As artificial intelligence is becoming more and more important across enterprises and industries, we’ve entered an era of intelligent automation. When we think of product recommendation engines, we might think of Amazon and Netflix; whereas considering that 35% of what consumers purchase on Amazon and 75% of what they watch on Netflix come from product recommendation engines, we have a good reason to do so.
A recommendation case study about Helsinki-based tech startup Fuzu demonstrated that a combination of natural language processing and a recommender system can significantly improve customer experience and increase the email click-through rate by 30%.
But what is a product recommendation engine and how to make one that doesn’t fail in the first month? In this article, we are providing a comprehensive guide based on actual cases.
The following topics are covered:
What is a product recommendation engine?
Why hard-coded engines are failing?
Types of recommendation engines.
Where recommendation engines can be used?
This problem might be a serious challenge in the world of high competition, as for instance even when consumers love a brand or product, 59% of them will stop using it after just two bad experiences.
Those questions and challenges could be handled by an AI-powered recommender engine. This type of engine can make product recommendations based on millions of data points – producing highly relevant results for the end-user and thus helping to sustain great user experience.
A product recommendation engine is a combination of machine learning models that are capable of matching the right services and products with the right clients at the right time, so as to increase sales, customer engagement and to improve customer experience.
How this can be implemented depends heavily on the type of business in question, but the general idea remains the same – provide your customers with the offers they are most likely to accept when they are most likely to accept them.
Many current product recommendation engines are “out-of-the-box solutions” that essentially try fitting people into boxes based on past behavior. But these solutions are very seriously affected by the so-called cold start problem whereby recommender systems can not make the most engaging product recommendations to the new users simply because they do not have any previous interactions. Also, this problem occurs when brands launch new products: no conclusions can be drawn when no one has actually purchased the product yet. However, there are ways for eliminating the cold start problem.
More advanced product recommendation engines use not only historical data on user interactions, which is often known as collaborative filtering, but also factor in two additional variables:
This means that even if you add a new product to your inventory, you can still offer it to the people most likely to buy it because you know the product and who are the people most likely to be interested in it.
It also allows you to provide people with novel content. It’s a common issue for recommendation engines to reinforce their own product recommendations, nudging people further and further into an echo chamber. Correlating relevant background information and item properties allows the recommendation engine to look beyond the products a person has already seen and present them with new experiences.
But let’s have a look at how “hard-coded” traditional models differ from AI-enabled product recommendation engines.
In a traditional model, the system works on a trigger and rule basis – a user performs a predefined action and the system sends them an offer. This action could be anything from a product view or a click to adding something to a shopping cart – anything that could signify interest.
However, this way has many disadvantages. For instance, it does not take into account daytime, changes in consumers’ behaviour, or seasonal fluctuations and trends. All of this requires manual adjustments and is much less efficient when there are thousands of various products. It might be a good solution for a small E-commerce venture, but not for an organization having thousands of products in stock.
An AI-powered product recommendation engine is a step forward in both its capabilities and also the data that it can use for its benefit. Some of its benefits compared to traditional would be:
This allows companies to improve their sales offers and make product recommendations based on location, or past or current activity.
There are various types of recommendation engines used today. Most of us have probably interacted with one recently, or do so from time to time.
Even though it sounds simple, companies spend years on experiments until they get the optimal results, but it totally pays off. For instance, Netflix believes that their recommender system is worth 1 billion USD, as matching their customers with the right content helps to almost eliminate any subscription cancellations. Meaning that recommendation engines not only help to increase sales but also solve customer churn problems.
As such projects are complicated, companies often fail when they start building these internally, without a proper data science partner. MindTitan has built various product recommendation engines for the telecommunication industry, retail, and pharmaceutical companies for their online and offline shops. Talk to our experts and we will help you to come up with the best approach to your problem.
There are various ways to use product recommender engines
The most popular ones are:
With all said, you probably now understand what a product recommendation engine means and how it is used in e-commerce. We have helped several big companies from Japan, Finland, and Estonia to successfully implement recommendation engines and are happy to share our experience. Book a free consultation, and let’s discuss your idea.
