--- title: "Wildberries" id: "1781" type: "page" slug: "wildberries" published_at: "2026-09-20T21:35:45+00:00" modified_at: "2026-09-21T01:37:26+00:00" url: "https://xedant.com/agents/analytics/docs/templates/wildberries" markdown_url: "https://xedant.com/agents/analytics/docs/templates/wildberries.md" excerpt: "A section for sellers on Wildberries (WB): 53 templates, ordered the way money moves —…" --- # Wildberries [https://xedant.com/agents/analytics/docs/templates/wildberries.md](https://xedant.com/agents/analytics/docs/templates/wildberries.md) A section for sellers on Wildberries (WB): 53 templates, ordered the way money moves — from the economics of a single sold unit, through cards and search, supply and warehouses, buyouts and returns, ads and bids, to financial reports, taxes, choosing a niche, and competitors. Here are all 53. Every template is a live walkthrough on one Wildberries seller’s demo data: open it in the Templates section of Analytics Agent and press “Use this template” to run it on your own data. ### WB Unit Economics: Price to Net Profit **Shows:** for every product, how the price is reduced step by step — commission, logistics adjusted for volume and warehouse, the low-buyout surcharge, storage, acceptance, acquiring, and tax — and what remains from one sold unit in the end. The same products are calculated under the rates before and after the July commission rise, and for each loss-maker the report names the price at which it breaks even. **You get:** a product that quietly runs at a loss shows up at once — together with the price that saves it. ### Buyout-Adjusted Unit Costs **Shows:** the real cost of delivery per bought-out unit, not per package: returns and cancellations travel too, and their shipping lands on the packages that did reach the buyer. Next to it, the same returns under the rules before and after March 2026. **You get:** you see which products lose money only because of a low buyout rate, and where to start — the size chart, the photos, or the price. ### DRR by SKU (Ad Cost Share) **Shows:** what share of each product’s revenue ads take (the DRR) — measured against accepted norms and against the product’s own profit. A verdict for each product: “turn off”, “reduce”, “scale up”, “don’t touch”. **You get:** ads on a thin-margin product stop quietly running at a loss. ### Seller Norms Traffic Light **Shows:** one product × metric screen — profit, ad cost share, buyout, returns, turnover — each metric against the norm for its own category, and each product gets an overall color from its worst metric. **You get:** no need to read five reports to find the sick product — it is named, with a note on where to start the week. ### Break-Even & the Minimum Allowed Price **Shows:** how many units per month you need to sell for the month to stop losing money, and on which day of the month that will happen. Separately, the price below which a given product must not be sold. **You get:** instead of the vague “seems like we’re doing fine”, a concrete number of units and a day of the month to reach it by. ### Fulfillment: FBO vs FBS vs FBW vs DBW **Shows:** each product’s economics recalculated under all four warehouse models — from storage on the platform’s warehouse to shipping from your own — with a winner named for every product. Where the winner differs from your current model, the report collects a list to switch. **You get:** you see what your habitual model costs, and where a switch pays off. ### Warehouse Coefficients Economics **Shows:** each product’s profit recalculated for every warehouse with its coefficient — from cheap Tula to expensive Khabarovsk. Beside it, the “cheap warehouse” trap (few buyers there) and a relocation table with the monthly gain. **You get:** it becomes clear at which warehouse a product actually earns, not just sits cheaper. ### WB Finance Calendar **Shows:** a weekly review — revenue, ad cost share, buyout; a monthly recalculation of each product’s economics under that month’s rates; a quarterly check of fixed costs and break-even. **You get:** instead of firefighting, a schedule — what to check every week, and what once a quarter. ### WB Seller Funnel: Impression → Buyout **Shows:** where buyers are lost — at the impression, the click, the cart, the order, or the buyout. Losses are counted in lost orders, and where data is thin, an honest note says so. **You get:** you know exactly what to fix — photos, price, description, or the size chart — instead of guessing. ### CTR vs CR: Clicks but No Orders **Shows:** for every card, click-through rate and order conversion side by side, both against the category median, with the suspects named: price, weak rating, dead size rows. **You get:** cards that collect clicks for nothing go into the fix queue, and you see right away what to change in them. ### Card SEO Audit **Shows:** a search-rules checklist — title, description, photos, attributes, keywords — for every card. Completeness is matched against real CTR, and the rework queue is sorted by missed clicks. **You get:** cards with a short description and buried keywords stop losing impressions you have already paid for. ### Search Visibility: Queries Won & Lost **Shows:** a query table for every “product — query” pair: