How to Benefit from Machine Learning in the Marketplace
The concept of machine learning
Machine learning started its history in 1986 thanks to Geoffrey Hinton. Now it runs the world pretty much. Its main idea is to use statistics to seek patterns in tons of information and apply them. Such data includes words, numbers, clicks, transactions, etc. Machine learning powers plenty of services that we use these days. Among them are:
- Search engines, e.g., Baidu, Google, Yahoo!, Bing
- Voice assistants such as Alexa, Siri, etc.
- Social networks like Twitter, Facebook
- Recommendation systems, e.g., Spotify, Netflix, etc.
Each of these platforms is collecting as much information about you as it is possible. They know what series you like to watch, what content in social media attracts your attention, what links you click, etc. As a result, all these platforms know what you want next or in terms of voice assistants what words better match your expectations. Times passed, and now new machine learning technology is becoming increasingly popular. It is called deep learning.
Deep learning: what is it?
Deep learning gives machines improved ability to find the smallest patterns. It is a common name for neural networks family, that has many layers of computational nodes. A lot of layers allows network to be more flexible and detect the smallest features. Layers function side by side to analyze tons of data and come up with the final prediction.
How to Benefit from Machine Learning in the Marketplace?
It is possible to make the marketplace work and be profitable with the help of machine learning. In fact, such algorithms have been used for a long time already to fuel the marketplace. Nowadays people are buying products they never thought of before. Machine learning algorithms study the preferences and activities of customers, analyze them, and offer products that fully meet customer’s expectations.
All these marketplace approach changes aren’t an accident. As we are in an era where AI controls marketplaces, it is not surprising that all personal information is studied for making necessary corrections and, thus, delivering the best possible experience for each person. Companies that understand the benefits of machine learning are one step ahead of their competitors. This is how many products and services were delivered to hundreds of houses. Missing the opportunity to leverage these advantages may be considerable damage to your business. There are several examples how machine learning can improve your product efficiency and achieve impressive results.
1. Predict preferences of the customers
It is one of the most popular ways to use machine learning in a marketplace business. AI algorithms learn different aspects of the user’s behavior and come up with the recommendations of goods and services that a particular user is likely to purchase or order.
Machine learning algorithms may improve the user’s experience by analyzing such information as:
- Machine learning algorithms may improve the user’s experience by analyzing such information as:
- Personal profile (hobbies and location)
- Search history (chosen filters and prior search results)
- History of buying goods
- The behavior of alike users (e.g., people with similar interests or coming from the same location)
- The most leading items from the category
The system will introduce very accurate information if the person uses a definite platform actively, for instance, adds items to the cart, purchases goods, etc. As a result, you will get precise data on what the customer likes, and therefore you can offer the most suitable option.
2. Set and adjust prices
Usually, a seller faces some challenges while setting the price for goods or services:
- Adjusting the cost depending on the market fluctuations
- Establishing a proper price
Many sellers are wondering how to set the price correctly. This point is crucial, especially when it comes to unique goods and handmade items. When such questions arise for a taxi driver or plumber, the person can just monitor the prices of the competitors and choose appropriate costs. Things are getting more complicated when it comes to the market changes, as a lot of factors impact the final decision. A person must consider such details as the price of materials, length and complexity of the workflow, etc. It is difficult for people to mind all these things and calculate the honest price when deciding on the cost of the artist’s work. This is when AI enters the stage and does it in several minutes.
For example, Airbnb widely applies artificial intelligence for adjusting the prices. All apartments that you can find on the platform are different. The cost depends on a wide range of things such as flat conditions, location, season, etc. Google also follows such logic and proposes the best time for people to book the tickets and comes up with predictions on price movements for a chosen destination. In its blog, the company's specialists state that people can decide on the best time for flight thanks to the analysis of flight data history, scrolling through search results. The passengers will be able to check whether the prices are typical or higher and find out available predictions on their decreasing.
3. Foresee and prevent fraud
Being an active participator of a shared economy marketplace, you have to trust valuable property to a stranger. Imagine that you offer to rent cars or apartments. Here is when machine learning may make all these interactions safer and relationships between you and your client more trusted. One of the most common ways to do it is by incorporating the system of references and reviews. Other people can get acquainted with these reviews and decide on further cooperation. Still, such a system has a significant drawback. While preventing fraud repetition, it doesn’t guarantee the prevention of fraud itself. Precisely this is the finest hour of machine learning. Its algorithms identify anomalies while analyzing the transaction history and extra data from external sources. As a result, the system may restrict access for a particular person in case some extra verification procedure is required. All in all, machine learning is an efficient tool for preventing significant types of fraud, such as spam messages, duplicated accounts, payment fraud, etc.
