FAQ

Author: Robin Jia

Last Update：2025/04/18

 

FAQ

Q: How do I get access and authorize Xnurta AMC Hub?

A: To use Xnurta AMC Hub, you need an Amazon Advertising account with Amazon Marketing Cloud (AMC) access enabled. If you don’t have AMC access yet, contact your Xnurta representative to help set it up. Once your account has AMC permissions, simply authorize your Amazon advertising account on the Xnurta platform and select the AMC instance you want to use. After that, you can start using all the features of the AMC Hub.

 

Q: What are the main features supported by Xnurta AMC Hub?

A: Xnurta AMC Hub offers two core capabilities: Analytics Models and Custom Audiences. The Analytics Models section includes 10 built-in analysis models covering various aspects of advertising performance (reach, conversion paths, attribution, audience insights, etc.), and the Custom Audiences section lets you define audience segments based on user behavior and use them for ad targeting. In summary:

• Ten Analytics Models:
  
  • Unique Reach – Measures unique users reached by your ads (deduplicated reach) and related metrics to help understand your overall ad reach.
  • Audience Label – Evaluates ad performance across different audience segments or labels, helping identify high-performing and high-potential audience groups.
  • Path to Conversion – Analyzes the full journey users take from initial ad exposure to final conversion, showing the sequence of touchpoints that lead to a purchase.
  • Cross-Product Association – Looks at relationships between products in purchases (e.g., which products are often bought by the same users), revealing cross-selling or bundle opportunities.
  • Time to Conversion – Measures the time span between a user’s first ad interaction and the conversion, providing insight into how long conversions take.
  • Overlap Analysis – Shows the overlap of audiences between different campaigns or channels, i.e., how many users were reached by multiple campaigns.
  • Geographic Analysis – Breaks down ad reach and conversions by geographic region, helping you see performance differences across locations.
  • Multi-Touch Attribution – Allows custom attribution modeling to credit multiple ad touchpoints for a conversion, so you can analyze how each ad exposure contributed to the result.
  • Customer Lifetime Value (CLV) – Tracks the long-term value of customers acquired in a given period (cohort), by monitoring their cumulative purchases over time (new-to-brand analysis and repeat purchase behavior).
  • Search Term Analysis (STA) – Group users based on different search behaviors and track their subsequent conversion actions.
• Custom Audiences – Lets you create audience segments based on users’ historical behavior on Amazon. You can define audiences by combining conditions such as viewed ASINs, purchase actions, ad exposures, etc. Once you create a custom audience, it automatically syncs to your Amazon DSP/SA account, where you can directly use it for targeting in your ad campaigns.

 

Q: Does using AMC Hub require any technical skills or background?

A: No. Xnurta AMC Hub is designed to be user-friendly and eliminate technical barriers. You do not need any programming or database expertise to use it. The platform provides an intuitive graphical interface and pre-built analysis models. With point-and-click or drag-and-drop operations, any user can customize queries and analyze AMC data. In short, no special technical background is required – even non-technical users can effectively use AMC Hub for data analysis.

 

Q: How does AMC Hub ensure data security and privacy?

A: Data security and user privacy are top priorities for AMC Hub. Xnurta AMC Hub is built on Amazon Marketing Cloud’s infrastructure and thus inherits AMC’s robust security and privacy protections. All data extraction, transfer, storage, and analysis operations are carried out under Amazon’s strict privacy guidelines, ensuring that each advertiser’s data remains isolated and secure. Moreover, Xnurta, as the service provider, adheres to data privacy regulations and our customer agreements — we will not access, use, or share your data without your authorization. In short, the data you handle in AMC Hub is used only for your analysis and is safeguarded at the same high standards as on Amazon’s own platforms.

 

Q: Does AMC Hub support Amazon’s paid data products like FSI? Are there extra fees to use them?

A: AMC Hub does support some of Amazon’s paid data integrations, and there are no additional fees from Xnurta to use them. Specifically, AMC Hub supports FSI data in certain models. FSI is an Amazon paid data add-on that includes conversion data from users who were not reached by ads in the past 28 days. Enabling FSI allows those organic (non-ad-driven) conversions to be included in your analysis. Currently, FSI data can be incorporated into models such as Cross-Product Association, Time to Conversion, Multi-Touch Attribution, Customer Lifetime Value, Search Term Analysis, as well as in the custom audience creation feature. 
Importantly, there is no extra charge to use AMC Hub or to utilize these AMC paid data features through our platform. In other words, if your Amazon account has access to FSI data, you can enable and use that data in AMC Hub’s supported models without paying any additional fees to Xnurta. 

