Top Practical Ways Brands Can Leverage AI in eCommerce

As a top stakeholder in your eCommerce C-suite, you can’t afford to fall behind on the technology needed to drive your business success. One of the emerging technologies in-the-know heads of eComm are leveraging today is the highly controversial but far less-scary-than-it-seems idea of AI. Artificial intelligence is a valuable resource that deserves its rightful place in many aspects of eCommerce, including your tech stack.

But how that AI is used is a big question many C-suites struggle with today. And that is the exact question we aim to answer with this guide.

Our goal is for you to finish this piece not only with a firm grip on the multiple practical uses of AI, but with a fresh perspective on your own business and processes that could be improved with the power of this new technology.

This is a 25-minute read, so grab a coffee and get comfortable. We’re about to dive into the formidable world of AI and not only make sense of it, but show you how to use it to your advantage in eCommerce.

We hope you find this an interesting read – enjoy!

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Introduction

If you are dipping your toes into the possibilities of AI but can’t find a matter-of-fact resource, this guide makes AI more approachable. This is the place to discover practical ways to leverage AI in eCommerce, specifically in managing your product information. Although you might be a little too far removed from the trenches to understand the challenges product information management (PIM) poses for your team, you play a significant role in enabling your to improve customer experiences. And that’s where AI can help.

As a PIM/DAM provider, we’ve looked at the pros and cons of AI and determined it is a safe, extremely productive part of a comprehensive PIM/DAM today. Since it is already being deployed to great effect in other industries, it’s worth a gander to understand what’s readily available for eCommerce applications. Understanding how to leverage this technology gives you a competitive edge in the quickly changing and ever-expanding eCommerce landscape.

This meticulously crafted white paper unravels the mystery of AI capabilities without unnecessary high-tech mumbo jumbo that’s virtually useless to someone not ensconced in the tech industry. We decided that rather than tossing a lot of the surface-level AI chatter about aimlessly or adding confusion to the mix by going too far the other way, we would cover the most critical facts you need to know about AI. Our goal is to enlighten you on the many benefits of AI, how it works with the help of machine learning (ML), and why it’s so essential for your team to find practical ways to leverage AI to remain competitive.

Two types of AI

There are also two types of AI:

Software such as search engines, security recognition systems, chatbots for customer service, virtual assistants, etc.
“Embodied” AI such as autonomous cars, robots, the Internet of Things, drones, etc.

AI can be applied to eCommerce in several ways, from personalization to product descriptions/ recommendations to fulfillment and logistics in the supply chain. In fact, many third-party logistics providers use embodied AI to streamline the order-picking process, while marketing companies use AI software to generate copy for eComm ads and social media posts. As a result, you could already be indirectly benefitting from AI.

Why is AI important to understand now as opposed to waiting?

The companies already deploying AI are gaining significant competitive advantages in two impressive ways:

1. Increased sales

AI helps overcome common challenges that can negatively impact sales, including:

Increased average order value

AI replaces the brick-and-mortar salesperson, enhancing the shopping experience with personalized recommendations. Things such as generating other customers viewed, other customers bought, and bundling idea messages using what’s called Natural Language Processing (NLP) is one example of how AI uses machine learning (ML). This function allows AI to interpret, manipulate, and comprehend human language, making it easier for customers to search your site for items. Also, AI can make recommendations using text, images, or visual elements.

For example, an image on a platform like Pinterest can allow AI to find the same or similar products using image recognition software. As a result, users can easily track down items to complete a home décor project, put together an outfit, or find more affordable options for a very high-end brand. Of course, you can also use AI for traditional upselling and cross-selling methods, recommending complementary products such as matching shoes to a purse, blender accessories, or gaming system speakers. AI is intuitive, gently coaxing customers towards their purchase without feeling invasive like a salesperson breathing down the customer’s neck.

Reduced cart abandonment

Average cart abandonment rates are about 70%. AI can help reduce abandonment by tracking customers as they use your site. When a customer fails to complete their order, AI reacts. For example, suppose a customer abandons their cart because they fail to meet the free delivery threshold. In that case, AI can recommend a more expensive item, or complementary items, with a friendly reminder that the customer can get free delivery by spending x more dollars. In addition, once the customer leaves your site, AI can create a targeted ad that follows them around the internet to encourage them to check out and send a cart link via an email reminder.

2. Reduced costs

AI improves sales while reducing costs, creating a very symbiotic relationship that improves your profit margins in a few ways:

Logistics:

As a backend supply chain technology, AI provides inventory tracking and planning in real-time based on good old-fashioned supply and demand logic. It leverages machine learning to provide predictive analytics forecasts. It also improves visibility at the final mile of the supply chain, such as demand levels, transit times, delays in shipping, etc. All these functions are performed without team input, reducing overhead, improving customer service, and ultimately creating brand loyalty by keeping your promises.

Scalable support:

Tech, like chatbots, allows you to provide scalable support no matter how quickly your customer base grows. You can engage in meaningful conversations with customers using chatbots that leverage machine learning to understand customer needs and answer intelligently. As a result, your system encounters fewer and fewer questions it can’t answer, so it rarely passes the customer on to a human support team member.