impressions, CTR against the category median, position in search results, clicks through to order. The lost-query rule finds queries with plenty of impressions but no buyers arriving. **You get:** you see which search queries a product is losing, and how many clicks and orders that costs. ### Ranking Factors Decomposition **Shows:** seven factors — conversion, seller rating, reviews, card completeness, sales velocity, buyout, delivery time — matched against the product’s real position in search results, both across all products and within categories. **You get:** your effort goes to what actually moves a card up, not to what is merely assumed to matter. ### Demand Forecast with Scenarios **Shows:** next month’s demand for every product as a range, not a single number. Several forecasting methods are compared on history, and scenarios — a promotion and a seasonal peak — are set separately. **You get:** buying to a range instead of a single number lowers both risks — stockouts, and excess goods in the warehouse. ### Card Audit: Why It Doesn’t Sell **Shows:** for every card, a list of obstacles out of the eight measured (price above the category median, low rating, too few reviews and photos, truncated title, buried keywords, dead size rows, slow delivery), the weight of each obstacle, and the order to fix them. The queue is sorted by traffic bid. **You get:** you know what to fix in a card first — fixes start where the most impressions are at risk. ### Reviews and Rating Impact **Shows:** cards grouped by review count and rating level, with CTR, conversion, and buyout shown changing along with them. Debatable conclusions are tested, not passed off as truth. **You get:** you see what review work actually pays back — and stop spending effort on things that don’t move sales. ### Supply Planning: Volume and Frequency **Shows:** how many units to send to the warehouse and how often. Volume comes from sales velocity, lead time, safety stock, and losses. Named separately: products that will run out before the next batch arrives, and products with money frozen in excess stock. **You get:** the order goes where it is needed, and money no longer sits in a product that already sells at a loss. ### Reorder Point and Safety Stock **Shows:** at what stock level it’s time to order more, and how much safety stock to hold so you don’t run dry. Beside it, products split by demand predictability — and the rule itself is rechecked by recalculating it on history. **You get:** you see where safety stock saves you, and where it only freezes cash. ### Out-of-Stock Losses **Shows:** what an empty shelf costs — out-of-stock periods valued at the sales velocity before them, plus the ads that kept pouring into an empty card all that time, and the ranking drop after restocking. **You get:** you see which is cheaper — holding stock or losing sales — with the price of both options. ### Stockout Date Forecast **Shows:** the date a product runs out, given as an interval rather than one number. From it, the report derives the last day you can still place an order so the supply arrives on time. **You get:** the order is placed before the product runs out, not after. ### Warehouse Allocation **Shows:** how to spread a supply across warehouses so regions don’t get lopsided: each region’s share of demand, an adjustment for what already sits in the warehouse, and the shipping and acceptance cost of every option. **You get:** buyers in remote regions get the product fast, and part of logistics gets cheaper at the same time. ### Stock ABC/XYZ and Turnover **Shows:** products laid out by contribution to profit and by demand predictability. Shown separately: which products bring revenue but no profit, and how many days each stock sits. **You get:** money stops freezing in products that sell slowly and earn little. ### Logistics Tariffs: Volume, Acceptance, Storage **Shows:** a reference on what logistics is made of — how a package’s volume is calculated, how volume bands work, the warehouse coefficient, the localization index (a live slider right in the report), acceptance and the fine for exceeding the plan, storage per liter per day. On your own products, it shows where a package crosses into a more expensive band. **You get:** you can check every line of your invoice and see in advance which packages get more expensive because of volume. ### Logistics Plan vs Fact: The 7-Step Check **Shows:** the deduction register from your seller portal, checked line by line against your own calculation from the tariffs. A discrepancy is flagged only when it stands out both in money and in percent, and every flag is explained — package contents, a fine, or a frozen index. **You get:** you see whether the platform has a claim on you that you don’t understand — and what exactly it comes from. ### Buyout Rate by SKU and Category **Shows:** what share of shipped items stays with buyers — for every product and category, over 30, 60, and 90 days, with honest accuracy bounds. Separately, the economics of the surcharge that switches on when buyout is low. **You get:** you see who is just short of the low-buyout surcharge, and what even a small change would bring. ### Returns Audit: Causes and Money **Shows:** the return rate of