B2B trading partners rely on many commercial strategies to boost revenue, margin and profitability. AI can enhance nearly all of them today and even add a few new ideas to the list. One area where manufacturers and distributors can very quickly realize significant top-line AND bottom-line growth with AI is price optimization.
How organizations price their products & services has always been a difference maker. Every 1% improvement in price typically yields a 10% or more improvement in profit. Commercial decisions like offering, quoting, and selling the right products to the right customers at the right price and time can be game changers when executed well. The challenge is always scale. Large manufacturers and distributors serve thousands or tens of thousands of customers across many geographies with extensive product portfolios and complex sales channels. Finding the right price for a customer group or single geography takes a lot of analytical work, let alone arriving at the right price for an individual customer.
This is where AI and machine learning shine. The technology can seek patterns across transactional data, market trends, product preferences, and competitor actions to find pricing and selling insights that individuals couldn’t possibly see on their own. AI approaches can analyze trends, predict outcomes, and fuel product recommendations an individual customer is likely to buy, forecast demand, sense any anomalies and alert the business, and generally improve customer experience.
First Things First: Get to Know the Different Types of AI
AI-driven revenue optimization sounds exciting - and the growth potential it delivers certainly is. But before jumping in, it’s important to recognize not all AI is created equal for every use case. While certain types of AI are good for some things, they can also be a bad fit for others. Unfortunately, many people don’t know the difference. They hope that by sprinkling a little AI magic on the problem, they’ll end up in a better place.
In pricing, an efficient misuse of AI could actually deliver bad results faster. A simple example is an LLM (Large Language Model) like ChatGPT is amazingly good at understanding language and creating natural language responses. But this type of AI is not an appropriate tool for quantitative modeling or forecasting. Even more fundamental, you should never put proprietary company data into a public tool. Companies are better served to use a closed AI system for revenue optimization.
While the executives who make pricing decisions need to know about the different types of AI, the same is true for non-quantitative employees like marketing, product, and sales managers. Think of it like having a toolbox at your disposal. You wouldn’t want to use a hammer to drive in a screw for fear of the damage it could cause. Likewise, some AI methods are inappropriate in the wrong situation and could create more problems than good.
How to Start Multiplying
According to the 2024 Pricing Excellence Report, analytics is the most sought-after skill for those who make pricing decisions (75%). When it comes to AI specifically, just over half (51%) report already using it to some degree or have plans to implement the innovation in the next 12 months. As someone with influence over price, what can you do to prepare for AI-fueled optimization? Collaborate and get creative.
- See the opportunity. You can’t control much of what happens to you, but you can always control if and how you react. Recognize the disruption AI is creating and treat it as an opportunity. It may not be an opportunity for you yet, but that’s what you need to examine: who will this benefit and how? How might this change the dynamics in your organization and across your partnerships?
- Know the capabilities. Become proficient in *applied* AI. This doesn’t mean know the nuances of building complex models or data science techniques yourself. Instead, understand the capabilities – and the limits – of AI and ML. Don’t be the one that’s proud of your hammer as you bang away at a Phillips-head screw.
- Experiment. Try out the widely accessible forms of AI, especially the easy-to-interact with Generative AI solutions like ChatGPT, Copilot, and Gemini. Mere experimentation can lead to “aha” moments for people who are not data scientists or even quants.
- Collaborate in Community. Set up or join an affinity group around AI within your organization. You can do this easily with MS Teams, Slack, etc. Crowdsource skill acquisition and lean into group discussion to accelerate your (and others) knowledge. Where I work, we created sub-groups based on AI possibilities and risks.
- Consider (and reconsider) your business model. Study how AI might change the fundamental business models in your industry. Could new insights change how customers purchase goods & services or alter the competitive landscape? How will the supply chain be altered? Data insights fuel new value-added services and revenue opportunities.
Back in 2013, manufacturers were taking into consideration the strides of revenue optimization. Fast forward to today and modern AI capabilities have added layers of insights to this intelligence, making the work more accessible and scalable. What are you waiting for?