4. Improve customer support
Recent research showed that 85% of provided customer support would not involve a human being in two years. The central role will be given to chatbots that are driven by machine learning algorithms. They will answer the most frequently asked questions and monitor the user's behavior. A real person will deal only with some unexpected issues. Such a tendency will play into the hands of entrepreneurs who will not have to hire many dedicated specialists, pay them salaries, rent and maintain an office, etc. Chatbots are able to communicate with numerous users at the same time without harming efficiency. As a result, businesses will speed up their workflows and reach the stated goals quicker.
5. Retarget and upsell
It would be a big mistake to think that all people who come to your marketplace are going to buy something. Often users visit numerous marketplaces in order to read the item description, check detailed data, read other customers’ reviews, compare prices, etc. Of course, all these visitors are your potential customers, but for some reason, they are not ready to buy goods that they are interested in. Thanks to AI algorithms, you can try to make them return to your marketplace when they are ready to purchase a particular item.
Machine learning provides entrepreneurs with an opportunity to retarget and upsell purchases. It uses the data that is found in customer’s profiles, analyzes their behavior, and gives you essential information. You can use it to predict who from the audience may become your potential buyers so that you can wisely spend a retargeting budget on.
In the long run, each person can be clustered and tagged with a definite group number. So similar users are in the same groups. Using AI you can build a portrait of average user for the specific group to create new more efficient selling campaigns and implement various marketing strategies.
6. Analyze trends
People prefer dealing with personalized products, which means you should study trends inside the market more carefully. However, things become more complicated when you want to collect information about various types of users. Machine learning techniques help you better understand your target audience, find out the preferences of the clients, and know what trends to focus on. You need to collect and analyze such user's data to foresee your potential clients' needs:
- Search information
- Behavioural data
7. Provide personal recommendation systems
Let’s face the truth: all marketplaces want to boost conversations, hence get more sales. Professional personalization algorithms are an efficient tool for analyzing the search and expectations of the target audience. As a result, you know their needs and can provide them with the necessary goods. In terms of a technical point, the marketplace personalization means its extension with a recommendation system. The primary recommendation systems with a colossal impact are:
- Price offerings
- Specifying categorization
- Resources and time evaluation
- Audience notification in real-time
- Recommendation for goods
Additionally, you can use AI algorithms for personalizing such options as:
- Goods filtering
- Wish list listings
- Item reviews
- Product views
- Interaction with ads
- Area “People also buy” and “You might also like”
Successful personalization of the marketplace is a great idea that results in greater user experience. The chances that a potential shopper will leave your store without purchasing necessary items are meagre.
8. Manage interest and supply
Recent Statista research stated that e-commerce earned more than $8.2 billion during Christmas time in the USA in 2019. But it is crucial to understand that holidays are not the only time when your sales can grow. Hence you should analyze and foresee the most suitable time to come up with special offers for the customers. This is when predictive AI techniques may be sufficient for your marketplace:
- You should start by collecting data on the cycles of interest for the target audience's services or products. Different accessible sources of analytics such as Statista or Google Trends will help to do this.
- Next, you need to get the internal marketplace data on your customers’ demand
- Finally, analyze all information and decide what products and when should be increased; make sure your company is capable of dealing with a grown demand.
Consequently, your marketplace will be able to offer holiday discounts coming up with efficient goods interest and manage to attract more clients.
9. Perform a visual search
You can better meet customer’s needs by incorporating image recognition technology. E.g., a person has only an image of the necessary product and no other information. You can help to find the good by adding the visual search feature. Such algorithms will monitor the entire marketplace’s database and match the most suitable items with an input picture.
As a result, the buyer will see the closest matches and be able to choose the necessary product among the delivered offers.
10. Work with a chatbot
Using chatbots in the company’s workflow is popular now. Such assistant can take part in several tasks in the marketplace. You can make a chatbot either a human-like or an interactive one, and be sure it will help your clients to:
- Find the necessary goods
- Compare several products based on their characteristics
- Make payments
- Arrange shopping list
- Check relevant products
AI chatbots will help you enhance the quality of provided services from excellent customer support to efficient lead generation.
Examples of Applying Machine Learning in Famous Online Marketplaces
Airbnb is a leading marketplace where people worldwide can lease their apartments or book any type of accommodations for a business trip or vacation. The service states that there are more than 150 million users and over 650 thousand hosts all over the world. Airbnb hosts may find it quite challenging to establish a proper cost for their accommodations because of plentiful factors that influence the final price. Among them are:
- Air conditioning
- Public transportation
- Closeness to the city centre, etc.
