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AI models


This page provides the general information about the intelligence running underneath the surface. The following sections contain information related to AI models implemented within Solver AI Suite.


Solver Segmentation

Customer segmentation is the practice of categorizing a diverse customer base into distinct groups based on shared characteristics, behaviors, or demographics. This strategic approach enables businesses to tailor their marketing efforts, product offerings, and customer service to effectively meet the specific needs and preferences of each segment. By identifying commonalities within a customer group, companies can create targeted and personalized experiences, optimizing resource allocation and improving customer satisfaction. Successful segmentation enhances marketing efficiency, customer engagement, and ultimately drives better business outcomes by ensuring that the right message reaches the right audience at the right time.

RFMT Segmentation

RFMT segmentation is a strategic approach utilized by businesses to categorize and analyze their customer base for effective marketing and service delivery. RFMT stands for Recency (R), Frequency (F), Monetary Value (M), and Tenure (T), representing the recency, frequency, and monetary value of purchases, along with the duration since the customer's first purchase. Customers are grouped based on these parameters, enabling personalized strategies to re-engage customers who have lapsed in activity, retain high-value and loyal customers, and optimize marketing efforts to maximize overall customer lifetime value.

Solver Recommender

Recommender systems are intelligent algorithms designed to predict and suggest items or content that a user is likely to prefer based on their past interactions, preferences, and behaviors. These systems play a crucial role in various online platforms, including e-commerce websites, streaming services, social media, and more. Their primary goal is to enhance user experience by providing personalized recommendations, leading to increased engagement and customer satisfaction. Recommender systems utilize various techniques, such as collaborative filtering, content-based filtering, and hybrid approaches, to analyze vast amounts of data and identify patterns in user behavior. By understanding individual preferences and similarities between users or items, recommender systems help users discover new products or content they might not have found on their own, ultimately driving sales, user engagement, and platform loyalty.


Personalized recommender systems are advanced algorithms that tailor recommendations to individual users based on their unique preferences, behaviors, and past interactions. These systems utilize a wide range of data, including user profiles, purchase history, browsing patterns, and demographic information, to understand and predict what a specific user is likely to be interested in. By employing sophisticated machine learning techniques, such as collaborative filtering and deep learning, personalized recommender systems analyze this data to identify patterns and similarities between users and items. This enables the system to provide highly relevant and customized suggestions, optimizing the user experience by presenting products, services, or content that align with the user's taste and preferences. The ultimate aim of personalized recommender systems is to enhance user satisfaction, increase engagement, and drive conversion rates by delivering a curated and enjoyable user journey.

History-based recommender

A history-based recommender system, personalized to each customer, looks at products they frequently purchase or the first and last products they bought. Using this information, the system predicts and suggests products that align with their preferences, making their shopping experience more individualized and enjoyable.

Time-based recommender

A recommender system based on time patterns examines a customer's purchase history to detect recurring buying intervals, like purchases made at regular time gaps. Understanding these habits, the system predicts and suggests products that fit the expected timing of their next purchase. This ensures recommendations align with the customer's established purchasing schedule, enhancing their shopping experience by offering timely and fitting suggestions for their upcoming purchase.

Attribute-based recommender

An attribute-based recommender system is designed to provide tailored recommendations by considering both the attributes of the customer and the characteristics of the items. It takes into account specific traits or preferences of the customer, such as demographic information, past purchase history, and stated preferences. Simultaneously, it analyzes the attributes of the items available, which can range from product features, genre, style, or any relevant categorization. By aligning these attributes and preferences, the recommender system suggests items that best fit the customer's profile, ensuring a personalized recommendation that matches their unique tastes and requirements.

Community-based recommender

A community-based recommender system employs a collaborative filtering strategy to recommend items to customers. This approach taps into the collective wisdom of a community of users with similar preferences or purchase histories. By analyzing the behavior and interactions within these communities, the system identifies patterns and correlations among users. If a user shares interests or purchasing behaviors with others in their community, the system recommends items that those similar users have liked or purchased. This harnessing of collective preferences helps in suggesting items that align with a specific user's taste, leveraging the power of peer insights and recommendations.

