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A Leading American Technology Company Improves Its Pricing & Promotion Strategy

Case Study

A Leading American Technology
Company Improves Its
Pricing & Promotion Strategy



Specialty Retail (Computer Hardware and Software)


Reduce margin erosion and maximize sales with optimal pricing and process automation

Product Used


Optimal pricing strategy for online pricing was achieved, incorporating the product life cycle and major events

Revenue leakage due to unintentional double discounts and incorrect portfolio was reduced by $5 million

Lead time for promotions planning and execution was reduced by 93%

Visibility of promotion and pricing extended up to 20 weeks as opposed to 1-2 weeks before implementation, this helps to optimize the inventory plan as well

Overview The client is one of the largest electronics retailers in the world, offering thousands of products across multiple categories such as personal computing, imaging/printing, and computer accessories through its network of exclusive and retail partner stores.

The eCommerce division of the firm was facing multiple challenges with respect to its promotions and pricing strategy.

With fast-refreshing product lines, identifying the right price was a challenge, leading to increasing margin erosion.

Lack of product life cycle-based decision-making led to undesirable consequences such as over-discounting and inadequate focus on aging inventory.

Instant rebate was a complex iterative process, requiring significant time and effort on an ongoing basis.

Promotions strategy often did not align with supply lead time, leading to increased customer dissatisfaction and potential loss of sales.

With changing consumer preferences, the client was witnessing fast-refreshing product lines that were getting increasingly deep and complex. The client was looking for an all-encompassing solution that would optimize its pricing strategy in accordance with the product life cycle and power key pricing and promotion processes with Algorithmic Decisioning and smart automation.

Powering Algorithmic Pricing for Category Managers

Algonomy’s Merchandise Analytics fit in perfectly with the client requirements of a scalable platform that integrates data from multiple systems, provides algorithm-driven prescriptive insights, and improves system-wide transparency. Its ML-based pricing models capture the nuances of the product life cycle and optimize promotions and pricing with smart recommendations. Its automation features further streamline planning processes to make it more efficient.

Merchandise Analytics helped the client achieve an optimal pricing strategy and transform its instant rebate and promotions from a set of complex iterative processes to a highly automated, algorithm-driven, and collaborative system.

Advanced price elasticity and prediction models based on ML algorithms helped the category managers achieve optimal instant rebate by identifying the right product and right price. Prices for new products were optimized using advanced algorithms that factor in category and product intelligence. The ability to specify business constraints such as inventory, IR budget, best margin, cannibalization effects, etc. gave the category managers further control to achieve the desired business objectives.

Smart features such as scenario simulation and auto-scheduling provided teams with greater control to optimize their day-to-day activities. With scenario simulation, the teams were able to simulate scenarios on the fly and stay on top of key metrics to make override decisions. Auto scheduling allowed teams to apply recommendations to automate the workflow. Multi-user and multi-team collaborative features helped the client improve coordination and speed up approval mechanisms to optimize instant rebate processes.

The ROI of Algorithmic Merchandising

As a result of the above, the client achieved significant improvement in its profit margins and was able to optimize its pricing and promotions processes. Revenue leakage was plugged by $5 million with zero instances of accidental double IRs. The client reduced their lead time for instant rebate implementation from 7 days to 4 hours. The client also reduced the planning efforts of category managers, allowing them to focus equally on all categories. The added capability of scenario simulation helped the client bring agility into planning and avoid the disconnect between supply chain and promotions.

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Leading American Technology Company

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