Product information is the backbone of omnichannel retail. Consumers expect high-quality product information with correct, up-to-date information Product information drives the entire consumer funnel online from search & browse to purchase. Product information is also organic content that brands and retailers can leverage on online real estate that is available.
“Nearly 50% of brands say unifying online and in-store operations and data will be their biggest challenge over the next year.”- Shopify retail statistics.
What is the challenge with product data:
In-store and online data gaps
Consumers want consistent treatment and a seamless experience from merchants, whether in-store, online, via mobile device or at a kiosk. The product data requirements for store operations are mostly operations-centric while the data requirements for omnichannel are more consumer-centric most retailers, merchants, manufacturers, marketplaces have evolved into silos. It's not uncommon for the store's segment to be in the Midwest while the e-commerce division is in Silicon Valley. Technology and data silos compound organizational silos, causing execution difficulties.
— An online order scheduled for pick-up at the retailer's nearby store will be missed if retail and online inventory data isn't integrated and updated in real-time.
— Managing in-store returns for internet purchases is a challenge if in-store and online systems don't always communicate.
A unified organizational approach to product data is required to enhance omnichannel experience & business. Creating cross-functional and cross-organizational product data teams with all involved stakeholders is key to embracing this new normal.
Data collected across an organization frequently exist in silos, which is one of the major issues for retail. Departments use different applications and often store information in ways that are frequently incompatible with one another and from various sources. A typical manufacturer has 8 - 12 applications that handle facets of product data.
Data comes in many forms in a contemporary retail setting, including unstructured data, media assets and structured data from files and databases. This raises the difficulty of integration.
A unified data strategy that includes a PIM, a Data Lake or a data warehouse is required not only for product data but all other customer, customer services, sales, marketing and operational data. Unified data can provide a deeper and broader view for every stakeholder. In addition, AI & ML can automate the aggregation, integration and reconciliation of data across multiple platforms creating a unified store of information.
Managing product information is labour intensive, especially with the addition of digital assets, media and images – searching, importing, optimizing, linking, publishing, etc. Often these assets are managed on local drives and are not searchable. Every time a product is launched, data updating and validation are manual. Once the data is fed into the system, it is so unmanaged that it affects SEO rankings negatively along with the search on the website.
Automation is key to managing ever-growing product data. Leveraging AI and ML is key to indexing, and searching digital assets. AI and ML can also enable extracting information from digital assets like attributes and automating extending the product information in the PIM for the digital shelf.
Most product content is created once and used over the life of the product. Unlike traditional information on packaging, digital content can evolve over the life of the product leveraging consumer insights, competitive positioning and market conditions. Most retailers and manufacturers are not geared to leverage this opportunity as product content creation is labor intensive & time consuming process.
Poor Data Visibility
Digital is a highly measurable channel. Everything from search from product views to purchase is effectively measured and tracked by marketing & sales teams. Unfortunately, this information does not flow back to product data managers. The list of issues with product content could include errors in listing, sub-optimal ranking in search, insufficient attributes for browse filters, and ineffective product titles.
Optimization and management of product data for the digital shelf have to include visibility into the performance of the data that will allow content owners to proactively take corrective steps. AI & ML can effectively monitor product content and also provide proactive insights on enhancements.
The growth of user-generated content on social media and user reviews is a rich source of information. This firehose of data is challenging for any organization to leverage and can provide a real-time pulse of the consumers and a rich source for personalization Social media interactions, online reviews, and call center conversations are typically unstructured data and difficult to leverage using traditional relational systems designed for rows and columns. Most statistical models used to forecast future purchases and promotional responses are based on a narrow set of demographic and transactional data, ignoring the wealth of consumer data now available.
The evolution of natural language processing and computer vision now enables extracting insights from these unstructured sources a reality. AI & ML can very effectively understand the user content for brands from social media images or identify the key features consumers care about in a product category. Leveraging AI can not only provide rich insights into consumers but can help tailor product information that best matches the consumer pulse.
Assuming most retailers and brands are already leveraging PIMs and MDMs to manage their data. Our 2022 strategy for product information is relatively easy to implement but driven by the digital-first approach:
Automation along the process can simplify this new strategy. Leveraging AI & ML in the process can improve efficiencies and improve productivity.