AWS Partner Network (APN) Blog

Telco Product Catalog Rationalization with Hansen using AWS Generative AI

By Juan Carlos Sanchez – Director, Product & Solutions Marketing, Hansen
By Albeda Siddique – Senior Telco Solutions Architect, AWS
By Nayef Khan – Senior Telco Solutions Architect, AWS

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Communication Service Providers (CSPs) face increasing pressure to deliver personalized offers to their customers, while managing complex product portfolios in the face of fierce competition. Creating and managing these diverse offerings is time-consuming, and remains a challenge.

This blog highlights Hansen’s collaboration during the 2024 TM Forum Catalyst Program, where leading technology and service providers developed an artificial intelligence (AI) driven offer lifecycle management solution. The Hansen-led team, including Amazon Web Services(AWS), NTT Data, AwareX, Prodapt, Telefónica, O2 Telefónica (Germany), Verizon, Viasat, and Aussie Broadband created a chat-based AI-driven offer lifecycle management solution using large language models (LLMs). This solution includes an artificial intelligence (AI) assistant that helps CSP product and marketing teams analyze their created offers based on their commercial success and provide recommendations to rationalize the catalog across existing brands, providing a sound basis for developing and commercializing future offers.

Hansen helps communications media, energy, and utilities companies create and deliver products faster. As an AWS Advanced Independent Software Vendor (ISV) partner, Hansen provides software that increases business agility for customers in more than 80 countries.

Offer lifecycle management challenges in the age of personalization

Customer offers include a mix of products, goods, and services with associated commercial conditions and pricing. Offers built by the CSP product management teams include an evolving range of items, including handsets and other specialized devices, media, network services, and associated resources. All of these elements must be maintained within their product catalog. CSPs manage multiple catalogs, each addressing different lines of business. Typically, new product items are added to each catalog and managed manually, so marketing team members can create sets of distinct, targeted customer offers across their commercial brands.

“We all have a common objective – see how gen AI can create better processes for offer lifecycle management, which is quite time consuming and complex for us today”Patricia Ventura Gómez, Head of Channels and Customer Engagement, Telefónica

“Automation can simplify offer lifecycle management, making it easier for customers to access only the products they need. By optimizing the catalog and removing unnecessary offers, we can reduce complexity and improve the customer experience”Sudhakar Lanka, Principal Engineer, Verizon

Solution overview

Catalog-based innovation revolutionizes offer lifecycle management (OLM) in telecommunications. By leveraging cloud and artificial intelligence technologies, CSPs can now solve several critical challenges. These solutions enable CSPs to audit large offer sets and identify successful product clusters efficiently. The technology creates standardized catalog structures across offerings with minimal effort, while using data-driven insights to reduce time-to-market for new offers. This approach improves offer profitability through intelligent analysis and maintains a streamlined catalog, helping sales staff present products more effectively to customers. The solution uses Open Digital Architecture (ODA) compliant APIs for ease of integration with Business Support System (BSS).

By applying generative AI services and tools, while rethinking current work practices, CSPs can develop new, innovative capabilities and processes to assist the teams involved in OLM. This includes product managers, marketing managers, with support from operations, IT, and other technical staff.

Hansen’s AI-assisted Catalog Rationalization analyzes offer performance and recommends catalog optimizations to product and marketing teams, and creates a data-driven foundation for future offer development.
Florian Estendorfer, Catalog Product Manager at O2 Telefónica in Germany provides his views of the benefits of the solution:

“It’s very impressive to see how the gen AI assistant delivers results in seconds, when it previously took a huge effort to simplify a portfolio of thousands of offer variants. Depending on the filters used, we were able to immediately identify poor-performing or duplicate offers, while getting suitable suggestions for smart replacements that can be added to the product catalog. In the long run, it’s easier to identify the best offers that are simple, attractive, and profitable. Other benefits include cost savings and faster time-to-market of new offers. It will also help accelerate business transformation in the case of brand migration, while maintaining high customer satisfaction from the best offers.”

Architecture of AI-assisted Catalog Rationalization

The AI-assisted Catalog Rationalization solution uses Amazon Bedrock’s gen AI capabilities, providing access to multiple large language models (LLMs) through a unified API. The Hansen-led TM Forum Catalyst project team used Amazon Bedrock to evaluate three LLMs, choosing Anthropic Claude 3 for implementation. Effective offer simplification requires understanding product relationships and customer preferences.

The figure below explains the integration between Hansen Catalog, Hansen Informatics and Amazon Bedrock (LLM, Amazon Bedrock Knowledge Bases and Amazon Bedrock Agents), and how it can be securely deployed in AWS. It also highlights the process flow and important technical decisions made that uniquely position this solution as a key BSS enhancement in OLM.

Solution Architecture: an AI-driven offer lifecycle management solution.Figure 1: Architecture for the AI-driven offer lifecycle management solution

The offer lifecycle management solution follows these steps to evaluate and optimize product offerings:

  1. The Product Manager from CSP applies filtering functions to select offers to analyze on the Hansen Catalog.
  2. Hansen Catalog sends the offer using Amazon Bedrock Agent for further analysis.
  3. Amazon Bedrock uses Claude 3 LLM and the knowledge base to analyze offer. This solution uses Amazon Bedrock Knowledge Bases with an Amazon OpenSearch Service vector embedding to store complex product attributes.
  4. The knowledge base relies on offer catalog product data and offer performance metrics data to evaluate current offer and provide recommendations. Data from the Hansen Catalog (TMFC001 Product Catalog Management) is saved in Amazon Simple Storage Service (Amazon S3). This data is then synchronized with the knowledge base. The LLM uses this embedding to identify similar offers, understand product feature relationships, and recommend simplified, high-performing offers.
  5. The catalog data is kept updated through integration with Hansen Catalog.
  6. The performance data is updated from Hansen Informatics to provide data around the success of a particular offer.

The recommended offer is presented to CSP’s customers through existing sales channel and fulfilled using the order management platform.

Conclusion

The AI-assisted Catalog Rationalization solution demonstrates how combining compliant Hansen’s Catalog and Informatics with Amazon Bedrock’s advanced AI capabilities can transform offer lifecycle management. This approach enables CSPs to streamline their product catalogs efficiently, identify their most successful offers within minutes, and create more attractive portfolios for their customers.

To learn more about the AI-assisted Hansen Catalog, click here. To learn how to build and scale generative AI applications, visit Amazon Bedrock.

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Hansen – AWS Partner Spotlight

Hansen is an AWS Advanced ISV Partner that provides software and services to the communications media, energy, and utilities industries. Serving customers in over 80 countries, its solutions help them create, sell, and deliver new products and services, helping achieve greater commercial agility.

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