every product against its category norm, plus a breakdown of causes — size, “didn’t like it”, defects. Every return is valued in money at the rates of its own date, and refusals at pickup are shown separately. **You get:** you see which returns hit your money hardest, and what to work on — the size chart, the description, or quality. ### Return Cost under 2026 Reverse Rules **Shows:** what one return costs in full — return shipping, storage, write-off, markdown — and how many sales it eats. The same return is calculated under the rules before and after March 2026. **You get:** you see whom the new reverse logistics rule cost the most, and which packages it hit hardest. ### Return Cycle and Re-Listing **Shows:** how fast a returned product becomes sellable again — the length of each stage, stuck returns, and the amount of money sitting in goods that cannot be sold. **You get:** shortening the return cycle directly frees up money already invested in stock. ### Realization Limit Control **Shows:** how many round trips a unit survives before it loses its looks, and what shipment limit to set because of it. Wear is measured by product group and extrapolated where data is thin. **You get:** a product no longer reaches buyers looking shabby after several trips, and the limit protects the card’s rating. ### Markdown Erosion of Margin **Shows:** how much profit the discounted sale of returns quietly eats: returned units laid out by fate — sold at full price, sold at a markdown, written off, or still sitting. Losses are counted in profit, not in revenue. **You get:** the markdown line, invisible in the usual return rate, stops eating your profit unnoticed. ### Returns Recovery – 8 Steps **Shows:** eight steps to cut returns, and under each — its own products, causes, and the amount the data actually supports. Steps without a measurable effect are honestly shown without money figures. **You get:** return work turns into a plan with a price on every step, not a general plea to “work on quality”. ### WB Ad Metrics Passport — What I Have on Every Metric **Shows:** one table across all campaigns — impressions, clicks, CTR, cost per click and per thousand impressions, conversion, return on ad spend, and ad cost share. Empty cells are not replaced with zeros, and atypical values are flagged. **You get:** you see which metric failed where — and the “cheap clicks, no orders” trap too. ### DRR in Four Honest Views — Operational, Fact, Corrected, TACoS **Shows:** DRR calculated four ways — by orders, by revenue actually kept, adjusted for buyout, and across all sales. Beside it, a table of which view answers which question. **You get:** ads stop looking more profitable than they are because of cancelled and returned orders inside the calculation. ### WB DRR Benchmarks — Is My Ad Load Normal for My Goods Right Now? **Shows:** each product’s DRR compared with the norm for its category and stage — a new card or an established one. A separate check of the rule “ads must not cost more than the product’s profit”. **You get:** ads are dialed down where they eat more than they bring, and within the norm they are not cut for nothing. ### WB Auto Ads — Did the Campaign Start Working, or Should I Not Touch It Yet? **Shows:** the first days of auto ads — impressions, clicks, orders, and ad spend share by day, the learning window, and a verdict for every campaign: “don’t touch”, “keep”, “watch”, or “adjust”. **You get:** you don’t panic-disable a campaign on day three — and don’t overpay for it for months. ### WB Manual Campaigns — Keys, Bids, Minus Words **Shows:** the economics of every keyword — CTR, cost per click, conversion, and DRR, with a “little data” mark where it applies. A minus-words queue, a bid redistribution list, and a breakdown of query groups. **You get:** junk keywords stop burning the budget, and an underfed profitable keyword finally gets its bid. ### WB Auction Positions — Bids and Slots, Tested **Shows:** the “bid → position” curve built from your own measurements, the price of the next position for every campaign, observed competitor bids, and a card check before you raise bids. **You get:** bids go up where that moves the position — and you don’t chase a slot that costs three times more. ### WB Boost Incrementality — What the Boost Actually Added **Shows:** weeks without paid promotion (boost) compared with weeks with it — how many sales it added, and how many it simply took from organic ones, with an accuracy estimate. Verdicts: “scale”, “wait”, “turn off”. **You get:** paid impressions stay only where they bring new sales, not where they reshuffle sales you would have had anyway. ### WB Ad Metric Conflicts — the Monday Watchdog **Shows:** a weekly check of metric pairs — CTR rising while conversion falls, cost per impression rising at the same reach, and other mismatches. Noise is cut off, and every finding comes with an action. **You get:** an ad problem surfaces the same week, not a month later in a summary report. ### WB Realization Report, Decoded **Shows:** the platform’s weekly documents broken into readable lines — commission, logistics, return shipping, storage, acceptance, ads, acquiring, tax — and every amount re-derived from the rates of its own date. **You get:** you