BevGenie replaces the monthly reconciliation ritual with automated data harmonization. Depletions, retail scan data, distributor scorecards, and internal ERP records are matched, aligned, and mastered into one commercial view, so the insights team spends its month answering why the number moved rather than proving which number is right.
Because every source names things differently and counts on a different calendar. One distributor's file calls an account "Joe's Wine & Spirits", the next calls it "JOES WINE SPIRITS LLC", the scan data uses a chain identifier, and the ERP uses an internal code. Matching those by hand, month after month, is the work that eats the day.
The cost is not really the day. It is that the analyst who spends it is the person best placed to explain the miss, and by the time the reconciliation finishes the period has closed. The organization ends up paying an expert to do clerical work, then asking them for a diagnosis with no time left to produce one.
Entity naming differs across every distributor, retailer, and internal system, and no two agree.
Entity resolution matches accounts, products, and territories across sources automatically, and keeps matching as new files arrive.
Shipment weeks, depletion months, and retail periods each close on different days, so nothing sums.
Timelines are aligned during mastering, so a comparison across sources is valid without a manual bridge each period.
The person who could explain the miss spends the period assembling the file that shows it.
Harmonization is automated, so the insights team moves from preparing data to directing the commercial decision it supports.
They agree once the entities behind them are resolved to the same identity and their timelines are aligned to the same periods. Until that happens the three sources are not in conflict, they are simply describing different universes, and no amount of dashboard work reconciles them. This is a data structure problem rather than a data quality problem.
Regulatory filings, distributor sales and depletion signals, retail and on-premise point of sale, and your own shipment and field records land in one foundation.
Accounts, products, and territories are matched to a single identity across every source, including the variants no lookup table ever catches.
Periods and hierarchies are reconciled so a comparison across sources means something without a manual bridge.
Questions are asked in plain language against the mastered view, and the answer arrives with its sources attached.
Only if the diagnosis does not depend on a reconciliation that finishes after the period does. When harmonization runs continuously, the question can be asked on day nine instead of day thirty-four, which is the difference between a correction and a post-mortem. Being right but late costs the same as being wrong.
The recurring pack assembles from the mastered view rather than from a chain of exports and pivots maintained by one person.
A variance table says the number moved. Attribution says whether pricing, execution, distribution, or demand moved it, and which team owns the fix.
Sales, finance, and leadership ask their own questions against the same foundation, so the insights team stops being the query queue.
Syndicated providers measure the market. BevGenie reasons over your position inside it. Those are different jobs, and most insights teams need both: a market-level read of the category, and a system that joins that read to your own shipment, depletion, and field data to say what you should do about it.
| Layer | What it is for | What it does not do |
|---|---|---|
| Market measurement Providers such as IWSR and NielsenIQ | Sizing categories and tracking how the market and competitive set move. | Join that market view to your own internal records, or name the account-level action. |
| Sales, depletion, and regulatory data Depletion reports, TTB COLA filings | Product movement and label-approval signal across the markets they cover. | Cover every market, or reconcile itself against your distributor and retail data. |
| BI and dashboards | Presenting a modelled dataset once someone has built the model and the view. | Resolve entities across sources, or explain why a number moved. |
| Data warehouse and ETL | Storing and moving data reliably at scale. | Carry beverage-specific knowledge, or produce a commercial recommendation. |
| BevGenie | Harmonizing all of the above with your own data and answering why the number moved and what to do. | Replace market measurement. BevGenie reads those sources rather than competing with them. |
A commercial review needs one measure per decision, not a pack of forty. Depletion velocity against a category baseline, distribution against target accounts, realized price against the strategic floor, and execution compliance on live programs cover most of what a supplier or distributor actually decides in a month.
Year-over-year comparison hides a category that moved. A baseline built from comparable markets does not.
Total account count rewards breadth. Coverage of the accounts that should carry the brand rewards the right breadth.
Average selling price blends a disciplined market with a leaking one. Floor compliance by market separates them.
Measure whether a program ran before concluding it failed. Most disappointing programs were partially executed.
The ones that come up in every technical evaluation, answered directly.
Security and data handlingEntity resolution is the process of recognizing that records from different systems refer to the same real thing: the same retail account, the same product, or the same territory, written four different ways. In beverage alcohol it is the precondition for every cross-source comparison, because distributor files, retail scan data, and internal systems each name accounts and SKUs on their own conventions.
No. BevGenie is a decision intelligence system, not a data warehouse, an ETL tool, or a reporting tool. Warehouses store and move data well. Dashboards present it well. Neither carries the beverage-specific knowledge needed to say whether a velocity dip is a pricing error or a competitor's line extension, which is the layer BevGenie adds.
Most teams keep both. Market measurement providers size the category and track the competitive set at market level. BevGenie reads sources like those alongside your own shipment, depletion, and field data and reasons over your position inside the market. The two answer different questions, and BevGenie is built to consume market data rather than replace it.
None to get a first answer. Mastering and entity resolution happen inside the ConsumerIQ platform, which is the point: asking a customer to clean distributor files before the system can read them would reproduce the problem the system exists to solve. A demo runs on your own categories and markets with no preparation from your team.
No. ConsumerIQ does not intentionally use customer content submitted to BevGenie to train public foundation models. Customers retain ownership of everything they upload, and customer content is logically isolated between organizations. The Responsible AI page and the trust FAQ cover this in full.
Yes, and that is the difference from a dashboard. A dashboard answers the questions someone modelled in advance, which is why a new question means a new build. BevGenie is asked in plain language against the mastered foundation, so the cost of asking a question drops and teams start asking questions shaped by the decision rather than by the tool.
Yes. A data engineer on our side brings your data in as a private data mart that only your organization can reach, so your team does not run the project. BevGenie reads the schema and catalogs the tables, columns, and relationships. Connections are read-only, with a statement timeout and a row cap on every query, and the credentials behind them are encrypted with AES-256-GCM before storage.
Yes, at three levels. Reporting standards such as fiscal year start, reporting currency, volume unit, default period, and home state are settings that apply to every calculation. A company knowledge base holds the business facts your team confirms and treats them as ground truth. Individual metric definitions carry across conversations for the person who set them. All three are visible on a settings page where an administrator can correct or remove any entry, and known measurement caveats travel with the data itself so a rule reaches the answer rather than sitting in a document.
Typed Excel or CSV for result sets, with real number types and a totals row, and several tables can land as separate sheets in one workbook. PDF for reports, conversations, single answers, and sell sheets. PowerPoint or PDF for slide decks. The generated documents carry your brand colors and logo. What BevGenie produces covers each output.
Bring the reconciliation that eats your close and see it run on your own sources.