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Q: How often is SellerSprite data updated?
A: Different types of data on SellerSprite are updated at different intervals to ensure fresh and accurate information:
1. Extension Data: Information shown by the SellerSprite browser extension (e.g. price, BSR, keyword rankings) is fetched in real-time from Amazon’s front end whenever you query, so it’s always up to date.
2. Sales Data: Sales estimates and figures are refreshed daily. Each day’s data is updated with the previous day’s sales numbers on the following day.
3. Keyword Data: Keyword metrics (like search volume) are updated monthly. At the beginning of each month, SellerSprite updates keyword data for the previous month. Additionally, keyword trend data is updated weekly, with each week’s update reflecting data from the prior week.
4. Monitoring Data: All tracking/monitoring data (for products or keywords you track) is updated daily. The updates occur on a rolling basis, meaning each tracked ASIN’s update time may vary (not a fixed schedule).

Q: How do I upgrade the SellerSprite Chrome Extension when a new version is released? ‍
‍ A: To upgrade to the latest version of the SellerSprite browser extension, simply remove or uninstall the old version from your browser, then download and install the new version from the Chrome Web Store (or the appropriate browser store). Your SellerSprite account credentials remain the same, and you can log into the new extension after installation. (There is no automatic in-place update, so reinstalling ensures you get the newest features and fixes.)

Q: What if the extension is slow or crashes frequently?
A: If you experience lag or crashes with the SellerSprite extension, it is usually due to heavy data load and insufficient computer resources to handle it. Here are some solutions to improve performance:
1.Enable “Light Mode” in the extension settings. Light Mode reduces resource usage by turning off some non-essential features, making the extension run leaner.
2.If possible, use a computer with higher specifications. We recommend at least 16GB of RAM and a robust CPU for smooth performance when handling large amounts of data. Upgrading your hardware can significantly reduce crashes or freezing.

Q: Where does the extension’s data come from? Is it using Amazon’s official API?
A: Amazon does not provide any open API for product data, so SellerSprite’s extension gathers data through other means. Specifically:
1. Live Amazon Front-End Data: The extension directly fetches data from Amazon’s website in real time. This includes information like product titles, prices, BSR (Best Sellers Rank), ratings, etc., just as you see on Amazon’s pages.
2. Processed Estimates: Some values (e.g. 30-day sales estimates, profit margins) are calculated by SellerSprite’s proprietary algorithms using real market data and historical trends. These are not provided by Amazon’s API (since none exists for this purpose) but are derived from SellerSprite’s internal data analysis.

Q: Is the “Keepa Trends” feature on the extension free to use? Are all features free?
A: Yes – Keepa Trends is completely free for all SellerSprite users. The Keepa Trends tool in the extension pulls historical data (prices, BSR ranks, ratings, deal history, etc.) via Keepa’s API, and SellerSprite provides this to users at no cost sellersprite.com . In fact, as a seller-centric platform (and a popular alternative to tools like Jungle Scout and Helium 10), SellerSprite offers many free features and tools to help Amazon sellers. Keepa Trends is one of those free tools, alongside others like the Sales Estimator, Index Checker, and more, which you can use without additional charge sellersprite.com . (Note: Advanced features beyond the free toolkit may require a subscription, but the extension’s core tools including Keepa Trends remain free.)

Q: How can I verify the accuracy of SellerSprite’s keyword data? ‍
‍ A: SellerSprite’s keyword database is built to be highly accurate, and the team encourages users to verify it through a few means. First, SellerSprite incorporates Amazon’s Brand Analytics (ABA) data as a benchmark for keyword rankings. Brand Analytics is first-party data from Amazon available to brand-registered sellers, and SellerSprite updates ABA-based keyword rankings on a weekly and monthly basis for all departments. This helps ensure that the keyword popularity and ranking information align with what Amazon itself reports. Additionally, SellerSprite’s keywords are all derived from real Amazon search queries and refined with hundreds of metrics to predict their search volume and relevance. The internal tests and continuous calibration have resulted in an overall prediction accuracy of over 90% for keyword metrics. In practice, this means you can trust that a keyword’s reported search volume and rank are quite close to reality. For further verification, you could compare a keyword’s trend on SellerSprite with other sources like Amazon Suggest or Google Trends as sanity checks. SellerSprite prides itself on data accuracy, completeness, and freshness, so any discrepancies can often be traced to timing (data update schedule) or Amazon’s shifting algorithms, rather than fundamental inaccuracies in the tool.

