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Retail & CPG

One sales picture from every retailer, market and Nielsen file

CPG brands buy their sales data from Nielsen, receive it from each retailer and collect it from every market, and no two sources name a product the same way. Mammoth matches it once, keeps it matched, and feeds the dashboard or Power BI report your team already reads.
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  • Nielsen and retailer files
  • Excel from every market
  • Distributor feeds
One pipeline
  • Connect
  • Clean
  • Automate
  • Share

Recorded once, re-run on a schedule.

  • One harmonised sales dataset
  • Power BI, BigQuery or a Mammoth dashboard

Retail & CPG teams running this on Mammoth

  • Starbucks logo
  • Arla Foods logo
  • Bacardí logo
  • Golden Acre Foods logo
  • BAT logo
The actual problem

Each retailer names your products its own way, and Nielsen names them another. Each market sends its file in its own layout and currency. So before anyone can say why a segment dropped, someone spends days lining up SKUs by hand. Next month the new file lands and they do it again.

The work

Four jobs CPG data teams do by hand, and the brands that stopped

Each row is one brand's story. The panels beside them use made-up rows to show the shape of the work.

Product matching

Match every retailer's product names to one SKU

Golden Acre Foods matched its SKUs against Nielsen by hand, across four major retailers, for hours every week. The mapping now runs on barcodes, on a schedule the commercial team owns.

  • Match on the barcode, not the spelling
  • New lines that match nothing are listed for someone to map
  • The mapping is kept, so next month's file needs no rework
Golden Acre Foods logoRead how Golden Acre Foods did it
Match productsIllustrative
  • Retailer AOAT LATTE RTD 250ML
  • Retailer BOat Latte Can 250ml
  • Retailer CLATTE OAT 25CL
  • NielsenOAT LATTE READY TO DRINK 0.25L
Barcode5012345678900Oat Latte 250ml

Four spellings, one product. New lines that match nothing are listed for someone to map.

Markets

Put every market's file into one format

Arla Foods received sales data from 17 countries in different formats, over a billion rows a month, and hand-processed it into static spreadsheets that took up to 20 days. Every region now feeds one automated workflow.

  • Each market's layout is mapped once and reused
  • No IT hours to produce or maintain the reports
  • Regional teams read their own dashboards, with no SQL
Arla Foods logoRead how Arla Foods did it
Combine marketsIllustrative
MarketArrives asIn
GermanyDE_sales_wk41.xlsxEUR
TurkeyTR-Satis-41.csvTRY
SwitzerlandCH sales 41.xlsxCHF
United Kingdomuk_wk41_final_v2.csvGBP
One sales tableOne currency · one hierarchy
Channels

See on-trade and off-trade side by side

Bacardí's on-trade and off-trade figures arrived as Excel workbooks from more than 170 markets, so comparing the two halves of the business meant reading across pages. One pipeline now puts them in a single view.

  • One currency and one product hierarchy in every market
  • Commercial teams open dashboards without an IT ticket
  • Reporting cycles down from days of manual work to minutes
Bacardí logoRead how Bacardí did it
Join channelsIllustrative
CasesOn-tradeOff-tradeTotal
Brand A41,200118,600159,800
Brand B12,90064,30077,200
Brand C27,4009,80037,200

Brand C sells mostly in bars. Two separate reports never put that side by side.

Refresh

Keep it current without anyone opening a file

BAT collected competitor listings from six or more retailer and distributor feeds by hand, so a price change took days to log. Automated pipelines now check every source daily.

  • Feeds collected on a schedule, not by hand
  • Checks run before anything is published
  • The analysts own the pipeline, with no engineers involved
BAT logoRead how BAT did it
RefreshIllustrative
  1. 1Nielsen weekly fileLanded 05:40
  2. 2Scheduled run06:00
  3. 3Checks passed0 unmatched
  4. 4Export to BigQuery06:07
  5. 5Category dashboard06:07

The dashboard updates when the run finishes. Nobody opens the file.

Customer stories

Results in Retail & CPG

Every figure here comes from the customer's published story.

Templates

Start from a dashboard that already works

Mammoth templates captured as they render, on sample data. Point one at your own data and it fills in.

  • Sell-Through by Category dashboard: Sell Through, Planned Sell Through, Weeks Of Cover and Net Sales tiles.
    Sample data: illustrative figures, not business results.

    Sell-through dashboard

    Is the season selling through at the rate the buy was planned on?

    See it full size →
  • Promotion & Discount Effectiveness dashboard: Uplift Units, Markdown, Gross margin and Net Sales tiles.
    Sample data: illustrative figures, not business results.

    Retail promotion dashboard

    Did the promotion make money, or just move it?

    See it full size →

Before you ask

The questions this industry always raises

Does it work with Nielsen data?
Yes. Starbucks and Golden Acre Foods both run Nielsen data through Mammoth. It comes in as the files you already receive. The matching and cleaning is recorded once and runs again on each new file.
Will it handle our volume?
Starbucks and Arla each put over a billion rows a month through Mammoth, across 17 countries.
Do we have to give up Power BI?
No. Starbucks' harmonised data lands in Power BI through BigQuery, and Arla feeds Power BI too. If you have no BI tool, Mammoth builds the dashboard itself.
How current will the numbers be?
As current as the files behind them. Nielsen and retailer data tend to arrive weekly or monthly, and a scheduled run picks each delivery up. Scheduled refresh is weekly on Starter, daily on Team and every 3 hours on Pro.
Does every sales and category manager need a paid seat?
No. Viewers are free and unlimited on every plan. At Bacardí, global adoption grew 60% once commercial teams could open dashboards without asking IT.

See it on your own data.

Start free and connect one source, or book a demo if you'd rather we scoped it with you first.

Try it freeBook a demo
  • ✓ 14-day Pro trial · No credit card

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