest of the catalog. Bid differently on gateway products. A higher ACoS is defensible on a product that recruits customers, because the return arrives on their second and third orders — not on the one you paid for. See Understanding ROAS and Key Amazon Advertising Metrics. Check your entry price. Gateway ASINs are frequently the cheapest or smallest pack. If yours is not, ask whether a trial size would recruit faster. Re-read it after a launch. A new product can quietly become the gateway within two months, and the budget usually takes far longer to notice. What not to conclude NTB is not incrementality. The customer was new to your brand. It does not prove the ad caused the purchase. A low NTB share is not failure. For a brand with strong repeat purchase it is a sign of a healthy base. Read it beside repeat volume, not alone. One month is one month. AMC reports monthly cohorts, and seasonality moves NTB share sharply. Compare the same month year on year where you can. Related - NTB Purchases: New Business by Campaign — the same question asked of campaigns. - NTB Customers: New vs Repeat, Counted as People — the third NTB view, and how it differs. - What 'New to Brand' Actually Means — the definition in full. - Why AMC Data Is Monthly and Sometimes Empty — the reporting cadence.

NTB Purchases: New Business by Campaign

Every ad-attributed purchase is either from somebody new to your brand or somebody buying again. NTB Purchases splits them, campaign type by campaign type, so you can see which formats are actually recruiting and which are collecting orders you were probably going to get anyway. Watch the walkthrough Prefer to watch? Eighty-two seconds on the per-ASIN definition, and why DSP sits at the bottom. The definition here is narrower than you think Read the panel at the top carefully, because it does not say what its sister dashboards say: Purchases are considered new-to-brand (NTB) if a customer purchased a specific ASIN for the first time during the previous 365-day period on Amazon. That is per ASIN, not per brand. NTB Customers and NTB ASINs Analysis both define NTB at brand level. This dashboard counts a first purchase of that product. The practical consequence: a loyal customer buying a different product of yours for the first time counts as an NTB purchase here, and does not count as an NTB customer there. That is why the two dashboards' NTB shares differ — 52.52% against 55.21% on the same account, same month. They are not meant to match, and reconciling them is wasted effort. | Dashboard | NTB means | Counts | |---|---|---| | NTB Purchases | first purchase of this ASIN | orders | | NTB Customers | first purchase from your brand | people | | NTB ASINs | first purchase from your brand | people, per product | The headline numbers Campaigns analysed, NTB purchases, NTB sales and NTB purchase share | Tile | Reads | |---|---| | Campaigns Analyzed | active campaigns with NTB purchases | | NTB Purchases | ad-attributed first-time orders | | NTB Sales | revenue from new-to-brand buyers | | NTB Purchase Share | NTB as a percentage, non-NTB beside it | Campaign Types Comparison Two stacked charts sit side by side — the same split applied to purchases and then to sales: Stacked split of every ad-attributed purchase into new-to-brand versus repeat across ad product types — the longer the NTB segment, the more that campaign type is acquiring fresh customers. Same split applied to revenue — useful for spotting campaign types where NTB customers also drive the biggest order values, not just order counts. On this dashboard the NTB segment is the darker one, on the right. That is the opposite colour convention from NTB Customers, and the opposite side from NTB ASINs. Check the legend every time. Reading the split across formats A representative month looks like this: | Ad type | NTB share of purchases | |---|---| | Sponsored Brands | 64.8% | | Sponsored Products | 60.5% | | Sponsored Display | 47.2% | | DSP | 29.8% | The result that surprises people is DSP at the bottom. Display is supposed to be the upper funnel — and it is, when it is bought that way. A DSP setup weighted toward remarketing and retargeting audiences reaches people who have already visited your pages, and those people are mostly not new to the