Q: What is the difference between static and dynamic keyword mining in SellerSprite?
A: Keyword Mining is a feature that helps sellers discover related keywords, and SellerSprite offers two modes: static and dynamic.
1. Static Keyword Mining: This method expands the seed keyword itself to generate ideas. It finds keywords that contain the entire seed phrase (in any order or as a phrase match). For example, if you use static mining on “portable charger,” the tool will return long-tail keywords that include those words, such as “portable charger for iPhone,” “portable phone charger,” “charger portable power bank,” etc.. It’s essentially an expansion of the original term with prefixes, suffixes, or other words, ensuring the seed term remains in each suggestion. These results are typically sorted by search volume (with higher volume keywords listed first). Static mining is great for uncovering long-tail variations of your main keyword.
2. Dynamic Keyword Mining: This mode is more advanced and market-focused. Instead of strictly containing the same phrase, dynamic mining looks at the sub-market or category that the seed keyword belongs to, and finds other core keywords that drive traffic to that same market ‍ . In practice, using dynamic mining for “portable charger” will identify that this keyword is part of the broader power bank market, and then it will suggest other keywords central to that market, like “power bank,” “battery pack,” etc., even if those terms do not include the words “portable” or “charger”. The results are ranked by relevance to the sub-market and by how much traffic they’ve brought to that market in the past six months (rather than pure search volume). Dynamic mining thus reveals keywords that shoppers use interchangeably or similarly when looking for the same type of product, giving you insight into alternate terms and broader search behavior.

In summary, static mining finds variations of your keyword , while dynamic mining finds the various keywords for your niche (the ecosystem of search terms for the product category). Using both in tandem can provide a comprehensive keyword list – static for capturing all the long-tails of your main term, and dynamic for making sure you’re not missing other high-traffic terms that target the same product market.

Q: In SellerSprite’s dynamic keyword mining, what does the “Relevance” score mean?
A: The relevance score is a metric (on a scale from 1 to 100) that indicates how closely related a keyword is to the target sub-market or niche identified by your seed keyword. A score of 100 means the keyword is highly relevant – essentially, it’s very central to the product market and likely brings in a lot of traffic for that niche. A lower score means the keyword is more loosely related. SellerSprite calculates relevance based on how much traffic a given keyword has driven to the sub-market in the past six months. For example, if you perform dynamic mining on “lemon squeezer” (which points to the manual juicers sub-market), you might see “lemon juicer” with a relevance of 95 and “citrus squeezer” with a lower score. That tells you that “lemon juicer” has brought more shoppers to the manual juicer product pages than “citrus squeezer” has in recent months. In practice, a higher relevance score means that keyword should be a higher priority for optimization and advertising, because it’s proven to drive significant traffic for products in that category. It’s a handy way to gauge importance of keywords beyond just search volume – relevance marries volume with contextual importance in your niche.

Q: How can an ASIN have high sales but low keyword popularity (low traffic keywords)?
A: It may seem counterintuitive, but there are cases where a product sells very well even though the keywords it ranks for are not highly searched. Here are a few reasons this can happen:
1. Niche Dominance: The product might be in a small niche where no single keyword has huge search volume, but the ASIN ranks #1 for many of the niche’s long-tail keywords. Each individual keyword’s traffic might be “low popularity,” but combined they generate substantial sales. Essentially, the ASIN is capturing traffic from a wide spread of specific, low-volume searches rather than a few big high-volume keywords.
2. External Traffic & Branding: Not all sales come from Amazon’s organic search. An ASIN could be getting traffic from external sources (social media, email marketing, influencers, etc.) or repeat customers and thus have strong sales without corresponding high rankings on popular keywords. In such cases, the tool might show low keyword metrics because those sales aren’t driven by common search terms.
3. Product Type and Buyer Behavior: Some products fulfill very specific needs or have usage scenario keywords that are unconventional. As discussed, a product’s search traffic can come from related or complementary keywords. If those related keywords aren’t obviously popular for the category, the product might appear to have “low popularity” keywords yet still benefit from them due to high conversion rates. Also, branded keywords (people searching the exact brand or product name) might not register as high popularity in generic keyword tools, but can lead to high sales for that ASIN.