 

Q: What is the difference between “prebuilt data” and “custom data” in AMC Hub?

A: The key differences lie in how the data is pulled and the level of flexibility:

• Prebuilt Data: These are model results that Xnurta pulls automatically on a fixed schedule with preset parameters. They are typically updated once per month and are available for you to view without any manual action. For example, the system will query the analysis results for the previous month for each model, so you can directly view them. The parameters for prebuilt data (such as the time range and which campaigns/products are included) are fixed, providing a consistent, standard view of your performance (usually focused on the most recent period). Because of data volume considerations, prebuilt models generally only include the recent data (approximately the last month). If you need data from further back in time or with different parameters, you would use a custom query.
• Custom Data: This is data you pull on-demand by setting your own parameters in a model. You can choose the exact date range, select specific campaigns or ASINs, apply filters, and then run the query to retrieve that specific dataset. Custom data queries can be run ad-hoc (whenever you need) or set to update on a schedule you define. With custom data, you have the ability to retrieve longer historical ranges or highly specific segmented data beyond what the prebuilt settings offer. In short, prebuilt data is like a regular monthly report that the system prepares for you, whereas custom data allows you to actively fetch exactly what you want to see. Both types of data can be exported to Excel for further analysis if needed.

 

Q: Can I upload my own first-party data to AMC Hub?

A: Currently, Xnurta AMC Hub does not support directly uploading first-party data within the platform. However, if you have already uploaded first-party data via the Amazon Marketing Cloud console (your AMC instance), the Custom Audiences feature in AMC Hub can consume that data to create audiences (including Lookalike audiences). For example, after you upload encrypted member ID data into your AMC instance, you can select that first-party data source when building a custom audience in AMC Hub. This allows you to leverage your existing first-party data for more targeted ad audiences.

 

Q: After setting up a new AMC instance, how long does it take for data to show up in AMC Hub?

A: When you create and authorize a new AMC instance, you won’t see the data immediately; there is a short waiting period for everything to sync. In general, the timeline is as follows: First, Amazon typically takes about 1–2 business days to review your request and link your ad account to the new AMC instance. Once that is done, the Xnurta platform will begin pulling in the prebuilt model data, which usually completes within another 48 hours or less. So roughly, you can expect it to take about 3–4 days from the time you set up an AMC instance until all the model data is available in AMC Hub. (If you’re linking an already-existing AMC instance that was authorized externally, data querying begins immediately after authorization, and you should see data ready in about 2 days.)

• A natural delay in processing and finalizing the last few days of any month. Conversions and events that occur in the final days of the month may only get recorded into AMC during the first few days of the following month. So, the data for a given month isn’t finalized right at midnight of month-end — typically by the first week of the next month, all of the prior month’s data will have arrived and settled.
• Attribution Window: Amazon’s advertising attribution window is about 14 days. This means if a user was exposed to your ad near the end of the month but converted a bit later (within those 14 days), that conversion won’t immediately show up in the previous month’s attributed metrics. If you pull the data too soon, some conversions that technically stem from last month’s ads might still be pending attribution, making the initial reported numbers for the month a little low.

Given these factors, it’s advisable to wait a short while into the new month before quering the full data for the previous month. Usually by the second week of the new month (or at least a few days into the month), the prior month’s AMC data will be complete. At that point, any processing lag or attribution delays have been resolved, and you can trust that the data is accurate and final.

 

Q: How long does a custom model query take to complete?

A: The time it takes to run a custom model query can vary depending on several factors: the date range you request, the number of campaigns or data points involved, and the complexity/volume of the data. In general, most custom queries finish processing within 24 hours of submission. If you’ve chosen an especially long time span or included a very large number of campaigns (resulting in a huge amount of data to crunch), it may take longer – in extreme cases, if a query runs over 24 hours, it might time out and fail. Also, if you submit many queries at once, they will be queued and processed one after another, which means some might complete after the 24-hour mark due to waiting time. In practice, the majority of typical queries complete within a few hours, and only very heavy data requests approach the upper end of this timeframe. If you notice a query is taking unusually long, you can check its status in the “My AMC Models” list; if needed, consider narrowing the query’s scope and trying again to speed it up.

 

Q: How can I check the progress or status of a custom model data pull?

