The machines have risen and — sorry, John Connor — but people are embracing them because they’re just such good shoppers. Businesses are on board, too, because automation tools powered by machine learning (ML) and artificial intelligence (AI) can make it easier and more profitable to run a business.
E-commerce ML and AI tools are rapidly expanding because of the reach of e-commerce. The ability to interact with thousands of people at any given moment helps these bots learn and grow, improving what they can do. From personalizing shopping experiences and improving search results to increasing the efficiency of marketing and cutting your shipping costs, e-commerce ML is booming.
For your business, that likely means that you’re going to be investing in ML and AI services. Want to know where to start? We’ve identified seven of the most significant improvements and offerings of ML, all of which have budget-smart options for stores large and small.
1. Smarter Search and Recommendations
Sometimes, your customers are looking for a specific product but may name it incorrectly. If you’re only using keywords for that search, then people might not see what they want even if you sell it.
Machine learning is all about matching intent and outcome, which is especially useful for product and service searches. Many ML systems will learn about your products over time, including what people are searching for to find them on your site — as well as relevant information from referral sources. In most cases, ML first expands search by incorporating a broad set of synonyms and adjectives your customers use commonly.
Over time it can start to learn which products are viewed similar audiences and make suggestions by attempting to understand what’s relevant to the search intent, not just words. You’ve likely seen this on Amazon’s recommendation tools, which not only guess at what they think you want but also show you what people who’ve searched for something similar ending up getting.
The Amazon engine relies on ML to understand the patterns of search and recommendations specifically to encourage a purchase. As people buy more or less based on its results, the tool adapts. Thankfully, many of these advances are now available on ecommerce platforms and websites of even small shops through third-party developers and plugins.
2. Consumer Personalization
For online shopping, one of the most frequent starting points of machine learning and artificial intelligence is in personalizing the customer experience. This can be the secret sauce to landing more leads, converting more first sales, and establishing a relationship that leads to satisfied customers and high lifetime values.
When the right person gets the right message at the right time, you’re encouraging them to convert.
AI and ML tools help this happen by understanding past website visitor actions and what they’re doing now. Learning that context and action sequence helps them start to predict what people will do when they receive a specific deal, see a particular product, or get a landing page that’s unique to them. Analytics processing all this information — often called Big Data — now specifically target user experience and web design as well as offers to help encourage sales.
There is no specific route to adopting e-commerce machine learning in this case. You’ll have many options and have to determine what’s right for you. Personalization can change how often someone sees product reviews or their placement on a page. Or customers might get an email with a coupon for a product they put in their cart yesterday but haven’t bought. A younger audience might get drip emails focused on social proof, while older visitors see highlighted reviews that talk about product longevity.
The possibilities are limitless, which is why AI and ML are a part of it. They can create, test, and check more patterns than most of us marketers can. And, they’re good at gathering information on results.
3. Virtual Assistants and Chatbots
It’s likely that you’ve already interacted with a virtual assistant today, and the same is true for your customers. These services power many of our interactions, from asking your smartphone about the weather and voice searches to interacting with brands on Facebook or getting order tracking details on a website.
Assistants and chatbots are designed to help us answer a question or find a product/solution quickly. Both use machine learning and AI technology to learn about a person or task, such as answering questions. The chief distinction between the two is that assistants are something we secure and use for ourselves while chatbots are something a separate company offers. So, your WMS might have a virtual assistant inside of the software that helps you make decisions, while your bank will have a chatbot that helps you look up branch hours.
Chatbots are growing in ecommerce because they can get your customers to the best option for their needs or alleviate the burden on customer service agents by addressing frequent questions. Sophisticated chatbots can ask multiple questions to give a detailed answer or act as a filter to help people go from hundreds of options for finding the perfect purchase.
LEGO even rolls out chatbots during the holiday season to help people on its website and Facebook page buy the right gift for someone else. These ask about age and hobbies plus the shopper’s budget to recommend multiple LEGO sets that will address all of the customer’s needs.
The wonderful thing about machine learning chatbots is that they improve over time. You initially need to teach them about your business and products, plus provide answers to usual questions. Afterward, as they start interacting with customers and using these answers, the bots refine what they share and when. As they continue to chat, bots learn and can even start to predict the next questions or needs for customers.
This information informs not only your customer service capabilities but also product needs, customer demands, and where you can improve. If all of your customers are asking about shipping options or size charts, you’ll know that you can benefit by making that information more readily available on product pages. If everyone is asking for the pink umbrella ahead of a rainy season, it makes good sense to stock up now.
One challenge with your chatbot is the data it collects. Like your other customer information, this needs to be carefully managed and limited. People don’t want you or your bots to get overly personal, so greeting a brand-new customer by name could come off as creepy.