In short, an ASIN can thrive on multiple lower-volume, high-intent keywords or non-search traffic. SellerSprite’s keyword metrics might label those individual keywords as low popularity, but the product’s aggregate performance across many such keywords (or channels) results in strong sales. It’s a reminder to sellers that looking at only big search volume keywords doesn’t always tell the whole story of how a product is generating its sales.

Q: Why do I often see a different number of products on Amazon’s website than the number of products SellerSprite shows for the same search or category?
A: This is usually due to Amazon’s personalized and regionalized search results . SellerSprite strives to provide consistent, objective data – for example, when it reports the number of products for a keyword, it’s typically using a default U.S. location and no personalization. On Amazon’s actual site, however, the number of products you see can vary because:
1. Location Differences: Amazon tailors search results based on the shopper’s location (IP address or chosen delivery address). Since late 2019, Amazon started prioritizing showing items from nearby fulfillment centers for better Prime delivery speed. This means two people in different states (or different countries) searching the same term might see a different total product count as Amazon includes/excludes products based on stock availability in regional warehouses. SellerSprite’s data fetch uses a fixed location, so it might show, say, 50,000 results for a keyword while your local search shows 45,000 because of regional filtering.
2. Personalization and User Profile: Amazon also takes into account your browsing history, purchase history, and other profile data to surface products it thinks you’re more likely to buy. This can sometimes affect the count or the products shown (some products might be omitted or added in your view). SellerSprite’s count won’t have those personalized adjustments, leading to discrepancies in numbers.
3. Search Query Variance: As mentioned earlier, Amazon often doesn’t give an exact item count, especially for broad searches – it might just say “1,000+ results” or “20,000 results” which can jump around as you navigate pages. SellerSprite might have a more exact count from its querying method. Moreover, if your Amazon search defaults to a specific category vs. “All Departments”, the count will differ. SellerSprite’s numbers are generally based on an unfiltered search in “All” category (unless otherwise specified).

So, if you notice a mismatch, it’s not an error in SellerSprite – it’s a reflection of Amazon’s dynamic search environment. SellerSprite provides a standardized viewpoint (often using a Seattle/New York IP for Amazon.com data, for example) to give all users a common reference, whereas your Amazon app or browser might be showing a tailored view. Both are “right” in their own context. Use SellerSprite’s numbers as a baseline for research, and be aware of Amazon’s adjustments when you check on your own.

Q: Can a main account track and view the operation or history records of its sub-accounts?
A: Yes. All sub-accounts are tied to the main account, which has administrative privileges. The primary account holder can view the usage history of sub-accounts and can also manage them (including deleting or disabling sub-accounts). This helps a team leader monitor the activities performed by team members on their respective child accounts.

Q: Can a main account track and view the operation or history records of its sub-accounts?
A: Each SellerSprite account (whether main or sub-account) can only be actively logged in on one device or location at a time . If you try to use the same account on a second computer or browser at the same time, you will be logged out from the first. There are two ways to use SellerSprite on multiple devices/locations:
1.Take turns (stagger usage): Log in on different devices or locations at different times (not concurrently).
2.Use sub-accounts or higher plans: Upgrade to a plan that supports multiple users. For example, purchase additional child accounts or use a business/advanced plan which includes multiple user seats. Then each user (or device/location) can log in with a different sub-account simultaneously.
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This ensures data security and consistency, as SellerSprite does not allow one account to be shared across multiple active sessions at once.

