How AI benefits your eCommerce business?

AI has become a game-changer for eCommerce businesses, offering a wide range of benefits and transformative opportunities. By leveraging advanced algorithms, machine learning, and data analytics, AI has the potential to revolutionize the eCommerce industry. From enhancing customer experiences to streamlining operations and boosting sales, AI’s impact on eCommerce is undeniable. 

Here are some ways AI could benefit your eCommerce workflows:

Automate administrative work 

ECommerce businesses have several workflows that can be automated with AI such as data analytics, customer responses, research, etc. Tasks that are repetitive or follow a pattern are easier to automate. For example,

• Making a query in the database and programming it to run every Monday for the sales report.
• Setting up email responses for similar email requests such as customers inquiring about store policies.
• Shipping products from the warehouse once payment is approved.
• Handling refunds when the warehouse receives the returned package.
• Low stock alerts when stock value falls below a certain threshold.

AI removes some manual work from each workflow. As new tools emerge, AI could potentially fully automate the workflow by removing manual inputs. While that can be a concern about losing jobs to technology, it also brings more skilled opportunities such as training AI and scaling the work.

Leverage chatbots

Chatbots are changing customer service and sales roles while transforming communication between customers and service reps. Chatbots help eCommerce brands answer several customer queries as they identify a pattern in customer requests. It makes sense from a business point of view to automate repetitive requests as it saves them a ton of money.

While this can be a threat to many customer service-related professions, it can be beneficial for those who understand how to teach AI to cater to repeated requests. Computers understand If-then logic and most service/retail-based industries with a lot of traffic can train AI the way they train any employee. 

For example, Domino’s Pizza Facebook Messenger chatbot can take as many orders from around the region without adding more customer service staff.

With NPL, chatbots could serve more complex requests. For example, helping bank customers do Know Your Client (KYC), open their accounts, suggest investment options, and keep them updated with their account growth.

Find insights into complex data

AI can handle complex data that humans cannot process even with tools. That is where data analytics and AI work together. Outcomes of analysis can quickly be actioned by AI. 

Advertising data is one prime example of where AI helps eCommerce sellers optimize their campaigns. Optimization requires finding the most effective keywords, channels, and content strategies to get the highest ROI on their ad spend. By developing benchmarks, AI can make decisions based on whether a search term is performing.

While smaller brands may be able to manually analyze advertising data if given the right expertise, larger brands with a range of products require automation tools to interpret data on a much wider scale. With an increasing number of variables being tracked, AI can analyze data with increased efficiency. For example, Amazon ads automation can now view CPCs and Conversion rates in context, factoring seasonality, time of day, and placement into the optimizations.

Through various ChatGPT plugins,  AI has taken a leap. One tool called the Code Interpreter analyzes data and creates reports based on data provided in a CSV file. Innovations like this can help eCommerce businesses compete as they begin to operationalize more efficiently than their competitors, gradually making more effective and timely decisions.

Product recommendations for a better experience

ECommerce sellers focus on seamless shopping experiences to increase sales. AI assists this experience by recommending Amazon products to customers.

A study by Monetate shows that customers who interacted with a suggested product experienced a 70% higher conversion rate within the same session. In subsequent return sessions, the initial product recommendation continued to have a positive impact, although to a slightly lesser extent (55%).

While product recommendations require customer profiling by tracking the user journey, it helps customers find the right products and increases sales for sellers. However, personalization can make shoppers uncomfortable with their privacy. After analyzing the purchase history of a teenage shopper, Target began sending discount coupons for diapers and baby formula which got the family furious. Later it was found the family didn’t know the teenager was pregnant but the algorithms knew.

Given that shoppers allow product recommendations on specific platforms they trust, AI can transform shopping in the future. Online shopping may be as simple as clicking an “order what’s best for me” button, using AI that synthesizes your historical shopping behaviors thus choosing the products you would most likely choose. 

Inventory management

For the past few years, supply chain vendors have invested in AI. It can help eCommerce store owners maintain inventory levels. It can also alert store owners if inventory levels of particular products drop. In fact, AI programs can even automatically order the inventory for a store. 

Looking at a macro level, ML is used for demand forecasting. Predicting sales of a particular product in a region can help with lead times, which is crucial for planning the next batch. Using real-time signals, planning can be more accurate.

The biggest marketplace, Amazon, needs a strong supply chain system and has its skin in the game through AI. Through supply chain optimization, Amazon leverages data such as seasonal factors, geographical considerations, and previous demand patterns to recommend lawn care supplies during the spring. It also stocks its warehouses accordingly. By employing this approach, Amazon reduces transportation expenses associated with rush orders.