Basket-based recommender

A basket-based recommender system focuses on understanding relationships within items in a customer's purchase basket and predicts related products based on those associations. By analyzing the items a customer has already bought, the system identifies patterns and connections between these purchases. It learns from the combinations of products in the basket, deciphering which items are often bought together. Utilizing this learned knowledge, the system can then suggest additional products that are likely to be of interest to the customer, complementing their previous purchases and enhancing their shopping experience.

Description-based recommender

A description-based recommender system leverages all available textual data and descriptions associated with items to identify products that align with a customer's preferences. By analyzing the text, including product descriptions, features, reviews, and other relevant information, this recommender system gains a comprehensive understanding of each item's attributes. It then matches these attributes with the customer's known taste and preferences, allowing the system to recommend items that are most likely to resonate with the customer. This approach ensures that recommendations are not solely based on purchase history but also take into account the specific qualities and details of the items, resulting in more tailored and fitting suggestions for the customer.


Non-personalized recommenders, often referred to as general or global recommenders, offer suggestions without considering individual user preferences. Instead, they focus on overall popularity, trends, or item attributes. These systems provide recommendations that are the same for all users or a particular user group. For instance, a non-personalized movie recommender might suggest the most viewed or highest-rated movies to all users without taking into account an individual's movie taste. While they may lack the tailored precision of personalized recommenders, non-personalized recommenders are valuable for introducing users to popular or trending items and can be particularly useful in scenarios where a user's preferences are unknown or for new users joining a platform.

Best sellers

A best-sellers recommender system operates by suggesting the most popular and top-selling products to users. This recommendation approach is based on the assumption that products frequently purchased by a large number of customers are likely to be appealing to others as well. The system analyzes purchase data, often in real-time, to identify the products with the highest sales or most views. By showcasing these highly sought-after items, the recommender aims to guide users towards products that are already well-received by a broad audience. This strategy helps users discover items that are in high demand, making it particularly useful for those seeking widely appreciated and trusted options within a diverse range of products.

A trending products recommender system operates by identifying and recommending items that have recently gained significant popularity and are currently trending. By analyzing real-time data, customer engagement, social media activity, and other relevant metrics, this system highlights products that have experienced a sudden surge in interest or sales. These trending items reflect current consumer preferences and emerging market trends, making the recommendations highly relevant and timely. Users benefit by being exposed to the latest and most talked-about products, enabling them to stay up-to-date with what's popular and in demand within the market at any given moment.

Similar products

A similar products recommender system suggests items that closely resemble a given item based on various criteria such as features, attributes, category, or customer interactions. This type of recommender employs similarity measures and algorithms to calculate the likeness between products. By analyzing item characteristics and user behavior, it identifies comparable items that share commonalities and are likely to appeal to the same audience. For example, if a user is viewing a specific laptop, the system may recommend other laptops with similar specifications or features. This enables users to explore alternative options that align with their preferences, aiding in making informed decisions and broadening their choices within a specific product category.

A related products recommender system suggests items that are frequently purchased together with a given item. This recommendation approach is rooted in the concept of item association and co-occurrence in baskets or purchase histories. By analyzing vast amounts of transaction data and purchase patterns, the system identifies products that tend to appear together in users' baskets. For instance, if a customer is viewing a smartphone, the system may recommend accessories like phone cases or screen protectors that are commonly bought alongside smartphones. This assists users in discovering complementary products or items that enhance the usage or experience of the main product, offering a holistic shopping experience.

Solver Predict

Prediction algorithms play a crucial role in various domains by utilizing historical data and statistical techniques to forecast future outcomes or trends. These algorithms analyze patterns, correlations, and hidden relationships within the data, enabling predictions regarding unknown or future events. In fields like finance, they're used to forecast stock prices or market trends. In healthcare, prediction algorithms can anticipate disease progression or patient outcomes. Moreover, in marketing, they aid in foreseeing consumer behavior and preferences. The goal is to provide actionable insights that inform decision-making, allowing organizations to strategize effectively, optimize resource allocation, and mitigate risks. By harnessing the power of prediction algorithms, businesses can gain a competitive edge, enhance operational efficiency, and tailor their approaches to meet the evolving needs of their respective industries.


