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Dallas Crawford

Customer Care

Transferring Plant-Floor Efficiency to Pricing Efficiency

Machine learning algorithms are the ‘robots’ in the pricing process

Published: Tuesday, September 15, 2020 - 12:03

Manufacturers know the value of automation on the plant floor. The world is more interconnected, with more competitors, and consumers are more informed and thus more selective with purchasing decisions. With increased competition and disruption, manufacturers must leverage automation to achieve operational efficiency.

Automation of any process delivers higher productivity, lower costs, improved workplace safety, enhanced precision, and ultimately allows associates to focus on more valuable activities. Technology, and specifically machine learning, has helped expand the breadth of automation by becoming more accessible and affordable for manufacturers of every size.

Transferring plant-floor efficiency to pricing efficiency

Robotic automation on the plant floor has helped companies produce high-quality goods more quickly and efficiently. Robots perform dull, repeatable steps with reliable accuracy and do not get tired, distracted, or endure repetitive injuries.

Pricing automation is simply transferring the same plant-floor efficiencies to pricing best practices. Physical strain is unlikely from a pricing process, but mentally it can be taxing and often impossible when determining the optimal prices for unique products.

For a company with 2,000 customers and 500 active items, there are potentially 1,000,000 unique prices that need to be delivered. Complicating matters, varying market conditions and a competitive landscape can vary by region. Additionally, constantly changing costs on the supply side exacerbate the volume of unwieldy pricing data for manufacturing teams to manage.

Machine learning algorithms are the ‘robots’ in the pricing process

As with robots, manufacturing pricing teams must set some boundaries, train the movements, and let the models perform the drudgery of determining the best prices based on predicted revenue, margin, and volumes. These models mine all historical transactions on a nightly basis, ensuring that predictions are based on the most current market realities. This allows a focus on more valuable activities like selling, managing relationships, and negotiating contracts with key suppliers. Pricing is simply too daunting to try without the “robots”—i.e., machine learning models with process automation. 

Most manufacturers are not currently using artificial intelligence (AI) and machine learning to set prices based on customer behavior, segmentation, and demographics.

The costs of not automating pricing are significant

Hard costs, including additional headcount, full-time employee hours, lower margins, and lost sales, are quantifiable metrics to consider when justifying automated pricing technology. 

There are also soft costs to consider, from delayed response times as market dynamics shift, and the cost of time spent on data entry and manipulation (rather than analysis and customer engagement).

Lower margins and lost sales can be quantified in customer churn as well as the inability to effectively react to market changes, including competitive pricing, demand shifts, and product life-cycle management.

During the pandemic industrial organizations have seen what happens when there is an inability to manage supply-side changes, including cost changes, supply constraints, and product quality challenges.

CPQ systems are forgetting a key bit

Within the pricing automation market are configure-price-quote (CPQ) solutions that are designed to enable salespeople and customer service to provide a quote to a customer based on any combination of products, features, and services.

The ease of dragging features and options onto a base product to quote a net price is certainly valuable. However, most manufacturers sell discrete SKUs and do not need to spend additional resources in acquiring, implementing, and maintaining a full CPQ system.

For example, have you ever purchased a vehicle online and played with the ability to drag and drop lots of optional features to see the impact on your net price? That is what a CPQ solution puts in the hands of salespeople to ensure they quote the full package for their clients. Most companies do not have that level of optionality for their products, so the bells and whistles of a full CPQ are overkill.

Even for manufacturers who need advanced configuration capabilities, the focus of CPQ solution providers on responsiveness and workflow has minimized the importance of ensuring that the base model—and any additional options—have sound pricing recommendations feeding the assembly of a quote.

You could say that CPQ solutions have slowly deprioritized the most critical and fundamental component—price. Without strong pricing science, the configure-and-quote components are moot. To achieve truly efficient manufacturing operations, many companies may not need a true CPQ, but rather a cost-effective pricing solution—cPq—where the emphasis is on price.

The prequalification process for cPq companies

Companies with more than $50 million in revenue are prime for rapid cPq deployment. While smaller organizations would certainly benefit, they may have a fairly simple pricing process and, for them, neither CPQ nor cPq might make sense.

Most manufacturers for which cPq does make sense, want guidance on how much to charge to capture maximum revenue or profit, which includes examining the solutions or methods currently used to set prices for products.

Too often, key pricing decision makers have had to rush out of a meeting about pricing changes, return to their office, and run reports or scenarios. This required yet another meeting to evaluate how those changes might impact the bottom line. Even efficient manufacturers, when losing market share to competitors or constantly reacting to price changes, struggle to align customer, location, or category-specific pricing to corporate objectives—including growing margins, revenue, and increasing market share.

Sadly, too few companies have reporting available that clearly demonstrates the impacts of pricing across revenue, volume, mix, and margin.

Getting started with cPq and analytics for pricing strategies

The onboarding process for a cPq solution must start with a pre-sales meeting to determine if process changes and consulting are required to align a company’s needs with technology solutions.

Once a proven cPq solution is determined, a post-sale implementation and execution modality is launched. This includes assessing infrastructure readiness, data readiness, and systems integration. The return on investment (ROI) is considered when the post-implementation is quantified.

Beyond the quantitative-pricing ROI in these technologies are the qualitative best-in-class outcomes of highly efficient manufacturing operations.

For many companies a full-fledged CPQ solution may not be the answer and may actually be money poorly spent. For those companies with fewer SKUs and options, but which still need nimble pricing data, a cPq system will deliver the pricing agility they need at an ROI that makes sense.


About The Author

Dallas Crawford’s picture

Dallas Crawford

Dallas Crawford is an Advanced Analytics Executive at QueBIT with over 10 years of experience helping customers leverage predictive analytics to make informed strategic decisions. Prior to QueBIT, he spent 7 years at IBM serving clients in the Distribution Sector and led multiple key Predictive Analytics and Reporting projects. Dallas graduated with an MBA from Florida A&M University in 2008.