How does AI help Amazon sellers?

Amazon sellers have the advantage of working with the biggest marketplace that keeps up with innovation. Amazon provides tech, marketing, and fulfillment for Amazon sellers at a handsome fee. But it also gives an opportunity to Amazon sellers to test their products while not being too worried about innovation. If a seller has a unique product and provides value, there is a good chance of success on Amazon. 

Amazon sellers can leverage the latest AI tools to improve their eCommerce merchandising from creating content to advertising. Given the competition on Amazon, leveraging AI may become imperative in the future, but a head start for your business would only do good.

From an eCommerce merchandising perspective, here are the top areas you should use AI.

Creating content

Amazon sellers can leverage AI tools such as ChatGPT to create a content strategy, and a brand story, and write SEO-optimized product listings. AI can do a quicker job of adding your most relevant keywords to the product listing. It can also write product titles, descriptions, and other content pieces to give you some ideas to build on. 

Trellis Content module lets you do keyword research and create content on the platform itself with just a click of a button. Moreover, for images and infographics, there are several other design tools that can help you create and edit images using prompts.

If you have a range of products, it could be a real-time and cost saver. As the marketing world adapts, NPL tools can help you grow your business.

The biggest wave at the moment is using prompt engineering to create content that serves a purpose. While content creators still explore the possibilities, Amazon sellers can learn how to create product content that sells.

Leverage dynamic pricing 

Being one of the biggest purchase factors, pricing correctly is important for Amazon sellers. Ironically, this usually puts merchandising teams in a state of paralysis. 

Those who are a little more courageous and do research, planning, and competitive analyses, can still get it wrong and leave money on the table. Getting the pricing for your product through AI-based testing and modeling is not only a viable solution but may be the most comprehensive. 

AI-based dynamic pricing involves adjusting product prices in real-time based on various factors such as demand, competition, and market conditions. This pricing approach allows Amazon sellers to optimize for profit, maximize sales, and price out competitors. With dynamic pricing, you can adapt to changes in supply and demand dynamics every day. 

Trellis Pricing Module uses AI to test product pricing with a pricing range input and suggests optimal pricing to maximize profit or sales. It also considers low inventory to keep you from running out of stock. Based on these factors and many more, you can model price elasticity and demand velocity to give you the best competitive pricing, every day.

Advertising optimization

Advertising campaigns are integral to Amazon sellers’ eCommerce merchandising strategy. More than three-quarters of Amazon sellers have used it at least once. Whether launching a product or marketing a best-seller, brands spend on Amazon ads to boost sales and improve BSR. However, ad campaigns are becoming more expensive and brands struggle to maintain margins and lower ACoS. 

Advertising campaigns need to be optimized to make more sales or sell at an optimal margin. In many cases, if a brand launches a product, its initial goal is to sell despite high ACoS. Since high ACoS is not sustainable, sellers need to optimize their campaigns in other places to reduce ACoS and increase profits. 

Ad optimization requires a lot of cross-campaign data analysis. AI has helped Amazon sellers through complexities to make better decisions. For example, Trellis’ Advertising Automation Module uses AI to suggest and update bids on the most effective keywords. It can maximize the output of these campaigns by overlaying different contexts such as ad creative, ad type, and seasonality to make more profitable decisions. This can help to inform and implement strategic decisions across your brand.

Strategizing promotions

Amazon brands that provide discounts and special offers witness a remarkable 5.2 times increase in sales and have a higher probability of acquiring new customers. On the other hand, brands that do not offer discounts still generate sales, but their performance is only a fraction of what brands offering discounts achieve.

Amazon customers expect promotions and discounts. Their first priority is to get the best deal, whether through Prime membership or Today’s deal page. Amazon offers various promotions such as lightning deals, coupons, Prime discounts, and other discounts to satisfy this user behavior. Amazon sellers use these opportunities to boost sales, launch products, and clear inventory. 

However, there is one caveat. Amazon sellers can’t predict the ROI of running a promotion until it ends. Trellis Promotions Module uses AI to predict ROI before launching a promotion campaign. It uses historical prices and promotions data to help sellers determine whether a particular promotion will get them the sales velocity that they are looking for. 

What’s next with AI

AI solutions will keep getting better and can assist eCommerce sellers to automate their workflows, save costs, and improve decision-making. While there are many other AI tools for product research and market analysis, Amazon sellers with products should focus on adapting to AI and focus on eCommerce merchandising.

To learn more about how to leverage AI for your Amazon business through a single platform, book a demo with our experts at Trellis.