Q: If I use the same SellerSprite account (and extension) across different Amazon seller accounts, could it cause account linking issues on Amazon?
A: No – using SellerSprite will not create any link between the Amazon seller accounts you manage. SellerSprite is an independent Amazon data analysis tool (often used as a Jungle Scout or Helium 10 alternative) and it does not require being connected to your Amazon Seller Central. Because it doesn’t integrate or ask for Amazon login credentials, it poses no risk of account association. In other words, you can safely use the same SellerSprite account for multiple Amazon stores without worrying about Amazon linking them.

Q: How does SellerSprite decide which products (ASINs) to include in its database for analysis?
A: SellerSprite uses an ASIN inclusion principle to focus on the most relevant products in each marketplace. At the start of each month, it collects ASINs that rank within a certain threshold in Amazon’s Best Sellers Rank (BSR) for their main category (e.g. top 500,000 in each category, or top 800,000 for the US site). These ASINs are then used for sales tracking and market analysis. While this method covers the vast majority of active, high-ranking products, there are a few cases where an ASIN might not be included:
1.Extremely Niche or Lower-Ranked Items: Categories with an enormous number of products (e.g. Apparel) or very low-selling items may have some ASINs falling outside the cutoff (beyond the top 500k/800k ranks). SellerSprite focuses on the top portion to ensure data relevance.
2.New Fast-Rising Products: A brand new product that wasn’t ranked high at the beginning of the month might be missed initially if it suddenly surges in sales mid-month. Such a product would be picked up in the following month’s data refresh if it sustains a high BSR.
3.Variant Listings without a Shared Parent Rank: If a product variant doesn’t share the parent’s BSR (for example, some color/size variations have separate category rankings), those with minimal sales might not have a detectable BSR and thus could be absent from the data.
4.Minor Categories: Products in very small sub-categories (especially those that only have sub-category ranks and no main category BSR) might not be captured. For example, an item only ranked in a niche subcategory without a broader category rank may be overlooked by the inclusion criteria.

Despite these limitations, SellerSprite’s database is extensive – roughly 20 million ASINs on Amazon US alone (with a historical total of ~60 million ASINs collected, including past seasonal items). The team continuously invests in expanding coverage and updating the data more frequently as the user base grows, so the completeness and timeliness of ASIN data are always improving.

Q: How does SellerSprite forecast a product’s sales (monthly sales estimation)?
A: SellerSprite uses an algorithmic approach to estimate sales, primarily leveraging the relationship between a product’s Best Sellers Rank (BSR) and its sales performance. The sales forecasting principle can be summarized as follows:
1. Data Collection: Every day, SellerSprite’s system crawls Amazon to gather basic product data – especially BSR, as well as price, reviews, etc..
2. BSR-to-Sales Mapping: Based on over three years of historical sales data and real seller feedback, SellerSprite has mapped out how BSR correlates with actual sales for each category and marketplace.
3. Daily Sales Estimation: By looking at an ASIN’s average daily BSR (for a given period), SellerSprite estimates how many units that corresponds to in daily sales.
4. Monthly Projection: The tool multiplies the average daily sales by the number of days in the month to project the total monthly sales for that product.
5. Trend Adjustments: The algorithm also takes into account the product’s historical sales trends and seasonality to refine the prediction, so recent surges or slowdowns can adjust the forecast appropriately.

‍ Important: If a listing has multiple variations that share one parent BSR, each child ASIN will show the same sales estimate since the ranking is collective. In reality, that number represents the entire listing’s sales divided among all variations. SellerSprite’s estimator may show each variant with similar sales figures because it’s interpreting the one shared BSR. (Competitor analysis tools will typically attribute the same total sales to each child ASIN for consistency.)