A: In the AMC Hub interface, you can monitor the status of each custom query in real time via the “My AMC Models” list (typically on the main models page). Every custom model task you’ve submitted will be listed with a status indicator. The primary statuses include:

• Pending: Your request has been submitted and is waiting in the queue to be processed.
• Processing: The system is currently running the query – retrieving and computing your data.
• Completed: The data pull has finished successfully. You can now click on that model in the list to view the results.
• Failed: An error occurred and the data pull did not complete.

After submitting a custom query, you can check this “My AMC Models” list at any time to see the latest status. If a query shows as “Failed,” you can hover over the status (or click it) to see a tooltip with the error message, which can help you identify the cause of the failure.

 

Q: If a custom model data pull fails, what are the possible reasons?

A: Based on our experience, some common reasons for custom query failures include:

• Data Volume Too Large: If your query requests an excessively large dataset (for example, a very long date range combined with a high number of campaigns or filters), the system may time out or hit limits. Essentially, the query can fail because it couldn’t complete within the allowed time or resource constraints. To resolve this, try reducing the date range or splitting the query into smaller chunks to decrease the amount of data processed in one go.
• Privacy Budget Limits: If you submit too many queries with very similar conditions in a short period, Amazon’s AMC may trigger a “Privacy Budget Error”. This is a privacy safeguard mechanism – it prevents attempts to triangulate individual user data by making repeated similar queries. Once this error is triggered, subsequent similar queries will be blocked. In such cases, you’ll need to contact our support team to help reset or unlock your AMC instance before continuing. It’s best to avoid firing off numerous nearly identical queries back-to-back.
• Network/System Glitches: In rare instances, a query might fail due to a temporary network issue or server-side glitch (for example, if there’s a momentary outage or maintenance). These occurrences are infrequent. If you suspect this is the cause, simply wait a bit and try the query again later.

If a custom data pull fails, first check if any of the above might be the cause and adjust accordingly (e.g., simplify the query or spread out similar requests). If the problem persists after those adjustments, please reach out to Xnurta support for further assistance. We can investigate the specific failure and help you get the data you need.

 

Q: Can I modify or cancel a custom model query after I’ve submitted it?

A: Yes, you can cancel it as long as the query is still in progress. If your custom model task is still in “Pending” or “Processing” status, you have the option to cancel it. To do this, go to the “My AMC Models” list, locate the query you want to stop, and click the cancel button for that entry. Once clicked, the system will halt the data pull. This is useful if you realize you made a mistake in your query parameters or you no longer need the data – you won’t have to wait for it to finish. Keep in mind that if a task has already been completed (status shows “Completed”), it cannot be undone or modified; in that case, you would need to submit a new query with the desired changes. (There is no way to directly edit a query that’s finished, but you can always cancel and resubmit while it’s running.)

 

Q: Can I export the analysis data and charts from AMC Hub?

A: Yes. AMC Hub provides easy export options for your analysis results. On each model’s results page, there is a “Download Data” button that allows you to export the currently displayed data table as an Excel file. This makes it convenient to take the data offline for further analysis or to incorporate into your own reports. These export features let you seamlessly include insights from AMC Hub in your presentations or decision-making documents without having to manually copy and paste figures.

 

Q: Why do the numbers in AMC Hub not match the data in Amazon’s own ad platforms?

A: It’s common to see some discrepancies between AMC Hub data and the figures in Amazon’s advertising dashboards (like Seller Central or the DSP console) because they are derived and calculated differently. Here are the main reasons:

• Different Data Sources: AMC Hub pulls data via Amazon Marketing Cloud, which is separate from the data source that Amazon’s native ad platforms use. The AMC data is more raw and user-level, whereas the ad console data is aggregated and campaign-level. Thus, the way impressions, clicks, and conversions are counted can differ. In short, the two systems are not in perfect one-to-one alignment due to these source differences.
• Privacy Filtering: AMC applies strict privacy rules. If a query result involves very small numbers of users, AMC may suppress or omit data to protect user anonymity. As a result, some conversions or metrics that appear in the Amazon ad console might be hidden or shown as zero in AMC if the user count is below a certain threshold. This privacy filtering can cause AMC’s totals to be lower than those on the ad platform for those particular segments.
• Attribution Differences: Amazon’s native reports use fixed attribution logic (for example, click-through attribution within 14 days for conversions). They only count a conversion if it meets those criteria (an ad click led to a sale within the window, etc.). AMC, on the other hand, often provides unattributed or flexibly attributed data. This means AMC will show conversions that happened in the presence of certain ads or conditions, even if those conversions weren’t directly credited to an ad click under the standard rules. For example, in the Search Term Analysis model, if a user searched for a term and later purchased a product, that purchase is counted under that search term’s data in AMC – even if the purchase wasn’t directly caused by clicking an ad for that term. In Amazon’s campaign report, that sale might not be attributed to the term at all (unless the user clicked your ad). Therefore, AMC might show higher purchase counts for a given search term than the Amazon console does. Similarly, the Multi-Touch Attribution model in AMC uses custom attribution rules, which will naturally diverge from Amazon’s default single-touch attribution.
• Scope of Data: The scope of data included in an AMC instance is usually broader than a single Amazon platform. AMC Hub can combine data from your Amazon store (including organic sales), your DSP campaigns, any first-party data you’ve uploaded, and even additional paid data sources like FSI if enabled. In contrast, Amazon’s native dashboards typically show data in silos — for example, Seller Central shows your store’s sales and Sponsored Ads, and the DSP console shows only DSP campaign data for that advertiser. AMC Hub aggregates across these sources. This means some “total” figures in AMC could be higher because they encompass multiple channels. For instance, AMC might show a higher overall sales number because it’s counting both ad-attributed sales and other sales, whereas Seller Central might only be considering sales attributed to ads (depending on the context of the report).
• Metric Definitions: AMC Hub often provides different metrics or calculates them in ways that don’t directly mirror the native reports. It emphasizes user-centric metrics (like Purchase UV, which is the count of unique purchasers, or distinguishing New-to-Brand customers) and cumulative or holistic measures, whereas the ad console focuses on ad-centric metrics (like clicks, spend, ROAS for that campaign, etc.). Because of this, even when metrics seem similar, they may not be directly comparable. For example, the Customer Lifetime Value (CLV) model in AMC tracks cohorts of new customers acquired each month and measures their subsequent purchases over time. A given month’s CLV sales figure only includes purchases from customers who were first acquired in that month. It intentionally does not include purchases from customers who were acquired earlier (since those belong to past cohorts). By contrast, Amazon’s standard sales report for that month includes all sales from all customers (new or existing). Naturally, the CLV model’s “Total Sales” for a month will usually be lower than the total sales reported in Seller Central for the same month, because the Seller Central figure also contains repeat purchases from older customers. (Another example: the Path to Conversion model in AMC might sum up the cost of all ads a user saw on their journey, across DSP and Sponsored Ads, whereas the DSP platform itself only shows the cost of DSP ads. So if you tried to compare “Total Cost” or “NTB purchases” between the two, they wouldn’t match because one is combining channels and not using Amazon’s one-ad attribution rule.)

In summary, these discrepancies are expected and stem from AMC Hub providing a more user-behavior-oriented, cross-channel view of performance, versus Amazon’s native tools providing a channel-specific, strictly attributed view. Each is useful in its own context. It’s best to use AMC Hub to gain deeper insights (e.g., user journeys, combined impact of all ads, incremental lifts) and use the Amazon console to measure the direct performance of specific ads or campaigns. The differences in numbers do not indicate an error but rather reflect these different analytical perspectives.

 

Q: In the CLV model, what does the “Accumulative” toggle do?

A: In the Customer Lifetime Value (CLV) model, the “accumulative” option adjusts the way metrics are presented over time. When you turn on the accumulative toggle, the report will cumulatively add the performance of a cohort of customers across months. Here’s how it works:

• For absolute metrics like Total Sales or Purchase UV (unique purchasers), turning on accumulation means each month’s figure will include that month plus all previous months for that customer cohort. So the values become running totals. For example, if in January the cohort had $10k in sales and in February they contributed another $5k, the February value with accumulation would show $15k for that cohort (Jan + Feb).
• For calculated metrics like LTV (Lifetime Value per customer), the calculation uses the cumulative values when accumulation is on. LTV is typically calculated as total sales divided by the number of new customers in that cohort’s start month. With accumulation enabled, the “total sales” part of that formula becomes the accumulated sales up through the current month, while the denominator (the number of acquired customers in the initial month) remains the same. As a result, if those customers keep buying in later months, the LTV metric will increase over time. For instance, if the cohort’s initial LTV after one month was $50, and some of those customers made additional purchases in the second month, the LTV might increase to $80 (because the total revenue numerator grew).

In summary, enabling the accumulative toggle lets you see how the value of a cohort of customers grows over the subsequent months beyond their first purchase month. It answers the question, “We acquired these customers in Month X – how much revenue have they generated in total by Month Y?” rather than just looking at each month in isolation.

