Is your product data AI-ready?

This year, global AI investments are already at $196.63 billion. By 2030, the AI market is predicted to expand to $1.85 trillion, which is nine times larger than the figures today.

AI has evolved beyond just speeding up workflows and is now used to transform ecommerce experiences for customers. More than 75% of online customers appreciate a personalized and consistent brand experience across the web while they shop. AI features play a significant role in tracking market trends, customer behavior, purchase history, browning patterns, and providing relevant product recommendations and suggestions.

Netflix, for example, has around 269.6 million subscribers around the world. The streaming service provides unique TV and movie recommendations based on each individual user’s preferences and watch history. This involves gathering data via analytics models and then plugging it into Netflix’s AI recommendation engine to analyze the data and provide relevant suggestions to the watchers. This is how big data and AI go hand-in-hand to build our personalized Netflix experiences, and similarly other ecommerce experiences.

Why product data quality is key to AI success

In spite of such a surge in AI investments across the world, only 25% of businesses are satisfied with the features and outputs of their AI solutions. The reality is that AI is not self-facilitated and relies on accurate data for its performance. The technology is only as good as the data you feed it.

With this in mind, the potential pitfalls of unsupervised AI cannot be overlooked. Imagine a scenario where AI is tasked with categorizing product attributes. Even a minor 1% error rate could lead to significant repercussions. For instance, in a catalog boasting one million SKUs, this could mean a staggering 10,000 products wrongly classified, risking their visibility on critical platforms like Meta or Google.

Before integrating new AI tools into operations, businesses must ensure that the data plugged into the tools is reliable and consistent. Otherwise, these big tech investments can backfire quickly without clean data.

Let’s explore how you can prepare your product data to leverage it for AI features that will ultimately enhance your business operations.

Read More: 5 PIM trends shaping modern commerce

3 steps to make your enterprise AI-ready

For large, global companies with huge data sets, it’s crucial to implement AI strategically. The following steps can help ensure your AI goals are on track.

• Clarify the objective of the AI use case

Start by revisiting the strategic goals of your enterprise and build a business value map. Analyze the inefficiencies and restraints in your business operations and identify opportunities to improve. After drawing lines between your current pain points and potential solutions, draft the scope for AI in your enterprise. By not overlooking this step, you can move forward with confidence that your AI initiatives align with your fundamental business goals.

• Conduct a product data health check

Once you have defined the AI implementation objectives for your enterprise, evaluate your company’s product data base – how is data currently stored, who is responsible for managing it, how is information shared, where is it shared, what purpose does it serve? Understanding the current state of your product data is a necessary step on the path to improving its quality to ensure successful AI outputs.

• Build your AI team

Identify people within your company, or lean on your outsourced partners, who have the skills and knowledge required to understand data management, statistical analysis, and the principles of machine learning. This is essential to make informed decisions when developing and deploying AI models effectively.

Your AI-ready product data checklist: Ensure smooth implementation

To help you prime your product data to be used for ecommerce AI solutions, we created the following checklist.

• Consolidate and integrate your data sources

Product data can be found scattered across a wide range of sources, such as internal databases, customer relationship management (CRM) systems, cloud storage solutions, third party marketplaces, ecommerce sites, sales channels, etc.

To avoid gaps or discrepancies in your product data, you should keep a centralized repository that acts as a source of truth. Breaking down silos among your team and having a streamlined approach to gather information about your products ensures you have an accurate, comprehensive product feed to create the most impactful product content. Otherwise, you could risk false stock counts if you’re pulling product inventory data from multiple systems, for instance.

• Validate your product data

Once you’ve sourced your data, the next step is to assess its quality. Look for inconsistencies, errors, duplications, and missing information. The richness of data fields significantly impacts AI analysis effectiveness. For instance, if you want to use AI to personalize a product description for a specific target audience, the more product attributes you can plug will have a direct impact on how robust the generated description is.

Read More: 7 creative strategies for customer retention in ecommerce

• Structure your product data

Artificial intelligence detects patterns in data and then makes predictions based on these patterns. So, inconsistent data formats make it difficult for AI models to make accurate predictions and provide correct outcomes. You need to organize and structure your product data in a consistent manner to avoid errors in AI results. Here are a few quick tips on how structured data can aid in AI integration:

• Reformat data from different sources to follow the same structure. For instance, if one department uses “MM/DD/YYYY” and another one uses “DD/MM/YY” for expiration dates, decide on a common format and properly adjust all sourced data sets. This is especially important for dates, numerical values, and categorical entries.
• Use labels, tags, and metadata to properly annotate your data. This helps AI systems understand context and relationships between different data points.

Source: Gartner

While smart technologies have had an extensive impact on the global ecommerce sector, such as generative AI which is now valued at $5.92 trillion, realizing their effects comes down to having good data – and transitioning your data management practices to align with your AI goals.

Is your product data ready to see the ROI of AI? Learn more about how Productsup’s platform is equipped to take your raw, complex data and enhance it with AI to deliver flawless product content journeys. Explore our AI resources:

Explore our AI resources for further learning:

• Overview of all AI features within the Productsup platform
• The state of smart commerce: AI’s impact on buying and selling
• How Productsup’s AI tools create smarter product content journeys
• Work smarter, not harder in managing global marketplace data