According to Technomic, the foodservice distribution industry is heavily saturated and competitive, and companies need to balance smart technology investments with meaningful and consultative customer partnerships.
Merger and acquisition activity, private or own-label product competition and a customer landscape shifting toward consolidation, and GPOs are pushing company leaders to find new, innovative strategies for profitable growth. This shift in the customer landscape paired with the massive complexity of the typical food distribution business has made it increasingly difficult to maintain margins and grow revenue.
As companies race to automate processes, digitize operations and focus on customer service, pricing remains an area ripe for improvement. Pricing is, by far, the most effective profit lever available to any company. However, setting and realizing the best price for every selling circumstance isn’t straightforward. Cost volatility coupled with the sheer number of products, product categories, and customers makes it nearly impossible to set and quote a market-aligned price for every deal.
Whether foodservice distribution companies have outgrown their pricing practices, or they want their salespeople to stop over-discounting, or they can’t identify untapped revenue and profit opportunities due to the complexity in their business, these companies can reap the benefits of a data science-based approach to pricing through price optimization software solutions.
Case Study: How a Leading Foodservice Distributor Grew Profit 10%
One leading U.S.-based foodservice distributor replaced manual pricing methods, overcame cost volatility and improved profit margin with an AI-enriched pricing optimization software solution. Below is a summary of their pricing journey.
Shifting Product Costs and Manual Pricing Practices Hurt Profitability
Despite the company’s rich history spanning nearly a century, and standing as one of the top 10 foodservice distributors in the U.S., fluctuating product costs and manual pricing practices were reducing its profitability. With hundreds of thousands of customers, 36,000 unique product SKUs and approximately 450 sales reps, the distributor’s pricing had many complex variables.
In fact, the company only had two centralized pricing analysts to determine millions of price points each week. They did their best to set and adjust prices to weekly changes in supply costs, update and publish the weekly price lists, and give sales reps high-level margin targets for orders.
But, ultimately, field sales reps determined the product sales price, and with so many accounts to manage, they often guessed at what price to charge. Often, the final price paid by the customer failed to reflect actual market price or their willingness to pay.
Without any structure in discounting, price points were widely scattered, leaving money on the table. When company executives reviewed P&L reports, it was clear that they needed a solution to address the squeeze on margins and remove the subjectivity that dominated their current price-setting methods.
An AI-Enriched Price Optimization Software Solution
Company executives implemented an AI-enriched price optimization solution because it could manage the complexity of pricing in their business - and do it at scale. It works using artificial intelligence, machine learning and predictive analytics algorithms that continuously consume the company’s existing customer, product and transaction data to generate market-aligned pricing based on factors, such as:
- profit and margin objectives
- product mix
- product costs
- price elasticity
- price sensitivity
The solution generates price micro-segments, then performs two types of optimization: alignment optimization to ensure the rationalization of good, better, best price relationships within and across segments, and measurement of price response through elasticity optimization to determine each customers’ sensitivity to price. For example, the below image is an example of one of the thousands of segments generated through this process. In this example, the blue line represents a lower price sensitivity, whereas the red line represents high price sensitivity.
Optimized Pricing Drove Business Performance
After just nine weeks, the company grew profit margin by 10.1 percent or 140 basis points for the regions using the solution. With positive sales feedback and business results, they accelerated deployment across the rest of the company. The distributor realized these results:
- Improved ability to recover weekly supply cost changes via price
- Enhanced pricing accuracy and consistency
- Streamlined pricing process efficiency
- Boosted sales rep confidence in quoting prices to customers
- Increased customer satisfaction with market-aligned prices that accurately reflected their price sensitivity
To learn more about how the foodservice distributor realized these results with price optimization and their change management approach, download the full case study.
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