5 product information management (PIM) trends shaping modern commerce

Despite all the innovation and complexity surrounding modern commerce, everything always comes back to the basics – someone is trying to sell an item to a customer. As soon as you put a price tag on that item, give it a name, or describe what it looks like, you now have product information.

Effective product information management (PIM) is, always has been, and always will be crucial for reaching buyers, guiding shopping journeys, and driving sales. In fact, the PIM market is expected to grow 13.9% in 2024, reaching $1.5 billion.

So, how has PIM technology evolved? Where are the current challenges in product data management? What are the latest strategies? Let’s look at the top PIM industry trends, as well as the leading solutions addressing them.

Top 5 PIM trends to watch

1. Increasing importance of data quality

The importance of high-quality product data cannot be overstated. According to Forrester, businesses that prioritize data quality see a 20% increase in online sales and a 25% decrease in product returns. Product data needs to be maintained constantly so that it is accurate, complete, and consistent. Not only does this ensure all channel requirements and local regulations for product data are met, but also that customers have the information they need for purchasing decisions and overall satisfaction.

Brands across various industries can benefit significantly from improving their product data:

• Health & Beauty: Brands like L’Oréal and Beiersdorf leverage high-quality product data to provide detailed ingredient lists, warning labels against allergies, and verifications for non-harmful chemicals, making it easier for customers to identify the products that fit their unique needs.

• Electronics: Companies like Apple and Samsung rely on accurate product specifications and high-quality images to inform customers about features and functionalities. This ensures that customers can easily compare products and select the right one for their preferences.

• Home goods: Retailers like IKEA can use detailed product data to provide assembly instructions, dimensions, and material details, helping customers make informed purchasing decisions and reducing post-purchase dissatisfaction.

• Automotive: Car manufacturers like Ford and Tesla benefit from comprehensive product information, including specifications, features, and comparisons, which aid customers in making well-informed decisions about their vehicle purchases.

2. Omnichannel integration

Today’s consumers look for product information in multiple locations, meaning brands need to make their product data available in all of the spots they’re looking at. Data shows omnichannel campaigns achieve a 287% higher purchase rate than single-channel campaigns. But it’s not just about showing up where consumers spend time; brands need to deliver a seamless shopping experience across all channels (ie. Amazon, Google, and Meta) and mediums (ie. online, mobile, and in-store). PIM can ensure consistency across these various customer touchpoints, including new ones entering the industry.

• Rise of retail media networks

The growth of retail media networks (RMNs) is a significant development within the omnichannel landscape. Retailers like Amazon, Walmart, and Target have created their own media networks, allowing brands to advertise directly on their platforms. According to Insider Intelligence, $55.31 billion will be spent on retail media in the US in 2024 with a projection to hit over $100 billion in three years. This emphasizes the importance of having accurate and consistent product data for effective advertising.

Source: emarketer.com

Retail media networks provide an additional channel for brands to reach consumers, but they also require precise and high-quality product data to ensure that advertisements are relevant and engaging. Brands that utilize retail media networks need to manage the increased volume and complexity of product data across these platforms. - Explore how RMNs can enhance your brand's visibility and drive sales with continued reading on our blog or by watching our webinar on-demand.

3. AI integration and digital shelf analytics

AI capabilities are transforming product information management by enabling faster data adoption and providing valuable digital shelf analytics. AI-powered solutions can automatically tag, classify, and onboard product data, significantly reducing manual effort and accelerating time-to-market.

• Enhanced efficiency: AI reduces the time and effort required for data entry and management, allowing product teams to focus on strategic initiatives.

• Improved accuracy: Automated data tagging and classification minimizes errors, ensuring consistent and accurate product information.

• Faster time-to-market: AI-driven processes accelerate product data onboarding and syndication, enabling businesses to launch products faster.

Through investment in advanced AI capabilities, enterprises can also leverage digital shelf analytics to gain insights into competitive market intelligence. This enables them to stay competitive and drive sales growth by making data-driven decisions.

4. Managing disparate sources and channel volatility

In a highly saturated and rapidly changing commerce tech market, businesses must manage the complexity of disparate supplier sources and the volatility of digital channels. They need to be able to source data from multiple systems, like product lifecycle management (PLM), and repackage it as compliant product information tailored for each sales channel. Streamlining this process through integrations ensures accurate, up-to-date, and contextually relevant product information, reducing the impacts of commerce complexity, such as online returns or negative customer reviews.