Keep in mind that sales estimates are approximations . Certain situations can cause deviations between the estimate and reality ‍ :
1. Bulk Orders vs. Single Units: BSR is influenced by order count, not units sold. For instance, one order of 10 units has the same BSR impact as one order of 1 unit. If many buyers commonly purchase multiples in one order, the BSR-based method might overestimate actual units sold.
2. Promotions and BSR Spikes: Sudden BSR improvements (e.g. jumping from rank 100,000 to 2,000 due to a flash sale or deep discount) can temporarily inflate the sales estimate. The averaging method may lag in catching up to rapid changes.
3. Stockouts: If a product goes out of stock, its BSR will start to worsen (the numeric rank increases) but not immediately drop to zero. The system might still predict some sales for days where the item was unavailable. SellerSprite partially mitigates this by monitoring the Buy Box status, but minor inaccuracies can occur.
4. Incomplete BSR Tracking: In some cases an ASIN’s BSR isn’t recorded every single day (due to the rolling update schedule or Amazon not updating the rank). If data is missing, the tool relies on the nearest available BSR readings, which may not perfectly reflect that day’s sales.
5. Minor Category Ranks: Products that only have a sub-category rank (and no main category BSR) cannot be directly compared across the broader market. Any attempt to estimate sales from such a rank is prone to large error, so SellerSprite actively filters out or improves handling of these cases.
6. Extreme Seasonal Events: In high-sales periods like Prime Day or Black Friday, overall sales volumes jump dramatically. A product might sell far more this month than last month even if BSR looks similar, making predictions based on last month’s BSR-to-sales relationship less reliable.
7. Top Sellers’ Variability: For top-ranked products (e.g. top 100 in a category), small changes in rank can reflect big swings in sales, but the BSR might remain relatively stable. This can introduce larger estimation error at the very top end of the market.
8. Category Changes: If Amazon moves a product to a different category, the BSR relationship changes. SellerSprite’s model will need a few days to adjust to the new category’s dynamics.

Overall, SellerSprite’s sales estimates are best used as a directional guide. Experienced sellers often look at historical sales trends provided by the tool, rather than fixating on a single month’s number, to get a more reliable picture of a product’s performance.

Q: Why can the total monthly sales for a given market change drastically from one day to the next in SellerSprite’s Market Research?
A: It’s possible to see the “Monthly Total Sales” for a niche or category jump or drop significantly within a short period. This usually happens because one or a few top products had an abrupt change in sales , which skews the entire market’s aggregated estimate. SellerSprite’s market research data for sales is based on recent BSR-driven sales estimates of sample products. If a top seller in that sample suffers a big sales drop on a particular day , its BSR will shoot up (meaning a worse rank), and consequently the tool will predict a much lower sales figure for it. Because that product previously contributed heavily to the total, the whole market’s total 30-day sales projection can fall sharply until the product’s performance stabilizes. The reverse can also happen – if a product runs a major promotion and its BSR skyrockets (to a much better rank), the model might suddenly add a large amount of sales to the market total. In summary, BSR is a fast-changing metric , and a swift rank change in a high-weight product will cause noticeable volatility in market-wide sales estimates. This is normal, and it’s why looking at longer-term trends or averages (rather than daily snapshots) is recommended for market analysis.

Q: In the Market Demand and Trends section of a market report, why do some of the top keywords seem unrelated to the product category?
A: This can happen because the industry trend keywords reflect how customers search and what else they might be interested in, not just the product’s direct name. SellerSprite identifies the top five keywords that drove the most traffic in that category over the past three years (to show seasonality and trend). Sometimes, these include keywords that at first glance appear unrelated to the category. For example, the Slippers category showed keywords like “robe” and “maternity robe” in its top trending searches. Slippers and robes seem unrelated, but in practice they are complementary products in a use-case (comfort at home). Many shoppers looking for robes may also consider buying slippers, so Amazon’s search algorithm cross-displays slippers when “robe” is searched. This behavioral link means “robe” actually drives traffic to slipper listings, making it a significant keyword for the slippers market. Another example: in Travel Mugs & Tumblers , trending keywords included “gifts for men” and “dad gifts.” A travel mug can be a popular gift for men, so broad gift-related searches can lead customers to travel mug products. In essence, these seemingly unrelated terms are often scenario-based or complementary keywords . They reveal that a product’s organic traffic isn’t only from literal product-name searches, but also from related queries and use-case searches that overlap with the product’s audience.

