Analytics & Insights
An Agile Approach to Measure Customer Lifetime Value
Measuring the health of your customer baseRead More
Authors: Pero Dalkovski and Jeff Renz
Margin optimization has been a cornerstone of business since the beginning of commerce. That said, recent technological advancements in the areas of artificial intelligence (AI), coupled with the abundance of data to support effective advanced analytics, have provided unprecedented opportunities for organizations to improve profitability – intelligently and tailored vs. one-size-fits-all.
Historically, profitability measures were implemented with broad categorizations and rule sets built on cost-plus models and acceptable margin floors. However, the explosion of available data along with advancements in data management and AI technologies open up new and innovative ways to tailor pricing, reduce costs, and find revenue opportunities. All with the goal of optimizing profitability.
For example, key technological advancements in the following areas are driving profitability opportunities.
Graph database technologies and knowledge graphs provide unprecedented visibility into the interrelations between factors such as customers, geographies, 3rd party costs, vendors, pricing, sales, operations, servicing, and billing. In other words, it shows how a specific customer’s situation relates to other factors unique to your organization and the external business environment. This allows you to:
Imagine enabling your sales organization with intelligent price recommendations – powered by a robust knowledge graph considering all of these interrelated data points – in order to quickly present an ideal price to a customer. This would be a price that provides the highest probability to win the deal while also preserving the optimal profitability for your business. Now, that is a game changer. If you still end up losing the deal, it’s most likely a better holistic outcome for your business – an outcome backed by rich and diverse data.
Technological advancements in the field of AI have reached the point of enabling intelligent margin optimization for the enterprise. How, you ask? You might start with reducing manually intensive operations with technologies like robotic process automation and then move on to exploring opportunities within your value chain by leveraging text analytics, machine learning, and augmented data management. A practice historically referred to as margin assurance or revenue assurance, can now be supercharged by these technological advancements uncovering more revenue uplift and cost reduction opportunities.
Tools such as Microsoft Azure Machine Learning, Informatica’s intelligent Enterprise Data Catalog, Ne04j graph database, and libraries like the Python Natural Language Processing Toolkit, help power this capability. These tools can quickly:
All leading to increased profit without bringing in a single new customer to increase revenues or finding new vendor alternatives to lower your cost of service delivery.
If you haven’t yet explored these technological advancements in data and AI, you are almost certainly leaving cash on the table. In today’s ultra-competitive business landscape, organizations at the forefront of implementing these advanced profitability maximization measures will outperform and secure competitive advantage over their competition. There has never been a better time to embark on this journey than now – what’s holding you back?
Pero Dalkovski co-leads RevGen’s analytics and insights practice. He has spent his career helping clients drive business value from data with increasingly sophisticated tools and techniques.
Jeff Renz is a senior business intelligence architect. Jeff is passionate about exploring emerging technologies and sharing his expertise by speaking at conferences and local user groups.