You reduce the costs of each purchase and empower customers to make purchases without delay. Almost any question is addressed, from brand queries to shipping costs and color availability to your return policy. Other support capabilities include the automatic generation of FAQs for common questions, 24/7 response for late-night shoppers, and ensuring customers receive answers in real-time to reduce shopping cart abandonment.

Marketing:

AI also empowers marketing teams through AI-generated copy, such as product descriptions. Instant copy generation based on your brand tone and product attributes provides a ready-to-edit text block that speeds up the copywriting process. This saves time and money, allowing marketing teams to focus more on strategy and less on time-consuming creative processes.

Practical applications

That brings us to Product Information Management for retailers, manufacturers, resellers, and distributors. They all leverage product data to keep their businesses running and growing. While the purpose of product data varies from business type to business type, these businesses can only survive with product data.

For example, manufacturers now understand that a significant portion of their products are ultimately sold online. Whether they produce an assembled product ready for the end user, or components for other manufacturers to use to compile their products, the details of product information required are the same. Also, their leg of the supply chain is essential to getting products to market, whether it is via wholesalers, distributors, or retailers. Each aspect of eCommerce has the same purpose: getting products to the market quicker to beat the competition.

When your eCommerce tech stack is enhanced with AI, you can leverage it to manage product data. For example, your product information management system (PIM) organizes and stores large amounts of information that must be accurate, detailed, and up-to-date. AI makes a PIM more efficient and robust, especially when you sell through different marketplaces. For example, it can save you time by searching, collecting, and compiling existing product attributes from major sources such as Amazon and Shopify. This function facilitates the creation of product information copy ready to use in your sell sheets, eCommerce platforms, marketplaces, marketing, and more.

This is an invaluable tool when managing complex data. We’d say that in the eCommerce landscape, PIM is the best business software to leverage AI because of the importance and scope of information it stores and manages. It is the most practical way to leverage AI, automating manual tasks across your departments, enhancing efficiencies, and allowing your brand to focus on creating personalized customer experiences. In essence, it is the ultimate AI tool for your eCommerce business.

1) What is the difference between AI, machine learning, and deep learning?

AI is usually associated with computers carrying out tasks like a human. However, AI can be applied to far too many tasks to think of it in such broad terms. Although AI can also carry out human tasks, how it performs those tasks is related to the specific job and what it needs to perform them. That’s why it’s essential to understand that AI relies on different capabilities through various subsets, including machine learning and deep learning. Let’s delve deeper to understand the differences between AI’s three most common components and their roles in facilitating AI performance.

So, what is AI?

You probably know that AI stands for artificial intelligence. It refers to the “computer science” used to create intelligent machines capable of simulating human thinking and logic. AI simulates human intelligence based on what developers believe to be the key processes used to form ideas and process logic. It then applies it to varying scenarios through machines. If it sounds too futuristic or even far-fetched, AI is very real. It is already being used in specific applications, from expert systems to NLP and speech recognition for security purposes to search engine algorithms.

How AI works

AI enables technical systems to perceive their environment, understand what it means, and use that understanding to solve problems. As a result, end-users can use that solution or apply that new understanding to achieve a specific goal. When the system encounters the data it collects through methods such as camera sensors or the criteria it is fed, it can process that information and respond based on what the user is trying to achieve. As a result, AI enables these users to complete all kinds of tasks and solve all kinds of problems faster and more effectively.

The process involves developing algorithms, models, and systems capable of performing tasks requiring human intelligence, such as perception, reasoning, learning, problem-solving, and decision-making. As a result, AI can be implemented using various techniques and subsets, which is where machine learning and deep learning come in.

When we think of AI for chatbots and virtual assistants, for example, they use machine learning to improve their knowledge to answer more questions and provide a broader range of “services” to users.

Then, what is machine learning?

As mentioned above, machine learning is a subset of AI. This is one of AI’s amazing yet scarier elements, as it allows the technology to use mathematical models of data to help it learn new things without direct human instruction. It’s like a self-learning mode that uses experience and new data to understand and improve upon its own capabilities. As a result, it continuously improves processes based on experience or data and identifies algorithms to spot patterns, make predictions and act upon that information when needed.

There are then three subsets of machine learning:

Supervised learning using labeled examples
Unsupervised learning using patterns in unlabeled data
Reinforcement learning from trial and error

Using the chatbot example, machine learning allows the bot to learn and improve from data without being manually programmed to answer specific questions. Instead, it develops algorithms to make predictions in the conversation and uses that to answer customer questions accurately.

And deep learning?

Deep learning is another subset of machine learning. It goes one step further by mimicking the human brain and recognizing patterns from complex data and sources, whether it is images, sounds, texts, or data. With that information, deep learning can make predictions or provide insights on a far more complex and accurate level. It trains artificial “neural networks” with multiple layers of three or more so it can attempt to emulate human brain behavior to perform highly skilled tasks. For example, deep learning is used for image and speech recognition.

When deep learning is applied to chatbot AI, it becomes more personalized, using sentiment analysis on user messages to make more accurate predictions of what a customer wants. Using multiple neural networks, it tries to understand the emotional tone or sentiment expressed by the user so it can put the conversation in context.