Hosts have to analyze current interest and supply in order to establish competitive costs and, therefore, attract more potential guests.
This is when machine learning techniques enter the stage. The engineers have presented a feature that allows analyzing the demand and supply for dates and offers a cost to enhance the chance of reserving the accommodation at the best price. To understand how this mechanism works, let's look closer at listing an apartment on Airbnb and check how definite factors influence the house's cost.
For this experiment, imagine that we need accommodation in London. A host is going to rent a private room with its own bathroom and 1 bedroom.
Next, the person chooses other essential amenities that are expected to make life during travelling as comfortable as at home. In such a way, the owner justifies a higher price of the property.
Afterward, the host uploaded the required images with catchy titles and a short description.
Afterward, the host uploaded the required images with catchy titles and a short description.
Completing all required points, the host receives a base price determined by a machine learning system. It can vary depending on the seasons, either low or high. You will be able to correct the cost basing on the interest. So for our example AI system calculates the host’s earnings and a private room in London with all the mentioned facilities will bring £63 per night.
You do not have to agree on the suggested price and can set your own one. Still, hosts who accept it have 5% more bookings, according to Airbnb's survey.
Airbnb chooses the best image for your advert
While other services just choose the first or a random image for the apartment, machine learning algorithms at Airbnb analyze all uploaded pictures. As a result, the host automatically get the winning image that is later established as the main and shown at search engine results. Such an approach lets enhance the first impression that users get about the apartment and confirms that exactly it is the best place for having a comfortable stay during a trip. Moreover, the service is equipped with plenty of various improvements, which make the image more captivating.
Another popular service that also faces difficulties with setting the correct price is Uber. The main challenges appear because of a low income from definite trips and unsatisfying driver's productivity in peak times. This is when machine learning plays a vital role in coping with growing requests. The engineers developed Michelangelo, a machine learning system, in order to set correct cost for trips depending on location, time, ride history, etc.
Key capabilities of Michelangelo:
The system foresees the places where the person may go. A prediction depends on the available location, previous ride history, and time of the day.If the user is a newcomer and doesn’t have a previous ride history, Michelangelo will offer the most beloved destinations at this time from the current place.Engineers also added the feature that adapts prices for various users’ categories. For the person who lives in a wealthy district the journey cost will surely be higher than for those who are coming from a poorer location.Machine learning technology can predict the possible imbalance between interest and supply. Then it will tell the driver where he should go to enjoy the best opportunity to ride and earn.
Uber: Support System
According to Uber, the company has about 15 million rides every day. Not each client needs a support system while others do. For example, a person may lose an umbrella or any other personal thing. Hence, the company has plenty of requests during the day. Michelangelo helps cope with all these issues by speeding up and automating the process of reacting to customers’ requests. It deals with thousands of issues and successfully resolves them.
A popular platform UberEats also uses machine learning models represented by Michelangelo. It offers the most suitable restaurants for the customers, ranks them, and their menus. Uber app forecasts the time for expected meals. The system checks and analyzes a lot of historical information and different signals for actual time, such as prediction of time that restorant requires to cook a meal, number of free delivery men in this area, current kitchen load etc.
Amazon is one of the pioneers that used AI algorithms for product recommendations based on users’ likes. These algorithms went through the changes over the years and now are getting more efficient and dynamic. The company's CEO Jeff Wilke says that earlier, they didn't know what shoes the person is likely to buy along with a particular top; the algorithms needed more time to predict such things, and the results were not accurate enough. Now new technologies are smarter and come up with recommendations quicker. As a result, the company may present a popular top earlier to meet the customer’s needs and boost sales.
Amazon Visual Search
A professional Visual Search technology allows clients to use visual data to look for, discover, and purchase goods. The marketplace creates AR solutions on smartphones, overlaying related data over the cameras on mobile phones. As a result, users can look for products in accordance with shape, color, and even texture. Such an approach boosts sales on Zappos and Amazon as the clients quickly find necessary watches, bags, or any other good. Even more, Amazon's visual search develops computer vision opportunities for supporting the initiatives of the marketplace across the whole good delivery process: from the time when the item is photographed and included in the catalog to the moment when it is delivered to the client.
Among other machine learning and AI efforts of Amazon are Alexa voice assistant, access to the cloud-based instruments, letting the clients get necessary goods, and instantly leave Go stores, guiding robots to take responsibility for some operations, etc. Meanwhile, AI technologies are crucial for all businesses run by Amazon; the range of all its apps is stunning. This is the main reason for the impressive popularity of Amazon and its remarkable profit.
