Turn your sales exports into decision-ready views of product, SKU, channel, retailer/customer, region and time performance—without treating shipments, sell-through, promotions or margin as the same measure.
Global service • Focused plan from $49 • Standard turnaround starts at 3–5 working days for a prepared single-source dataset
Start with a narrow prepared-data snapshot, move to multi-view analysis, or scope a connected analytics build when several systems, refresh cycles or BI deployment requirements are involved.
A focused decision from one prepared sales export
Brands that need several commercial views from prepared sources
Complex, recurring or multi-system reporting requirements
Describe the sales files you have, the commercial question you need answered and whether the data represents shipments, distributor/retailer sales, POS, ecommerce or another sales view. Rudrriv can confirm the appropriate scope before work begins.
A generic revenue chart can hide the difference between shipments into a channel and products actually selling through, or mix together SKU, promotion, customer and geography structures that need different treatment.
A brand may sell directly online, ship to distributors, supply retail chains, work through marketplaces, or use several routes at once. Each source can have different product codes, customer naming, periods, currencies and definitions. The analysis starts by identifying what each dataset represents before combining it.
Sales leaders, category or commercial teams, founders, business heads, ecommerce managers, distributor-management teams and finance partners may use different cuts of the same underlying data.
Month/quarter reviews, unexplained revenue movement, SKU rationalisation, retailer negotiations, promotion reviews, channel expansion, new product launches or a reporting process that has outgrown spreadsheets.
If the real requirement is data warehouse design, automated integrations, forecasting models, CRM implementation or a full BI environment, a broader data engineering or reporting-dashboard scope may be more appropriate than a standalone analysis.
The useful output depends on whether the supplied files can be aligned across product, customer, channel, geography and time. These are common consumer-goods data categories—not a claim that every project needs every source.
Useful for shipments, invoices, order values and customer/account reporting.
Can support downstream account, store or sell-through views when definitions are clear.
Useful for direct or marketplace channel analysis when order and product identifiers are consistent.
Product, customer, promotion and cost masters improve grouping and interpretation.
A useful consumer-goods analysis connects the right metric to the right level of detail. The final views depend on the fields, definitions and business question agreed for the project.
Understand contribution, mix, growth, decline and concentration across products.
Compare where revenue or volume is coming from and how channel mix is changing.
Review regions, territories, seasonality and period-over-period movement.
Compare discount and promotion periods when the data supports a defensible comparison.
Separate gross activity from returns or adjustments where those fields are available.
Add commercial profitability views only when reliable cost or margin data is supplied.
These are practical situations the service can support when the required data is available. They are not fabricated client case studies or guaranteed outcomes.
Sales are changing but leadership needs to know whether the movement is concentrated in a few products or spread across the range.
Analysis focus: SKU contribution, growth/decline, mix and concentration.Account teams need a consistent view of performance across customers, territories or distributors before a review meeting.
Analysis focus: account contribution, period trend, product mix and regional breakdown.D2C, marketplace or retail sales are changing at different rates and the business needs to understand the mix rather than only total growth.
Analysis focus: channel share, growth, SKU mix and value/volume movement.A promotion generated activity, but the team needs to compare the period, products and customer segments involved before drawing conclusions.
Analysis focus: sales/units during promotion, comparison periods and data gaps.National totals hide differences between territories, stores, regions or markets and resource allocation needs better visibility.
Analysis focus: regional contribution, trend, product mix and account concentration.Monthly reporting is manual, inconsistent or overloaded with charts and needs a clearer KPI structure before automation is considered.
Analysis focus: KPI definitions, hierarchy, reusable views and handoff requirements.Good analytics depends on clear division of responsibility: you provide accurate business context and usable data; Rudrriv performs the agreed analysis; the deliverables reflect only the questions and fields supported by that scope.
The files, definitions and business context needed to interpret the sales correctly.
The agreed preparation, metric logic, analysis, visualisation and review work.
Decision-ready outputs that match the agreed engagement rather than a generic dashboard bundle.
These items are normally kept outside a focused Sales Analytics engagement unless explicitly added.
These requirements can materially change effort and are confirmed before work begins.
The close-out is designed so the customer can understand what was calculated and what remains dependent on source systems.
The sequence keeps metric definitions, data readiness and review ahead of final presentation so the output is easier to trace back to the supplied source.