What’s the connection between AI and machine learning?

AI allows an “intelligent” computer to perform tasks independently and apply “thought processes” to its function like a human. That intelligence is courtesy of machine learning. Deep learning neural networks contribute to that intelligence because it is modeled after the human brain. As a result, machine learning and its subsets allow computers to achieve AI.

2) What do we mean by eCommerce?

As you know, eCommerce is the buying and selling of goods over the internet. Different platforms allow customers to shop online, including brand-specific websites, mobile apps, social media, and marketplaces like Amazon. The transactions are made free of human contact and rely on digital interfaces instead. These interfaces can be machine-to-machine or machine-to-human.

Multi-channel vs. Omnichannel

There are two possible eCommerce strategies:

Multi-Channel: A multi-channel strategy creates a sales presence across multiple sales channels to increase the odds of customers finding and buying your products. Because each channel has its own validation, interface, and approach to selling, you need to ensure the product information is correct and compliant with each channel’s formatting requirements. You can choose the channels that align with your target’s habits and create content to suit the customers using the marketplace. There is no need for integration across your channel marketing in this strategy. As a result, what happens on one channel does not impact other channels. Suppose you are selling on Amazon, for example, and a customer abandons their shopping cart when they discover the delivery date doesn’t work for them. In that case, you respond by sending a reminder email with a link to their cart. This singular act has no impact on the other channels.

An omnichannel strategy also includes a presence across several channels, but in this case, you want integration. Instead of focusing on the marketplace’s needs, you focus on your customers’ needs. As a result, you develop an understanding of typical buying behavior across the channels, so your content targets those needs. You create product information and content consistent across each channel so customers enjoy the same experience that is brand focused from channel to channel. Using the same Amazon example, your omnichannel strategy triggers a different response if a customer abandons their cart. Marketing is not limited to a single email but instead ensures the customer is reminded about the full cart across all touchpoints with your brand, including social and Google ads. The ads are personalized with messaging enticing the customer to return to their cart and complete their purchase. A customer’s action triggers a chain reaction that eases them along their journey and encourages sales.

B2B vs B2C

eCommerce companies tend to focus on either business-to-business (B2B) or business-to-consumer (B2C) sales:

eCommerce for B2C: B2C targets consumers with online stores focused on attracting individual buyers. In this case, the buyer experience should mimic the experience of walking into a store, with high-quality product images and easy navigation that allows shoppers to browse the “aisles” based on logical groupings and departments. For example, a fashion retailer would include sections such as Mens, Womens and Childrens, broken down into sections such as accessories, tops, bottoms, shoes, etc. An electronics store would have sections such as appliances broken down into stoves, fridges, freezers, microwaves, home entertainment broken down into sound systems, TVs, home theaters, etc., and computers broken down into sections such as desktops, laptops, tablets, accessories, etc.

You want to make the customer journey easy, so they find what they need intuitively. Once they find what they want, effective product information and images allow them to experience the products as if they were handling them in the store. AI allows you to improve customer experience to present customers with the items they need when they need it. It also uses chatbots to answer questions, much like a sales assistant in a store, so buyers feel confident they have the information they need to make a purchase.

eCommerce for B2B:

B2B is selling to businesses allowing them to make convenient online purchases for items critical to their operations. Today, the B2B eCommerce experience isn’t that different from the B2C experience — businesses also want to find what they need quickly and easily. There are different types of B2B eCommerce models, including:

Manufacturers:

Manufacturers create products they either sell to wholesalers, retailers, or other manufacturers. Ecommerce allows them to reach these traditional businesses and sell directly to consumers online. This removes the middleman and allows them to control their brand’s reputation.

Wholesalers:

Wholesalers purchase products in bulk from manufacturers to sell to retailers or distributors. They might also be selling raw materials to manufacturers or distributors to create a product or to be packaged for sale. B2B eCommerce allows wholesalers to reach a broader audience, showcase their inventory, and streamline the buying process. But, again, they remove the middleman, which in this case is the wholesaler’s sales representatives.

Distributors:

Distributors work closely with manufacturers not interested in selling, packaging, and shipping orders. Distributors handle the details that get a manufacturer’s products to market, from advertising to order fulfillment. This might be buying an item like nuts from a grower and packaging them for sale to grocery chains. B2B eCommerce platforms help them get the products to customers sooner and reach a broader audience with personalized customer service added into the mix.

AI helps you create a simplified eCommerce experience when managing and presenting complex product information to your customers. For example, manufacturers can use AI in a PIM/DAM to store information in a centralized location and provide the level of detailed text, sell sheets, and visuals that allow retailers and distributors to experience the products firsthand. This speeds up the process of getting the manufacturer’s product to market, providing a competitive edge.

