Machine Learning isn’t just a buzzword anymore – executives from all industries are regularly discussing implementing Machine Learning solutions or are even already deploying them. However, they are still mainly focusing on its capability to automate processes and cut costs. In our opinion, tapping into its full potential means exploring its ability to foster top-line growth.
Pricing is the most effective profit driver: This insight, gained by Hermann Simon in his pricing bible Confessions of the Pricing Man – How Price Affects Everything never gets old – especially in the digital age. However, the way companies can conduct pricing is changing rapidly. Technological progress enabled by Machine Learning (ML) and Artificial Intelligence (AI) paves the way for innovative and effective pricing techniques and models: digitally-enabled pricing. But how exactly does this work?
How we define Machine Learning
Machine Learning is commonly known as the field of study that gives computers the ability to learn without being explicitly programmed. It is the driving force behind autonomous driving, product recommendations at Netflix or Amazon, machinery with predictive maintenance, and much more.
The concept of Machine Learning is not new; it has been around since the 1950’s, but due to the recent advancements in computing power and the expansion of various technologies, it has grown in popularity especially in the corporate world.
Today, Machine Learning is not just for tech companies looking to enhance and smarten their products. We rather see it being used across almost all functional business areas as a tool to automate processes and make faster, smarter, and better business decisions.
Today’s customers expect a different product approach
The digital era is often dubbed as the age of the consumers, since customers increasingly expect products and services to contain maximum value and personalization at the lowest cost. One solution to this seemingly impossible equation is Machine Learning. By automating some processes and improving others, costs can be reduced while at the same time enhancing certain benefits. However, always remember: Successfully applied Machine Learning improves the experience of your customers – it doesn’t automate it.
3 ways Machine Learning helps grow profits
There are plenty of Machine Learning applications that help drive top-line growth. These are the three areas where we are seeing the most benefits:
- Hyper-personalization: Companies are using Machine Learning to understand what customers really want, often even before they realize it themselves. Whether this is a sophisticated movie recommendation engine, a more accurate churn prediction model, or a tool to help sales reps cross-sell to their existing customers – ML solutions help your team from product development to sales by predicting and fulfilling your customers’ wishes.
- Sales force efficiency: By automating repetitive tasks, Machine Learning enables your sales force to spend more time on what is really important: building relationships and actually selling products and services. It also provides sales teams with the tools to make more profitable and individually tailored decisions in the field, including price recommendation engines and guidance on next best actions to convert prospects. This then leads to increased revenue and profit.
- Optimizing pricing and promotions: Last but not least, Machine Learning enables you to understand your business’ key demand drivers. Predictive promotion decisions, data-driven list prices, and dynamic pricing: You can gain valuable insights in all these areas via Machine Learning applications.
How to deploy Machine Learning successfully
Before diving into these new technological possibilities and blindly setting up Machine Learning solutions, you need to find out how such a project fits into your overall business and commercial strategy. Simply implementing Machine Learning doesn’t make your pricing smarter, and it is definitely not always suitable. If, when, and to what degree Machine Learning meets your company’s needs are some of the key questions you need to address at the very beginning.
From an organizational point of view, successful Machine Learning requires cooperation between different departments such as analytics, IT, and business functions. The strategic and cooperative aspects of Machine Learning are some of the main reasons why it needs careful and committed management.