With a majority of modern services and products now being offered predominately online, it can be hard to get to know your customers. Unlike running a local store where you get to know each person that comes in, online businesses can struggle to know exactly what their users are expecting.
So, how can your business use this, and what is the recommended steps to take?
Thanks to advances in machine learning, and deep learning specifically, it is now possible to get to know millions of customers completely online simply through their data. By using a data model to filter through your users' favorite products and interests, it is easier than ever to make recommendations to them for what they would enjoy to use or buy.
Read on to discover everything there is to know about recommender systems - and how can you use one to get to know your users and boost sales right now!
If you have ever seen an advertisement on a web-page that seems strangely targeted to your hobbies, then you have probably already come across a recommender system without realizing it. Suggestions on Amazon and "What to Watch Next" on Netflix are all powered by recommender systems.
A recommender system, or recommendation data model, is essentially a type of machine learning model that filters throughout your previous interests and purchases and rates what you may be interested in. This ensures you only see products and information that you might truly be interested in and enjoy.
A recommendation system works in two different types of ways. Firstly, it will see what people who have similar tastes to you will like. This is done by gathering meta-data, such as whether they recommend a specific show on Netflix, and seeing what people tend to go on to watch after that show is over.
The other way a recommendation system will work is that it will assign a specific 'likelihood' rating to users and items they might like. It will then use filtering to remove any unlikely items or anomalies and only show you items linked to what you have already shown an interest in.
In more technical terms, a users' purchases or interests will be stored within a user and item matrix. Most ratings are either measured using a numerical system such as '1 out of 10' but some may use a binary rating. This is usually measured in a simpler way such as 'Clicked' or 'Watched'.
Most items in a User/Item matrix will be left blank, as this is what the machine is designed to fill in. These blanks will eventually be populated by recommended items or shows.
This matrix can then be compared with other users to find the 'nearest neighbor', or most similar user, and through deep learning, a machine can learn more about what users might like the most.
So, the next time you see a content-based recommendation or product recommendations, just know a complex machine learning model (commonly using the Singular Value Decomposition Method or clustering) is behind it.
There are many different types of techniques and implementations out there. The two main categories are memory based and model based:
The main characteristic that sets these solutions apart is they assume you have no model to make predictions and simply make choices based on information from the user-item interaction matrix.
To decide what to show a product to a new person, this method tries to match this person to existing "profiles" mapping normal interactions from both to find similarities. The goal is to predict the best item for this new person that is popular among similar users, based on interactions with the item.
The idea of item item is to focus on items that a candidate might like based on other items they interacted with positively. Two different items (a product/page/email) are considering similar if the majority interacted with both in a similar fashion. The main difference from user user is that we are now focused on interaction similarities within an item matrix, not different users. One of the main benefits of this technique is we usually end up with an item vector, listing the most likely items in order, and allows us to focus on different ones based on ROI or price.
Content-based solutions are used by a range of different businesses. They're probably the most commonly used method and one we have all encountered at some point. Sites such as Amazon and the Google Play Store are just some of the many examples out there.
A content recommendation system will see what the user already has an interest in, and recommend similar products or items. Think about the "What We Think You Might Like" sections on many e-commerce sites and you are on the right track.
Unlike a content-oriented system, collaborative filtering systems will use what other users similar to you are interested in. Commonly, content-based methods and collaborative filtering systems are used in conjunction with each other to ensure users find what they are looking for.
Some of the most popular examples are Netflix's "What to Watch Next' and Spotify's "More Like" systems.
By using what users with similar tastes to you have enjoyed in the past, it is more than possible to offer accurate recommendations. A collaborative filtering system is especially accurate as it takes in data from lots of users, not just one. In the world of machine learning, the more data the better.
Unlike a content-oriented system, collaborative filtering systems will use what other users similar to you are interested in. Commonly, content-based methods and collaborative filtering systems are used in conjunction with each other to ensure users find what they are looking for.
Some of the most popular examples are Netflix's "What to Watch Next' and Spotify's "More Like" systems.
By using what users with similar tastes to you have enjoyed in the past, it is more than possible to offer accurate recommendations. A collaborative filtering system is especially accurate as it takes in data from lots of users, not just one. In the world of machine learning, the more data the better.
Knowledge-based methods work similarly to content-based recommender techniques. By learning about specific user/item interactions, a machine learning model can figure out a pattern in what a user is more likely to purchase or enjoy.
There are a few examples of this, as it is extremely hard to implement in a consumer-based business. This is because it requires a complex knowledge of set items. However, if done well, it is the best way to attain accurate recommendations.
One of the most effective methods of boosting sales and improving customer retention is to combine more than one recommender method. Perhaps the most common mix is a content based and collaborative filtering recommender.
As this will compare your tastes with people who have similar likes to you, the machine learning system will be able to offer more accurate recommendations.
The main downfall to a hybrid technique is that it will often be more expensive than just specializing in one. However, the cost is more than worth it as hybrid methods get results.
