AI-Powered Analytics For Pricing: Driving Real-World Advantage For Manufacturers

“We’re looking at a new AI-powered analytics solution.” That’s what manufacturers are telling us when we ask about their pricing strategy. Though it’s still the early days of the fourth industrial revolution, manufacturers are increasingly turning to AI-powered analytics for pricing to manage volatility, improve margins and stay competitive.

The real value comes not from simply “adding AI” but from integrating it into end-to-end processes, where data is transformed into insights, insights into actions, and actions into measurable margin improvement. Manufacturers that succeed with AI-powered analytics for pricing will be those that treat it as a catalyst for operational change, not just another tool layered on top of existing practices.

Getting from Point A to Point AI

A universal need that AI-powered analytics can fulfill is setting the right price, but it all starts with your data. That’s the input for your AI machinery, so make sure it’s clean. Data mining tools can adapt to small gaps and smooth out irregularities, but large blind spots in data—or conflicting data—will generate misleading results.

And how can you get more data to feed the hungry AI machine? Enlist your distributors. Use your rebate program to incentivize them to share information with you. Here’s what you can learn from this approach:

• Distributor inventory (inventory sales to distributor, minus rebate units claimed)
• Customer geo-information (at minimum, ZIP codes should be reported)
• Distributor selling price (sensing local market demand)
• Product mixes (upsell, cross-sell or bundled promotion opportunities)

A smart manufacturer should be able to derive greater value from this data than the cost paid to obtain it, and you can exploit the data to identify trends, such as where you need to focus your advertising efforts, how best to motivate your distributors, and evaluate your pricing.

The competitive edge of AI-powered analytics for pricing

AI applies machine learning (ML) to crawl through your growing (clean!) data set to find new trends and demands to fold into predictive calculations.

Manufacturers can also rely on ML to help them understand how their customers will react to varied pricing strategies and then identify the best price based on their goals. Knowing the potential impact of sales promotions or rebates, as well as competitors, weather conditions, seasonal sales forecasts, economic conditions, operating costs, and regional demand, all play into formulating pricing guidance.

ML is a powerful tool that can handle millions of SKUs and sales transactions to optimize prices regionally and dynamically, based on the manufacturer’s pre-defined rules. ML continually absorbs information, building its own pricing automation by using its algorithms to identify and learn from the data’s patterns.

One manufacturer using ML to optimize pricing and provide real-time price intelligence to its sales reps reported 35% of the ML-recommended prices were adopted. This manufacturer realized a $1.3 million revenue increase and a 10% increase in win rate.

Adding it all up with AI-powered analytics

Growing a manufacturing business requires informed decision-making. AI-powered analytics for pricing can quickly provide key insights that identify business risks and opportunities with greater ease and efficiency. Manufacturers who use software that manages the full lifecycle of their pricing optimization programs will have a strategic advantage in the marketplace. In the end, better data leads to better insights and better insights lead to more profit. Vistex can help you make sure it all adds up.

To learn more, download the eBook, The Price is Right: Going from Reactionary to Strategic Pricing.