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Using content recommendation engines to increase sales and improve customer engagement

AI and Machine Learning have become integral to most businesses’ decision-making processes and they can also power recommender systems that create a personalised online experience for your audience. Indaru has experience developing and putting in production custom of recommender systems in different industries.

What is a content based recommendation system?

Recommender engines are a subset of ML used to make suggestions for products, services or content and are powerful marketing tools that can enrich the user experience. One of the first content based filtering e-commerce systems was introduced by Amazon in 1998 and was used to recommend books to customers based on their past purchases.

Content based recommender systems examine user actions and identify patterns to generate recommendations. The content recommendation engines will analyse past purchases, engagement time, keywords or search terms used, reviews, clicks and likes to match a user profile to specific content or products.

Benefits of recommender systems

  • They provide highly personalised recommendations that are tailored to the individual user. This can lead to a better user experience, higher conversion rates and thus more sales.
  • The hyper-personalisation can reduce customer churn by providing relevant recommendations that will keep the customer engaged and coming back for more.
  • Because recommender systems learn over time, they can continue to evolve and get better at making recommendations as more data is collected.
  • Content-based filtering systems allow for a “cold start” (aka limited data). They require user activity to begin making relevant recommendations, so are suitable for businesses that do not have masses of data at their disposal.
  • Content-based filtering feels more transparent to the user and it is obvious to them why they are seeing the recommendation (e.g. they have browsed parka jackets and are now seeing recommendations for similar products). This makes users more likely to click through to recommendations because it is relevant to them.

Our custom made recommender systems remove the need for resource-draining manual tasks by automating data collection, storage and analysis.

How content recommendation engines work

We have already touched on how content based recommendations work but there is a lot more going on beneath the surface. In fact, when programming a recommender system, there are four specific steps involved:

1. Data collection

For any recommendation engine, data is always the starting point. The AI will need to collect user data through explicit and implicit means in order to feed the algorithm the information it needs to produce recommendations. Implicit data is collected from user behaviour such as demographics, purchase history, time spent reading a blog, adding to cart and adding to a list. Explicit data is collected from user input such as likes and dislikes, reviews and ratings or comments.

User profiles will be created based on demographic, psychographic and behavioural data. The next step is assigning attributes to each object (i.e. product or content) so the ML model can match user profiles with products or content that share similar attributes.

2. Data storage

Where you store the collected data is vital in terms of not only security but also scalability. There are a number of options available to you based on the type of data being stored. Structured data can be stored in an SQL database while unstructured data can be stored in a NoSQL database. You could also opt to make use of cloud-based storage or a mixture of storage solutions. We can help you select the right data storage solution for maximum efficiency.

3. Data analysis

The third step involves setting up the system to analyse the data. There are three data analysis models to choose from:

    • Data can be analysed periodically in batches. For example, maybe you want to analyse which products are added to cart on a daily basis.

    • If a 24-hour analysis is not a fast enough turnaround time, you can opt for near real-time analysis where data is processed every few seconds or minutes. This is helpful if you’d like to collect user data in a single browsing session.

    • Finally, real-time analysis enables you to analyse data as it comes in and thus provide recommendations in real time.

The type of data analysis model you select will depend on how quickly you want the system to produce recommendations based on processed data.

4. Recommendation algorithm

Finally, we will select the appropriate filtering approach in order to train the machine learning model to make the correct recommendations. There are three filtering models that can be applied:

Collaborative filtering is a method of making predictions about the interests of a user by collecting preferences from many users based on their interactions with content or products. These interactions can be classified as adding products to their cart, disliking a series on Netflix or clicking on a Facebook ad. For example, if multiple users with similar user profiles added a specific product to their wishlist, the recommender system would start recommending this product to people with similar interests.There are two main types of collaborative filtering recommender systems: model-based and memory-based.

  1.  Model-based methods use a specific ML algorithm to learn the relationships between items and users from the training data. This approach usually requires more computational resources than memory-based methods but can generate more accurate recommendations.
  2. Memory-based methods store information about past user behaviour in an easily accessible format. This data is then used to calculate similarities between users or items and make recommendations based on these similarities. This approach is typically less resource intensive than model-based methods, but can result in less accurate recommendations.

Content-based filtering is a method of making recommendations based on the similarity between content attributes and user preferences. In the case of music recommendations, it could take specific attributes like genres, artists, labels and producers into consideration to suggest songs or albums the user may like based on tracks they have recently listened to. This filtering approach doesn’t require any previous user data as it creates recommendations as it receives incoming information.

Hybrid filtering makes recommendations by combining collaborative filtering and content-based filtering. This approach is used by most businesses today as used alone, each method has its shortfall but when combined these challenges fall away. By combining both content based recommendations and collaborative filtering, the strengths of each method can be used to provide highly accurate recommendations, even for new products and content.

Why choose Indaru?

Our multidisciplinary team of marketing experts have experience with international FMCG, entertainment and eCommerce portals, enabling us to add great value with the development and implementation of content based filtering recommender marketing.

Manually trying to assign attributions is an impossible task, especially with extensive product, service and content catalogues. Our custom made recommender systems remove the need for resource-draining manual tasks by automating data collection, storage and analysis.