Online marketplaces have become a staple in our daily lives, from buying and selling goods on Amazon and eBay, to finding a place to stay on Airbnb, or hailing a ride on Uber. These companies have revolutionized the way we conduct transactions and connect with service providers.
But what’s the secret sauce behind their success?
A well-designed and efficient matching algorithm.
A matching algorithm is a tool that helps connect two sides of the market in a marketplace. Its purpose is to match the two sides of the market efficiently and effectively, based on customer needs and preferences, as well as optimize for what the business is looking to achieve, whether that be volume or profitability.
A few examples include matching a ride-hailing service’s riders with drivers, connecting people looking for accommodation with hosts on Airbnb, pairing buyers with products on an e-commerce platform, or matching talent with jobs on freelancer websites.
More concretely, matching algorithms are a set of rules and processes that use data and logic to deliver a recommendation, usually based on mathematical models and optimization techniques, and often incorporating AI elements. (You can even build matching algorithms through GPT-3!)
This can be done through various techniques such as rule-based systems, decision trees, or more advanced techniques like machine learning which take into account a larger set of variables and learn from past data to improve future matches and dynamically adjusting rules to ensure a successful, accurate, and relevant match.
Matching algorithms are the backbone of many successful online marketplaces, connecting two sides of the market in the most efficient way possible. They play a crucial role in determining success, creating a seamless and efficient experience for both sides of the marketplace while maximizing business KPIs.
The reason they’re important lies in their ability to accomplish two key tasks:
Matching algorithms allow for creating a more personalized user experience. By analyzing customer data and preferences, these algorithms can match customers with the most relevant options for them. This ensures that customers are more likely to find what they’re looking for, and ultimately drives customer satisfaction and retention.
Another advantage of matching algorithms is that they can help to optimize for the business’s goals. For example, if a business is looking to increase volume, the matching algorithm can be optimized to match customers with the most popular options. On the other hand, if a business is looking to maximize profitability, the matching algorithm can be optimized to match customers with the most profitable options or match based on predictive customer lifetime value. With the right algorithm, this is done at scale, which helps streamling marketplace operations.
In short, matching algorithms are a crucial tool that helps online marketplaces connect the two sides of the market efficiently and effectively. They are essential for meeting the needs and preferences of customers, while also optimizing business metrics to ensure the long-term sustainability of the marketplace.
Okay, now that we know what matching algorithms are and why they are important, let’s get into what makes them successful. When looking to build or optimize a matching algorithm, there are a few key considerations to keep in mind. Most importantly, a model is only as the data is built on, or the input – the data you feed into the model.
The most important step in building a successful matching algorithm comes from understanding your customers. What is their job-to-be-done? It’s critical to understand what each side of the market is looking for and what their priorities are. In ride-hailing, riders prioritize convenience and speed, while drivers prioritize high-paying fares. In the job market, job seekers prioritize job location, salary, and benefits while employers prioritize qualifications, experience and skills. An effective matching algorithm will take into account the wants of both sides of the market to ensure the best match.
But it’s often more nuanced than that. Not all customers are the same and some may value different things more than others. In ride-hailing, certain segments might prioritize finding a ride faster, at a slightly higher cost. Others might be willing to wait a bit longer, if it means they can find a cheaper ride. In the job market, certain people are actively looking for new roles, while others are happy in their role but would possibly consider other opportunities if the fit is right. Your matching algorithm should often take these nuances into account, if you are looking to meet the needs of different customer segments and profiles.
To do this, practically speaking, you need a mix of both quantitative and qualitative inputs to fully understand your customer. What are the customers here for? What are you here to do for them?
To understand your customer through data, there are multiple possible techniques to apply, and there is no singular silver bullet that can get you to where you need to be. One data methodology that can help understand your customer is K-means clustering, which is a technique used to group similar data points together, based on their characteristics or attributes. By applying this technique to customer data, you can identify patterns and trends that will help you understand your customer better.
