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Overcoming Growth Headwinds: AI/ML-Driven Strategies for Revenue Optimization in Distribution
The distribution industry faces unprecedented challenges—market saturation, heightened competition, and changing customer expectations—that are stifling growth and profitability. While Artificial Intelligence (AI) and Machine Learning (ML) are often hailed as game-changers, their true value lies in strategic, practical applications.
This blog explores how distributors can leverage AI/ML to optimize pricing, retain customers, and capitalize on cross-sell opportunities. By addressing key issues such as price realization gaps, customer churn, and missed revenue opportunities, distributors can overcome growth headwinds and accelerate profitable growth.
Building a Dynamic Pricing Capability (In Under 90 Days)
Many mid-market retailers and wholesalers' pricing, finance, and merchandising executives have inherited outdated pricing solutions. These legacy pricing processes don't quickly scale and heavily rely on expensive human, mundane tasks. There are no intelligent dynamic pricing capabilities that automatically set prices based on sales patterns, corporate objectives, and changing marketplace behavior. It often results in missed profit opportunities and liquidity problems.
Fortunately, you can build simple yet effective dynamic pricing solutions in 3-to 4 months that achieve up to 80% of its incremental Gross Profit $ potential. You can design and implement using practical methods and accessible technologies that empower your teams to take complete control without expensive 3rd party support.
Read more below about how you can achieve this and unlock an extra 1-3% in GP% in year 1.
Solving Unproductive Inventory Challenge with Dynamic Markdown Pricing
Is the lack of robust clearance pricing strategy and manual pricing processes causing unnecessary and costly inventory buildup in your warehouses?
A leading $5B consumer durables wholesaler in North America wanted to unlock significant liquidity tied up in unproductive inventory by deploying in-house markdown optimization capabilities. Please read the below case study to understand how we helped them solve a $150MM unproductive inventory problem with dynamic clearance pricing.
This article is one in a series of five detailed case studies we will publish bi-weekly on key B2B Commercial Analytics topics. It gives insight into our outcome-based analytics process, including both technical and change management pieces.
Building Dynamic Pricing for Fortune 500 Specialty Retailer - Case Study
In this case study, we delve into the strategic implementation of dynamic pricing by a Fortune 500 specialty retailer, aiming to enhance market share and profitability in their brick-and-mortar (B&M) channel. Despite the success of dynamic pricing in their e-commerce channel, the retailer faced unique challenges in translating this strategy to their physical stores, constituting 90% of their sales. The initiative encountered skepticism and logistical hurdles in frequently changing price tags. In partnership with Revology Analytics, the retailer overcame these challenges through a practical, simple-to-understand dynamic pricing solution, resulting in increased gross margins, fast and cost-effective implementation, and successful stakeholder engagement in their dynamic pricing journey.
Solving Unproductive Inventory Challenge with Dynamic Markdown Pricing - Case Study
Learn how a leading North American consumer durables distributor improved liquidity and profitability with Revology Analytics' Markdown Price Optimization solution. Discover how the solution increased Gross Profit by 5% annually, enhanced inventory liquidity by 30%, and saved significant labor hours for pricing and sales teams. Explore the benefits of advanced inventory management for sustained growth and financial stability.
(All I want for Christmas is) better discounting habits!
A brief guide for Manufacturers and Distributors to boost profits by 5-10% using simple, effective pricing and analytics techniques.
If you're a manufacturer or distributor, you know that discounting is vital to the success of your sales force. But are you discounting enough? Are you discounting too much?
This article will discuss the importance of discounting in B2B settings and how to determine the right discounts for your customers and products. We'll also discuss when to use each type of discount and offer tips for increasing Net Revenue and Gross Profit with analytics and surgical discounting.
RA Quick Insights: Top Pricing Quick Wins for Distributors
Below are my top Revenue Growth Analytics quick wins that Distributors should implement (or right-size) to boost gross profits $ by 5-20% and increase liquidity.
RA Quick Insights: Using Sales Stack Ranking to Grow Net Sales & Profits
Commercial Analytics, aimed at improving Gross Profits and Sales Productivity, doesn't have to be complex to drive the proper outcomes with Pricing and Sales teams.
𝐒𝐚𝐥𝐞𝐬 & 𝐃𝐢𝐬𝐜𝐨𝐮𝐧𝐭𝐢𝐧𝐠 𝐒𝐭𝐚𝐜𝐤 𝐑𝐚𝐧𝐤𝐢𝐧𝐠 is another simple yet effective Revenue Analytics technique to steer Sales behavior in the right direction and drive incremental Net Sales (and Gross Profits).
RA Quick Insights: Mistake in Customer-Facing Analytics Solutions
I often see Manufacturers and Wholesalers making the mistake of trying to revolutionize entrenched Customer habits vs. creating easy-to-use, pragmatic analytics solutions that Customers understand and care about.
RA Quick Insights: Using Discount Curve Analysis to Evaluate Pricing Behavior
This week, I wanted to show you another simple yet effective revenue analytics technique to steer sales behavior in the right direction and drive Gross Profits.
The Unproductivity Problem with Your Inventory
Unproductive inventory often accounts for 20-30% of wholesale businesses' merchandise. For many, it's just the cost of doing business. But it doesn't have to be that way. Unproductive inventory ties up your cash, erodes your profitability, and puts you at the risk of not meeting debt obligations for your asset-backed loans. Automated markdown pricing is one of the most effective ways to address this problem, and simple analytical solutions (no "ML and AI) can often yield significant benefits. Learn more below how.
I welcome your comments or similar experiences.