Thanks to technological advances, more and more retail businesses are going online. The COVID-19 pandemic propelled this growth even further, as lockdowns forced people to shop online more and businesses to go digital.
The eCommerce industry’s value is expected to grow by $11 trillion between 2021-2025. This opens up tremendous opportunities. But with so many players cramping up the space, online retailers will have to step up the game. Major players like Amazon and Alibaba are always on the prowl for innovative ways to improve their services and keep customers hooked. And they rely on machine learning (ML) technologies to get this done.
In 2021, 12 to 24 million online shops were vying for consumers’ attention. And this number is only expected to grow. To increase their chances in this highly competitive space, even small players need to adopt the technologies driving the growth of major eCommerce brands.
You may have heard about AI chatbots and ML fraud detectors. You may even be familiar with how AI and ML work but don’t really know how they translate in the eCommerce space.
We’re here to guide you on the real-world ways to use machine learning in eCommerce. We’ll show you how the likes of Amazon and Alibaba use ML to their advantage and how even smaller players can catch up with these innovations.
Recommendation engine
A recommendation engine uses ML algorithms to filter data and suggest items relevant to a user. It processes large volumes of information to predict which content, products, or services are most likely to catch the customers’ attention and interest.
Recommendation engines create a win-win situation for businesses and customers. They help businesses by increasing engagement, sales, and customer loyalty. And they benefit customers by providing personalized assistance that makes it easier for them to find what they need or want.
Amazon is particularly famous for its spot-on recommendations. Its system automatically analyzes data, selects and optimizes algorithms, and instantly recommends relevant products. It’s so effective that it drives 35% of the company’s sales.
Rakuten, Japan’s largest and most popular eCommerce site, employs reinforcement learning algorithms to study customer reactions to products. They help make data-driven decisions on what action to take or what products to recommend in case of similar consumer behavior.
Another example is Unicsoft’s track record. Our big data and machine learning experts helped to build a recommendation engine for a big platform featuring user-generated content. As soon as the recommendation engine was launched, the owners of the platform, which was very similar to 9GAG and Reddit, witnessed dramatic growth in audience monetization. At the same time, click-and-go for partners’ offers grew by 20% compared to the previous period. The platform’s overall audience increased by approximately 8%, which was definitely a positive effect the client was counting on.
Customer segmentation and personalization
Customer segmentation is grouping customers into segments that reflect common patterns in their demographics, interests, behavior, and characteristics. It helps companies know their customers better and market to them more effectively.
ML methodologies do a particularly great job analyzing customer data, uncovering patterns, and gaining insights. The customer segments they identify are more precise than those determined using manual or conventional methods. Common ML algorithms for customer segmentation are k-means clustering, BIRCH, and DBSCAN.
Amazon Personalize features advanced user segmentation. It creates audience segments based on more than just demographic information and business rules. Backed by ML technology, it groups users based on their preferred items, brands, categories, and other similar information. It empowers highly targeted marketing campaigns that boost engagement and retention while minimizing marketing spend.
BecauseMarket, an eCommerce brand for personal care needs, uses smart segmentation to analyze its target customers and offer a seamless experience. In particular, they determined that seniors with incontinence might be too embarrassed to ask for adult diapers in physical stores and would appreciate the products being delivered discreetly to their doorstep.
Dynamic pricing
Dynamic pricing is a flexible and responsive approach to setting prices. Instead of sticking to fixed prices, businesses consider several factors, including demand, supply, competition, and operating costs. It’s hard to do this job manually.
ML can step in to process vast amounts of data and adjust pricing according to business rules. These rules can consider not only shifting market forces but also customer’s location, the day of the week, the season of the year, and other details. It can even analyze personal information about a specific customer to predict how much they might be willing to pay.
Dynamic pricing lets you increase prices for products in demand, revving up profits. You can also sell overstocked products at lower prices to prevent inventory waste and drive revenue even when times get rough. And you can rest assured that your prices stack up well against competitors.
When discussing successful dynamic pricing strategies, Amazon springs to mind yet again. The eCommerce giant makes over 250 million price changes in a day, with the average product price changing every ten minutes. This strategy helped Amazon establish a low-price reputation that attracts customers in droves.
Demand forecasting
Demand forecasting allows businesses to predict customer demand for a specific period. This way, they can improve their supply chain operations, capital expenditures planning, inventory management, and more. Historical data, market forces, and other relevant information are all accounted for.
ML techniques greatly assist demand forecasting. They speed up data processing, automatically update forecasts drawing on recent data, identify hidden patterns, and make projections more accurate.
Let’s consider the case of Rakuten. Around 200 million products get traded at any given time on this platform. The Japanese eCommerce giant relies on supervised machine learning to process historical data and come up with product sales predictions to inform its decision-making.
Optimized search results
It’s not enough for your eCommerce platform to be well-stocked. Customers should also find the products they need easily. Around 25% of search queries on eCommerce platforms are misspelled, which can translate into missed revenue.
Optimized search results allow buyers to find a specific product even if they type in misspelled keywords. This feature also pulls up similar and related products. It’s powered by ML algorithms that identify patterns in user searches, past purchases, clicks, and other data.
Optimized search results help improve user experience, retain customers, boost product visibility, increase sales, and promote upselling.
Zalando, a German fashion store, developed a neural network that works through misspelled words in various languages. Backed by ML technology, the platform takes only two seconds to sort through 300,000 products and suggest the right products.
