Return-to-work strategies, work stoppages, social distancing, and PPE (personal protective equipment) are the vernacular of manufacturers today. The evolving manufacturing enterprise is adapting and adjusting to this new pandemic reality, says Thomas R. Cutler, president and CEO of TR Cutler, Inc.
Manufacturers can prevent financial hemorrhaging and even realise greater profitability during COVID-19 via proper price alignment with data analytics. Several pricing experts shared insights how price alignment is both a methodology to surviving and even thriving during the pandemic.
The pricing experts:
Lydia Di Liello is the CEO and founder of Capital Pricing Consultants, a revenue management and business consultancy dedicated to significantly improving profitability for clients through strategic, operational, and tactical analysis and recommendations. Di Liello brings more than 25 years of global revenue management and pricing expertise. She is a member of the Professional Pricing Society Board of Advisors and holds an MBA from Youngstown State University.
Dallas Crawford is an advanced analytics executive with over 10 years of experience helping customers leverage predictive analytics to make informed strategic decisions. Prior to QueBIT PriceAlign, 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.
Dan Barrett is an advanced analytics technical manager at QueBIT PriceAlign. Barrett served as Controller and CFO for a manufacturing organisation for more than 12 years. He successfully led evaluations of Advanced Analytics solutions in hundreds of evaluation cycles with some the manufacturing and retail organisations. He directed the development and field enablement of Distribution and Manufacturing Performance Management Solutions while at IBM and Cognos.
All three experts were asked to elaborate how manufacturers should analyse customer pricing needs using data analytics.
DiLiello suggested the first step is to focus on points of volume. High volume equals high need. Without data analytics those analyses cannot be accomplished. She also suggested that these data will allow manufacturers to identify places where volume has changed significantly (either up or down) as a clear indicator of changed customer behavior and needs. These data allow sales teams to investigate and ask the customers why they are buying more or less of those identified SKUs. Price aligning requires a deeper look for overall trends in data.
Crawford insisted that price aligning with data analytics can be done readily in real-time. The ability to concentrate on high and low volumes that fall outside a normal distribution of trend or seasonality can inform where margin opportunities exist. This is particularly valuable during the pandemic where buying patterns are in greater flux. Higher volume provides opportunities to adjust prices based on demand or to capture market share. Lower volume provides opportunities to analyse pricing, determining if market share is being lost or if the product’s lifecycle is near its end.
Barrett noted the best method to analyse and optimise customer pricing is to model the data and ensure that the analytics platform supports it at a very discrete level. Customer data by location or product SKUs allow for optimal delivery of unique pricing. Understanding and forecasting the customers’ pricing elasticity is key to determining the optimal price.
Next, the experts were asked how pricing sensitivity during the pandemic is being impacted by machine learning (ML), Artificial Intelligence (AI), and business intelligence (BI).
DiLiello was cautionary noting a concern that AI will interpret and “learn” erratic and poor pricing behaviors from undisciplined companies which are reactive in their pricing approach. She added this will create future data sets that reflect low price variation which may begin to suggest lower price points automatically bringing the company pricing down.
Crawford suggested that PriceAlign’s ML models can be more adaptive than typical time series models in the pandemic environment. Adding more qualitative and timely input variables (like fuel costs) allow the model to be more contextual and tolerant to the aberrant data collected during this unique event.
The pandemic gives manufacturing organisations which embrace the PriceAlign technology the opportunity to study and model the effects of these unique data to be better prepared for future events. Shutdowns will potentially cause incomplete data; future training of Machine Learning models will have to deal with it.
Barrett is convinced the strength of any AI & Machine Learning-based solution rests in the ability to quantify the relationship between these variables and demand. While the pandemic is an important driver for price aligning, it remains just one factor impacting manufacturers on the demand and supply side.
This article originally appeared in The Evolving Enterprise.