Recommendation engine: a brief guide

User needs are complex and understanding them becomes crucial in providing relevant products. The human mind may be complex, but nothing will ever compare to data-based decisions that gather accurate information over long periods of time. Allowing your ecommerce business to rely on insightful big data technologies alongside machine learning, your B2C business will see drastic increases in conversions and marketing ROI. How does a recommendation engine solve this?

Did you know?

McKinsey estimates that 35 % of what consumers purchase on Amazon comes from product recommendations.

Staying relevant to your audience

A personalized recommendation engine provides users with individual item recommendations, allowing them to focus on content that matters to them instead of browsing through unrelated offers. Thus, relevant products that might otherwise be overlooked get to users in a quick and clean manner.

Reaching your customers everywhere

A tailor-made recommendation engine can reach its audience through multiple channels: widgets on the home page, category lists in combination with user-defined filters, product detail pages, ads, and e-mail campaigns.

Considering real data

The Machine Learning model works by considering product attributes (brand, price, profit margin, stock availability), shopper behaviour (pages visited, products bought, add to cart, favourites lists), as well as the behaviour of other clients (to incorporate “hot” listings on the market). Moreover, the model can update daily or even in real-time (if the data allows it), to account for changes in products and stock levels, and to learn from new consumer interactions.

Choosing what fits best

There are different types of recommendation engine algorithms that deliver successful conversion.

One type is the collaborative filtering algorithm which is based on collecting and interpreting large volumes of customer behaviour data. It compares similar actions of different potential customers and predicts what a particular user might be interested in.

Another type of machine learning algorithm is the content-based recommendation engine. It takes into account customers’ profiles as well as attributes of the products that users interact with. This algorithm is strongly focused on item properties and the similarity between them.

Overall, the benefits of implementing a recommendation engine through big data technologies are countless, from increased conversion rate, average order value and no. of page visits to continuously decreasing bounce rate averages and cart abandonment rate.

Useful resources:

Read the case study for to see how a recommendation engine helped their real estate portal

AI in Ecommerce (InsightOut Analytics)

How retailers can keep up with consumers (McKinsey)