Churn prediction, a vital application of predictive modeling, involves forecasting the likelihood of customers leaving or discontinuing their relationship with a service or product. It plays a pivotal role in customer retention strategies for businesses across various sectors. By analyzing historical data and customer interactions, churn prediction models identify patterns and behaviors that precede customer attrition. Factors such as declining usage, infrequent purchases, or dissatisfaction indicators are taken into account. Leveraging machine learning algorithms, these models generate predictions, helping businesses proactively target at-risk customers with tailored interventions like personalized offers, improved customer service, or engagement campaigns. Successful churn prediction aids in reducing customer attrition rates, preserving revenue streams, and fostering long-term customer relationships.

Customer lifetime value

Customer Lifetime Value (CLV) prediction is a strategic business practice that involves forecasting the total value a customer is expected to bring to a company throughout their entire relationship. It integrates historical data, purchasing behavior, and engagement patterns to estimate the revenue a customer will generate over a specified period. By utilizing sophisticated analytical models and machine learning algorithms, businesses can calculate CLV accurately, considering factors such as average purchase value, purchase frequency, customer retention, and future purchase projections. Accurate CLV prediction is critical for guiding marketing strategies, optimizing customer acquisition costs, and tailoring retention efforts. It enables businesses to allocate resources efficiently, prioritize high-value customers, and design personalized experiences, ultimately maximizing the overall profitability and sustainability of the customer base.

Solver Chatbot

Chatbots, powered by artificial intelligence, have evolved into essential tools for businesses, serving a diverse range of roles across industries. These computer programs simulate human conversation and interaction, providing instant responses and assistance to users via text or speech. Their role is multifaceted, including customer service, sales support, appointment scheduling, and information dissemination. Chatbots streamline customer engagement by handling routine queries, resolving issues, and directing users to relevant resources. They operate 24/7, enhancing accessibility and customer satisfaction. Furthermore, chatbots collect valuable data on user interactions, enabling businesses to gain insights into customer preferences and behaviors, which in turn inform product development and marketing strategies. Overall, chatbots significantly improve operational efficiency, customer experiences, and the scalability of businesses by automating and enhancing communication processes.

Smart Chatbot

Employing a smart chatbot equipped with advanced features, the system effortlessly manages and comprehends customer inquiries. When a question is presented, this adept chatbot swiftly accesses the database, utilizing an array of techniques like similarity metrics and complex algorithms. These methods are like specialized tools that help the chatbot find patterns and similarities in the way questions are asked and answered. This sophisticated process is designed to carefully scan through the entire database, aiming to pinpoint the question that most closely matches what the customer has asked. The ultimate goal is to make the interaction as smooth and effective as possible, ensuring the customer obtains the information they seek or the assistance they require.

Search technology plays a pivotal role in our modern digital landscape, revolutionizing the way we access and retrieve information. It serves as a powerful gateway, allowing users to quickly and efficiently find relevant data across vast repositories. Whether it's web search engines, enterprise search systems, or within applications and databases, search technology enables users to input keywords or queries and retrieves the most relevant results in a fraction of a second. Its role is to streamline information discovery, making it accessible and actionable. By incorporating advanced algorithms and indexing techniques, search technology constantly evolves to comprehend context, user intent, and nuances in language, enhancing the accuracy and relevance of the results. In essence, search technology facilitates seamless navigation through the vast sea of information, empowering individuals and organizations to make informed decisions and gain knowledge at their fingertips.

Smart search, empowered by advanced AI and semantic understanding, revolutionizes the traditional search experience. This intelligent search technology goes beyond mere keyword matching and delves into the intricate semantics of a customer's query. By comprehending context, intent, and linguistic nuances, it discerns the true meaning behind the words. This enables the search system to consider the broader context and connections between terms, deciphering what the user is seeking. The smart search engine then swiftly navigates through the vast pool of data, pinpointing the most relevant items that align with the customer's query in a more meaningful and accurate manner. By prioritizing semantic context, smart search significantly enhances the precision of results, ensuring that users find exactly what they are looking for, making the search process more efficient and satisfying for the user.

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