AI in eCommerce

AI has totally altered the eCommerce playing field. Luckily, it is still early enough days for you to use AI to help your brand stand out in this extremely competitive landscape. First, however, you need to leverage AI in the right places to stand out from the other trailblazers already embracing this empowering technology, such as:

Optimized customer experiences: Since 49% of online purchasers need to scroll beyond the first web page, you can make it easier for visitors to find products with AI-powered search engines. They use NLP to better understand customer queries at the search stage so the items ranked are relevant. Product recommendations also play prominently in improved customer experiences. They leverage machine learning and use purchasing history and shopping paths to develop logical and much-appreciated product recommendations, whether it is finding similar products when an item is out of stock, bundling products frequently purchased together, or sharing the products bought based on the buying habits of similar customers. This adds value for customers because they find what they need or learn about items they didn’t know existed, and to you by increasing average order value and customer lifetime value through repeat purchases

Increased Revenue: Improved efficiencies using AI allows you to improve your profit margins with less effort required for each purchase. You don’t sacrifice the level of customer service provided thanks to AI machine learning. Chatbots allow you to engage in intelligent conversations, maintain brand tone, offer sales support, and push orders forward, anticipating needs by putting conversations into context.

3) Where is AI being used now in eCommerce?

Optimized customer experiences and increased revenue are the expected outcomes when using AI in eCommerce. However, how your competitors achieve this is important. Let’s look at a few examples.

Recommendations (‘Related Products’)

AI allows you to simultaneously understand customer behavior and the products they are considering so it can actively engage them and share relevant information as they shop. In hand with what is happening now, AI considers purchase history to predict needs and provide a more personalized shopping experience. The combination of these capabilities streamlines the product discovery process and introduces cross-selling and upselling opportunities.

Chatbots

We’ve already referred to chatbots several times as the computer version of customer service and support. Their ability to engage in conversation and use machine learning’s deep learning function creates very personalized, positive brand interaction. Chatbots leverage NLP to understand customer intent beyond what they are saying in their conversations. As a result, responses are far more relevant and helpful.

Forecasting

Machine learning uses prior events, actions, or behaviors to make accurate predictions. While this helps with customer interactions, it also can calculate market trends and consider outside factors, such as inventory shortages, to make insightful supply chain management forecasts. AI also uses changes in consumer demand and applies them to historical data to instantly spot relationships in large datasets. As a result, you meet your customers’ needs and maintain a more scalable and sustainable supply chain to reduce inventory inaccuracies.

Price optimization

ML understands product performance in relation to your competition. As a result, you can research competitor pricing and create optimized pricing schedules that undersell the competition without reducing your profits, thanks to a larger sales volume. Furthermore, in hand with AI, dynamic pricing allows you to respond in real time, so you are always out-pricing the best price.

Fraud detection

Algorithms detect suspicious activity, such as fraudulent transactions, to minimize payment fraud. For example, if a customer enters incorrect credit card info or sets up multiple accounts to commit loyalty program abuse, AI detects it and sends an alert. AI can also detect harmful user-generated content to protect your brand.

Visual search

Our Pinterest example above allows customers to search for products using images rather than text. This is extremely helpful when customers don’t know what an item is commonly referred to, such as a style of furniture or hat. They can also depend on images they find visually appealing to find similar items quickly.

Sentiment analysis

AI can analyze customer feedback and reviews to understand brand and product sentiment. Sentiment provides the insight you need to make your brand and product more appealing and make informed marketing decisions to help meet your goals.

Supply chain management

We’ve mentioned AI forecasting as a benefit to improve your supply chain. However, you can also use AI to examine factors like demand patterns, transportation logistics, and inventory levels. As a result, you can improve your decision-making capabilities to develop a more efficient approach to inventory management, reduce costs, and improve delivery times at the final legs of your supply chain.

Voice assistance

According to Gartner, voice commerce will reach $80 billion globally by 2023. AI voice assistance allows customers to use voice search to find items and ask questions. AI shows search results and answers questions using machine and deep learning. Voice assistance also applies to personalized recommendations.

Product content

NLP machine learning creates compelling text and metadata for your product descriptions based on product criteria and attributes and brand tone. As a result, your omnichannel customer experience is more straightforward, with multiple unique versions of content created, ready for editing and uploading into your PIM.

4) What makes AI more practical than human labor?

Leveraging the power of artificial intelligence (AI) is increasingly becoming an important factor for gaining a competitive edge. One of the most significant advantages of AI is its ability to operate tirelessly.

Unlike human workers, whose productivity can decline over long periods, AI systems consistently perform at optimal levels. They don’t require rest, food, or sleep, meaning they can work 24/7, significantly enhancing operational efficiency and productivity.

Beyond that, AI technology is renowned for its accuracy. Humans, despite their best efforts, can make mistakes, especially when performing repetitive tasks. The human brain can make significantly more logical decisions than a machine; the brain must process information on many things at once (i.e., breathing).

AI, on the other hand, can perform significantly more logical transactions than any one human is capable of. It follows instructions with absolute precision, reducing the likelihood of errors and resulting in more predictable and reliable outcomes. This level of exactness is particularly useful for clients seeking to optimize item data, who can depend on the AI for accurate data management and product information processing.

Furthermore, AI systems have less inherent bias than humans. The algorithms base decisions purely on data and programmed algorithms, eliminating the influence of personal beliefs or emotions. AI systems are also extremely adaptable— they can learn from new information and experiences, modify their operations accordingly, and continually improve their performance.

