What data does ClearDemand need to get started?

Your product hierarchy, pricing, and sales history, plus the competitors and markets you want tracked. We handle competitive data collection ourselves. Real retail data is always a little messy, and the platform is built for that rather than for a perfect dataset. Most implementations start with competitive intelligence, which asks the least of your team.

How does competitive intelligence connect to the pricing engine?

Directly, with no export step in between. ClearDemand collects more than 7 million competitive prices a day across a 240+ retailer network, matches them to your items with machine learning built for fresh, private label, and pack size differences, then feeds the result into the price recommendation itself. Competitive data that arrives as a separate report never reaches the decision.

Can price and promotion run on the same model?

Yes, and that is the reason to run them together. Shoppers experience everyday price and promotions as one price story, so the decisions behind them should share a demand model. McKinsey’s 2026 grocery research found roughly 71% of shoppers prefer lower, steady everyday prices over frequent promotions on higher base prices, which is a hard tradeoff to manage in two separate tools.

Does ClearDemand change prices automatically?

No. A person approves every price. The platform evaluates the full assortment, applies your rules, and surfaces only the decisions that need judgment, ranked by P&L impact. Your team accepts, edits, or overrides, and every override is captured with its reasoning and fed back to the model. Every recommendation, override, and outcome is logged and auditable.

How does ClearDemand decide which prices to surface for review?

By exception. The platform evaluates the full assortment every cycle, applies your rules, and ranks whatever is left by P&L impact, so a lean team works the decisions that matter instead of the whole list. Competitor moves and cost letter changes trigger updates between cycles, and your KVIs refresh most often. You set the alert criteria.

How do you prove the platform created value?

By backcasting. Most platforms stop at forecasting and tell you what might happen. ClearDemand measures what actually happened after the price hit the shelf and scores it against what would have happened without the change, at the decision level rather than the program level. Forecast accuracy is tracked at item, store, and week, so the model retrains on actuals.

Can it handle fresh and private label, or only packaged goods?

Both. Fresh, produce, deli, bakery, and meat are matched with pack sizes and units equalized, so comparisons are true price per unit. Private label gaps against the national brand equivalent are held automatically. FMI’s Power of Private Brands 2026 found 92% of US shoppers keep private brands at home and private brand dollar sales grew 2.8% year over year, outpacing national brands, which makes that gap a margin decision rather than an afterthought.

How mature does our pricing operation need to be?

Less mature than most teams assume. McKinsey's 2026 grocery research found pricing and promotions are among the least AI-invested functions in grocery, and that investment scales with retailer size. IDC’s 2025 Global Retail Survey of 980 retailers found 56% expect to increase price optimization technology spending within 12 months. Most customers start with competitive intelligence and add optimization as the data proves itself.

PLATFORM OVERVIEW

One system behind every pricing decision

Q: How does price optimization software actually work?

It runs in four steps. Competitive prices are collected and matched to your catalog, a demand model solves base price and promotions together against your rules, approved decisions push to your POS and shelf labels, and each one is measured against what would have happened without it. IDC has observed price optimization driving 1 to 3% profitability improvement in the first year, with promotions management alone contributing 5 to 6%.

ClearDemand runs competitive intelligence, price optimization, and promotion optimization on one demand model, purpose-built for grocery and convenience retail.

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ClearDemand price recommendation showing competitor, cost, elasticity, and margin inputs.

From competitive signal to shelf price, in four steps

Every recommendation moves through the same four steps. Competitive data is collected and matched to your catalog, price and promotion are solved on one demand model, approved decisions push to your POS and shelf labels, and each one is measured against what would have happened without it.

Analyze

See the whole market, matched to your catalog

We collect competitor prices, promotions, and assortment, then match them to your items so the data is usable.

Data collection

Patented collection across competitor sites, apps, and APIs at store level, with anomaly detection and quality audits.

Product matching

Machine learning matches fresh, private label, and national brands, with pack sizes equalized to true price per unit.

