Post-pandemic, consumer reliance on online shopping remains steady, meaning e-commerce businesses need to continue to adopt new technologies to scale their business operations.
Digital Asset Management (DAM) software can make it easier for creators to store, search, and organize their assets. Unfortunately, legacy DAM solutions are no longer sufficient to manage large volumes of product-related content. After all, using ‘old school’ DAM software requires a large staff who can manually optimize media and customize experiences for their audience—a practice that goes against agile methodology.
Staying competitive in today’s e-commerce environment requires brands to harness the power of AI and the efficiency of automation. A business using AI can quickly match audiences to relevant products and edit assets on the fly, creating more convenient and personalized shopping experiences. On the back-end, automation simplifies asset management, saving time and resources while increasing sales efficiency and marketing effectiveness.
During the pandemic, the US saw a 50% increase in e-commerce sales. This rapid shift to online shopping forced many businesses to find new asset management solutions. The right tool saves time for creative teams by taking on the labor involved in cropping, tagging, recoloring, background removal, and numerous other tedious tasks. AI tools can also automate higher-level functions, performing object recognition and asset categorization and efficiently organizing even legacy datasets.
Together, these tools free up a marketing team to address more strategic concerns, like finding opportunities to generate interest across new sales channels and touchpoints.
E-commerce activity generates a lot of data that can be used for discovery. However, creators and developers can’t use what they can’t access, and studies show that 73% of data is never used for analytics. This wasted data is more than just lost revenue: Storing and transmitting data is expensive and also poses environmental concerns. To optimize asset delivery and extract the most valuable data from e-commerce activity, businesses must enhance their DAM tools with AI and automation.
Let’s look at how AI and automation can help an e-commerce business achieve greater customer satisfaction, higher revenue, lower costs, happier employees, and more efficient and agile business operations.
Many websites collect cookies to track their customers’ buying patterns and enable personalized product recommendations. AI can analyze this information, so we can use it to automate outreach and customize customer campaigns and newsletters.
Effective tools can provide extensible APIs to automate DAM and target specific user segments and devices. For example, Cloudinary’s Admin API lets you retrieve and manipulate asset metadata as part of an automated pipeline. In conjunction with Cloudinary’s object detection tools, it’s a powerful tool to modernize legacy databases.
Most companies offer flexible return policies to stay competitive in a market where customers cannot appraise a product in person before purchase. It’s expensive to provide the customer with this freedom—product returns cost companies millions of dollars annually.
One of the most common reasons customers return products is because they feel they’ve received something different than what they saw before purchase, which could occur if the product page had insufficient photos or poor-quality images. For an e-commerce retailer, saving money by taking fewer photos is a false economy; a loss of revenue and the cost of processing returns can offset any savings.
AI-powered content creation helps ensure customers are happy with their purchases. For example, Cloudinary’s image and video transformation API provides a suite of tools to generate high-quality derivative assets from a small number of product images. For example, suppose you’re selling a sweater in a range of colors. Cloudinary’s image transformation API enables us to recolor a photo of it, so the product team only needs to photograph it once.
AI is also a powerful tool for matching visitors to the products they’re most likely to buy. By combining in-session user behavior patterns with cookies, an AI-based system can recommend appropriately sized clothing that matches the customer’s style.
Then, when a potential buyer is matched to a product, we can use AI-powered tools to generate interest. For example, on Mazda’s purchase page, customers can apply 3D model transformation functions to create a 360-degree view of their vehicle build with all the personalized upgrade options and the color they’ve selected.
AI also enables customers to preview personalized products. If a clothing retailer offers the option to add a custom inscription or design, for example, then an AI-powered displacement map can show what the final product will look like much more clearly than a simple overlay.
We can implement much of this functionality with a tool like Cloudinary’s content-aware object detection add-on. When used alongside the AI-powered background removal tool, we can generate and edit image assets for any context. For instance, consider an automotive manufacturer with a database of automotive add-ons. An AI could analyze image assets and apply smart tags to categorize product options. If the manufacturer offers numerous upgrade options across a range of a dozen or more vehicles, this will save a lot of time and work. The technology can even help with cleaning up legacy databases and regaining control over lost or mislabeled assets.
A well-organized asset database also creates happier customers. Suppose visitors to our storefront have access to a search field or chatbot for queries. In that case, we can combine this data with user behavior data we collected earlier and compare it against our meticulously and automatically tagged and organized product catalog.
As we integrate AI tools more deeply into our supply chain, we can also expect more efficient fulfillment as we optimize for customer preference, location, and even local weather. For example, we can integrate Cloudinary-managed assets with Next.js Middleware in Netlify to find out where visitors are located and inject shipping information. If customers find the status updates useful, they’re more likely to become repeat buyers.
AI also helps build customer trust. AI-powered tools can automatically synchronize sales across multiple devices, identify high-risk transactions, and offer discounts to loyal customers more intelligently than rule-based implementations would. We can even use virtual assistants to handle administrative tasks that impact the end-user experience.
For example, AI can help a storefront become more responsive by determining which media assets should be cached locally in a Content Delivery Network (CDN) or by identifying the most routine customer queries and offloading them to automated chatbots. An apparel storefront can provide a more bespoke experience by offering AI-powered fit and sizing assistance or even suggestions for complementary wardrobe choices.
When a customer decides to purchase, AI can help us ensure we’ve minimized human error in the inventory handling and fulfillment stages. If our product has a loyal following, we can keep customers engaged by providing AI-optimized, up-to-date stock arrival notifications.
If we allow end users to create their own content, such as photos in product reviews (or if we’re using AI to pull from external content stores), we should use a tool like Cloudinary’s asset moderation. Depending on the type and volume of content, we can configure these add-ons to flag content for manual or automatic review or a combination of both. For instance, we might want to automatically reject some content, such as low-quality images or images that have not been anonymized. Other content might need human approval, such as automatically smart-tagged product images.
To be competitive in sales within a digital ecosystem, you often need to analyze trends in external data. AI tools help us stay competitive with comprehensive industry monitoring and analysis. Rather than manually searching for a competitive edge, we can feed raw data into our models and expect better insights—notably, often without needing to perform the tedious process of data normalization.
Another common necessity of e-commerce businesses—namely, complex integrations—can break continuity between upstream and downstream portions of the sales pipeline, especially when integrating legacy applications. This process can create extra work and delays for the sales team, who either have to troubleshoot integrations or rely on support teams or developer teams to make changes. AI-powered automation can solve this issue and create a more extensible and easy-to-use pipeline for the sales team.
In an e-commerce business, payroll, accounting, and invoicing are all digital (and often cloud-first) processes. This makes them ideally suited to administrative automation and AI.
Cloudinary’s broad set of integrations enables Cloudinary-managed assets to be deployed through commercial platforms, like Adobe Commerce (formerly Magento) or Salesforce. We get the benefits of the financial tooling of top e-commerce and marketing frameworks while delivering quality, relevant content that’s been automatically curated by asset management technologies.
To grow an e-commerce business in a cloud-first world, you need the help of cutting-edge technologies. In the DAM space, AI can make the difference between a digital storefront that needs constant manual labor to stay effective and an e-commerce business that’s ready to sail the tide of internet commerce. To start integrating AI into your business plan, visit Cloudinary today.