Retail & E-commerce · Dynamic pricing
Banao builds demand-aware pricing engines that watch competitor moves, demand signals, and price elasticity per SKU — and surface a recommendation your category managers can act on or override in one click.
The goal is margin retention and revenue capture on fast-moving lines, not a black box that reprices without context. Every recommendation carries a reason, and your team stays in control.
Myntra— engineering pods on a fashion marketplace where pricing, discovery, and promotions run at scale.
A pricing model that nobody trusts gets overridden into irrelevance. We build recommendation systems your category teams will actually use — because they understand the output and can push back on it.
Automated tracking of competitor prices, promotions, and stock status across your category — cleaned, normalised, and fed into the pricing model so decisions stop being made on 48-hour-old data.
Price-elasticity estimates at the SKU or category level, updated on your actual demand signal rather than rule-of-thumb brackets. The model identifies where a small price move gains margin and where it loses volume.
A recommendation layer your category managers open each morning — price-up and price-down signals, the reason behind each, and a one-click override with a reason log. Confidence scores surface when the model is uncertain.
Clearance and promotional pricing modelled against sell-through rate, margin floor, and seasonal deadline — so markdowns move inventory without liquidating it at a loss before you needed to.
Pricing policy encoded as hard constraints — MAP agreements, margin floors, brand-price positioning — so the model never surfaces a recommendation that violates a business rule, even during a midnight flash sale.
Every price change and every recommendation is logged with its inputs. When finance asks why a category dropped margin last week, the answer is retrievable in minutes, not a post-mortem investigation.
Metrics shown dotted (··) are being finalised in our case-study metrics pack — published only once verified.
Myntra
Banao engineers worked inside pods on one of India's largest fashion platforms, on systems where pricing decisions run across hundreds of thousands of SKUs and promotions fire against tight margin targets. The problem is not speed alone — it is pricing at volume where a model error compounds fast.
Banao runs a ~300-person engineering company on its own AI products. Vikaas, our demand-generation AI, handles how we position and price our own service offerings — the same pricing-signal and demand-tracking logic we put in front of retail clients.
A pricing model that has to survive a real business decision — not a demo environment — is the standard we hold our own tools to. That is the version that reaches your category dashboard.
Pricing AI sold as a universal answer is how category managers end up overriding everything on day three. We will tell you before you build:
You have seen pricing tools that promised margin lift and got overridden by week two. We start by showing where the model would have moved margin on your actual historical data.
We ingest a sample of your pricing history, competitor data, and demand signal, run elasticity estimates on your real SKUs, and hand back a margin-opportunity map — yours to keep whether or not you proceed. If you go forward, the Sprint cost is credited against the build.
Competitor ingestion, elasticity modelling, recommendation engine, and the category-manager dashboard — wired to your catalog, ERP, and OMS. Policy guardrails and override logging are part of the deliverable, not a later addition.
We measure recommendation acceptance rate and actual margin movement per category, retrain on the pricing decisions your team makes, and tune the model to your business rhythms — peak, sale, and off-season.
How is this different from a rule-based repricing tool?
Rule-based tools move prices on a fixed formula — if a competitor drops, match or beat by X%. A demand-aware model also weighs your own elasticity, stock position, and seasonality, so it identifies when holding price protects margin better than matching. The output is a recommendation with a reason, not an automatic move.
Won't dynamic pricing frustrate customers who see prices change?
The risk is real on consumer-facing price drops that reverse quickly — the kind that erode trust. We build in change-frequency controls and category-level rules that prevent the patterns shoppers notice and resent. Category managers review and approve changes on high-visibility lines before they go live.
What data do we need to get started?
At minimum: 12 months of transaction data by SKU, your current pricing rules or sheets, and access to catalog and stock data. Competitor data we can ingest for you. The Discovery Sprint tells you exactly what is missing and whether it blocks the first use case.
Can our category managers still override the model?
Override is a first-class feature, not a fallback. The recommendation dashboard is built for the team to accept, reject, or adjust each recommendation with a reason logged. Those overrides feed the model so it learns your team's business logic over time instead of conflicting with it.
How long before we see margin impact?
A typical path is a 2-week Discovery Sprint, then 6–8 weeks to a v1 recommendation engine in a single category, then weekly iteration as coverage expands. Banao's ~300-engineer bench means the build starts quickly — and we measure margin movement per category from week one of production.
Bring 12 months of pricing history and your top three categories. In 45 minutes we will show you where demand-aware pricing would have moved margin — and what it would take to get there.
Book a 45-min scoping call
