Large retailer, reseller, and consumer loan provider
Success Story
Multinational reseller automates pricing and minimizes inventory risk
The Client
Profile: Retailer, reseller, and consumer loan provider with about 1,000 locations in the U.S., Mexico, and Canada
Structure: Publicly traded
Year founded: 1989
Yearly revenue: $850+ million
Applying widely appealing prices to individually sourced items is a tricky business.
Doing it right means striking a balance among meeting the seller’s needs, ensuring a margin for growth, and supporting inventory management. It requires recognition of an item’s features, accurate assessment of its value, estimation of current and projected stock, a solid outlook on demand, and an understanding of competitor prices.
Further, in this business model, it all has to be done on the spot by store associates in various, distinctly different locations.
No matter how experienced your team or stellar your service, ad-hoc pricing can’t deliver sustained growth or predictable inventory levels.
This company’s success stemmed from its genuine dedication to knowing and serving its diverse communities through its retail products, lending services, volunteer programs, and local partnerships. But leadership saw that a lack of data-backed pricing capabilities was straining operations and underserving the very communities it intended to help.
The company was asking, what do optimized prices look like in a business where pricing is an individual and often reactive activity? And how do we implement automated pricing that isn’t one-size-fits-all?
The Nousot solution: Pricing Optimization
Nousot partnered with this client to customize a Pricing Optimization model for selected products at its flagship locations, which were often challenged by inventory surpluses.
Client data used
The full history of transactional data for each selected product
How we enrich it
Nousot engineered a data pipeline specific to the products that this client needed to sell most critically. We included granular data on the characteristics, pricing, and performance of these products, along with ones like it, across competitors, consumers, and geographies. We then customized our Pricing Optimization product and GlobalPulse with this pipeline.
Deliverables
An autonomous predictive model that delivers a dynamic, optimal price for a product, plus three options for discount cadences over time, based on consumer propensity to buy that product at that location at various price points.
The model factors in not only internal, historical sales data, but also comprehensive geographic and market data for any given product plus similar and substitute products.
A custom dashboard of various product pricing curves and their estimated yield regarding revenue and profit.
Integration with the client point-of-sale system so that associates can access and use dynamic pricing recommendations during their customer interactions.
Automated monthly updates of both internal and external data to refresh pricing and discount recommendations continually.
Weekly walk-throughs of model results on 1,000 sample products to enable this client to analyze Nousot-provided prices and their potential impact on sales and inventory.
Timeline
1 week
to source client internal data
4-8 weeks
to engineer a custom data pipeline per product
1-2 days
to product an initial autonomous model
4 weeks
to analyze results, iterate with the client, and validate the model for production use
Business Outcomes
At-a-glance analysis of the impact of different pricing decisions on the bottom line in terms of sales and inventory
The ability to pull regional pricing levers to meet changing enterprise goals in terms of revenue targets, stock levels, operations, and customer sentiment
More empowered and efficient team members regardless of tenure or experience, improving customer service, training programs, and recruitment efforts
Support for the company’s new POS system, rolled out in Fall 2019
Automation aligned with the company’s strategic objectives of conducting operations and competing through increased digital transformation