The data of organisations is growing fast especially when the consumers' behaviours are forced to be changed to adapt to the new normal which is to the digital world due to the pandemic outbreak. The industries with high traffic on transactions such as banks, insurance and retails will experience high concurrency and high data throughput. The organisations not only have to handle the large volume of data but also to make sure the predictions and analytics are accurate enough based on the available data in order to make a better decision on how to take the best next step in achieving certain desired goals. However, the data nowadays is highly dynamics, it changes fast and grows fast which may cause you to unable react quickly to the changes, for example, find out the reasons of high churn rate and how to reduce it immediately with the right approach. This shouldn’t be seen as a problem because it is actually a “goldmine” that could help you to have a deeper understanding about your customer behaviours and thoughts if you “dig into the goldmine” with the correct approaches and tools.

The recommendations system or engine has been rising in e-commerce market during the recent year to provides personalised recommendations tailored to the customer’s needs and wants that trigger them to make the purchase decision quickly without being too obviously pushy or annoying. Recommendations not only can be applied to retailers, but banks and insurance companies can also use recommendations to provides the best loans/insurance schemes to optimise revenues based on customer’s risk and optimise collection by providing alternative options that increase customer willingness in repaying the debt or the insurance premium.

Understand your customers’ need is a very important stage before recommending because a good recommendation improve the customer experience while a bad recommendation might cause you to lose a customer. Thus, transforming the large volume of data into deeper insights about your customers' behaviours helps you to produce greater recommendations that could drive your target audience to take certain action such as making a purchasing decision, repaying loans or paying the insurance premium on time. There are some real use cases in e-commerce that achieve significant value from deploying the right recommendation engine, which are:

• Improve Operational Efficiency and Cost Efficiency (Netflix) - 75% of what people watch on Netflix is ​​generated by their recommendation engine and their recommender system saves them about $ 1 billion.
• Traffic Boost and Increase in Conversion Rate (Sephora) – Jaclyn Luft, site personalisation manager at beauty retailer Sephora sees 50% increase in CTR on their product pages and a nearly 2% increase in overall conversion rate on their homepage with AI-powered recommendations system.
• Enhance customer retention with more engaged shoppers (Amazon) – Almost 35% of Amazon’s total revenues are from recommended personalised products.
  
  

How to Choose a Right Recommendation System for Your Organisation

1. High Scalability & Flexibility

Adopting a just nice recommendation system for your current data size based on yearly estimated data increment will only meet your criteria in short term but you might need to upgrade or even replace another new one when the recommender is not capable enough to handle a sudden increase in data especially when an unexpected event happens that causes a sudden increase in data. Therefore, a recommendation system should be highly scalable to cope with fast growing of data size. Besides, it should be able to flexibly deal with various recommendation targets and business requirements based on the consumers' needs and behaviours.

2. Learn & Improve Fast

One of the desirable characteristics of a smart recommendations system is the ability to support fast iterative deployment like algorithm development. Consumers will not always have the same preferences or behaviours since they are highly dynamic which can change in a single day. Thus, a recommendations system must be able to learn and improve fast. With Machine Learning, technologies, it allows the recommendations system to use the data it gathers to produce accurate predictions about customers preferences by enhancing its effectiveness timely.

3. A Closed-loop System & Effective Automation

A closed-loop system is a set of mechanic that automatically regulates a process variable to the desired state or setpoint without human interaction. (Tech Target, 2017) A smart recommendation system should be a closed-loop system to provides best recommendations to customers directly without involving human in nearly the entire recommendations process from collecting data, analysing and delivering recommendations directly to customers. This allows organisations to save costs while improving operational efficiency.

4. Behavioural Science

The data itself will not tell us the underlying meaning about customers thought, preference and behaviour. The key to unlocking the answer is by applying the behavioural science theories in the system. Some of the examples of behavioural science theories that could help the system to have a better understanding of human behaviour are Fogg’s behaviour model, flow theory, self-determine theory and more. A recommendations system that is backed by behavioural science theories will be able to identify ingredients for behaviour changes and increase the reproducibility of interventions in different settings that efficiently enhance the recommendations with behavioural analytics, influential approach and behavioural targeting.

How a Recommendations System Helps Your Organisation

• Towards Real-Time – It ensures data flows in the recommendation systems correctly and in nearly real-time manner while reacting faster to customer’s interests, unlike the traditional analytical workload. This allows your organisations to achieve higher operation efficiency and enhance results with recommendations that highly related to customer’s interests.
  
  
• Understand Item from various aspects – It deep dives into each item from different aspects such as named entity, brand, gender group, age group and attributes. It’s not only important to understand your customers, understanding the items or services that you provide with hierarchical concept mining through unsupervised learning allows you to upsell efficiently.
  
  
• Personalisation – When you know your customers well, you will be able to personalise the recommendations so that each user sees different items at different time. This will help your organisations to increase sales while enhancing customer experience from receiving highly relevant recommendations.
  
  
• Ensure results with performance tracking – The recommendations system doesn’t stop after recommending, its continuous and timely assessment of A/B test results will help to track the performance and enhance results instead of just solely depending on the predictions.

Recommend Smartly to Achieve Numerous Advantages

Many top leaders in e-commerce have rapid growth and see significant success with the heavily dependent on a recommendation engine. However, there are several recommendation systems in the market and choosing the right system is extremely important because the recommendation engines are becoming more accurate than ever before. The different industries might have different needs in their recommendations engines. It should provide you accurate insights about your target audiences while enhancing your customer experience in the journey with the best relevant recommendations that ultimately not only helps you to optimise results but also to enhance operational efficiency and save cost.