When it comes to sites - such as Netflix and Disney+ - that are based on monthly subscriptions, there has to be a reason why the customer should pay for another month. Recommender systems are most commonly used to ensure the user always discovers something new to watch.
By ensuring the user receives regular recommendations that suit their taste, they are more likely to renew their subscription for another month. At the end of the day, the user experience is what keeps them coming back to you.
Often overlooked as a way to analyze a market, a recommender can be used to discover user preferences and see what people are most interested in. By utilizing user ratings and the number of users watching a show, businesses can ensure they offer similar products.
Much like the previous section, if you offer your user something they may be interested in, they are much more likely to regularly use your business. If you believe your business would benefit from such a system then might be able to help you.
Most online stores, such as eBay and Amazon, will constantly offer their customers recommendations by sorting through their search results and what they have purchased. This is because a recommendation system is a perfect way to offer a compelling user experience.
By getting to know your customers through content-based approaches, you will ensure that they keep coming back to you. As you learn what does and doesn't sell, you can offer your main target audience exactly what they need. This will quickly lead to more sales and more profit for you!
If you have ever been to a website and been recommended a product that would suit your needs, then you understand how powerful this tool can be. As a business, you can use recommender systems to ensure your user is always provided with the best possible experience.
Even in industries where there isn't a large amount of competition, a good user experience will still lead to more sales and more profit. By using the right software, such as buyer intent models or purchasing models, you can provide your users with the right recommendations and ensure they keep coming back.
The most common types of businesses that will use a recommendation system are ones that feature a monthly subscription, such as Netflix, and ones that sell product items, such as Amazon.
Any business that features a large number of products, whether shows or trade goods, will be able to use user/item collaborative filtering to try and ensure their customers stay with them.
One of the most difficult challenges to implementing a recommendation system is something known as a cold start. This is essentially where, as the system is new, there isn't enough data stored about users and items to start recommending products to your customers.
The best way to navigate this common issue is to set-up the system in the background, but wait for a few months, or longer in some cases, before you begin sharing recommendations.
This will help make sure enough data is stored so that a user truly gets accurate recommendations. Although this may take some time, it is more than worth it in the long run.
Almost all fields of machine learning must consider ethics during development. As a recommender system stores data about a user, there may be sensitive information that a user does not want to share.
This topic is frequently discussed in the world of computer science, with recent examples being the outrage regarding Facebook storing data about their users.
If you are storing data about your customers, you should always be asking for their permission or at least alert them to this - whether through a pop-up when they enter your site or during the sign-up process. Avoiding ethical issues is extremely important, no matter what your business excels in.
If your business is looking to increase your average customer order size, or want to improve the experience for a low converting user using your product, then implementing one is highly recommended.
It may seem like a complicated task, but we can handle the recommender systems' development and integration for you. Get in touch with us, as we may be able to offer you just what you need to boost your conversion rate and improve user sales.
This depends entirely on what your business predominately offers its users, and what types of recommendations you may need. Most successful techniques will use a mixture of more than one model style. Starting with very low-level data like google analytics or data from behavioral analytics tools is a great way to build an initial system, and scale from there.
Making sure you choose the right one for your business is the fastest way to boost sales and improve your conversion rates. Ensure you get ahead of the competition and check out how predictive analysis software (what we use to build recommendation systems) can improve ROI and conversion rates.
The use of a recommendation system is a proven way to massively increase your sales. This varies from anywhere between 10% and 50% depending on how accurate your recommendations are, and the price point of your product or service. One of the tools you can deploy alongside this is a dynamic product pricing model, allowing you to increase conversions and sales, plus optimize your profit margins.
There is a wide range of benefits to using a recommender system, from increased customer diversity (Brynjolfsson, 2006) to increased conversion rates. If you are looking for a way to expand your business, consider implementing model-based recommender systems into your software.
There is a multitude of factors that can alter how fast it takes for accurate recommendations to appear - from how many products or SKUs you offer, to how many users you currently have data stored about.
As a lot of recommender systems are prone to something known as 'Cold Starts', it may take a little time for the system to get enough data to accurately recommend items.
If you run an e-commerce website with millions of products, then more data will be needed than if you run a SaaS with a specific product.
Recommender models require lots of (useful) data to accurately get results. The sooner you implement such a system, the sooner you will begin to see results. The training is similar to that of neural network based systems.
Tracking user interactions with your new recommender system is a great way to collect deep learning generated data that gives you insights your competition wishes they had. This data can be used to optimize your new model or start creating new models like buyer intent or behavior analysis. Normally what you do is build data capture services on top of your model that allows you to scrape valuable data as your software runs.
What's very interesting is a lot of this data is higher-level data build on multiple levels of existing lower-level data. What this means is that your competition that is not running these systems probably doesn't have these same insights you have, making it extremely high ROI in your space.
Interested? Let's talk about how we can increase conversions and customer value with these same ideas for your business.