see not just the total payout but what it is made of — and one-off jumps between weeks stop looking like losses. ### WB Weekly Sales Dynamics — the Snapshot and Its Limits **Shows:** a weekly slice for every product — orders, buyout, returns, stock left, and a profit estimate. Periods are compared only over full weeks, and the difference between two exports shows what the platform has recalculated. **You get:** you see how the week is going and which export numbers will still change — without mixing up an estimate with the actual payout. ### Net Profit: From Accruals to Fact **Shows:** the path from revenue to the money that stays — eight platform deductions, purchase and packaging, fixed costs — with every step taken from its own source. A common mistake in the tax base is shown separately. **You get:** you see how much actually stays from every currency unit earned, and where the rest goes. ### Seller Tax Regimes Compared **Shows:** all tax regimes calculated on the same revenue and costs — NPD (the self-employed tax), USN “Income” and “Income minus Expenses” (the simplified regimes), AUSN (the automated version), and the general regime. Separately, the point after which switching regimes pays off. **You get:** you see how much tax you pay now and how much would remain under another regime — with the assumptions printed out for your accountant. ### Hidden Deductions: Acquiring and SPP **Shows:** what acquiring and the loyal-customer discount (SPP) quietly take — for every product, by how much they cut profit, and which verdicts change once you count them. **You get:** products that looked profitable only because the SPP discount was forgotten are finally counted honestly. ### Storage and Acceptance Control **Shows:** the monthly storage and acceptance invoice checked against your own calculation — storage days from stock balances, acceptance from supplies and tariffs, and the fine for exceeding the plan on a specific truck. **You get:** an overcharge for storage and acceptance is found at once, and products that sit too long go onto the sale list. ### The Seller’s Financial Mart — the Whole Money Picture on One Screen **Shows:** one summary of the whole money picture — revenue, every deduction, ads, buyout, returns, stockout risk, and tax — by week and by product, with a note on which template each metric comes from. **You get:** the whole shop on one screen, with an honest comparison of what the seller portal shows against what actually remains. ### WB Niche Entry Analysis — Is This Niche Worth Entering, and How Big a Bet **Shows:** a map of the niche before you put money in — how many sellers there are and how they split the market, the price corridor, revenue missed on competitors’ stockouts, and an entry verdict recalculated under different estimates. **You get:** the decision to enter a new niche rests on several estimates at once, not on a single number that can be wrong. ### Competitor Cards and Prices **Shows:** your cards compared with the field of competitors on price and listing quality, the entry barrier identified — and the competitors themselves dictate the order of fixes. **You get:** you see what is missing to stand next to the niche leaders, and in what order to do it. ### What to Sell — Category Trends **Shows:** which categories grow, where sellers crowd, and where prices leave room to earn — the two-year demand trend, competition, and the price band. Seasonal entry points are shown separately. **You get:** a new category is chosen by the numbers, not by a tip from a chat. ### WB Analytics Services Scorecard — Which Service for Which Task **Shows:** twelve analytics services compared by task, with a score and by plan price, and for every task a best pick plus a backup — under a budget you set with a slider in the report header. It also says plainly what you don’t need to pay for. **You get:** the service is chosen for a specific task and budget — and you don’t pay for your own numbers, which are already in the realization report. ### Own Data + Services: One Mart — Facts Beside Estimates **Shows:** your own facts from the seller portal standing next to an analytics service’s estimates on one screen, with every column saying outright whether it is a fact or an estimate. **You get:** you see where the service reads the market correctly and where it goes wrong — and estimates don’t get mixed up with your own numbers. ### Competitor Reviews: Weak Spots **Shows:** competitor reviews broken down by theme — size, delivery, quality, photos not matching — with parsing mistakes shown honestly. Each competitor’s main complaint is turned into a claim for your own card. **You get:** the niche leader’s weak spot becomes a ready-made argument in your own card. ### Competitor Bids and Auction **Shows:** competitor bid ranges for every query, joined with your own tested “bid → position” curve, a ladder of required bids, and a “don’t chase” rule for when the gap in weights is too big. **You get:** you see where a competitor’s bid is worth beating — and where it is cheaper and safer to improve the card itself. [← Templates library](/agents/analytics/docs/templates) · [Business group](/agents/analytics/docs/templates#wp-block-themeisle-blocks-advanced-heading-biznes)