brand. That is not a failing of DSP. It is a description of which audiences you bought. The check is on DSP Campaigns: if remarketing and retargeting dominate the campaign list, a low NTB share is exactly what that spend was purchased to produce, and the honest question is whether the prospecting side is funded at all. Sponsored Brands at the top is the mirror image. It runs on broader, more discovery-shaped queries, so it recruits — usually on modest volume. Down to individual campaigns Per-campaign NTB and non-NTB splits for purchases and sales The chart groups by ad product type; the table beneath it goes campaign by campaign, with the NTB and non-NTB split of both purchases and sales, the percentage beside each, and a change against the previous month. That is the level at which you can act. "Sponsored Products is 60% new-to-brand" is context; "this generic broad campaign is 85% new-to-brand and this brand-specific one is 42%" is a budget decision — and it is exactly the split you would expect, since brand-specific terms reach people who already know the name. Compare the two charts, not just one The purchases chart and the sales chart rarely agree, and the gap is the useful part: NTB share higher in sales than purchases means your new customers spend more per order than your repeat ones — often a sign the format is recruiting into a premium or bundle product. NTB share lower in sales than purchases means new customers buy the cheap entry item, which is normal and healthy if the second order is bigger. A format strong on both is the one to fund. It brings new people and they spend. What to do with it Judge prospecting formats on NTB, not ACoS. A campaign type with 65% NTB and a poor ACoS may be your cheapest source of customers. Judging it on efficiency alone guarantees you cut it. See Understanding ROAS and Key Amazon Advertising Metrics. Set a floor for acquisition. If NTB purchase share across the account drifts down month over month while total sales hold, you are gradually converting your advertising into a remarketing programme. That looks efficient right up until growth stops. Cross-check against Path to Purchase. If DSP shows low NTB here, the path data usually shows why — DSP appearing late in journeys rather than first. See Path to Purchase: Which Ad Sequences Actually Convert. What not to conclude NTB does not mean incremental. It means the buyer had not bought that ASIN in a year. The ad may or may not have caused it. Do not compare the share to NTB Customers. Different definitions, as above. A single month is a cohort, not a trend. Seasonality moves NTB share hard — gifting periods inflate it, replenishment periods depress it. Related - NTB Customers: New vs Repeat, Counted as People — the same idea counted differently. - NTB ASINs Analysis: Finding Your Gateway Products — which products recruit. - What 'New to Brand' Actually Means — the definition in full. - DSP Campaigns — checking what your display spend actually bought.

NTB Customers: New vs Repeat, Counted as People

NTB Customers counts people, not orders. That single difference is what makes it the most useful of the three new-to-brand dashboards, because a customer is the thing you actually acquire — orders are just what they do afterwards. Watch the walkthrough Prefer to watch? Seventy-eight seconds on counting people, and the budget trap in the two charts. The definition A customer is considered new-to-brand (NTB) the first time they purchase from your brand in the previous 365 days. This view breaks the ad-attributed audience into NTB versus repeat purchasers per campaign and ad product type. Brand-level and person-level. Somebody who buys four times this month is one customer here, and four purchases on NTB Purchases. That is why the two dashboards report different shares on identical data, and why neither is wrong. See NTB Purchases: New Business by Campaign. The headline numbers Total users, NTB users, NTB share and campaigns | Tile | Reads | |---|---| | Total Users | customers exposed to ads who purchased | | NTB Users | first-time brand purchasers among them | | NTB Share | NTB as a percentage, repeat beside it | | Campaigns | active campaigns analysed | Total Users is worth pausing on. It counts people who saw an ad and