While humans can sometimes resist change due to various factors, AI readily integrates change, thereby driving continuous improvement and growth. With such strengths, AI stands out as an incredibly valuable asset for any forward-looking organization.

5) Why PIM/DAM is one of the most practical ways to leverage AI

When it comes to eCommerce, optimized product data is the key to using AI tools to increase sales. Everything we touched on above is only optimized when your product data is at-the-ready and managed/updated automatically. This is where a PIM/DAM comes in. High-quality product information includes text assets (descriptions/attributes) held by the PIM and digital assets (image/video) held by the DAM.

Trying to create thousands of product descriptions based on marketplace and channel formats/requirements and your various target segments is a monster to manage. This massive task also tends to be inherently fraught with human bias, which increases the risk of feeding your machine learning systems and these eCommerce features incorrect or skewed information.

When you consider the challenges of a legacy PIM, it is not prepared to deal with digital assets. As a result, you need to find a DAM solution. Also, your PIM feels outdated if you don’t have AI to assist with product information management, such as product descriptions, leveraging unstructured data to create attribute-specific text, product intelligent chats, and more. However, when you have PIM/DAM legacy software, your product information management is next-level ready, providing all the information AI needs to manage tasks efficiently.

So how does AI help a PIM/DAM?

Providing correct information

A PIM/DAM centralizes your product information to optimize AI features. You know the information fed to your AI is always current and correct with a PIM/DAM. For example, a chatbot becomes far more helpful when it can source product data stored in your PIM. As a result, you can provide accurate answers that help move customers along the final stage of their buyer journey. Another example is AI-generated unique product descriptions ready to use across your sales channels and marketplaces.

Simple product descriptions with all the specifications and attributes sourced from your PIM provide copywriters with the perfect base to fine-tune copy ready to upload to your PIM to be shared across different marketplaces. One final example is the ability to search existing product descriptions at major marketplaces like Amazon and Shopify to collect up-to-date attributes. Our PIM adds it to your system, then monitors the source and automatically updates your description when changes are made.

Unstructured data

Your PIM stores every piece of updated product information while your DAM manages your digital assets. PIMs manage structured data, such as text, while DAMs manage unstructured data, such as images and video. When your PIM includes AI, you can actually use AI image recognition to turn unstructured data in your DAM into structured data for your PIM. It also allows for visual searches.

Modern PIM/DAMs

When a PIM includes a DAM, it creates a modern PIM that allows you to easily manage all your structured and unstructured data in one place. Pimberly is an example of the modern PIM because, since inception, we have been both a PIM and a DAM. However, today our PIM has maintained its modern standard by adopting AI to enhance its capabilities.

6) Pimberly AI

Pimberly AI is one of our newest additions to the platform providing three major functions:

Attribution

Pimberly AI makes it easy to manage complicated product attributes from different sources such as existing product descriptions found online, reviews, images, and other unstructured data so you can:

Identify and extract attributes from various sources
Analyze text data
Identify product attributes through image recognition
Leverage historical data
Increase sales with product recommendations

Analyze text data: Using multiple sources, our AI analyzes text data extracting the most relevant product attributes. That information can then be applied to all kinds of marketing, from landing pages to emails and ads to brochures to establish your authority and speed up your time to market.

Leverage image recognition: Our AI image recognition capabilities identify product attributes and apply relevant tags based on an 80% threshold confidence level or user-defined features. It also applies deep learning to continuously adapt and improve its knowledge to assist with the following time-consuming tasks:

Locating digital assets
Labeling meta-tags
Searching image content
Creating smart, categorized photo libraries
Assisting in the development of effective targeted advertising
Creating image tags for SEO and enriched product information
Considering user-defined criteria to ensure tags are product-relevant

Utilize historical data: Our PIM/DAM machine learning continues to learn and update its knowledge, to adapt to customer needs. As a result, you can create customized product descriptions highlighting the attributes and features most important to each customer. Customers feel confident in their purchases and don’t need to shop elsewhere.

Share product recommendations: Product recommendations increase average basket value and empower your marketing team to understand customer behavior using AI. Purchase and shopping patterns are tracked and analyzed so you can upsell, cross-sell, provide replacement options for sold-out items, create bundles, and more.

Product descriptions

AI-generated product descriptions write themselves based on character limit, keywords, and tone of voice. Our AI then creates three variations of clean, optimized copy, ready for editing and uploading across all marketplaces. Our clients use our AI-generated product descriptions in different ways, such as:

A/B Testing: The three variations are ready for A/B resting, so you can continuously find the most appealing wording to sell products more effectively. Used in different marketplaces, you can track performance and decide what products appeal the most to customers on each platform and what tone works best on that particular channel. All of this requires minimum effort on your copywriter’s part, as the multiple versions are standard.

Original Content for Similar Products: Your copywriters are tasked to create unique versions of product descriptions for every single product across every single platform and marketplace. This is brain-draining work that leads to boredom and increases the risk of writer churn. Good writers are hard to find, and once they get to know your brand voice, the last thing you want them to do is leave. We purposefully had our AI create three variations to give your writers a break. They can leverage the three versions to provide unique content to remain compliant with marketplace guidelines. Creatives meet the requirement to provide unique product descriptions ad nauseum without reaching the point of despair.