5. Personalization and customer experience

Research shows 80% of consumers are more likely to make a purchase when brands offer personalized experiences. The greater emphasis on personalization is driven by the need to stand out in a crowded market. Customers now expect more than just a transaction; they want an experience that feels unique and relevant to them. To meet these expectations, companies must ensure that every interaction – from product recommendations to marketing messages – is tailored to the individual consumer.

Personalized product information includes dynamic content that changes based on user behavior, preferences, and demographics. This could mean showing different images, descriptions, or even different product selections to different users. The ability to personalize product content at scale requires robust data integration and analytics.

Emerging product data pain points

1. Data silos and fragmentation

As businesses grow to new markets and expand their sales channels, they often encounter data silos and fragmentation. These silos exist not only across sales channels but also within internal departments or regional teams. Disparate systems and sources of product information lead to inconsistencies and errors, impacting the customer experience. Research shows 80% of organizations struggle with data silos, which hinder their ability to manage product information effectively.

Source: datadynamicsinc.com

2. Manual data entry and errors

Companies spend an average of 10% of their time on manual data entry. This consumes resources and makes the data prone to errors. Inaccurate product information can lead to customer dissatisfaction, increased returns, and lost sales, underscoring the need for automated workflows.

3. Complex data syndication

With multiple sales channels and partners, businesses face the challenge of syndicating product data efficiently. Ensuring that product information is consistent across all channels requires agility when handling complex data syndication processes.

4. Dependence on IT for routine tasks

Many businesses still heavily rely on IT departments for routine data management tasks, which can lead to delays in implementing changes and updates. Leveraging AI-powered tools that eliminate the need for coding and streamlining these processes with automation features can enhance efficiency and reduce dependency on IT resources.

5. Low visibility of performance data

Without a centralized view to track how product information performs across channels, businesses struggle to optimize ROI and ROAS. Poorly performing products can drain advertising budgets, while top sellers may not achieve optimal exposure. This lack of insight makes budget allocation difficult and can lead to higher operational costs.

How Productsup fits into the PIM space

As part of the Forrester Wave™: Product Information Management, Q4 2023, Forrester evaluated Productsup for our PIM-like functionality. We were ranked as a Strong Performer amongst the leading 11 PIM providers. The report highlights our extensive data enrichment and syndication capabilities as key components driving the next wave of PIM innovations.

However, our P2C platform is not exclusively PIM software, but rather, a comprehensive solution for managing the entire product content journey. PIM systems act as a “golden record” for product content, serving as central repositories for consistent, formatted product information. However, this only covers part of the journey.

A product-to-consumer (P2C) software like Productsup is equipped to manage the entire path product content takes from suppliers to buyers – and back again. P2C systems act as an “agile data record”, allowing for the collection, transformation, and transfer of product content. These systems either integrate directly with other PIM solutions or offer their own PIM-like functions. By bringing the functions of various commerce solutions (ie. PIM, Digital Asset Management, Master Data Management, Enterprise Resource Planning, etc.) together into a single management view, P2C condenses the number of systems required to manage the product content journey from start to finish.

Here’s how:

1. Centralized data management

Productsup provides a centralized platform for managing product information, eliminating data silos, and ensuring consistency across all channels. This centralized approach reduces errors, improves data accuracy, and enhances the customer experience.

2. Automated data integration and validation

Our platform automates data integration and validation processes, reducing the need for manual data entry and minimizing errors. By leveraging advanced AI technology, Productsup ensures that product data is accurate, complete, and up-to-date.

3. Efficient data syndication

Productsup simplifies data syndication by automating the distribution of product information to multiple channels and partners. Our platform supports various data formats and ensures that product information is consistent across all touchpoints, improving the efficiency of your product content strategy.

Discover how lastminute.com, a leading online travel and leisure retailer, streamlined their data syndication processes across multiple channels, using Productsup's robust capabilities. [Read more]

4. AI-powered content generation

Our platform is engineered with advanced AI technology that can turn raw product data into high-quality content tailored for different audiences, channels, and regions. Instead of manually creating relevant product descriptions, titles, keywords, etc., teams can generate multiple versions of product content at scale. The AI tools can adjust text for specific channel requirements, translate to different languages, optimize for SEO, search and extract patterns, and manage dynamic content.