One example of how K-means clustering can be applied to understanding customers and improving matching in a different industry is in the realm of online dating. By using K-means clustering to group users based on their demographics, interests, and behavior on the platform, a dating app can create more accurate matches by connecting users who are more likely to be compatible. For example, by clustering users based on factors such as age, location, and preferred relationship type, the app can more effectively match users who are looking for similar types of relationships in similar geographic areas.
Data alone isn’t enough to paint a clear picture of your customers needs and wants – qualitative factors are also critical to explore. Again, there are multiple ways to do this, but a super effective methodology is the jobs-to-be-done customer framework.
The jobs-to-be-done framework is a customer discovery method that focuses on understanding the underlying motivation for why a customer is using a product or service. It helps businesses understand what “job” the customer is trying to accomplish, rather than just looking at the features of the product or service they are using. This helps businesses understand the customer’s pain points, needs, and goals, and how they can create products or services that better meet those needs.
When it comes to building and optimizing a matching algorithm, it’s not just about matching customers – but also about driving success for the business. What does success look like for you? These metrics will vary depending on the marketplace and the goals of the business, but they are critical to define as they will shape how the algorithm is built. These metrics can include key performance indicators such as transaction volume, profitability, or operational efficiency, or a mix of them.
For example, a ride-hailing app may prioritize customer retention and high ride volume to optimize for market share of rides. On the other hand, an e-commerce platform may prioritize customer lifetime value and customer acquisition cost to ensure long-term profitability. By understanding which metrics are most important to the business, the matching algorithm can be optimized to prioritize these metrics and drive success for the marketplace.
It’s important to note that the optimization of these metrics should be done in a way that doesn’t compromise the needs and preferences of the customer, otherwise, it will result in a poor user experience, ultimately leading to less retention and profitability. For example, a ride-hailing app that prioritizes high ride volume over rider satisfaction may see a spike in short-term revenue, but in the long-term, it would lead to poor reviews, which could decrease the number of customers who use the service.
Moving on to building the algorithm – which model should you choose? As always, there’s no straightforward answer and what you choose will depend on your specific goals and needs.
One thing to keep in mind is that you don’t need to build something super complex if you are just starting out. A basic approach can be updated incrementally as you gain more experience and data.
It doesn’t have to be a live self re-inforcing model, you can hard code certain things and update incrementally. Doesn’t have to be some fancy model. Start with a basic approach.
That being said, here are a few common models applied to matching algorithms.
All of these models can be enhanced with machine learning techniques such as gradient boosting or random forests, which improve the model’s ability to generalize and make accurate predictions. GPT-3’s embedding model can also be used in matching algorithms. For example, a fashion marketplace could use GPT-3 to create embeddings for different clothing items, and then use those embeddings to match customers with similar fashion preferences.
Instead of trying to build a silver bullet solution that solves all problems in one go, it’s often better to take an iterative approach. In other words, start simpler and build additional layers based on the results, making it more complex as necessary and over time.
Let’s take Tinder for example. When it first launched, it used a simple algorithm that only took into account a user’s location and age to match them with potential partners. However, as the app grew in popularity and more data was collected, the algorithm became more sophisticated. Today, Tinder’s algorithm takes into account a wide range of factors such as users’ interests, swiping behavior, and even their activity on the app to make more accurate matches, introducing a range of features such as “smart photos” and “boost” to further improve the user experience and increase the chances of successful matches.
What you are optimizing for will also likely change over time. Matching algorithms are a living and breathing entity that adapt with the changing needs of the marketplace. As the marketplace evolves, the matching algorithm may need to evolve as well. For example, in the early stages of a marketplace, it may be necessary to focus on building up the supply base, so volume is prioritized. As the marketplace matures, the focus may shift to revenue and profitability, so lifetime value is prioritized.
As we’ve seen, matching algorithms play a crucial role in building a successful marketplace. We went through more details about them, why they’re important, and things to consider, as well as looking at some industry use cases.
If you’re looking to build or refine your marketplace’s matchmaking, we’d love to chat! We specialize in working with marketplaces and creating end-to-end matching solutions. Reach out via chat or e-mail below!