In the current business landscape, there is an incredible need to stay competitive, increase conversion rates, boost loyalty programs, and improve customer experience during shopping. This is why recommenders have become an integral part of data-driven strategies.
Depending on your end goal, available data, and technical resources, you can use different recommender models to achieve them.
Let’s start by explaining starter-pack recommenders and see how helpful they are.
Then, we’ll dive deeper into AI-powered recommenders and explain how they support your business goals and plans.
Finally, let’s discuss the
Non-personalized recommenders usually provide recommendations that are the result of generic patterns identified within the aggregated data. At Things Solver, we refer to them as starter-pack recommenders.
For example, recommendations based on all-time best-sellers or recently trending products are the most popular non-personalized recommenders. They usually work in a solid number of cases but don’t really show that we listen and understand our customers.
Another type of non-personalized recommender includes new product recommendations. These are related to the “cold-start” problem where most recommender models can’t address the need to recommend something new yet relevant to the customer in question.
Of course, there are ways of overcoming this. They require using intelligence and analytics in order to find a workaround – leveraging recommender patterns of similar products or identifying early adopters who express interest in new offerings.
Overall, starter-pack recommenders are cheap, simple, and convenient solutions for scenarios where customer identification or long-term purchase tracking is challenging. In this case, the only way to go is relying on aggregated statistics, such as:
- Cash desk recommendations,
- Promo banners in stores/streets, or
- Homepage recommendations on the webshop.
However, if this information is available to you, non-personalized recommenders can be extremely outperformed by personalized recommenders.
Let’s see how.
As your data resources grow, so does the opportunity to build an effective recommender model. Businesses must recognize this potential, as failing to harness available data would be a missed opportunity.
This is where personalization comes into play. Personalized recommenders rely on patterns identified within the data they have access to.
Let’s demonstrate this through two different examples – history-based and intent-based recommenders.
The simplest, yet most effective is the history-based recommender. It suggests products that customers have previously bought and are likely to purchase again.
- Is it personalized? Yes, as each customer receives a distinct set of recommended products.
- Is it relevant? Absolutely, given the inclination to purchase products from past transactions.
- Is it AI-powered? Not necessarily, as it can be crafted using various filters, groupings, and aggregations.
Another useful recommender model is the intent-based recommender. This recommender suggests products that a customer has repeatedly viewed on a web shop or added to their cart without making a purchase.
It is personalized, it is relevant, and it is a result of a simple combination of filters and aggregations.
As you can see, personalized recommenders are a must-have when customer identification is possible, and a history of interactions or product purchases has been accumulated. They are great for the so-called exploitation strategy – proposing relevant products based on the customer’s interest.
But what about other interests the customer hasn’t yet explicitly shown?
This is one of the areas where AI may come in handy! It enables us to navigate this challenge and uncover customer intentions before they are explicitly revealed or even developed by the customer.
AI accomplishes this by examining customers who are similar or by analyzing products connected in some way to those of the customer’s interest.
AI-powered recommenders are an effective way to analyze customer preferences, intentions, and needs on every single occasion.
Various recommender systems have been developed, leveraging AI strategies through different lenses. These systems range from the simplest ones like collaborative filtering to more sophisticated approaches such as content-based recommenders powered by NLP algorithms or recommenders using graph neural networks.
AI-powered recommenders excel at uncovering long-term patterns that reflect customer preferences, even in light of recent events.
For instance, they can identify a tech-savvy customer with a penchant for black-colored gadgets and recommend the latest black iWatch while the customer browses through Apple products.
While AI recommenders deliver effective and fully personalized results, they can sometimes pose resource and speed challenges. Various models rely on distinct parameters, with some heavily influenced by the volume of products or transactions, while others depend on the available product attributes.
Consequently, their performance may strain memory and CPU usage, leading to extended processing times.
To address these challenges, it’s crucial to tailor the technology to match specific business needs and available resources. Leveraging cloud-based services, maintained applications, and serverless solutions, along with powerful data processing frameworks like Spark, can prove invaluable, as we’ve discovered on our journey to developing high-performance recommenders.
Incorporating recommenders into the business model
Tailor-made recommenders serve as a premium asset for a thriving marketing strategy and boosting sales and revenue. Nevertheless, while having pertinent recommendations is essential, it doesn’t guarantee success.
The story gets more complicated when we factor in the right timing and communication channels. Customers possess varying needs, with differing levels of significance depending on the situation.
Therefore, the primary focus should be on creating a recommender system that encompasses a recommendation model and enhances it with supplementary insights to address the following questions:
- What to recommend?
