Aurora Retail Analytics Demo
A live, end-to-end example of how transaction data can be turned into decision-ready views across forecasting & targets, inventory economics, promotion profitability, and customer & product growth.
This is not a template product. It’s a working example of what we can build—tailored to your KPI definitions, hierarchy, and data reality.
How to read this demo
- Each example below maps to a concrete decision and the dashboard view that supports it.
- Real client work is adapted to your definitions and your business structure (store/region/channel/category/etc.).
- A repeatable operating rhythm: observe → explain → act.
Off-track drivers: where are we behind — and why?
Business question: Are we on track vs targets, and what’s actually driving the gap?
- Turns “we’re behind plan” into a ranked list of the few drivers that matter most.
- Separates volume, pricing/discounting, and margin-rate effects so actions are obvious.
- Keeps the forecast inside the same view (with a scenario toggle) so planning isn’t divorced from reality.
Typical actions:
- Focus leadership attention on the top contributors to the gap (not everything at once).
- Fix availability where stockouts are suppressing demand and creating missed targets.
- Adjust promo plans where margin is the limiting factor.
What does “gap” mean here?
Inventory economics: stockouts and dead stock become measurable
Business question: How much demand are we losing to stockouts, where is it happening, and what’s the best next action?
- Reframes availability from “units on hand” into money at risk (lost revenue).
- Separates understock (lost demand) from overstock/dead stock (tied-up cash).
- Produces a prioritized action list: which products to replenish, transfer, or markdown first.
Under the hood: lost demand is estimated from recent sales patterns, so when stock runs out we can estimate what sales likely would have been.
What do “Cover days” and “Lost demand %” mean?
Lost demand % estimates the share of demand you couldn’t fulfill due to insufficient stock.
Promotions that pay back (measure & plan)
Business question: Did a campaign create incremental profit — and what should we run next?
Measure (avoid self-deception):
- Compares actual vs baseline to estimate what the promo truly added.
- Separates attributed sales from incremental margin.
- Explains why campaigns can “look great” on sales but fail on profitability.
Plan (turn insight into a shortlist):
- Learns from historical campaign performance to estimate expected uplift.
- Combines that with stock levels and an ROI hurdle to shortlist candidates.
- Suggests bundle ideas from “frequently bought with” patterns.
What do you mean by “baseline”, “incremental”, and “ROI”?
ROI is framed as “what we get back” for promotion cost (and can be aligned to your internal definition and hurdle rate).
Customer & growth levers (segments, value, retention, attach)
Business question: Which customer groups drive profit, who is drifting into dormancy, and where are the highest-leverage growth levers?
- Segment scorecards show size, value, margin, order frequency, and dormancy risk.
- Cohort retention reveals whether new customers are “sticking” over time.
- Attach-rate and service revenue show where margin can be compounded beyond product sales.
Typical actions:
- Target reactivation where churn-to-dormant rises.
- Run segment-specific offers (not “one campaign for everyone”).
- Improve service attach where it compounds gross profit.
How should I think about “LTV” here?
What data is needed to do this on real data?
The demo is synthetic; client work is built on your definitions and data reality. Here’s a practical checklist of what unlocks each area:
Minimum to get value fast
- Transactions (date, store/channel, product, quantity, price/discount)
- Product master (category/brand hierarchy — or your equivalent)
- Store list (and channel mapping)
- Costs (ideal; improves profit & promo analysis)
To unlock promo measurement & planning
- Campaign calendar (start/end, products/categories, mechanics)
- Promo identifiers on transactions (ideal, but not always necessary)
To unlock inventory economics
- On-hand inventory snapshots (or movements)
- Supplier lead times (optional, improves actionability)
To unlock customer & service levers
- Customer identifier (loyalty ID, hashed email, etc.)
- Returns/refunds mapping (for accurate margin & value)
- Service line items (warranty/insurance/delivery/installation), if applicable