Data Science and Pricing: Getting real world ROI
Article from Ryan:
Data is increasingly driving products, services, business models, and internal operations. Not long from now, what is today considered cutting edge will be table stakes and the minimum requirement to be competitive. This is especially true for pricing.
One challenge in using data science is the complexity, or at least the perceived complexity. It is daunting to look at the big, digital companies and their advanced systems. For instance, Amazon uses dynamic pricing adjusting prices every few minutes with advanced models of market behaviour, time-of-day psychology, personal buying habits, and individual purchase history based on countless experiments on tens of millions of customers. It is tempting to simply think “there is no way my company can do that”.
Data science is really a range of things including descriptive statistics, predictive analytics, machine learning, and artificial intelligence (AI). Companies tempted by the hype of AI often implement systems that are too expensive and not aligned with business goals. When your manager says, “do it with artificial intelligence”, they may not know what that means or whether AI is actually useful for your application.
Data science is a tool like any other. If it helps you meet your business goals, you should use it and, perhaps most importantly, use it appropriately. Don’t think about complex dynamic pricing if you haven’t got basic customer segmentation. Don’t think about some massive investment in an AI-based pricing system if you aren’t regularly doing a basic waterfall analysis. In my experience, many businesses can get great returns on smaller investments in data science.
The Stratence Partners Integrated Ecosystem (SPIE) approach is instructive. It uses off-the-shelf software like Power BI and Excel to address the critical issues in pricing including evaluating return on different strategies, implementing strategies in pricing policies, and using those policies in every-day commercial practices.
This pragmatic approach doesn’t start with AI or other overly complex systems. It starts by focusing on the actual issues. For example, many companies have complex, disconnected systems. This can make even the most basic data analysis difficult. Until that basic analysis is done, you can’t make evidence-based decisions.
This philosophy is in our data science practice, but also our pricing excellence model. It is tempting to go for big ambitious goals like implementing AI or Level 5 World Class Pricing excellence, but you can’t just skip to the end. You don’t need advanced AI to get away from reactive and ad-hoc pricing. Start with the data science that will take you from where you are today to the next level of pricing excellence. It’s not very glamorous, but perhaps all you need is good data cleaning and a basic linear regression to get a good return on investment today.
When you are ready to move to the next level, Stratence Partners and I be ready to help with advanced analytics, machine learning, and yes, even AI.
Ryan Maley is Vice President of Stratence Partners based in Chicago. His career includes leadership roles in operations, strategy, marketing, product development, and information technology with both manufacturing and services companies. He has an MBA from the University of Illinois and a brand-new master’s degree in applied data science from the University of Michigan.