Recommendation Engine

It’s an entirely digital world – there is no doubt, and there is no looking back. Companies are developing more digital tools everyday than one can possibly keep up, customers are getting used to the online environment and the internet, and online connectivity is has become a core part of our everyday functioning.

From essential work like banking and finance to shopping or watching entertaining shows and movies – everything is being done online. You can order online from multiple geographical locations through amazon, you can watch shows across the world on Netflix, and you don’t have to worry about going to the bank to work out your investments. A large number of products are readily available to the customer – and the choices are continually increasing.

In such a scenario, good customer experience would depend on understanding what customers want and curating products they genuinely like – from the sea of available products.
This is where recommendation engines come in.

How does it impact the business?

By improving user experience, Recommendation engines can give your company the much-needed edge over your competitors.

It can –

– Drive traffic to your website.
By targeting the correct users as well as giving the users better ads, recommendations, and products, it can increase traffic and footfall to your website.

– Improve engagement
It can give customers a much more engaging experience. An engaged customer is more likely to return and less likely to churn.

– Improve customer acquisition
If you provide the correct choices, the customers are more likely to buy. Thus this can increase customer acquisition.

– Increase average order value
Customers are more likely to increase their orders if they regularly find the correct products.
It can improve products and content.

Recommendation engines can provide information about customer preferences and the type of products or contents that create more engagement. This can help the company by guiding in the direction of growth and content creation.

What do you need for a recommendation engine?

So can your company leverage the power of recommendation engines?
To benefit from a recommendation engine, you should make sure –
– That you can generate good high-quality data regularly,
– You have some data about the customers’ value transactions,
– You have a good team of data scientists and data engineers to maintain and improve the algorithm continuously. Machine learning and deep learning models can deteriorate and become obsolete very fast, depending on new customers and new behavior patterns if not monitored and modified.

The more detailed data you collect, the better will be your recommendation engine’s performance. But it’s never too early to start and then improve and scale up with time.

How does it work

Recommendation engines rely on two sources of information:
1.Users
2.Items or products

Recommendation engines use this two-sources to curate products for users.

The two most popular approaches to building a recommender system are called collaborative filtering and content-based filtering. This is, of course, a comprehensive and oversimplified classification, but it can help us understand the fundamental working of such systems.


Content-based filtering

In this type of model, the properties of products are used to determine the similarity between products. Then based on the customers’ buying or surfing history, similar effects are recommended to the customer.

There are a lot of mathematical models to determine the similarity between products. Different models try to achieve other goals. How similarity is defined in terms of products largely determines which is the appropriate model to use.

Collaborative filtering
In Collaborative filtering, the similarity between products is not measured by using the properties of the products. Instead, closeness, like-dislike, or affinity of users towards the products is calculated. This information can come from multiple sources – ratings, comments, or just the number of times the product has been bought or viewed.

Once this is established, then the similarity between users is measured. Recommendations are made with the assumption that similar users like similar products.

Which model should you use?

Both models have their pros and cons. Most recommendations are a hybrid of the two models above.

But as a business owner or a data scientist, it is essential to know what advantages each could offer.

The advantage of content-based filtering is that even if you keep expanding on your product inventory, the lack of historical data about the product doesn’t affect the model’s performance. The similarity-based on the characteristics of the product is enough to recommend.

For collaborative filtering, the advantage is that it can understand individual user patterns better. However, it requires historical data about user-product interactions, which may need time to build up.

So which do I use? How to proceed

There are no absolute answers. As such most models are hybrid of the two systems, and with time one can modify the models by experimenting with what works better.

Data scientists responsible for creating and maintaining recommender systems should always monitor the performance and keep experimenting with different kinds of models. There are multiple ways to measure performance, depending on business needs.

Some key questions which can help you measure the performance are-

– Is the recommender engine recommending various products? Is it stuck in some local cluster of products?
– Does it drive business growth appropriately?
– Has it really affected user behavior?

The business development team and the data science should always be in sync with each other to ensure the recommendation system impacts the business goals in an optimum way.

Our 33-pt proprietary BADIR framework can help solve business problems with machine learning and deep learning-based solutions. You can read more about BADIR here.

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