If the last few business cycles have taught us anything, it has been the importance of proactively managing inventory levels against the impact of business cycle swings and shifts in consumer demand. Retailers take great care in selecting the right products for the right locations in the right quantities. But once products are on the shelves and selling starts, imbalances can appear. Inventory builds up where it’s not wanted and runs out where there is higher demand.

That’s when pricing goes to work. It’s the key lever for recalibrating inventory to demand once products have hit the shelves. Coordinated pricing action for in-season and clearance season sales individualized to match each product and location’s own demand and inventory drives better results: better sales, better margin, better sell-through.

Especially toward product end-of-life, inventory must be steered to align with coincident consumer demand. Too often, however, pricing decisions throughout the product lifecycle are ill-informed by historical data and/or simplistic rules-based practices. To use pricing as an effective tool to improve inventory management, retailers need a forward-looking tool that anticipates consumer in-season behavior as it’s playing out and one that provides relevant pricing recommendations at a location/product level. In this way, inventory can be proactively managed to minimize possible margin and revenue losses synergistically across the combination of both in-season and clearance.

What do antuit.ai’s Lifecycle Pricing and Markdown Optimization solutions encompass?

Antuit.ai’s Lifecycle Pricing solution is a simple-to-use price management tool advised by a highly sophisticated Demand Intelligence platform that understands the interplay of demand and pricing at a granular level throughout the multiple phases of a product’s lifecycle. At the outset of product selling, sales growth and margin are paramount. But as the lifecycle nears end of season, it gradually become more important to control the inventory levels to maximize over-all profit and ROI. By combining a multiple-life-phase optimization approach with fast learning models of demand, pricing can help steer in-season promotions or clearance markdowns to proactively manage inventory to meet overall lifecycle business goals.

Markdown Optimization, an integral component of antuit.ai’s Lifecycle Pricing solution, helps retailers increase margins and sell-through with cleaner seasonal transitions. It couples a forecast - that incorporates all demand drivers across stores, online fulfillment and returns - with a workflow that enables planners to focus on SKUs by location where demand is predicted to be most variable.

Improving the visibility and management of pricing across the entire product lifecycle

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What kind of challenges does it solve?

For a $4B omni-channel retailer looking to coordinate in-season pricing with end-of-season liquidation, the goal was to minimize over and understocks caused in part by unsynchronized pricing across channels.

By evolving a 2-phase pricing process into a single process with phased pricing goals, pricing conflicts over the product lifecycle were reduced and greater efficiency around inventory management was achieved. In addition, backed by demand forecasts that take into account the different demand and inventory conditions at the location level modeled by channel, the right reactions for each channel, and overall, were taken. As a whole, strategic control of pricing was restored, enabling cleaner inventories, better value messaging to customers and better financial results.

For another $3B Fashion Department Store retailer with nearly 30% of sales from clearance, changes in customer demand cycles along with a less than responsive supply chain left too much inventory in some places and too little in others. Across the board price cuts helped drive down total inventory levels but did nothing to address out of kilter assortments so that, in addition to the margin hit from ill-timed discounts, sales also dropped as customers were unable to find the items they wanted.

By implementing a forecast-advised markdown cadence that employed the latest AI factoring and machine learning, markdown recommendations were able to reflect true demand for seasonal items, by product and by location. In 6 months, margins increased 4 points and sell-throughs increased over 10 points.

Working in tandem with Planners to perfect the season's performance

Lifecycle Pricing with Markdown Optimization from antuit.ai is a complete solution delivering:

User-Designed Workflow — Gain visibility and strategic control for improved productivity and adoption, guiding Planners to the SKUs that deserve their attention and saving their time by identifying the ones that don’t.

Inventory Fluidity — Understand store demand, online fulfillment demand, and returns for every Style/SKU/location in order to capitalize on the ability to meet demand with available inventory from all channels.

SKU/location Risk Assessment — Prioritize inventory pricing not only on current levels of demand and inventory on hand, but also on the forecasted variability in demand at a product/location level – where taking pricing action could mitigate the risk of holding too much.

Macroeconomic Forecast Sensitivity — Anticipate major changes in consumer price-to-value preferences that are the result of macroeconomic conditions like inflation where product mix and price elasticity can vary based on changes in consumer willingness-to-pay.

Lifecycle Pricing Continuity — Connect in-season and liquidation pricing decisions to maximize inventory margins and sell-through throughout the product’s lifecycle.

Antuit.ai delivered the solution in under 2 months with high quality development. The transition was seamless to our business operations and we are very happy with the recommendations we are seeing out of the system.

Julie Rankin, VP Enterprise Applications, Neiman Marcus Group

Our solutions are built upon antuit.ai’s world-class AI Demand Forecasting

Unified Demand Signal

Control for the differences between regions, stores, online, and even the fulfillment type, and serve as the connective tissue across financial, assortment, allocation, size, and pricing decisions.

Dynamic Aggregation

An analytic methodology to address data sparsity, avoid the impact of fringe sizes, handle new items, and protect unit minimums.

Omnichannel Profiling

Delivering demand profiles that consider store and online sales independently, but optimize for BOPIS and ship-from-store (SFS) aspects of inventory location.

Seamless Integration

Delivers pricing and forecasting results through API integration, feeding either antuit.ai’s application suite or existing ERP solutions.

Scalable Data

AI models capable of digesting data that accounts for every demand driver - including seasonality, price, product lifecycle, trends, and local events.

Cloud Native

Built natively in the cloud with scalable distributed processing.

Download Solution Sheet (PDF)