4. Choosing the Best Shipping Options
E-commerce tools can help you and your customers pick the best shipping and fulfillment options for your needs. They can automatically test different solutions with standard and DIM weight calculators to help you understand the cheapest and fastest methods for getting goods to customers.
At the same time, e-commerce ML solutions can ask your customers what they need in shipping and make recommendations. Think about the chatbot mentioned above and a conversation it has with a customer at checkout.
The customer clicks on shipping options and gets a list plus a prompt to have a conversation. It can offer real-time, accurate shipping costs based on different methods. Asking the customer if they need the order by a certain date can filter options and help speed people to a decision.
That same fulfillment ML tool can automatically pair tracking information with orders and customer data. So, when this customer emails or goes to your website to check order status, AI tools can automatically provide an answer within seconds.
Chatbots can tell people where a carrier says a product is at this moment, and get a custom response to them, by name, and with all the relevant details available. ML can also be used to track these interactions to help you know which customers might want more emails and notifications, or if people only check-in when something is missing. It can help you manage your interactions and keep conversations to what customers want.
You’re optimizing shipping spend and enriching the customer experience, all with the same data and logic from reliable e-commerce ML and AI tools.
5. Inventory Management Improvements
Not every ML tool is focused on your customers. There are many e-commerce AI options designed to help you run your business better. One of our favorites — and something that we use internally — are ML tools designed to help companies better manage inventory and set levels based on demand forecasts.
Giving AI access to your inventory levels and historical data can help you to predict sales and inventory usage across a year. It’ll also show you where you might be overspending or holding onto inventory for too long. For instance, we use ML tools to help customers optimize how much space they use in our warehouses, controlling monthly costs, and preventing them from having stock that gathers dust.
You might discover that high-ticket goods sell well at certain times, and you can increase holdings just before and then lower your resupply thresholds as the months progress.
At the same time, these tools can help you understand costs in your warehouse. You can quickly and accurately see product loss due to accidents, theft, and other concerns. You might realize that your shrinkage over time is significant for some products and warrants better protections. Or some companies see that a specific product is returned more often than others, while they also gain insight into labor and other costs associated with restocking.
Better inventory management and greater product understanding also inform sales and promotions. You’ll find that these ML tools can make smart suggestions for broader business use cases. It’s one reason that they’re common in many warehouse platforms and why 3PLs like Red Stag Fulfillment regularly review ML systems to see where we can find more significant insights and increased savings.
6. Fraud Detection and Risk Mitigation
The boom in e-commerce sales comes with an unfortunate side effect: increased fraud and theft through the supply chain and customer sales parts of your operations. One report projects that 2020 e-commerce fraud will reach more than $12 billion, and account takeovers are the biggest culprits.
E-commerce machine learning is figuring out how to combat this by monitoring customer account activities and looking for unlikely changes to someone’s email, phone number, password, or address. While scammers are getting more sophisticated, your tools to fight them can too. Your ML tools can look at larger patterns and greater trends than a human fraud team and make predictions based on most likely cases of fraud.
Turning on AI will help you review all transactions at a rapid pace to try and detect current fraud. As you learn more about past fraud or concerns, these tools are updated. They learn and adapt, helping you save money by minimizing the risk of online fraud and reducing the burden on human workers.
Perhaps the best news for your business is that these platforms are becoming affordable for even smaller e-commerce stores, and they’re generally more effective than traditional fraud protection tools.
7. Trend and Pattern Detection
At the heart of all AI and ML tools is the ability to detect and recognize patterns or trends. So, if you’re using these tools, look for ways to expand on the data they collect and things they notice. E-commerce ML services often make it easy to output data and run secondary analysis on it to discover what’s essential.
Some solutions on the market will do this for you, while others require your team to get deep into the data to figure out if patterns have broader implications.
In a few cases, this can be simple. Knowing the most popular searches can quickly help you refine product catalogs and know what to run specials on during the next holiday. Tracking packages and order-size by customer location may help you understand if you need to add another warehouse to save on shipping. Your AI may notice that one product gets a lot of traffic but has low sales, which could mean its outdated or the offer is wrong. There are a lot of suitable places to start.
When you get more advanced or increase your toolkit, both ML and AI technology can yield more profound insights. You may discover that sales follow specific patterns, and you can adjust inventory counts ahead of a surge to avoid stockouts. The same data can help you reconfigure the layout of a warehouse to move the next wave of popular products closer to your packing stations to speed up orders. You may even learn that it pays to only offer free shipping to customers in local areas because of how much your costs scale — and that a 10% off coupon saves money while still converting.
The possibilities are virtually endless because there’s always room to try something new. E-commerce machine learning is here and growing. We can’t wait to see what’s next and even try some of it in our own distribution centers.
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