Creativity: If your copywriters worry AI will replace them, they needn’t. AI generates ready-to-edit product descriptions that just need a touch of human creativity to perfect them. AI is the tool your copywriters need to get rid of the tiresome mechanical tasks they hate, such as sticking to character counts and, as mentioned above, that horrible repetition to suit the needs of each marketplace. In addition, AI provides the facts in editable format, so copywriters focus on the fun branding stuff instead of the boring attribute details.

Time saver: Pimberly AI saves eCommerce and creative teams significant time by effortlessly creating channel-appropriate product descriptions with the right character count.

Image Recognition

Pimberly AI uses image recognition to generate attributes by analyzing uploaded images to identify objects, decide what they are, and then add relevant tags to perform various tasks. Our PIM/DAM AI is all about high-quality product information that ensures customers have everything they need to make quick purchasing decisions. Image recognition also helps you leverage unstructured data in our PIM/DAM, turning it into structured data as product information descriptions. This symbiotic relationship creates text related to existing images to enhance accuracy and quickly pull both text and image into a single pipeline to generate your product attributes.

User-defined information combines machine learning with human knowledge. This is very important to the machine learning process. For example, AI can’t decipher when attributes are too similar to differentiate. In this case, your team can spot the differences and add that information to increase accuracy. Once the new information is added through machine learning, your team’s knowledge and confirmation increases our AI’s machine-learning capabilities so it constantly improves its performance. As a result, the more your team contributes, the less they’ll have to do the next time around. Your team’s input also includes adding that human element for less tangible details related to your brand or the experiences your products might offer.

Product image recognition also allows AI to find products with similar attributes building on the product recommendations you can make, further enhancing your customer experience. In addition, our PIM/DAM technology leverages AI to help streamline processes in the creation and use of product information.

7) The future of AI in eCommerce

We should probably use AI to help us predict its own future uses in eCommerce. However, as it stands, AI’s agility and learning capabilities make it hard to determine where AI will take eCommerce. Rapid advancement of AI in the current landscape has enormous potential to enhance the use of algorithms and machine learning to improve our lives. However, we also know that despite our advanced AI features, our developers are just getting started. Our team has its sights set on some exciting possibilities, including:

Digital twins for eCommerce

Digital twins use AI to create an exact replica, or 3D vector image, of a physical product. Right now, this tool has many uses for manufacturers that can create a virtual environment to test and perfect product design and functionality. However, soon these digital twins will be able to go straight to your eCommerce site.

While the average retailer uses ‘laydown shots’ and lifestyle photos to sell products online, digital twins provide a much more realistic image to showcase products on consumer-facing platforms like eCommerce sites and online catalogs. The digital twin will allow you to save time and money by creating and uploading them directly to online stores or your DAM, ready for use when you need them.

Synthetic images for eCommerce

Synthetic images are created using AI, computer graphics software, and various simulation methods. The images can mimic camera angles, lighting, and object placement to create compelling imagery, much like those produced by a professional photographer. They are used to train and test machine learning models based on known characteristics. The images allow researchers to create large datasets for training algorithms. This tool is ideal when you don’t have a physical object to replicate and want to create the same attractive, well-thought-out images captured by an experienced photographer so you can showcase product attributes. It is also an excellent tool to show customers what might be coming down the line for your brand.

8) Conclusion

This whitepaper was designed to provide you with a firm understanding of how AI can positively affect your business. We’ve explained what AI is, how it uses machine learning to improve itself, and the many ways AI helps your eCommerce operations become more profitable. In addition, you’ve learned that our PIM/DAM and other AI-powered software will adapt and improve to leverage other emerging technologies, such as digital twins and synthetic images.

You can also see that to introduce AI into your business, you don’t need to make a major investment or require a technical overhaul when you find AI-powered tools like our PIM/DAM. We helped demonstrate how our AI facilitates improved workflows for product information management and provided a comprehensive guide of top AI apps and platforms that can help facilitate efficiencies throughout your entire operation.

Our PIM/DAM is an essential eCommerce tool and a perfect example of how AI empowers eCommerce companies to leverage this emerging technology. Keeping both the unknowns and expectations of further AI development in mind, time is of the essence to establish yourself as an eCommerce trailblazer, smart enough to recognize the potential AI offers your business.

9) Appendix

The thing with AI is that it has so many applications it can be hard to know where to access it and how to leverage it. Our own PIM/DAM is just one example of how eCommerce C-suites can leverage AI to gain efficiencies and increase revenue. The keyword here is efficiency, as there is an AI tool in existence right now that is sure to help you improve workflows and increase productivity across all your operations and departmental functions. Here we provide a comprehensive appendix outlining the most impressive AI solutions, from HR and hiring to administrative assistance and marketing to signing contracts and service agreements.

Algorithmia

Is there a better name for an AI company than Algorithmia? Now a DataRobot company after an acquisition, Algorithmia’s goal is to help data scientists find and use algorithms. It was initially an exchange for algorithms on a one-off, single-user basis. As it has grown, it has set its sights on the enterprise market.