Q: In the Market Demand and Trends section of a market report, why do some of the top keywords seem unrelated to the product category?
A: This can happen because the industry trend keywords reflect how customers search and what else they might be interested in, not just the product’s direct name. SellerSprite identifies the top five keywords that drove the most traffic in that category over the past three years (to show seasonality and trend). Sometimes, these include keywords that at first glance appear unrelated to the category. For example, the Slippers category showed keywords like “robe” and “maternity robe” in its top trending searches. Slippers and robes seem unrelated, but in practice they are complementary products in a use-case (comfort at home). Many shoppers looking for robes may also consider buying slippers, so Amazon’s search algorithm cross-displays slippers when “robe” is searched. This behavioral link means “robe” actually drives traffic to slipper listings, making it a significant keyword for the slippers market. Another example: in Travel Mugs & Tumblers , trending keywords included “gifts for men” and “dad gifts.” A travel mug can be a popular gift for men, so broad gift-related searches can lead customers to travel mug products. In essence, these seemingly unrelated terms are often scenario-based or complementary keywords . They reveal that a product’s organic traffic isn’t only from literal product-name searches, but also from related queries and use-case searches that overlap with the product’s audience.

Q: What’s the difference between the number of products shown in a category versus the search results count for a keyword on Amazon?
A: The category product count refers to how many products are listed under a specific category on Amazon. For instance, if you navigate to the “Pillowcases” category page on Amazon, you might see a total count of all items categorized as Pillowcases. In contrast, a search result count is the number of products Amazon displays as relevant when you search for a particular keyword (like “pillowcases” or any other term). Several factors explain why these numbers differ:
1. Relevance vs. Listing Category: A keyword search will pull in any products Amazon deems relevant to that keyword, regardless of their category. So searching “pillowcases” might show results that include pillow protectors or sheet sets if Amazon’s algorithm thinks they are relevant, even if some items aren’t strictly in the “Pillowcases” category. The category count only tallies items officially listed under that category.
2. Multiple Categories in Search Results: Products in search results can come from various categories. For example, a search for an “iPad stand” might return products from Electronics, Home, or even Office categories, because many types of products could function as an iPad stand. The category page, on the other hand, is limited to one category’s items.
3. Amazon’s Search Algorithms: Amazon often personalizes and varies the number of results. Sometimes the first page might say “1,000 results” and by page 3 it shows “5,000 results” for the same query. Amazon also started giving very approximate counts (like “10,000+”) rather than exact numbers ‍ , so the count you see is not always precise.
4. Filtering and IP/localization effects: If you apply category filters in a search or if Amazon auto-selects a department for your search, the count can change. Moreover, Amazon tailors results based on your location (shipping address or IP). The same keyword might show a different number of results in one country or city versus another. SellerSprite’s data retrieval typically uses a consistent location (for example, a U.S. IP for Amazon.com), whereas an individual user might see localized results. This means the product count SellerSprite reports for a keyword (using a neutral location) could differ from the count you see on your local Amazon due to regional filtering.

In short, category count tells you how many products are in an official Amazon category, while search count tells you how many products match a search query (across all categories, influenced by relevance and personalization). Don’t be alarmed if SellerSprite’s numbers differ from what you see on Amazon’s interface – Amazon’s dynamic and location-based result counts are the reason.

Q: Why might SellerSprite’s predicted monthly sales for an ASIN differ from that ASIN’s historical sales shown under Competitor Analysis?
A: The discrepancy usually comes down to how Amazon reports sales rank for variations and how SellerSprite processes that data in different tools. Here’s what happens: Amazon now merges the BSR for all variations of a product. This means if a listing has, say, 7 color variations, they all share one BSR rank as a parent listing. SellerSprite’s Sales Estimator (predicted sales) treats each ASIN query independently. So if you input a specific variant’s ASIN, it will use that single shared BSR to estimate sales, essentially giving the total sales of the entire listing as the result for that one variant ‍ . If you then estimate another variant of the same product, you’ll get a very similar number. There might be slight differences between variants’ estimates because BSR updates hourly and each query might grab a slightly different rank snapshot, but in general each child ASIN’s “monthly sales” from the estimator reflects the whole listing’s sales.