bought — not everyone reached. It is the denominator for the share, and it is much smaller than your impression counts. Two charts, two different questions The dashboard is built around a contrast that is easy to miss because both charts look like they are about the same thing. NTB vs Repeat Users by Ad Product Type — a stacked bar per format: Stacked split of every ad-attributed buyer into new-to-brand versus repeat customers, broken down by ad product type (SP, SB, SD, DSP) — quickly see which formats are best at acquiring fresh customers. NTB Users by Ad Product Type — a doughnut: Share of total new-to-brand customers contributed by each ad product type — surfaces which formats deliver the biggest acquisition volume. The bars are a rate. The doughnut is a volume. They routinely disagree, and the disagreement is the finding. Read the legend before the bars. Here NTB Users is the lighter segment and repeat is darker — the reverse of NTB Purchases, where the darker segment is the NTB one. All three NTB dashboards use different conventions. The gap that matters A representative month: | Ad type | NTB rate | Share of all new customers | |---|---|---| | Sponsored Brands | 80.5% | 3.8% | | Sponsored Products | 71.6% | 77.6% | | Sponsored Display | 50.0% | 18.5% | | DSP | 27.2% | 0.1% | Sponsored Brands is the best recruiter and barely matters. Four buyers in five are new — and it delivers under 4% of the brand's new customers, because the volume is small. Sponsored Products is the opposite. A lower rate, and more than three-quarters of every new customer the brand gained. Both facts are actionable, and they point in different directions: - The rate says Sponsored Brands is efficient at acquisition — give it more room and see whether the rate holds. - The volume says Sponsored Products is your acquisition engine — do not let a cost-cutting exercise touch it. A budget decision that reads only one chart gets this exactly wrong. Shifting money from SP to SB because SB has the better rate can shrink total acquisition even as the percentages improve. The campaign table Per-campaign totals, NTB users and repeat users Beneath the charts, every campaign is listed with Total Users · NTB Users · NTB % · Repeat Users · Repeat %, each with a change against the previous month. Read NTB Users and NTB % together, one campaign at a time. A campaign at 84% NTB on 25 users and one at 81% on 296 users are doing very different jobs at the same rate — and only one of them is moving the business. DSP at 0.1% deserves a look A near-zero share of new customers is not automatically a problem — it is what a remarketing-weighted DSP buy produces, by definition. But it is worth confirming that is the intent rather than an accident. Open DSP Campaigns. If the campaign list is remarketing and retargeting with little or no prospecting, the number is explained. If there is prospecting spend and it still delivers no new customers, that is a genuine finding and the prospecting audiences need reviewing. What to do with it Track NTB Users, not just NTB share, month to month. The percentage can rise while the count falls — that is a shrinking business with a flattering ratio. Work out what a customer costs. Total ad spend divided by NTB Users is a rough blended acquisition cost. Rough, because not every new customer came from an ad. It is still a far better planning number than ACoS for anything acquisition-shaped. Compare it to your repeat rate. Acquisition only pays back if people come back. High NTB with low repeat is a leaky bucket, and the fix is the product and the listing, not the media plan. What not to conclude NTB does not mean incremental. These customers were new to the brand; the ad may not have caused the purchase. The doughnut is share of NTB, not share of spend. A format with 0.1% of new customers may be taking a large share of budget — the chart will not tell you. Do not reconcile it against NTB Purchases. Different definitions, different units. See the table in that article. Related - NTB Purchases: New Business by Campaign — the same split, counting orders. - NTB ASINs Analysis — which products recruit. - What 'New to Brand' Actually Means — the definition in full. - Protect the products that bring you new customers — the playbook.

