Analytics that answers in five minutes

The agent gathers data from 1C, CRM, and marketplaces, answers ad-hoc questions, and builds dashboards and reports.

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Выручка · дашборд
обновляется сам

1C, CRM, and marketplaces — in one picture

All integrations
Яндекс Метрика
DataLens
Sheets-iconCreated with Sketch.Google Sheets
1С
Битрикс24
amoCRMamoCRM
Wildberries
Ozon
Яндекс Маркет
MPStatsMPStats

Ad-hoc questions in plain words

Ask in plain language — the agent pulls the data, runs the numbers, and explains the result, no SQL or analyst.

Возвраты по WB · май
с источниками

Возвраты +14% к апрелю, основной рост — размерная сетка по новинкам. Сверил с отраслевыми бенчмарками и справкой площадки.

Dashboards and reports

Builds summary dashboards for revenue, pipeline, and channels, refreshes them on schedule, and sends to the right people.

Дашборд по Метрике каждое утро
Аномалии трафика → алерт в Telegram
Недельный отчёт по каналам
ABC-анализ продаж по SKU

Data from every system

Brings 1C, Bitrix24, amoCRM, and marketplaces into one picture — no manual exports or stitching spreadsheets.

Insights, not just numbers

Spots anomalies and trends, explains the causes, and suggests a next step — a ready conclusion to act on.

Почему маржа по рознице упала с 14% до 11,8% за три недели?

Главная причина — рост закупочных цен по 6 SKU, плюс скидки. Логистика почти не повлияла. Предлагаю пересмотреть наценку — расчёт приложил.

Расчёт маржи · розница

Document · XLSX
Merge Wildberries and Ozon sales for [period] and compare with [the previous period / the same period last year]. For each marketplace, category and SKU show revenue, orders, average order value and returns. Break every decline down into factors: order count fell, selling price fell or returns grew — and show each factor's contribution in rubles. Don't explain a decline by "falling demand" or "seasonality" until you show a figure that confirms it. If an item didn't sell because it was out of stock, say exactly that — that isn't a demand decline. Output a SKU table sorted by lost revenue, and the ten items that account for half of the decline.
Build a revenue dashboard in Google Sheets by channel [Ozon / Wildberries / others — list them] and refresh it [every Monday morning]. Overwrite the "Data" sheet in full, don't touch the charts sheet — it references "Data". Don't wipe the history of previous weeks: each week is its own row. If a source didn't respond or returned an obviously incomplete period — don't substitute a zero and don't carry the previous week over. Leave the cell empty, put the week into a "Gaps" sheet with the reason and mention it in the update message. After every run send a short summary: what was refreshed, which channels didn't come through, and the revenue change versus the previous week in one line.
Analyze Wildberries returns for [period] and compare with [the previous period]. Calculate the return rate against orders for each SKU, category and warehouse. Break it down by the reasons from the marketplace report; keep defects, "wrong size" and "doesn't match the description" separate — those are different areas of responsibility. Don't explain growth by seasonality until you show the same happened last year. If there is no data for last year, say so. Single out SKUs where the return rate is more than one and a half times the category average, and estimate the losses including logistics. Output a SKU table and the five items to start with.
Merge sales from 1C, Bitrix24, Ozon and Wildberries for [period] into a single table. Match items by [SKU / barcode / the attached mapping reference], not by name. Bring units of measure and currency to a single form, and fix whether we count revenue with or without VAT. Remove duplicates: one shipment that landed both in 1C and in the CRM as a deal is one row. If an item didn't match, or matched only on a similar name — don't merge it by eye, move it to a "Not matched" sheet with the candidate options. Output an XLSX: a "Sales" sheet broken down as date — channel — unified SKU — quantity — amount, a "Not matched" sheet and a "Mapping reference" sheet that I can extend.
Calculate margin by SKU by week for [period] using Ozon and Wildberries sales and cost of goods from 1C. For each week break the selling price down into marketplace commission, logistics, discounts and promos, and cost of goods. Find SKUs whose margin deviated from their own average for the period by more than [threshold]. For every deviation name the factor with a figure: how many rubles came from the increased commission, how many from the discount, how many from purchase cost. Don't name a cause if the breakdown doesn't show it — write "factor not determined". Take cost of goods as of the shipment date. Where it's missing, don't substitute the category average, move the SKU into a "no cost of goods" list. Output a table by SKU and week and the top deviations in rubles.
Compare [month] with [the previous month] across Ozon, Wildberries and 1C sales. Show revenue, order count, average order value and returns — in total, by channel and by category. For each row give the change in rubles and in percent and state what produced it: more orders or a higher average order value. List separately everything that makes the months non-comparable: a different number of working days, marketplace promos, a large one-off order, unsettled returns. Show large one-off deals both in the total figure and as a separate "excluding one-offs" line. Count revenue by [shipment / payment] — the same way for both months. Output a comparison table and the five categories that drove most of the change.
Calculate the sales funnel from [Bitrix24 / amoCRM] data for [period] and compare with [the previous period]. For each stage: how many deals entered, how many moved on, the conversion into the next stage and the end-to-end conversion from the first stage, the mean and median time in status. Count from the transition history, not from the deal's current status. Show deals that skipped a stage on a separate line rather than spreading them across the adjacent ones. Don't give a list of specific deals or recommendations on them — only figures and the differences between periods. If a stage has fewer than [30] deals, mark its conversion as unreliable and don't draw conclusions from it. Output a table by stage with the two periods side by side and the three stages with the largest conversion drop.
Calculate ad cost share for every campaign for [period]: Yandex Direct and the ad dashboards of Ozon and Wildberries. For each campaign: spend, revenue by the dashboard's attribution, ad cost share, order count, average order value. Within a single platform use one attribution model and state which one. Don't add up revenue from different dashboards for the same order — that's double counting. If a campaign's revenue can't be tied to its spend, move it into "attribution not determined" rather than crediting it with the category's overall sales. Separate campaigns with an ad cost share above [target] from those with too little data for a conclusion: at spend below [threshold] or fewer than [20] orders, mark the row as unreliable. Output a table of campaigns in descending order of spend and a bottom line: how much budget went into campaigns with an ad cost share above target.

