Supercharge the value of product data with GenAI

The challenges of product data management

Before diving into how Generative AI can address these challenges, let's first understand the key issues that organizations face when it comes to product data management.

Data volume and complexity: As businesses expand, so does the volume and complexity of their product data. This data comes in various formats, including text, images and videos, making it challenging to manage effectively.

Data quality and completeness: Ensuring data accuracy and consistency is a constant struggle. Inaccurate product data can lead to costly errors, delays and customer dissatisfaction. Organizations, especially distributors, retailers and others who source the full or near-end-stage products, struggle to consistently complete their data and often take shortcuts with valuable but ‘optional’ fields such as marketing descriptions, pictures, etc.

Data integration: Manual data entry and management processes are time-consuming and prone to human errors. This inefficiency can slow down decision-making and product development.

Customization and new product introductions: Rapid changes in consumer preferences and market dynamics demand agility and innovation. Businesses need to respond quickly and accurately to remain competitive and are increasing the number of SKUs offered via customization and new product introduction. These offerings increase complexity and put stress on processes used to create accurate product data.

The power of Generative AI

Generative AI has the potential to revolutionize product data management. It leverages machine learning algorithms to generate data, content and even creative assets.

Generative AI excels in the following capabilities:

• Recognize and extract: Locate and extract the exact text needed from documents such as invoices, contracts, or product descriptions using natural language questions.

• Summarize and infer: By analyzing product descriptions or customer reviews, an LLM can assist in automatically providing key insights and categorization to drive your business forward.

• Expand and complete: Generate likely options based on existing data or common patterns. Create automated product selling statements, images and marketing content.

• Translate: Translate content into any language seamlessly to serve all of your customers in their preferred language.

• Agents: Offer 24/7 customer support or make work easier for your employees by using agents.

Use cases: GenAI and product data management

Three use cases are rising to the surface as valuable ways to leverage GenAI to improve product data management.

1. Automate data decoration: Generative AI can automate the generation of various types of data, such as product descriptions, technical specifications and even images or videos. This reduces the manual data entry burden, turns copywriters and data entry specialists into editors, and ensures a continuous and consistent flow of high-quality data.

2. Organize and cleanse: By using Generative AI, businesses can get their product data house in order by ensuring consistency and completeness while raising the bar on overall data quality.

3. Expand customer exploration: Web and commerce chatbots are not new. Generative AI increases their functionality from a pre-made decision flow into a true digital agent that can answer the majority of unique questions from customers, simplifying their efforts to identify and purchase the best possible option for their specific needs.

Six steps to activate Generative AI

Here's a simplified roadmap for implementing Generative AI in your product data management.

1. Define your why: Determine the magnitude of your organizational challenges with product data management. Focus on business value first, and high level estimates of investment second. Investment will usually require an assessment of the data landscape to determine pain points and map out a path to get to clean data.

2. Prove value and pilot: GenAI models can initially be built with limited data sets. We recommend building the first iteration of your prompt as soon as possible to prove that the tool can execute the task at hand, and then analyzing for the key measures of value.

3. Pilot and plan: Once the prompt is built, start to use it in real business situations. Learn and iterate the prompt while you measure value and progress. At the same time, plot out what the full scale ‘product’ may look like in your organization by designing new processes, aligning on critical integrations and planning any associated features or functionality needed for a business-as-usual production version of the tool.

4. Provide human oversight: While Generative AI can automate many tasks, human oversight is essential to handle exceptions and ensure ethical use of AI-generated content.

5. Build and integrate: Build the production version with the appropriate integrations, process updates and functionality needed. Plan the adoption based on the breadth of users and stakeholders affected.

6. Monitor and expand as necessary: Regularly monitor the GenAI prompt to ensure quality output and make necessary adjustments. Treating each use case as a product to be nurtured is a helpful reference point to ensure you get the most value out of your Product Data.

Generative AI is a game-changer for product data management. It enhances data quality, accelerates innovation, and improves customer experiences, ultimately helping your business stay competitive in a rapidly evolving marketplace. Embracing this technology today can pave the way for a more efficient and innovative tomorrow.