Time to Conversion: Checking Your Attribution Window

Amazon credits a sale to an ad click for up to 7 days on Sponsored Products and 14 on the other formats. Time to Conversion shows you how much of that window you actually use — and for most accounts the honest answer is: almost none of it. Watch the walkthrough Prefer to watch? Eighty-two seconds on the nine buckets, and how soon you may judge a campaign. The question it answers How long does it take your customers to have an attributed conversion after last seeing your ad? Sometimes conversions happen rapidly within a few minutes, while other times it might take days for customers to complete the purchase. Two controls sit above the dashboard: Campaign Types (all, or one format) and Metric (Purchases, and the other conversion measures). The headline numbers Campaigns, total purchases, conversions within an hour, and fast conversion share | Tile | Reads | |---|---| | Campaigns Analyzed | campaigns with attributed conversions | | Total Purchases | across all conversion time buckets | | Within 1 Hour | conversions completing in under 60 minutes | | Fast Conversion Share | the same as a percentage, slower beside it | A fast conversion share around 56% is typical for a considered-but-familiar category. Over half of everything the ads got credit for happened inside the first hour. The nine buckets Volume in each time bucket, split by ad product type Volume of attributed conversions in each time bucket (under a minute → over a week), split by ad product type. Tall left-side bars = fast intent; tall right-side bars = decision-makers who think longer before they buy. < 1 min · 1–10 min · 10–30 min · 30–60 min · 1–2 hrs · 2–12 hrs · 12–24 hrs · 1–7 days · 7+ days The legend carries five ad types — DSP, SB, SD, SP and STV (Streaming TV) — so a format that appears nowhere else in TrackIQ's reporting shows up here if you run it. The shape is usually a U The distribution that turns up most often is bimodal: a large spike in the first ten minutes, a trough through the middle of the day, then a second rise at 1–7 days and beyond. Those are two different customers, and they are worth naming separately: The first spike is intent that already existed. Somebody searching for the thing, clicking, buying. The ad did not create the demand; it captured it. This is where most Sponsored Products volume sits. The second rise is consideration. People who saw the ad, left, thought about it, and came back. Higher-priced items, first-time buyers of a category, gift purchases. The trough in between is real, not noise. Nobody spends four hours deciding. They either buy now or they buy another day. What it changes Your attribution window. If 95% of conversions land inside 24 hours, the 7- and 14-day windows are contributing almost nothing — and the reporting lag they cause is a real cost. It explains why yesterday's numbers look thin, and it tells you how many days to ignore in a rule's look-back. See Look-back Windows and Adjustment Frequency. Promotion length. A 24-hour promotion on a catalogue that converts in minutes captures nearly all of the available lift. On one with a heavy 1–7 day tail, the same promotion ends before half the buyers have decided. How soon you judge a campaign. If your conversions arrive within an hour, a new campaign can be read in two or three days. With a long tail, reading it that fast will make it look like a failure. Which format you blame. Filter by campaign type. Sponsored Products usually skews fast; DSP and Streaming TV skew long, because they reach people who were not shopping when they saw the ad. A DSP campaign judged on same-day conversions will always look worse than it is. The campaign table Below the chart, every campaign appears with a column per time bucket, a count in each cell and a change against the previous period. Advertiser is shown too, so multi-entity accounts can tell the DSP advertiser from the seller account. Sort by the long buckets to find the campaigns doing the patient work — those are the ones most likely to be undervalued by a short attribution view, and the ones most at risk from a rule that pauses on recent performance. What not to conclude Fast does not mean better. A conversion at 30 seconds may have happened without the ad. A conversion at five days is more likely to have been influenced by it. This is time since last ad exposure, not time since first. A customer who saw four ads over a fortnight and bought an hour after the fourth appears in the < 1 hr bucket. Path to Purchase is the dashboard for the full journey. The buckets are conversions, not customers. Repeat buyers appear repeatedly. Monthly cohorts. Like every AMC dashboard, one calendar month at a time. A promotion inside the month will bend the shape. Related - Path to Purchase: Which Ad Sequences Actually Convert — the whole journey, not the last step. - Why SP, SB and SD Numbers Don't Add Up — the attribution windows themselves. - Look-back Windows and Adjustment Frequency — putting this into a rule. - Why AMC Data Is Monthly and Sometimes Empty — the reporting cadence.