FAQ

Where data is stored and how access is protected — covered separately: security overview

No. Phrase the question in plain words — the agent finds the right tables, pulls the data, runs the numbers, and explains the result. No SQL or in-house analyst required.

1C, Bitrix24, amoCRM, Wildberries, Ozon, Yandex Market, email, and spreadsheets — via API. It brings everything into one picture without manual exports or stitching.

The agent works from data in your own systems and shows the source of every figure. Calculations are reproducible — you can open up how a result was reached and verify it.

On the schedule you set: hourly, every morning, or before a meeting. The agent rebuilds the data and sends a fresh report to the right people on its own.

Ready-made use cases

All use cases
See where sales dropped on WB and Ozon

The month closed below plan and the reason won't surface: fewer orders, or lower prices? Ozon and Wildberries get read separately, factor by factor.

A revenue-by-channel dashboard refreshed on schedule

Every Monday someone rebuilds the same report by hand, and it still misses the meeting. Here it rebuilds itself in Google Sheets, on time.

Break down rising Wildberries returns by reason

Returns are visibly up and the dashboard reports won't say why. The rate gets counted by item and by reason, then converted into money.

Merge sales from 1C, CRM and marketplaces into one table

The same item is called three different things in 1C, the CRM and the marketplace. Sales land in one table, with the leftovers listed openly.

Find margin anomalies over the last weeks

Revenue holds steady, yet less money is left at the end. Margin — what a sale leaves after costs — gets counted per item, week by week.

Compare this month with last by revenue and orders

The owner asks one question: better or worse, and by how much. Both months are counted by a single rule, with the caveats spelled out.

Build the funnel from CRM and find where deals leak

Plenty of leads come in, few reach payment, and nobody can point to the exact stage. Stage-to-stage flow gets counted and compared with last period.

Calculate ad spend share for each campaign

Budget sits in Yandex Direct and two marketplace dashboards, and payback is estimated by eye. Spend as a share of revenue is computed per campaign.

Build a dashboard in Excel or Google Sheets

The file falls apart after every fresh export: ranges shift, slicers come loose. This one is built to survive the next paste-in and the one after.

Guides on this topic

Answers from your data — no analyst queue

Connect your sources in minutes and ask your data in plain words — the agent pulls it, runs the numbers, and explains.

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