The Trade Desk

A company designed to help digital advertisers run targeted digital advertising campaigns, The Trade Desk uses AI to optimize its customers’ advertising campaigns for their appropriate audiences. Their AI, known as Koa, was built to analyze data across the internet to figure out what certain audiences are looking for and where ads should be placed to optimize reach and cost. The Trade Desk also allows you to launch your digital ads independently but uses its AI to offer performance suggestions while your campaign is live.

Swim.Ai

Swim.ai’s goal is to enable businesses to mine continuously streaming data into actionable insights. Leveraging machine learning, the company’s “open core platform” augments the decision-making process by providing streaming data and contextualizing data sources. The SwimOS is open source.

Phrasee

Phrasee specializes in natural language generation for marketing copy. Its natural language generation system can generate millions of human-sounding variants of marketing at the touch of a button, allowing customers to tailor their copy to targeted customers. Retail, marketing, and AI are a combination of a rapid growth curve in the AI sector. For example, during the COVID-19 pandemic, several retailers, such as Walgreens, used Phrasee to boost customer engagement related to vaccination.

Pymetrics

Based in New York City, Pymetrics leverages AI to help companies hire optimal candidates by examining more than what’s traditionally included in a resume scan. Customers have their best employees fill out the Pymetrics assessment, which then creates a model for what future ideal candidates should bring to the table. In essence, the AI-based system is attempting to find more potential new hires that will fit in well with the existing top staff, using AI and behavioral science.

People.Ai

People.AI’s goal is to streamline the life of salespeople, assisting them in putting the reams of small details into relevant CRM systems, chiefly Salesforce. Think of all those pesky info bits from texting, your calendar, and endless Slack conversations — the company aims to help you with all of that. Plus, the system attempts to coach sales reps on the most effective ways to manage their time.

AlphaSense

AlphaSense is an AI-powered search engine designed for investment firms, banks, and Fortune “500” companies. The search engine focuses on searching for important information within earnings call transcripts, SEC filings, news, and research. The technology also uses artificial intelligence to expand keyword searches for relevant content.

Icertis

The remarkable truth about AI is that it keeps moving up the food chain in terms of the sophisticated tasks it can handle. Taking a big step up from simple automation, Icertis, with a decade under its belt, handles millions of business contracts through a method they call contract intelligence. Leveraging the cloud, the company’s solution automates certain tasks and scans previous contract details. The company has gained some big clients, like Microsoft, and was named a Gartner leader.

Bizzabo

Bizzabo acquired X.ai. Geared to assist the busiest of people, X.ai’s intelligent virtual assistant “Amy” helps users schedule meetings. The concept is simple: If you receive a meeting request but don’t have time to work out logistics, you copy Amy in the email, and she handles it. Through machine learning and natural language processing, Amy schedules the best time and location for your meeting based on your preferences and schedule.

One Model

Human resources can be a bifurcated digital workspace, with different apps for each task that HR handles. OneModel is a talent analytics accelerator that helps HR departments handle employees, career pathing, recruiting, succession, exits, engagement, surveys, HR effectiveness, payroll, planning, and other HR features all in one place and in a uniform way. The company’s core goal is to equip HR pros with machine learning smarts.

CopyAI

A fairly new startup in the AI copywriting space, Copy.ai uses basic inputs from users to generate marketing copy in seconds. It can create copy for a variety of different formats, including article outlines, meta descriptions, digital ads, social media content, and sales copy. Copy.ai has raised $2.9 million in funding from Craft Ventures and several other smaller investors. With its use of the GPT-3 language model to generate words, Copy.ai is a content-driven AI tool to keep an eye on.

C3.Ai

Focusing on enterprise AI, C3.ai offers a wide array of pre-built applications, along with a PaaS solution, to enable the development of enterprise-level AI, IoT applications, and analytics software. These AI-fueled applications serve a wide array of sectors and industry verticals, from supply chains to health care to anti-fraud efforts. The goal is to speed up and optimize the process of digital transformation.

Accubits

Accubits, a top-rated AI development company, focuses most of its energy on helping businesses enable AI for new efficiencies in their existing systems. Some of their AI solutions include intelligent chatbots in CRMs and predictive health diagnostics, both of which are designed to mesh with your existing software infrastructure. Accubits works across industries, like consumer technology, automotive, cybersecurity, health care, and fashion.

SS&C Blue Prism

SS&C Technologies completed an acquisition of Blue Prism, a leading RPA company. Blue Prism uses AI-fueled automation to do an array of repetitive, manual software tasks, which frees up human staff to focus on more meaningful work. The company’s AI laboratory researches automated document reading and software vision. To further boost its AI functionality, Blue Prism bought Thoughtonomy, which offers AI-based cloud solutions.

DocuSign

A well-known technology company in the contract world, DocuSign uses e-signature technology to digitize the contracting process across a multitude of industries. Many users don’t realize some of the AI features that DocuSign powers, such as AI-powered contract and risk analysis that is applied to a contract before you sign. This AI process lends itself to more efficient contract negotiations and/or renegotiations.