Path to Purchase: Which Ad Sequences Actually Convert

Every other report in TrackIQ credits a sale to one campaign. Path to Purchase shows the whole sequence — every ad format a customer was exposed to, in order, before they bought. It is the only dashboard that can tell you whether your formats are working together or simply taking turns claiming the same customer. Watch the walkthrough Prefer to watch? Eighty-one seconds on reading the NTB column instead of the sales column. The headline numbers Paths analysed, converted users, total sales and NTB purchase share Two controls sit above: Paths Include (all ad formats, or only paths containing one) and Ranked By (Sales, and the other measures). | Tile | Reads | |---|---| | Paths Analyzed | distinct conversion journeys | | Converted Users | customers who purchased after exposure | | Total Sales | revenue driven across all paths | | NTB Purchase Share | how much of it was new-to-brand | Fifty-odd distinct journeys is normal. Most of the volume lives in the top five. Best Performing Paths Top 5 conversion paths ranked by the metric you choose — each row shows the campaign-type sequence customers followed before buying. Each row is a sequence of chips ending in Purchase, with sales, purchases, NTB sales, NTB purchases and path sales % beneath it. The top five paths, ranked by sales Read the NTB column, not the sales column The paths ranked highest by revenue are frequently the worst at acquiring anybody. A representative month: | # | Path | Purchases | NTB purchases | NTB rate | |---|---|---|---|---| | 1 | DSP → SP → Purchase | 2,141 | 304 | 14% | | 2 | SP → Purchase | 2,452 | 2,262 | 92% | | 3 | SP → DSP → Purchase | 1,303 | 879 | 67% | | 4 | DSP → SP → SB → Purchase | 890 | 133 | 15% | | 5 | SP → SB → Purchase | 1,005 | 915 | 91% | The pattern is unmistakable once you sort by it: paths that begin with DSP are almost entirely repeat customers. Paths that begin with Sponsored Products are almost entirely new ones. That is the opposite of the textbook, where display is the top of the funnel. It is what happens when a DSP buy is weighted toward remarketing and retargeting — those audiences are built from people who have already visited your pages, so DSP appears first in the path only because it re-reached somebody the brand already knew. See DSP Campaigns. The path order tells you what your DSP is for. DSP appearing first on high-NTB paths would mean genuine prospecting. DSP appearing first on low-NTB paths means it is finding people you already have. Single-touch beats multi-touch more often than you'd think Path 2 — one format, straight to purchase — produced more purchases than path 1 and eight times the new customers. This is worth saying plainly because multi-touch paths look more sophisticated. Before concluding that DSP "assisted" path 1, ask the harder question: would those 2,141 purchases have happened anyway? Path 2 suggests many of them would. The DSP exposure may be a cost added to a sale that was already coming. The paths where multi-touch genuinely earns its place are the ones where the added format brings a materially better NTB rate than the same path without it. Compare path 3 (67% NTB) against path 2 (92%): adding DSP after SP made the journey less acquisitive here, not more. The methodology caveat that matters The dashboard states it, and it changes how you read the cost columns: Paths are based on the timing of first impressions, while costs and impressions reflect all exposures before purchase. So the sequence is ordered by when each campaign group first reached the customer, but the money attached to the row covers every impression along the way. A path can look expensive because of exposures that arrived late and contributed little to the ordering. Treat the sequence as reliable and the per-path cost as indicative. What to do with it Filter to one format and compare. Set Paths Include to a single format, then look at paths with and without it. If the format never improves the NTB rate of the paths it joins, it is riding along. Rank by NTB sales instead of sales. One dropdown change reorders the whole board around acquisition rather than revenue, and it usually promotes completely different paths. Take the finding to budget. If DSP-first paths are 14% NTB and SP-first are 92%, the question is not whether to cut DSP — it is whether the prospecting half of DSP is funded at all. See Decide whether your upper-funnel DSP is worth keeping. Check it against the other two dashboards. Low DSP NTB here should show up as low DSP NTB on NTB Purchases and NTB Customers. If all three agree, it is real. What not to conclude A path is not causation. It records exposure order, not influence. Somebody who scrolled past a display ad appears in the path. Paths do not include organic. Only ad exposures are visible. A customer who found you through search and was later shown a display ad looks DSP-influenced here. Top five is not all of it. Fifty-plus paths exist; the long tail is real, and the table below the chart holds it, with occurrences, impressions and total cost. Monthly cohorts. One calendar month at a time, like every AMC dashboard. Related - Time to Conversion: Checking Your Attribution Window — how long the journey takes. - NTB Purchases: New Business by Campaign — the same acquisition question by format. - DSP Campaigns — checking which audiences your display spend bought. - Why SP, SB and SD Numbers Don't Add Up — why single-campaign credit misleads.

