How Deep Neural Networks Are Changing Pricing Optimization

On the horizon, a new solution has started to form. Deep Neural Networks and ultra-powerful AI can take data that was originally too scattered and complex, and use it to find the perfect price every single time. 

Nick Boyer, the Senior Director of strategic consulting for PROS, spoke about this shift at Pricing Po50. He shared some insights into the drastic impact that this technology can have on the price optimization process, as well as how your team can start implementing it. 

Traditional Segmentation-Based Price Optimization 

Picture a sales team deciding on a base price for their products. There are several factors that they’ll consider when deciding on this number. 

• Country – The specific geographic market they’re selling in will have an effect on the price. This could be because of currency exchanges, the cost of moving products, or the demand in an area. 
• Brand – Most manufacturers have a “good, better, best” brand system which will affect how much customers are willing to pay for an item. 
• Product Category – Different categories can have completely different price points. For example, a laptop will be more expensive than a laptop case. 
• Industry Type – Which industry is the product selling into? Different industries often have different margins and pricing expectations. 
• Customer Size – The larger the customer, the more leverage they’ll have when it comes to price. 

This detailed segmentation helps them go into negotiations with a plan and know their base price. However, it doesn’t cover everything. This system completely ignores 

• Market Indices – The general health of the market in terms of the interest rates, currency, and commodities can have a big effect on the price. 
• Inflation Trends – Even if your base price is established, what if it was set up a few months prior and inflation rates have shifted? 
• Competitor Prices – It’s a challenge to learn about competitor prices in manufacturing, and aligning that information with your own system adds even more layers. 
• Diverse Viewpoints – Chances are, sales teams will always call the same two colleagues to validate their pricing decisions. 

There is a lot that this style of price optimization gets right, but there’s always room for improvement. It often boils down to sales teams using their gut feelings to land on a price that will work for the customers they know and work with. 

Neural Networks: A New Way Forward

With technology advancing at the pace that it is, sticking with traditional methods for price optimization could mean setting your team up for failure. Tools like Deep Neural Networks (DNNs) and Artificial Intelligence can work in tandem with sales teams to find the perfect price every single time. 

B2B manufacturers looking to optimize this process have a lot to gain from DNNs. 

Improved Pricing Accuracy 

Imagine being able to process, understand, and utilize all your historical pricing data to find exactly where the trends are headed. With the powerful processing that DNNs are capable of, they can easily analyze vast amounts of data. 

By examining the data more extensively than any human could, DNNs can uncover hidden correlations and dependencies that traditional pricing models may overlook. This leads to an understanding of market demand, competitive dynamics, and customer preferences. 

Demand Forecasting 

Another clear benefit of having AI comb through historical data is highly accurate forecasts. Seasonality and demand patterns can be difficult to track, but with a system that parses the information for them, companies can benefit from an understanding of the ebbs and flows. 

Even if patterns aren’t clear through an entire data set, DNNs can find them and apply them to your business strategy. 

Price Optimization 

Finding the perfect price points for products and bundles takes an understanding of so many factors. DNNs are able to track nearly everything that might affect the ideal price including 

• Customer behavior 
• Market trends
• Competitor pricing
• Cost structures
• Seasonality

Businesses can use this information to find the perfect balance between maximum profit and customer satisfaction. 

How To Implement a Deep Neural Network 

While it’s clear that the comprehensive data capacity and complex analysis made possible by DNNs can be a huge help to B2B manufacturing companies, the intricacies of implementing such a system can quickly become overwhelming. 

Before any major AI system is implemented, there are a few key steps that need to be taken. 

Step One: Data Preparation and Validation 

Any data that is used for data-driven decision-making needs to be taken through the data validation process. If there are mistakes or inconsistencies within your data set, the DNN conclusions may be inaccurate. 

This possess can be time consuming, but should include

• Thorough scrubbing and cleaning of available data
• Validation for possible organic patterns
• Correlation testing across various fields
• Data normalization 

All of this prep-work will help your team get the most out of a DNN. 

Step Two: Price Prediction Model Creation 

Once you’ve prepared your data and normalized it, it’s time to create the price prediction model. The model will be trained and adjusted using your data and the hyper-parameters of your choice. 

A successful price prediction model should eventually develop a deep understanding of the various factors that shape your company’s pricing strategies. This will include both customer-specific prices and market prices. 

From there, your deep neural network should be able to deliver feasible pricing recommendations to your team. 

Step Three: Interpreting The Model 

Interpreting the results of a DNN can be a tricky process. While receiving the recommendations is beneficial, interpreting them and learning from the underlying processes is even more valuable. 

For this reason, it’s important to take the time to conduct research into the individual parameters the DNN is using. This will allow your team to go into the various settings better informed to create an even more accurate model. 

Another huge benefit of taking the time to understand the model is the insights it can give you into customer preferences. You’ll be able to see what the AI detects as the biggest driving factors for the value a customer gives a specific product. 

If you notice the DNN consistently tells you that longevity of a product has the largest effect on the price that companies will pay for it, your team might want to invest in longer lasting products. 

The Bottom Line

In some spheres, there’s a fear that AI will replace humans. Boyer doubts that this will be the case, but he does argue that “humans with AI will replace humans without AI.” 

This means that if you and your team want to remain competitive and find a pricing model that helps you weather changing economic conditions, understanding and utilizing the tools that are becoming available is key. 

If you want to learn more about the trends and advancements that are affecting B2B manufacturing, be sure to check out the rest of Copperberg’s content. With tons of articles, and regular conferences and networking events, there’s something for everyone to learn.