Interactive examples
Explore the kind of thinking you would receive.
Each example starts from a business question, names who would use it, and ends with the decision it supports. All data is illustrative and labelled as such; retailer and store names are placeholders. No email required to explore.
Want one of these on your own data? Bring one retailer's export to a discovery call. Unsure of a term? See Retail math, plainly.
01 · Multi-retailer performance
Which retailer is really growing, and is it distribution or velocity?
- Business question
- Total units are up. Which accounts are driving it, and is the growth from more stores or more sales per store?
- Who uses it
- Owners and sales leaders before a quarterly review.
- How to explore
- Switch between units, stores selling and units per store. Show or hide retailers. Hover or tap for weekly values.
- Decision it supports
- Where to invest next quarter's trade spend and sales time, and which growth is durable.
Retailer C's units are up 14% on the quarter, but stores selling are flat: that growth is velocity. Retailer A's growth is almost all new distribution.
Index · base week 1 · illustrative supplier POS · dashed line = week 1 level
02 · SKU and store productivity
Which items earn more distribution, and which are taking up shelf they do not repay?
- Business question
- Across the range, where is velocity high but distribution thin, and where is the reverse true?
- Who uses it
- Account managers preparing a range review; commercial leads planning the next line review.
- How to explore
- Use the buttons to highlight the fast sellers with thin distribution, or the broadly stocked slow movers. Hover any point for the SKU.
- Decision it supports
- Which SKUs to put forward for expanded distribution, and which to defend, reposition or delist before the merchant does it for you.
Seven SKUs sell more than 2.6 units per store per week but are stocked in under 40% of stores. Each point of distribution on these is worth more than on anything else in the range.
Each point is one SKU · illustrative POS and store counts
03 · Inventory and availability
Where are we out of stock, for how long, and what is it costing?
- Business question
- Which stores and weeks had poor availability on a core SKU, and does the pattern point to our supply or the retailer's replenishment?
- Who uses it
- Operations and supply teams weekly; account managers before raising replenishment with the retailer.
- How to explore
- Darker cells mean fewer days in stock. Hover or tap a cell for the value. The outlined block is the pattern worth a phone call.
- Decision it supports
- Whether to escalate a replenishment issue with specifics, adjust your own shipments, or investigate phantom inventory in particular stores.
Seven stores in one region lost availability together in weeks 5–7. A cluster like this points to a replenishment or distribution-centre issue, not to seven coincidences. Weekly sales still showed units, which hid it from the sales report.
Sequential scale · one hue · 5 steps · illustrative store inventory
04 · Promotional performance
Which promotions were worth running, and which just moved volume forward at a loss?
- Business question
- For each promotion this year: how much did units lift, what happened in the weeks after, and what did it do to margin?
- Who uses it
- Commercial and trade marketing leads planning next year's calendar with the retailer.
- How to explore
- Each group shows weekly units before, during and after a promotion. Hover a bar for values and the margin effect.
- Decision it supports
- Which mechanics to repeat, which to renegotiate, and which to decline when the merchant asks.
The May BOGO produced the biggest week and the worst outcome: units fell 13% below baseline for a month after and margin dropped 21%. The March end-cap display lifted less but held a higher baseline afterwards and improved margin. Lift alone is the wrong scorecard.
Units per week · margin shown vs baseline weeks · illustrative
05 · Reporting automation
What does a monthly retailer scorecard look like before and after automation?
- Business question
- Where do the hours go in producing a recurring report, and which of those steps follow a pattern a system can run?
- Who uses it
- Commercial managers and analysts who own recurring reporting; owners deciding where AI pays back first.
- How to explore
- Compare the two columns. Steps marked in slate are now automated; the remaining time is judgment.
- Decision it supports
- Which reporting to automate first, and how much of your team's week it returns.
Before
- 1Download six portal files0.5 h
- 2Reconcile two files with mismatched SKU codes1.5 h
- 3Update pivot tables and charts2.0 h
- 4Find and check exceptions by eye1.5 h
- 5Write commentary1.5 h
- 6Format, circulate, answer questions1.0 h
Total8.0 h
After
- 1Files load on schedule; missing files flaggedautomated
- 2SKU codes mapped once in the data modelautomated
- 3Dashboard refreshesautomated
- 4Exceptions flagged against agreed thresholdsautomated
- 5Review drafted commentary; write the judgment paragraphs0.75 h
- 6Send; questions answered from the dashboard0.25 h
Total1.0 h
Seven hours a month returned on one report, and the scorecard goes out a week earlier. The person is still accountable for every number; the system does the assembly. Figures are illustrative of a typical workflow, not a measured client result.
06 · Embedded dashboard
A full interactive dashboard, embedded from a BI platform.
- Business question
- This slot is prepared for a published dashboard (for example a Tableau Public or Power BI view) once one is approved for the site.
- Who uses it
- Visitors who want to see a complete, filterable dashboard rather than a single chart.
- How to explore
- Use the dashboard's own filters. Expand for a larger view, or open it in a new tab. On a phone, read the summary and open the full version on a larger screen.
- Decision it supports
- Depends on the dashboard published here; the introduction will be updated with it.