Brand Keywords: Proving Upper-Funnel Spend Works

Upper-funnel advertising has a measurement problem: the whole point is that people do not buy immediately, so the campaign gets no credit. Brand Keywords is the dashboard that gives it credit anyway — by counting the brand searches your ads caused. Somebody who sees your ad and later types your brand name into Amazon has changed their behaviour because of it. That search is the evidence. Watch the walkthrough Prefer to watch? Seventy-seven seconds on counting the brand searches your ads caused. The headline numbers Total branded searches, unique keywords, campaigns and average per keyword Brand keywords are searches customers ran on Amazon for your brand after being exposed to your ads. This view shows which keywords drive the most brand searches and which campaigns are building brand awareness. | Tile | Reads | |---|---| | Total Branded Searches | brand searches generated post ad exposure | | Unique Keywords | distinct branded queries captured | | Campaigns Driving Searches | campaigns producing brand searches | | Avg per Keyword | searches divided across those keywords | The phrase that matters is post ad exposure. These are not all your brand searches — they are the ones AMC can tie to somebody who saw an ad first. Top Keywords by Branded Searches Brand keywords customers searched on Amazon after ad exposure, ranked by search volume. Use the top entries to refine messaging and sponsored-brand keyword bids. The list is more interesting than the totals, because the words people add to your brand name tell you what your advertising made them think of. A representative shape: | Query | Searches | |---|---| | brand + category noun | 392 | | brand alone | 355 | | brand + a specific product | 203 | | brand + a product variant | 146 | | brand + a spec or strength | 65 | Brand alone versus brand plus product is the split to read first. Brand alone means the ad built general awareness. Brand plus a product means it built awareness of a specific thing — which converts far better, because the shopper already knows what they want. A product appearing that you did not advertise is a discovery. Somebody saw one ad and went looking for a different item in the range. A competitor's product name attached to your brand is worth knowing about too, and it usually means your creative sat next to theirs. Branded Search Share by Keyword Each slice shows the share of total branded searches a single keyword captures — a quick read on which terms dominate brand discovery. Concentration here is normal and mostly healthy. Two or three phrases usually take more than half the volume. What you are looking for is change: a phrase that climbs after a campaign launches is that campaign's fingerprint. What it is actually for Proving upper-funnel spend works. This is the honest answer to "what did the display campaign do?" when it produced few direct sales. Branded searches attributable to that campaign are real demand it created, and they will convert later through cheaper channels. See NTB Purchases: New Business by Campaign for the acquisition half of the same argument. Deciding what to bid on in Sponsored Brands. Every phrase on this list is a term shoppers actually type. Bidding on your own brand is contentious, but bidding on brand + product phrases that your own advertising created is close to free money — the intent is already there. Catching brand confusion. Misspellings and near-misses show up here. If a substantial share of your branded searches are a variant you do not bid on, a competitor may be picking them up. Refining the message. If your campaign was about one benefit and the searches are all about another, the creative is landing differently than intended. The relationship with the Ads brand split The brand terms this dashboard uses are the same list that classifies your Amazon Ads queries as branded or non-branded. They are edited in one place — the Brand & Competitor Terms control on the SQP dashboard — and changing them changes both. See Generic, Branded and Competitor Terms. That is worth knowing before you edit: adding a broad word to the list will inflate branded searches here and reclassify a large slice of your advertising at the same time. What not to conclude A branded search is not a sale. It is intent. Whether it converted is a different report. Attribution is exposure-based. The customer saw an ad and later searched your brand. Some of them would have searched anyway — loyal customers do it constantly. Low volume is normal for small accounts. A few hundred branded searches a month is a real signal; do not expect thousands unless you are running significant upper-funnel spend. Monthly cohorts. One calendar month at a time, like every AMC dashboard. See Why AMC Data Is Monthly and Sometimes Empty. Related - Generic, Branded and Competitor Terms — where the brand list is edited. - NTB Purchases: New Business by Campaign — the acquisition side of upper-funnel proof. - Path to Purchase — where display actually sits in the journey. - Prove your display campaigns created demand — the playbook. - How Much to Spend on Branded Keywords — calibrating branded budget by market share.