Price comparison

Weighted CPI by banner, store, market, and zone, so you see your price image on the items that move your business.

OPTIMIZE

Know what happens before you change the price

Base price and promotions solve on one model, and every recommendation arrives with its unit, margin, and basket forecast.

Pricing rules

Hard rules never break. Soft rules flex the way you set them. Price families, zones, and ending digits, encoded once.

Promotions

Offers modeled on historical response, cannibalization, and affinity, so you fund the events that earn their keep.

Gap analysis

Your catalog against each competitor’s full book, by category and market, so you see where they beat you on coverage.

EXECUTE

From approval to shelf tag

Review, adjust, and approve in bulk, then push to POS, shelf labels, ecommerce, and mobile on the schedule you set.

Pricing approvals

Manage by exception. The platform evaluates every item and surfaces only what needs judgment, ranked by P&L impact.

Downstream integration

Approved prices push to POS, shelf labels, ecommerce, and mobile, with live connections to NCR HQ, PDI, SAP HANA, and NetSuite.

Assisted onboarding

We build and carry the integrations, from POS and ERP connection through go live.

Measure

Every decision, scored against no change

We forecast before the change and measure after it. Every decision is scored against what would have happened without it.

Value measurement and ROI

Every recommendation scored against what would have happened without it, at the decision level, not the program level.

Forecast accuracy

Tracked at item, store, and week, so the model retrains on actuals and the next cycle is sharper.

Leadership scorecards

Value rolled up by category, zone, and strategy, so the margin story is visible at the level the board reads.

competitive catalog matching view, competitor source to unified catalog.

price optimization engine with pricing rules and forecast.

approved price batch pushing to POS, shelf labels, ecommerce, and mobile.

pricing performance scorecard with gross profit, promotion ROI, and store compliance.

ADOPTION

What happens when a merchant asks why the price moved?

Most pricing AI is a black box. MIT found 95% of enterprise AI pilots produce no measurable P&L impact. Unexplained recommendations get overridden.

Every input behind the price, on one screen

The competitor that moved and by how much, the cost change, the elasticity headroom, the margin floor that held, and the item's price-image status.

The effect on the basket, before you approve

Units and margin for the item, plus what it does to the items around it. Cannibalization on the 24oz, halo on the decaf. Your team sees the basket, not just the SKU, before anything is approved.

Overrides make the model better

Some overrides are healthy, because merchants know the business. Each is captured with its reasoning and fed back, so the model learns your judgment.

See all resources

Four modules, one demand model

Hold price image, find the margin

Everyday price solved item by item and zone by zone, with your rules applied and the KVIs shoppers watch held sharp.

Explore Price Optimization

Fund the offers that earn their keep

Model deal mechanics, depth, and timing against the base price behind them, and see the basket impact before the ad drops.

Explore Promotion Optimization

The market, matched to your catalog

7M+ competitive prices a day across a 240+ retailer network, matched to your items and fed straight into the price recommendation.

Explore Competitive Intelligence

Clear inventory without giving away margin

Markdown timing and depth priced on demand response and inventory position rather than a blanket percentage off. Shipping Q4 2026.

Explore Markdown Optimization

INTEGRATIONS

Built to work with the systems you already use

We build and carry the integrations, from POS and ERP connection through go live. Live connections to NCR HQ, PDI, SAP HANA, and Oracle NetSuite, with delivery over SFTP, S3, Snowflake, and BigQuery, downstream to stores and shelf labels.

SOC 2 compliant and cloud native, with single sign on and role-based access control. We configure to your data model rather than asking you to conform to ours.

Delivery runs over SFTP or into your cloud warehouse today, with a customer-facing pull API on the roadmap.

Real results from our customers

How One Grocery Retailer Used Price Optimization to Boost Profit +2.78%

A well-known regional grocer measured every price change against a backcast of doing nothing, then reinvested where the basket had room. Frozen led at 4.81%.

Read the full case study

+2.78%

profit across key departments

+0.9%

total revenue growth

+4.81%

profit lift in frozen