One thing to keep in mind: if the cohort size is small, you might not observe a visible increase even with accumulation turned on. This could happen if there were very few repeat purchases from that cohort, or if the additional purchases involve so few users that Amazon’s privacy thresholds hide the data. For example, if only a handful of people in the cohort made second purchases (say fewer than 100 users), AMC may suppress the incremental data for privacy reasons. In such a case, the LTV might appear flat in the report even though there was a tiny increase. This is not a bug with the accumulative feature, but rather a result of insufficient data volume for that cohort.

 

Q: Can AMC Hub analyze the reach and frequency of my ads?

A: As of now, the platform does not have a dedicated model specifically for ad frequency analysis. However, you can use the “Unique Reach” model to understand how many unique users your ads reached, which in turn helps you infer frequency. The Unique Reach model tells you the number of distinct users exposed to your ads over a given time period (i.e., de-duplicated reach). To gauge frequency, you can compare the unique reach to your total impression count. For example, if a campaign delivered 10,000 ad impressions and the Unique Reach model shows 2,000 unique users reached, that implies an average frequency of about 5 impressions per user. This gives you a general idea of how often the same users saw your ads on average.

It’s important to note that AMC Hub currently does not provide a direct frequency distribution report like some native platforms do (where you could see how many users saw your ad 1 time, 2 times, etc.). For very granular frequency analysis, you would still need to rely on Amazon’s native ad console features or wait for a future update of AMC Hub that includes a dedicated frequency analysis tool. In the meantime, the Unique Reach model is the best proxy within AMC Hub for understanding reach and estimating frequency.

 

Q: In the Search Term Analysis model, what does “Related Search Terms” mean? Are these terms all keywords from my campaigns?

A: In the Search Term Analysis model, “Related Search Terms” refer to search terms that tend to be searched by the same users in close time proximity. In other words, if a lot of users who searched for keyword A also searched for keyword B during the analysis period, then keyword B is shown as a related search term to A.
 The model output is divided into two sections: “Overall Search Terms” and “Related Search Terms.”

• Overall Search Terms: This section lists all the search terms that triggered an impression of your ads, along with the subsequent metrics (such as Detail Page Views (DPV), Add-To-Carts, and Purchases by those users). That means every term in this list is indeed one that your campaign was targeting (or matched to) – when users searched that term, they saw your ad. So yes, these are all terms relevant to your advertising campaigns. You can think of this as the performance of each search term that was part of your advertising reach. Unlike the Amazon ad console, AMC Hub does not attribute those conversions back to the ad exposure itself; it simply reports on the behavior of users who searched those terms.
• Related Search Terms: This section adds a layer of user behavior correlation on top of the overall terms. It shows term pairs that are frequently searched by the same users. If term A and term B are often searched by the same user or audience group, the model identifies that relationship. For example, if many users who searched “running shoes” also searched “running socks”, then “running socks” might appear as a related search term for “running shoes.” This insight can help you discover additional keywords that your target audience is interested in, which you might want to include in your strategy.

Keep in mind that the conversion metrics associated with a search term in this model (for instance, the number of purchases after searching a term) do not imply that all those purchases were directly attributed to clicking an ad for that search term. Instead, it indicates that users who searched for that term also made those purchases during the time frame. All the search terms listed did trigger your ads (so they are part of your advertising reach), but the conversions shown are more about user behavior patterns rather than direct ad-attributed outcomes.

 

Q: Can I view a competitor’s data in AMC Hub?

A: No, you cannot. Amazon’s data privacy rules strictly ensure that you only have access to your own AMC data and not any other advertiser’s data. In other words, all the data you see in AMC Hub pertains to your brand’s accounts and campaigns – you won’t be able to retrieve any AMC information about your competitors. This policy protects everyone’s data privacy: just as you can’t see a competitor’s data, they cannot see yours via AMC either. Any analysis model or custom audience you use is limited to the data from your authorized AMC instance (i.e., your advertising accounts).

 

Q: When creating a lookalike audience, why do I get an error saying “no suitable audience found”?

