Next level e-commerce through Artificial Intelligence

Never before has it been possible to intelligently automate processes and tasks faster and personalize the customer experience more than with AI. A great opportunity for e-commerce companies to reduce costs and increase sales.

Considerable effects through the integration of AI

From +31% increase in CR (MQL to order) through optimized customer scoring to -80% reduction in manual effort for order processing in the online store - the implementation of AI offers extensive opportunities.

How can AI be implemented in the company?

Inspiration and ideas:

• Inspiration: Identification of existing business goals; evaluation of the possibilities of AI, definition of desired operational goals to be achieved with AI (e.g. increased efficiency, optimization of user experience)
• Ideation: Identification of current relevant problems that prevent the business goals from being achieved; definition of use cases in which AI can be used to solve the problems
• Prioritization: prioritization of the most worthwhile use cases based on benefits and feasibility
• Test use cases: Selection of high-priority use cases that can be tested in the first step with little effort

Data basis:

• Database: Checking whether the data required for test use cases is available in the company
• Feature engineering: Quality check of the data, preparation and extraction of features from raw data so that they can be used for the selected use cases

Implementation and proof of concept:

• Problem solving: evaluation of which algorithms/basic models are necessary
• Algorithms/basic models: training the algorithms with test data, validation of the use case hypotheses
• KPIs: Definition of success KPIs for test use cases
• POC: Implementation of the proof of concept with the help of A/B tests
• Scalability: Deciding whether test use cases are scalable on the basis of a business case

Strategy development:

• Strategic goals: Development of strategic goals for the company as a whole that are to be achieved with AI by deriving them from business goals
• Production readiness: Definition of the basis for implementing selected AI services:
  • Definition of the target operating model
  • Definition of the necessary technical architecture
  • Definition of the systems
  • Deriving the necessary resources and expertise