ASIN Purchase Upsell: Finding Bundle and Remarketing Pairs

ASIN Purchase Upsell finds the products your customers buy together, and pairs them up: a lead ASIN, an overlap ASIN, and the share of the lead's buyers who also bought the other one. It is the raw material for two things — a bundle, and a remarketing campaign aimed at people who have already bought from you. Watch the walkthrough Prefer to watch? Eighty-three seconds on lead and overlap pairs, and why the denominator matters. The headline numbers Lead ASINs, overlap ASINs, overlap users and average overlap ratio Identify products that customers tend to buy together. Each row pairs a lead ASIN with an overlap ASIN — the overlap ratio shows what share of the lead's purchasers also bought the overlap product. | Tile | Reads | |---|---| | Lead ASINs | primary products in upsell pairs | | Overlap ASINs | companion products surfaced | | Overlap Users | customers buying multiple ASINs | | Avg Overlap Ratio | share of lead buyers who also bought the overlap | Read the base before the ratio This is the dashboard where enthusiasm most needs a check, and the check is in the tiles above. Cross-purchase is rare. An average overlap ratio under one percent, and overlap users in the double or low triple digits, is entirely normal — and it means every pair below is built on a handful of people. A ratio computed from three customers is not a finding. It is a hypothesis. So read the two numbers together: | Overlap users | Ratio | Reading | |---|---|---| | high | high | a genuine pair — act on it | | high | low | a popular lead, weak companion pull | | low | high | noise — a big percentage of very few people | | low | low | nothing here | The bottom-left row is the trap, because a high ratio is exactly what catches the eye. The two charts Overlap User Count by Lead ASIN ranks lead products by how many customers bought them and at least one other ASIN. Higher bars surface stronger upsell candidates to remarket. Overlap User Share by Lead ASIN shows what share of all cross-purchase behaviour each lead product accounts for — which products dominate cross-purchase behaviour. Both are counts, not rates, which is why they are the right place to start. Find the products with real cross-purchase volume first, then look at their ratios. The pair table Lead ASIN, overlap ASIN, overlap users, lead distinct users and the ratio Every pair is listed with both products — image, title, ASIN, SKU and a Dashboard chip each — plus: Overlap Users · Lead Distinct Users · Overlap Ratio · Lead Purchases, each with a change against the previous month. The arithmetic is straightforward and worth confirming as you read: overlap ratio = overlap users ÷ lead distinct users. Three overlap users against 165 lead buyers is 1.82%. Seeing the denominator beside the ratio is the point of the table. The charts give you candidates; this tells you how much to trust each one. What to do with a good pair Build a remarketing audience. The clearest use, and the one the dashboard suggests: customers who bought the lead product but not the overlap are the audience. That is a DSP remarketing campaign with an unusually specific creative brief. See DSP Campaigns. Test a bundle. If the pair is genuinely popular, a listed bundle removes a step for the customer. Test it before committing inventory — a high overlap ratio says people buy both, not that they want them in one box. Check the sequence. A pair tells you that both were bought, not in which order. If one is consistently the entry product, it belongs in your gateway list — see NTB ASINs Analysis. Sanity-check against the catalog. Some pairs are obvious (a product and its refill) and some are surprising. The surprising ones are where the value is, provided the base is large enough to believe. What not to conclude Overlap is not causation. Both purchases may follow the same need rather than one leading to the other. Pairs are not ordered. Lead and overlap are labels for the row, not a claim about sequence. This is ad-attributed behaviour, so purely organic cross-purchase is not represented. Small numbers move violently. A pair going from 2 to 4 customers is a 100% increase and means almost nothing. Watch the change column with that in mind. Monthly cohorts. One calendar month at a time. Related - NTB ASINs Analysis: Finding Your Gateway Products — which product starts the relationship. - DSP Campaigns — where a remarketing audience gets built. - Find your next bundle from what customers already buy together — the playbook. - Why AMC Data Is Monthly and Sometimes Empty — the reporting cadence. - Six Ways to Find Product Candidates — using cross-purchase data as a research method.