How Data-Driven AI with SAP CX AI Toolkit Can Transform E-Commerce

Artificial Intelligence Customer Experience E-Commerce Innovation SAP

In E-Commerce, effective AI technology deployment has the potential to deliver steep 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 article 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 AI Toolkit and Langia IT Solutions support customers in maximizing value from their data with market-leading AI capabilities. Start with understanding the business’s e-commerce maturity level.

Langia IT Solutions experts created a maturity roadmap for B2B sales organizations to plan and track their AI e-commerce journey. AI effectiveness increases as more elements – customers, product lines, services, and ordering processes – are added to the E-Commerce 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 roadmap 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.

SAP CX AI Toolkit Can Deliver the Data

Customer Relationship Management (CRM) has evolved to take every touch point in the customer experience (CX). SAP’s CX AI Toolkit portfolio seamlessly combines functions and data from commerce, marketing, sales and service to give a single, unified 360° perspective on the customer, enabling the insights and intelligent action to maximize customer value.

SAP CX AI Toolkit’s comprehensive reach means a wide variety of information can be collected from it to fuel AI, from data on customers and the products and services they purchase to data on the use of the customer portal. This may include:

• Automate Repetitive Tasks: Increase operational efficiency and free up time for high-value activities by automating routine tasks using accessible AI capabilities.

• Gain Insights from Enterprise Data: Utilize AI to analyze enterprise data for more intelligent decision-making across sales, service, and e-commerce operations.

• Access Anywhere: Empower teams to access AI capabilities seamlessly from their everyday platforms, ensuring ease of integration and flexible usage.

• Drive Efficiency and Effectiveness: Use proactive insights from enterprise data to understand customers and address their queries faster, enhancing efficiency and customer satisfaction.

• Cloud Deployment and Preconfigured Integrations: Seamlessly deploy the toolkit in the cloud with preconfigured integrations for SAP applications, ensuring scalability and adaptability.

• Enhanced Customer Experience: Improve customer experiences through enriched product tags, compelling descriptions, and visual search capabilities, creating more personalized and engaging interactions. 

Access to this comprehensive range of data enables targeted, AI-powered optimization of e-commerce processes. Fueled by this data, the SAP CX AI Toolkit provides an environment in which AI-supported processes can be individually implemented and operated. This way, the stored data can be processed and used to automate and optimize business processes.

Evaluating AI priorities starts with understanding the needs and goals of the business, set within the context of its e-commerce maturity level. This identifies what data is required and available to fuel the AI capabilities to deliver those goals. 

Some examples of e-commerce processes that AI can optimize: 

• Automated reordering

• Product advise via live chat or chatbot

• Digital product advisors and product configuration

• Sentiment analysis for feedback and service tickets and social media analyses for products and services

• Automatic control of e-mails in digital business processes

• Product data enrichment with automatically generated metadata

• Classification of support tickets

• Translation of content

• Product identification via uploaded product photos from the customer

• Predictive maintenance

• Risk evaluation 

• Creation of sales forecasts

How Langia IT Solutions Creates Value with SAP CX AI Toolkit

Langia IT Solutions offers a proven, robust, predefined model for customers to grow their e-commerce processes and capabilities with AI. It is supported by the power of the SAP CX AI Toolkit and backed by the long standing partnership between SAP and Langia IT Solutions, broad industry experience, and a strong partner network. Langia IT Solutions’ Microservices-based, API-first, Cloud-native, and Headless (MACH) architecture maximizes deployment agility across all service layers.