In e-Commerce, effective AI technology deployment has the potential to deliver step improvements in customer experience (CX) while increasing sales revenues and reducing the cost of sales. While it has already achieved tangible benefits through applications such as chatbots and product recommendation engines and remains a key technology topic for e-Commerce organizations, much of this potential is still to be realized.
Many organizations have focused their e-Commerce efforts on establishing competitive digital sales capabilities. Those starting to engage fully in AI may encounter hurdles to effective deployments, such as:
- Challenges in identifying specific use cases where AI can quickly add tangible value to the business.
- A lack of existing use-cases to build from. These use cases exist but may not be visible to the organization.
- Allowing preconceptions of AI in eCommerce to limit the scope, such as seeing AI as primarily a business-to-consumer (B2C) tool, missing its potential in business-to-business (B2B sales)
This blog describes a data-driven approach that enables sales organizations to realize the full potential of AI, detailing the wide range of data that can be collected and applied to deliver AI-powered optimization of e-Commerce processes, and highlighting how SAP CX and Arvato Systems support customers in maximizing value from their data with market-leading AI capabilities. Start with understanding the business’s e-commerce maturity level.
Arvato Systems experts created the B2B e-commerce maturity model as a roadmap for B2B sales organizations to plan and track their e-commerce journey. Maturity increases as more elements – customers, product lines, services and ordering processes – are added to the eCommerce platform and integration between the elements grows.
Understanding the business’s e-commerce maturity level is a key foundation for a successful, data-driven approach to deploying AI. Data is the essential fuel for AI, so the greater the range and volume of data available, the more value AI can deliver.
Mapping business priorities against the maturity model can help pinpoint where AI can add the greatest value and what data is needed to fuel it. For example, an organization that has achieved excellence with its e-shop offering could look to the maturity model to identify where it could go next, such as using AI to deliver greater customer personalization.
Each maturity stage requires more data points and processes to support it, so referring to the model helps validate if these are already in place and the business is ready.