USE CASES

Banking and Finance

Nowadays, almost all banks are struggling with big data stacks. Although most traditional methods are used to solve this struggle, there is a significant loss of time and resources. However, machine learning provides an end-to-end solution and simplifies the process. The banking sector is listed by analysts as one of the sectors that will benefit the most from machine learning. Until 2030, it is expected to save 1 trillion dollars in banking sector by using only machine learning solutions. It provides solutions far beyond rule-based software, especially in the detection of credit card frauds, credit risk scoring and payment predictions.

Real Estate

In the real estate market, it is not only difficult to invest at the right time, but it is also difficult to value a property. There is no doubt that a property to be bought or rented, both the landlord and the buyer / tenant wants to know the value. But it is often valued by using traditional method which is looking at similar properties. However, through scoring, all the data of both property and the similar ones are processed retrospectively and can develop both a value for today and a forecast for the future. Unlike traditional methods, our scoring engine allows to obtain a certain price. It can score the future value of a property and determination of the value of a property to be sold or rented.

Customer Service

When it comes to customer service, artificial intelligence blends knowledge and automation that provides unprecedented levels of customer insight, and further enhances the service process and quality. This level of service leads consumers to gain brand loyalty. With AnalytiXR, Optiwisdom can help your company to scale and improve customer relationship management quickly. AnalytiXR uses the latest scoring algorithms to effectively measure customer satisfaction, the most valuable customer, customer churn and gain. It provides insight into how to turn visitors into advocator with scoring customer satisfaction for each level of customer through the funnel built into the product.

HR

Machine learning has become a real game changer in human resources. Their main use is to evaluate the performance of employees by removing them from the subjectivity and to identify the employees closest to the resignation or in need of training. It is also used to perform the division of labor appropriate to the qualifications of the employees.Companies should hire the right candidates for the job and ensure that their valuable employees stay in the company for many years. The adoption of artificial intelligence in human resources will accelerate the recruitment process and follow the job satisfaction of the employees. This is exactly what our AnalytiXR product is designed for. By measuring employee satisfaction, turnover rate can be kept at an optimum level, objective employee performance evaluations can be made and measures can be taken for the position of employees close to resignation.

Marketing

Understanding the demands of the customers and matching the relevant products with the right customers at the right time is the primary desire of every marketer. Through machine learning, companies analyze the previous purchases of their customers and bring the product that need to the consumer in the next offer. As a matter of course, scoring techniques are used to select the appropriate advertisement and channel by determining which advertisement is more effective on which customer. In addition to these, discount rate calculations and pricing optimization save companies from and permanent price calculations. Therefore machine learning helps marketers produce effective strategies and build forecasting models based on customer behavior.

Telecom

It is possible for telecom companies to improve their customer experience with data from devices, networks, smartphone applications, geolocation information, and customer profile details. However, the analysis of such large-scale data leads to a more complex process. On the other hand, by means of artificial intelligence and machine learning, big data will be analyzed and utilizable information will be obtained, customer experiences will be improved and customer satisfaction will increase, thus company revenues will increase. Gartner predicts that by 2020, 20.4 billion devices will be in use worldwide and the need for artificial intelligence applications in the telecommunications industry will increase. Based on these informations, we aim to enable telecom companies to score, segment their customers and match the right customer with the best offers and services with our engines.

Energy

The use of illegal electricity and water causes leakage in government budgets. Detection of these illegal behaviors is tried to be done with general audits, but the audits provide instant solutions while the same behaviors occur in the long term. For this reason, theft detection can be detected instantly by scoring the amount of resources passing through power sources by using advanced technologies. Our scoring engine assigns theft areas and acts as a compass to auditors.

Retail

Artificial intelligence and machine learning perform a learning process about consumer habits based on current and historical data, both in a physical store and in an e-commerce site. After this process, retailers get knowledge more about each customer, so they can anticipate what the consumer wants, target it, prioritize products that match consumer expectations, efficiently manage stocks and even optimize long-term supply chain management.

Tourism

Tourism sector has adopted artificial intelligence before most of the sectors. Travelers take control by using online sites to buy tickets, read other users’ feedback, and booking hotel reservations. On behalf of tourism companies, scoring tourists, hotels, restaurants and other amenities then matching with the most appropriate user is indispensable in order to increase the satisfaction of travelers.