Personalized recommendations are becoming more and more important for businesses looking to attract and retain customers. According to Accenture, 9 out of 10 consumers are more likely to shop with brands that offer relevant recommendations and offers. Recommendations also have a big impact on a business’s bottom line as they can lead to a 16% increase in conversions. Therefore, it is not surprising that recommendation engines are on the rise. Industry Arc reports that the global market for recommendation engines will grow from $1.14 billion in 2018 to $12.03 billion by 2025.

Benefits of using a recommendation engines

With continued advances in artificial intelligence (AI), recommendations are no longer aimed at a general audience or even people belonging to a certain niche. Using deep learning-based recommendation engines, marketers today can target consumers with hyper-personalized recommendations on a personal level, based on metrics like personality, location, and personality. This will not only allow marketers to drive online traffic through retargeting advertising or email marketing, but also reduce customer discomfort and abandonment rate.

Personalized product recommendations also help engage customers by offering products or services that are highly relevant to them. This will incentivize a higher average order value and increase conversions. In the long run, using personalized recommendations will show your customers that you understand and value them, increasing customer satisfaction and loyalty.

Challenges to overcome before recommending engine implementation

While the tremendous growth indicates that businesses worldwide are discovering what recommendation engines can do for them, using this technology effectively also comes with many challenges.

1. Requires substantial investment

The recommendation engine is a huge investment, not only financially but also in terms of time. It takes a lot of time and expertise to build an effective internal recommendation engine. In addition to the required scientists, data, and other specialist staff, you need to factor in the cost of the discovery and analysis phase (including feasibility studies to ensure this is the right path for your business), deployment stage, minimum viable product (MVP), final release, and final deployment. Even with the recommendation engine running, the process isn’t over: the tool will need to be continuously monitored and fine-tuned to ensure it’s performing as optimally as possible, resulting in ongoing costs.

2. Too many choices

You can use a solution that is available from a 3rd party company, but there are many choices available in the market. So how do you know which solution is right for your business? Evaluating different solutions can be very time-consuming, as you need to evaluate the case studies, technologies, and solutions that will be integrated into your existing company setup.

3. Complicated referral process

Bringing a recommendation engine into your business can be tricky. Sometimes it might not be worth the effort, especially if the tool isn’t right for your industry. However, this can also be due to a number of other reasons, such as a lack of understanding of recommendation models, poor UX design of your website, and insufficient knowledge of your business. Using and assigning the right employees who not only understand the technology but also the complex operations of your business will allow you to unleash the full potential of recommendation engines.

4. Lack of data analysis capabilities

Like all AI-based technologies, data-driven recommendation engines if you don’t have high-quality data or can’t process and analyze it properly, you won’t be able to get the most out of it. recommendation tool. Deep learning-based recommendation engines can require high computational complexity. If the data supplied to the model is less accurate or less valid, the results will be less useful. So, before investing in recommendation engines, make sure your business meets the complex data analysis requirements required.

5. The ‘cold start’ problem

The ‘cold start’ problem is when a new user enters the system or new items are added to the catalog, and as a result, it becomes difficult for the algorithm to predict the new user’s preferences or the ratings of new items, resulting in less than exactly suggested. However, deep learning models can optimize the relationship between customers and products by analyzing the product’s context and user details such as product descriptions, images, and user behavior. It can then make meaningful recommendations for individual products or customers in different situations. Because these machine learning models do not rely heavily on user behavioral data, they are the solution to the cold-start problem.

6. Failure to capture changes in user behavior

Consumers do not stand still, they constantly behave and develop as people and customers. Keeping up to date with these changes is a constant battle. A powerful recommendation engine will be able to identify changes (or signs of impending change) in customer preferences and behavior, and continuously automate real-time training to make recommendations. suitable output.

7. Privacy Concerns

The more the algorithm knows about the customer, the more accurate its recommendations will be. However, many customers are hesitant to provide personal information, especially in light of several well-known customer data leaks in recent years. However, without this customer data, the recommendation engine cannot function effectively. Therefore, building trust between businesses and customers is key. Many businesses are thriving with referral tools. While they present enormous opportunities, it is important to be aware of the many challenges inherent to technology in order to make the most of it.