Fraud detection
Ecommerce fraud detection software prevents fraudulent transactions by identifying red flags. It has a bank of knowledge about typical and acceptable user behavior and can recognize the slightest deviation. It also records precedents or previously fraudulent transactions. ML fraud detectors track every action in real-time, sending alarm signals with zero delay.
Powerful ML solutions verify the legitimacy of transactions. This prevents false declines that often drive customers away. The detection system does its job without disrupting the checkout process, ensuring a smooth-sailing customer journey.
Fraud detection systems fight against intelligent human actors who quickly change tactics when exposed. It means they work with shifting data sets. Alibaba stepped up to the challenge by generating formidable digital adversaries to train their ML software. These adversaries anticipate how real fraud perpetrators might shift directions or adjust their approaches.
Conversational chatbots
While non-AI chatbots can only respond to keywords or button clicks, ML chatbots are capable of offering “human” conversations and responding to random questions. The more these AI chatbots interact with users, the more they learn about the eCommerce platform, its products, and how it can serve its customers.
After intensive training, these conversational chatbots learn to perform complex tasks like upselling relevant products, addressing long-term needs, and providing customized coupons.
With ML chatbots, your business can provide 24/7 service, gather customer data, increase sales, personalize the customer experience, build brand awareness, boost customer satisfaction, improve agents’ productivity, generate more leads, and reduce costs.
Here’s a good example. In the Christmas season, LEGO decided to boost its online sales by providing customers with gift recommendations. To make this happen, they released their first chatbot operating on Messenger. It was so successful that LEGO decided to keep it. Now, Ralph the chatbot helps customers pick a perfect present throughout the year. It first segments the customer into relevant categories and then personalizes its gift recommendations. Ralph’s highly engaging and conversational approach keeps users hooked.
Levi Strauss & Co. has its own chatbot, The Virtual Stylist, which uses a conversational tone to engage with customers. It explores people’s preferences and uses the information to provide fashion advice and product recommendations.
So far, we’ve only discussed ML technologies and how they relate to big brand names. Now let’s talk about how smaller players can tap into this advanced tech.
Machine learning for small and medium-sized enterprises
Cloud computing made machine learning technologies accessible to small and medium-sized enterprises (SMEs). In a 2020 survey by IBM, as many as 29% of SMEs have reported using AI technology.
This is how the cloud makes ML more accessible:
- Pay-per-use models offer a more affordable way to manage machine learning workloads
- Smaller enterprises can experiment with ML capabilities and scale up as necessary without investing in expensive hardware
- Cloud providers like Amazon Web Services (AWS), Google Cloud Platform, and Microsoft Azure made machine learning technologies accessible even to those with limited knowledge of AI and data science
ML opportunities for SMEs are not limited to cloud computing. Software developers can also use open source frameworks like CNTK and TensorFlow to run these technologies on the company-owned hardware. However, the companies might have trouble scaling workloads and training models requiring large computer clusters.
In any case, SMEs still have to grapple with barriers to entry when embracing ML technologies. Fortunately, technology consulting companies are on a mission to help startups and enterprises adopt advanced technologies with minimal friction.
Unicsoft, for one, has the expertise, experience, and tech stack necessary to help enterprises of all sizes adopt advanced tech. They can quickly deploy experts in machine learning in eCommerce, natural language processing, computer vision, and data analytics to get your eCommerce platform set up.
So how can you get started?
Tips to adopt machine learning in your eCommerce business
Adopting ML technologies does not have to be a daunting process. Simply follow these steps.
Explore the possibilities of ML
Understanding the capabilities of ML can help you make the most of what it has to offer. Reading this article has already gotten you off to a good start. Now you know how the big brands use ML and what potential benefits this technology offers for your business.
Consult with experts
You might struggle looking for answers on your own. Fortunately, there’s no shortage of third-party consultants willing to lend their expertise. For instance, Unicsoft offers consulting services in ML, data science, and cloud computing, among others.
Identify problems you’d like ML to solve
Before exploring your options, clarify the problems you’d like to solve. This way, you can easily communicate your issues to consultants and IT experts. The consultants can also help you dig deeper and understand what your business needs.
Take stock of your current technological capacity
To get a good grasp of ML solutions that will work best for your company, you need to be fully aware of your technological capacity. Taking stock will show your company’s limitations and potential. Yet again, you can work with consultants to figure this out.
Put together a team
After assessing your company’s needs and capacities, you can build a team to get you started with ML adoption. A dedicated team will help you stay on track. Once again, you can turn to Unicsoft for help on this one. They can quickly put together a team of data engineers, data scientists, DevOp, QA engineers, UX/UI designers, project managers, business analysts, and other pros.
Start small, measure, and scale
It’s best to start small when adopting ML technologies. You can use new solutions to analyze the data sets and test the insights. If you’re happy with the results, you can move forward with scaling your application.
Moving through the process step by step, you will safely get to your destination — an ML-powered eCommerce platform that trumps the competition.
Conclusion
ML technologies — recommendation engines, fraud detectors, and conversational chatbots — elevated the eCommerce experience for both sellers and customers. They’re some of the main reasons why big names like Amazon and Alibaba continue to rake in the bucks. They also motivate customers to keep going back online to enjoy fast and convenient transactions.
Fortunately, even small players can leverage ML technologies to step up their game. Amazon even makes its ML-powered recommendation engine and customer segmentation readily available via AWS. With the power of ML now more accessible than ever, you simply can’t lag behind. Reach out to reliable technology partners to explore how your own store can benefit from it.