Tetra Tech

Tetra Tech uses AI to take notes on phone calls, so people working in call centers can focus on discussions with the callers. It uses AI to generate a detailed script of dialogues using its speech recognition technology. Given the large market for call centers, and the need to make them more effective at low cost, this is a big market for AI.

Nvidia

Nvidia’s emergence as an AI leader was hardly overnight. It has been promoting its CUDA GPU programming language for nearly two decades. AI developers have come to see the value in the GPU’s massively parallel processing design and embraced Nvidia GPUs for machine learning and artificial intelligence.

ViSenze

ViSenze’s artificial intelligence visual recognition technology works by recommending visually similar items to users when shopping online. Its advanced visual search and image recognition solutions help businesses in eCommerce, m-commerce, and online advertising by recommending visually similar items to online shoppers.

ServiceNow

Element AI was acquired by ServiceNow. Originally based in Montreal, Element AI provides a platform for companies to build AI-powered solutions, particularly for companies that may not have the in-house talent to do it. Element AI says it supports app-building for predictive modeling, forecasting modeling, conversational AI and NLP, image recognition, and automatic tagging of attributes based on images.

Pointr

Pointr is an indoor positioning and navigation company with analytics and messaging features that help people navigate busy locations, like train stations and airport terminals. Its modules include indoor navigation, contextual notifications, location-based analytics, and location tracking. Its Bluetooth beacons use customer phones to help orient them around the building.

Directly

Considered one of the best AI-driven customer support tools on the market, Directly counts Microsoft as a customer. It helps its customers by intelligently routing their questions to chatbots to answer their questions personally or to customer support personnel. It prides itself on intelligent automation.

Rulai

You have surely encountered the limited conversational style of a chatbot; a few stock phrases delivered in a monotone. Rulai is working to change this using the flexibility and adaptability of AI. The company claims its level 3 AI dialog manager can create “multi-round” conversations without requiring code from customers. Clearly a major growth area.

Tamr

In a world run by data, in many cases, someone, or some system, has to prep that data so it’s usable. Data preparation is unglamorous but absolutely essential. Tamr combines machine learning and human tech staff to help customers optimize and integrate the highest-value datasets into operations. Referred to as an enterprise-scale data unification company, Tamr enables cloud-native, on-premise, or hybrid scenarios — truly a good fit for today’s data-driven, multi-cloud world.

Aurea Software

Aurea Software acquired Xant and returned the brand to its original and widely recognized name, InsideSales, that same year. InsideSales is a sales acceleration platform with a predictive and prescriptive self-learning engine, assisting in a sale and providing guidance to the salesperson to help close the deal. At its core is machine learning.

CognitiveScale

CognitiveScale builds customer service AI apps for the health care, insurance, financial services, and digital commerce industries. Its products are built on its Cortex-augmented intelligence platform for companies to design, develop, deliver, and manage an enterprise-grade AI system. It also has an AI marketplace, which is an online AI collaboration system where business experts, researchers, data scientists, and developers can collaborate to solve problems.

Lobster Media

AI meets social media. Lobster Media is an AI-powered platform that helps brands, advertisers, and media outlets find and license user-generated social media content. Its process includes scanning major social networks and several cloud storage providers for images and video, using AI tagging and machine learning algorithms to identify the most relevant content. It then provides those images to clients for a fee.

SenseTime

Based in Asia, SenseTime develops facial recognition technology that can be applied to payment and picture analysis. It is used in banks and security systems. Its valuation is impressive, racking several billion dollars in recent years. The company specializes in deep learning, education, and fintech.

Bright Machines

Automation in factories has been progressing for years, even decades, but Bright Machines is working to push it a quantum leap forward. Based in San Francisco, the AI company is leveraging advances in robotics like machine learning and facial recognition to create an AI platform for digital manufacturing. Its solutions can accomplish any number of fine-grain tasks that might previously have required the exactitude of a skilled human.

Graphcore

Graphcore makes what it calls the Intelligence Processing Unit (IPU), a processor specifically for machine learning used to build high-performance machines. The IPU’s unique architecture allows developers to run current machine learning models orders of magnitude faster and undertake entirely new types of work not possible with current technologies.

Deepmind

Acquired by Alphabet, Deepmind is a research firm that focuses on AI research, covering everything from climate change to healthcare and finance. Its goal is to build “safe” AI that evolves in its ability to solve problems. The company is based in London and recruits heavily from Oxford and Cambridge, which are leading universities in Europe for AI and ML research.

Domino Data Lab

Certainly an AI company with a certain buzz about it, Domino is a SaaS solution that helps tech and data professionals program and test AI models. Think of it as a gathering place, an aggregation of sorts, for the AI community. Expect Domino to grow rapidly in the years ahead. Based in San Francisco, the company touts itself as a platform for data science.

OpenAI

OpenAI is a nonprofit research firm that operates under an open-source type of model to allow other institutions and researchers to freely collaborate, making its patents and research open to the public. The founders say they are motivated in part by concerns about existential risk from artificial general intelligence. ChatGPT is a recent part of OpenAI that allows users to generate text from poetry to short stories. However, despite OpenAI being nonprofit, ChatGPT is now its own for-profit company.