- Where should the recommendation be placed – on a website, mobile app, or through other channels?
- When is the right time to trigger a recommendation – during the browsing process, on Friday evening, or outside of working hours?
- Who is the right audience for a specific offer or suggestion?
Throughout our recommender system journey, we’ve learned that the best way to go is to start by covering the most basic business needs and then add complexity and flexibility as the business’s needs grow.
Let’s explore how recommenders play a pivotal role in different industries, including:
- Telco (Telecommunications),
- Social media and content sharing industry,
- Food delivery industry, and
- Travel and hospitality.
For instance, in the retail industry, let’s say you have a customer entering your clothing store.
A recommender system can help you:
- Determine what clothing items to recommend to them,
- Whether to showcase items near the entrance, in-store displays, or via an online app, and
- Whether to trigger the recommendation during their visit or after they’ve left.
In e-commerce, personalized recommendations are essential for enhancing the shopping experience.
Imagine an online clothing store where customers aren’t only presented with items they might like based on their past purchases but also receive timely suggestions on complementary accessories and trending fashion items.
The right timing for these recommendations – during the browsing process or when they revisit the site – can lead to a remarkable 30% increase in conversion rates and a 20% rise in the average basket value.
Telco uses recommenders to help customers discover the most suitable mobile plans, devices, and add-on services.
For instance, a mobile service provider might use a recommender system to suggest data plans and phone models based on a customer’s usage patterns. These recommendations, strategically placed on the company’s website or mobile app, are timed to appear when customers are exploring their options or when they are most likely to consider a plan upgrade.
The banking sector can use personalized financial recommendations to guide customers toward making informed decisions about savings, investments, and loans.
For instance, a bank could use a recommender system to suggest investment portfolios tailored to an individual’s financial goals and risk tolerance. These recommendations could be presented at crucial moments, such as when customers log into their online banking accounts or when they receive monthly statements.
This approach not only fosters customer loyalty but also encourages responsible financial management.
Streaming services industry
In the world of streaming services like Netflix, recommender systems play a crucial role.
Netflix uses an AI recommender that factors in your viewing history, preferences, and even the time of day you usually watch. If you tend to watch documentaries during the weekend, it will recommend new releases in that genre on Friday evening.
This level of personalization not only keeps users engaged but also leads to increased subscription retention.
Social media and content sharing industry
Social media platforms like Instagram use recommender systems to suggest new accounts to follow and posts to engage with. They analyze a user’s past interactions, such as likes and comments, to recommend content that aligns with their interests.
The timing of these recommendations is crucial, with notifications often sent when users are most active on the platform. This approach can boost user engagement and increase the time they spend on the platform.
Food delivery industry
In the food delivery industry, a recommendation system can be used to suggest meals to customers. The system analyzes the customer’s previous orders, dietary preferences, and the time of day.
For instance, it might recommend healthy salads during lunch hours and comfort food options during the evening. The timing and relevance of these recommendations can lead to increased order frequency and larger basket sizes.
Travel and hospitality
In the case of a travel booking platform, their recommender system takes into account a user’s past travel history, interests, and upcoming holidays. Based on this data, it recommends destinations, hotels, and experiences that align with the user’s preferences.
These recommendations are strategically timed, with offers for summer vacations appearing months in advance, while last-minute hotel deals are sent out closer to the travel date.
These examples demonstrate how different industries use recommender systems to enhance their business models. Providing tailored recommendations, optimizing timing, and improving user engagement can truly lead you to better business outcomes.
Revolutionizing your business with Things Solver AI Studio
Throughout our journey with recommender systems, we have found that starting with the basics and then adding complexity and flexibility as businesses’ needs evolve is crucial.
Regardless of the industry in question and the company size, we have adapted our recommender types to suit your unique requirements. Our offer includes:
- Starter-pack recommenders for a straightforward approach,
- Intelligent personalized recommenders for advanced personalization, and
- AI recommenders for cutting-edge solutions.
What’s more, we continue to work on innovations that include:
- Developing hybrid recommender systems that combine different recommender models and incorporate reinforcement learning for more precise recommendations.
- Combining recommendations with search history and optimizing product ranking, our goal remains consistent: to provide the best possible shopping, telecom, banking, or food delivery experience to every customer.
Are you ready to give Solver AI Studio a try? Having the same goal in mind – enable the best possible shopping experience for every customer – we can come up with a strategy that aligns with your business goals and paves the way to personalized solutions. Don’t wait – book a demo today and let’s make things happen!