While many companies send notifications with the most popular positions, Starbucks uses real-time personalization technology to come up with the snacks that you will definitely like. A famous brand analyzes the personal preferences of a customer and offers only relevant items. It sends about 400 thousand of unique variants every week considering the tastes of a person.
Apart from tracking preferences, demographics, and purchase history, the company considers other information like weather forecasting. Basing on it, the system comes up with relevant offers. For instance, when it is cold outdoors, you will get the recommendations for the most suitable hot drinks.
How to involve machine learning algorithms in your own business?
It is crucial to understand that implementing the machine learning mechanisms in your business depends on their proper use. Following these useful tips, you can boost the chances for the positive outcome:
- You should collect both external and internal information related to a specific model for creating a data set. Mind, both the right answers and the data are significant. For instance, if you are eager to range the goods at SERP, you should save some information about the item goodness. Meanwhile, we cannot measure the goodness, it is possible to save the client’s clicks.
- Using the received data, you may start building machine learning algorithms.
- Next, you need to apply a training strategy for the machine learning algorithm to determine the relationships in the already collected information. Algorithm is trained to give correct answers on the according data records.
- You can try different combinations to choose the most effective models for your marketplace, depending on the results.
- Now it is high time to initiate the testing analysis to check chosen models. We load new data to the model and compare model answers to the known ones. It allows to estimate model accuracy.
- Afterward, you can start the model deployment. In other words, you may incorporate the model in the company’s workflow and get the first business results.
- This is not the end. You should continuously monitor how the model works and what result it shows. Due to some changes over time, you must be ready to adjust the model to do it more efficient.
What is the cost of the machine learning model?
The final price of the project depends on the workflow itself. Usually, the development process is made up of a few stages. You can estimate the approximate cost of the product if you have a general idea of these stages.
Discovery & Analysis
This stage main idea is to conduct the essential study, set company, and project goals. Everything starts with studying the client’s business workflow, available metrics, and data assets. At this stage, the team specifies success factors, budget, deadline, appropriate technological stack, and document all information. The participants explore whether AI technology can be applied in a particular case. If the answer is positive, the parties define a necessary work scope for moving to the next stage called a prototype development. If everything is prepared well, and the metrics are represented in the required format, then the next phase lasts for 5-7 business days.
Proof of concept implementation and estimation stage - up to $25,000
At this stage, experts aim to create a business model for testing and checking the concept. They can use the code-based prototype, drawing or text-based mock-up, etc. In general, the form depends on the project complication and used tools for its development, such as app simulation program, screen generators, etc. Then a prototype is shown to the client and discussed with the company.Prototyping is a fantastic technique that allows specialists to approve requirements and sketch choices. You do not have to spend a real fortune on prototypes; meanwhile, they are quick and flexible to modify. The prototyping stage reduces risks and expenses that are associated with the model implementation as all requirements and project peculiarities are discussed before the start of the development phase.We offer reasonable prices for prototypes. Typically, the prototype phase requires up to $25,000.
Minimum Viable Product (MVP) - $35,000-$100,000
This is a product with certain functional features that are designed with prototype findings in mind. The MVP is delivered to a small group of real consumers in the form of a simplified product version. Its main goal is to get feedback and make any necessary changes without burning the client's pocket as at this stage. The price depends on project complexity and size and, on average, is about $35,000-$100,000.
It is the last stage on which the product is developed by considering all predefined features and then presented to the customers. Previous phases gave an excellent opportunity to study all requirements and necessary features carefully, so the final version comes up with almost any risks.
The primary purpose of the online marketplace is matching the merchants with relevant suppliers, providing a smooth way of holding transactions. But if you want your product to get popularity and bring lots of customers to you, then the business optimization is a must. Now the process of optimization is quick and straightforward thanks to machine learning technology. Let's once more look at the ways of using AI in your marketplace:
- Prediction of customers’ preferences
- Setting and adjusting prices
- Fraud foreseeing and prevention
- Custom support improvement
- Retargeting and upselling
- Analyzing trends
- Providing personal recommendation systems
- Managing interest and supply
- Performing visual search
- Applying chatbots
- Refining marketplace services or goods with extrinsic
Now you are aware of the advantages that machine learning algorithms can provide for your business. If you want to experience them in your marketplace, you need to do such steps:
- Determine the KPIs that may be enhanced and choose the preferable strategy. For example, you can diminish operational costs, sell more new items, boost repeat sales, etc.
- Decide on the area where the clients need more guidance.
- Check the information that you have already collected: demographics, personal, attributive, and behavioral.
- Send your requirements to our company and get a consultation that is free of charge.