On the other hand, the Competitor Lookup (historical sales) in SellerSprite is designed to show the performance of the full product listing. It will usually attribute the same total monthly sales to each variant (essentially the parent listing’s sales) for consistency. SellerSprite has standardized the historical sales for variations so that no matter which child ASIN you look at, it shows the total listing sales, avoiding confusion. Thus, you may notice that the Sales Estimator (predicted sales) for a single variant and the Competitor Lookup’s historical sales don’t match one-to-one – one is giving an estimate at a point in time per variant query, and the other is giving an aggregated confirmed sales figure for the listing. This is expected behavior given the merged BSRs.

In summary, all variants share the same sales pool , and SellerSprite’s tools present that data differently: the estimator gives you a quick prediction (which ends up being the listing total for each variant), while the competitor analysis shows the unified historical total for comparison purposes.

Q: I just added a product to the Product Tracker – why is there no data showing up yet? ‍
‍ A: When you add a new ASIN to SellerSprite’s Product Tracker, the system needs some time to start gathering data for it. Please allow up to 24 hours for the first update cycle. The tracker updates various metrics (price, rank, stock, sales, etc.) daily, but it doesn’t have historical data for a product you just added. After a day, you should begin to see the product’s information and its first day of tracked data. From then on, the Product Tracker will update daily so you can observe trends over time. This 24-hour initial delay is normal for all new products added to tracking.

Q: How frequently does the Keyword Tracker update rankings?
A: The Keyword Tracker in SellerSprite updates once per day on a rolling schedule. “Rolling” means not all keywords update at the same exact hour, but rather the updates are staggered throughout the day. Each ASIN-keyword pair you’re tracking will get refreshed roughly every 24 hours (it could be morning one day, afternoon the next, etc.). When you first start tracking a keyword for an ASIN, the initial data point may take 24-48 hours to appear. After that, you’ll get a new rank each day. This daily frequency provides a balance between keeping data fresh and avoiding excessive calls that might slow down the system.

Q: Why might the keyword rankings I see in Keyword Tracker differ from what I personally see on Amazon’s search at the moment?
A: It’s common to notice some differences, and it doesn’t mean the data is wrong. The Keyword Tracker’s ranking data is updated once a day , so it represents the position at the time of the daily snapshot. Amazon’s actual search results, however, are live and influenced by many variables – they can change hour to hour, and can vary based on your location or browsing profile. So if you look up a keyword on Amazon and compare it to SellerSprite’s last tracked rank (which could be, for example, from 6 hours ago and from a certain city’s perspective), it might not match exactly. Additionally, Amazon might show you a personalized order of results. SellerSprite’s data is non-personalized and aims to reflect a generic view. Given these factors, a small discrepancy is normal. Rest assured that SellerSprite’s tracking is authentic – it uses real Amazon queries behind the scenes (much like an Amazon FBA toolkit should) to get those rankings. Think of it as a once-daily benchmark. If a product’s rank jumps or drops significantly during the day, you’ll see that change on the next update. For critical keywords, it can be useful to occasionally double-check manually, but overall the tracker provides a reliable daily record of rank trends.

Q: How are tracking quotas allocated between main and sub-accounts?
A: When you subscribe to a SellerSprite plan, you’re given a certain number of tracking quotas (for product tracker and keyword tracker). These quotas are shared across your main account and any child sub-accounts . There isn’t a per-user split or fixed allotment per sub-account – instead, it’s a collective pool. For example, if your plan includes 100 keyword track slots, it doesn’t matter if you set 70 of them and your teammate uses 30, or any other combination – together you can track up to 100 keywords total. All sub-accounts created under your main account draw from the same quota bank. You can always see how many tracking slots are used and how many remain by visiting the “Tracking Center” in the Product Tracker section. It will show a summary like “X / Y used” for both product and keyword tracking. If you need more, you’d have to upgrade your plan or reduce some tracked items to free up slots.

Q: How often does the New Listing Tracker update?
A: The New Listing Tracker monitors fresh listings on Amazon and it updates data daily for those new ASINs. It pulls the information directly from Amazon’s front end just like the other trackers. Essentially, once a day it will refresh the stats of the new listings you’re watching (price, reviews, BSR, etc.), helping you keep an eye on how a new competitor or product launch is progressing. This daily update schedule ensures you can see day-by-day growth of a new listing in its crucial early stages.