Confirm the consumer-goods decision, period, audience and expected output.
Check fields, source meaning, hierarchy, completeness and obvious quality issues.
Define sales, units, margin, returns, promotion and comparison rules in scope.
Build product, channel, customer, geography and time views appropriate to the question.
Reconcile totals, check filters/calculations and record assumptions or data gaps.
Provide the agreed files, incorporate in-scope corrections and close with clear caveats.
Quality review focuses on traceability, calculation consistency and obvious source exceptions. It does not convert incomplete commercial data into information the source never contained.
The exact checks depend on the plan and source structure.
A small clean export can be analysed quickly; a project with mixed systems, unclear definitions or repeated stakeholder changes needs more time.
Answers focus on the data, scope and delivery decisions that commonly affect this type of engagement.
It means turning sales records into structured views of what is selling, where, through which channel, at what value or volume, and over what period. For consumer goods, the analysis often needs product/SKU, retailer or customer, channel, region, promotion and time dimensions rather than only a company-wide revenue total.
A useful starting point is a clean sales export with transaction or aggregated-period fields such as date, product or SKU, quantity, sales value, customer/retailer, channel and geography. Product master, cost, returns, promotion and inventory fields are useful when those topics are in scope.
Potentially, but only when the sources can be reconciled through usable product, customer, channel and time keys. Multi-source consolidation is normally custom scope because sell-in, sell-through and marketplace data can use different definitions and reporting periods.
Yes when the supplied data makes that distinction possible. Shipments to a distributor or retailer should not automatically be treated as consumer sell-through. The metric definitions are agreed before analysis so unlike measures are not blended incorrectly.
Depending on the data supplied, common measures can include gross or net sales, units, average selling price, growth versus prior period, product mix, channel mix, retailer/customer contribution, returns, gross margin, promotion performance and sell-through. Metrics that require missing fields are excluded or flagged as data gaps.
Yes when those fields exist in the product master or sales data. We can structure views at the level that supports the decision, while checking that product hierarchies and naming conventions are consistent enough to aggregate correctly.
Yes when promotion dates, discount or campaign identifiers and comparable sales data are available. The analysis can compare promoted and non-promoted periods or segments, but causal or incremental-lift conclusions require suitable data and may need a larger analytical scope.
Yes if reliable cost or margin fields are supplied and the calculation basis is agreed. Revenue-only data cannot support a dependable gross-margin analysis, so missing cost information is treated as a scope dependency rather than estimated without approval.
The first step is a data-readiness review. Duplicate records, inconsistent SKU names, missing dates, mixed currencies, incomplete channel labels or unexplained totals can change scope. Light cleaning may fit a standard plan; extensive reconstruction, mapping or historical reconciliation requires custom scope.
The Sales Snapshot and Multi-View plans can include a dashboard-style workbook or report. A deployed Power BI, Tableau or similar BI environment is treated as a separate implementation requirement because licensing, workspace access, refresh, security and data connections must be agreed.
A focused single-source snapshot is typically planned for 3–5 working days, while a multi-view analysis is typically 5–7 working days. Larger multi-system work usually starts at 7–15+ working days and is confirmed after reviewing data readiness and required integrations.
The main drivers are the number and condition of data sources, SKU/customer hierarchy complexity, reconciliation effort, custom KPI logic, currencies, promotion or margin analysis, BI implementation, refresh requirements, stakeholder review cycles and urgent deadlines.
The starter price does not include ERP or marketplace API integration, ongoing refresh automation, enterprise BI deployment, complex forecasting, causal promotion modelling, data warehouse engineering, custom software, or rebuilding large unstructured datasets. These can be discussed as custom scope when relevant.
Revisions are used to correct agreed logic, labels, filters or presentation based on consolidated feedback. A materially different data source, KPI definition, reporting structure or business question is treated as a scope change rather than an ordinary revision.
Only data needed for the agreed analysis should be shared. Credentials and unnecessary personal information should not be placed in the enquiry form. Where account access or sensitive commercial files are required, the access method and handling expectations should be agreed before work begins.
Rudrriv reviews the requirement and consumer-goods context, may request clarification or sample data, and then confirms scope, pricing and delivery expectations before the engagement proceeds.
You do not need to upload confidential files here. Describe the business question, the type of sales data you have, important dimensions such as SKU/channel/region, and any fixed review date.