A: If you encounter a “No suitable audience found” message while creating a lookalike audience in AMC Hub, it usually means Amazon’s algorithm could not find enough similar users based on your seed audience. This can happen for a couple of reasons:

• Seed Audience Too Small or Invalid: The source (seed) audience you provided might have too few users, or possibly none at all. For example, if the criteria for your seed audience are so restrictive that almost no users qualify (or if you entered an identifier like an ASIN incorrectly, such that it doesn’t match any users), then Amazon has no base to build a lookalike from. With such a tiny sample, the system can’t confidently expand to a larger audience, so it returns that error.
• Seed Audience in a Restricted Category: If the seed audience is defined by an ASIN that falls under a sensitive or restricted product category, Amazon’s policies might prevent creating an audience from it. In this case, even if there are users for the seed, Amazon will not generate a lookalike audience due to policy restrictions on that category.
  So if you get this message, it’s a good idea to confirm that you’re not using a product category that’s disallowed from Amazon Ads' official website: https://advertising.amazon.com/help/GX6YN67MVYYX5P4X

 

Q: After I create an audience in AMC Hub, how long until it appears in the Amazon ads interface?

A: Once you create a custom audience in AMC Hub, it isn’t available in your Amazon advertising account (e.g., DSP/SA audience list) immediately; there’s a bit of a delay for synchronization. Here’s the typical timeline: First, AMC Hub generates the audience’s user list. This generation step usually finishes within 24 hours (often much quicker if the system isn’t busy). Once the audience’s status is marked “Completed” in AMC Hub, the platform pushes the audience to your Amazon ad account. Amazon then generally takes up to around 48 hours to ingest and register that audience in their systems. In other words, from the moment you create the audience to the point it becomes selectable in, say, your ads console, it’s usually on the order of one to two days.

Lately, we have noticed some audiences taking longer than 48 hours to show up. Amazon is aware of these delays and is working to improve the speed of audience syncing. If it’s been a couple of days and your new audience still hasn’t appeared in the ads console, please reach out to Xnurta support. We can escalate the issue to Amazon and get an update. It’s best to be patient and avoid creating duplicate audiences in the meantime – eventually the audience should sync over, and creating it multiple times could lead to confusion if they all sync at once later.

 

Q: Is there any recommendation or requirement for the size of a custom audience in AMC Hub?

A: Yes, there are specific minimums (but no upper limit):

• Rule-based audiences (direct match): Must contain at least 2,000 users, or Amazon’s privacy safeguards will block creation; there is no maximum size.
• Lookalike audiences: The seed audience must have at least 500 users and no more than 500,000 users.

 

Q: Can I modify a custom audience after it’s been created?

A: Due to limitations in the Amazon Marketing Cloud platform, you cannot directly edit or delete the criteria of an existing custom audience once it’s created. However, AMC Hub offers two workarounds to keep your audiences up to date or create a revised version:

• Auto‑Update: When you create an audience, you can set an update frequency (e.g., daily, weekly, or bi‑weekly). If enabled, the system will automatically recalculate the audience at each interval, adding new users who now meet the criteria and removing those who no longer do. This ensures the audience remains “fresh” over time without manual edits.
• Copy Audience: To change the definition itself, use the Copy feature. In your audience list, duplicate the existing audience, adjust the conditions on the copy, and save it as a new audience. This effectively creates a new, edited version while preserving the original. You can then delete the original from Xnurta if you no longer need it. (Note: deleting an audience in Xnurta AMC Hub only removes it from the Xnurta list; it does not remove it from your Amazon ad account. If you wish to stop using it in campaigns, you must also disable or delete it within your ad platform.)    

In summary, direct edits aren’t possible, but you can keep an audience current with auto‑updates or “edit” its criteria by copying and modifying it.

 

Q: For an AMC audience with Auto Adjust Date (auto-refresh) turned on: if any refresh cycle drops below 2,000 users, will the audience become invalid? If it does, will it restart automatically once a later refresh exceeds 2,000 users?

A: The AMC system handles audience refresh as follows:

Scheduled refresh:
With Auto Adjust Date enabled, the system refreshes the audience on the set schedule.

When a refresh returns < 2,000 users:
The system does not overwrite the existing audience; the previous version with > 2,000 users stays active.

When a later refresh reaches ≥ 2,000 users:
The first refresh that meets or exceeds 2,000 users immediately replaces the old data.

30 consecutive days with < 2,000 users:
If every refresh stays below 2,000 users for 30 straight days, the audience is marked Inactive:

It can’t be added to new campaigns;

No further refreshes occur;

Campaigns already using it keep serving the last version that exceeded 2,000 users.

 

Q: I'm interested in a model, but the prebuilt metrics are not quite enough. Can I add more metrics?

A: Of course. We understand that each customer's marketing analysis perspective and focus are different. Therefore, in addition to the key metrics preset in each model, you can also flexibly add other metrics of interest in the custom model to meet personalized analysis needs. Some metrics in some models may require you to upload corresponding custom parameters (such as ASIN) before they can be used. See the "Model Customization" section of each model for details.