asw: maximus AI proposes the most optimal solutions for making business decisions. It is a universal platform - a system that learns from data and relies on asw: sapiens (BI system) and asw: dominus (ERP) system.
Applications of asw:maximus system
Client departure prevention module monitors clients' behaviors and identifies patterns of behavior that indicate the risk of a client leaving, i.e. the risk of cessation of purchase in the organizational units of the client company, as it is up to ten times more expensive to bring a new client than to keep the existing one. The results of the algorithm indicate what are the possible factors that have influenced the emergence of risk and, based on purchasing habits, the algorithm independently proposes the promotion that best keeps clients. The neural network accurately predicts how much of the income these clients or groups of clients will bring when they are held, so this is also taken into account when creating a promotion - for more loyal customers it's worth the effort.
The results obtained from the model are:
The Predictive Maintenance Module predicts system failures, and specifically in technical systems. The best time to solve the problem is the time before it happens. And this is achieved through artificial intelligence. The asw: maximus system for predictive maintenance monitors the flow of data from thousands of sources, whether they are towers for telecommunication networks or data from a warehouse, or the operation of an organizational unit. Based on historical data, the module recognizes patterns that preceded the failure or problem in business processes. This can respond to the challenge before it happens, and completely eliminate the consequences of errors and problems.
It is applied in infrastructure (for example SCADA systems for which the risk of failure is detected before it occurs), as well as in logistics and in other areas.
The Employee Quality Monitor module monitors and evaluates the quality of employees. Artificial intelligence goes through millions of recorded transactions and events in search of any anomalies - deviations from the expected business process. These anomalies are then displayed to the management and where necessary and removed. It is noted that positive anomalies, which increase profit as well as negative, which reduce profit or indicate the existence of abuse or bad workers. The scams that can not be detected for a person due to the amount of data, this tool illuminates and displays the data indicating that.
This tool can not only be used to detect fraud, but also for the performance of sales workers. There are articles that are often sold together, and often these additional items are like accessories and the biggest source of profit. The system recognizes which workers sell, and who do not sell these additives, and should, and on the basis of this, get another information on the quality of these workers. If an employee in the clothing store often offers and sells belts, ties and wipes along the suits, while others rarely offer it, artificial intelligence points to both of them, so the first one can be rewarded and the other can influence to improve their engagement.
The Price Optimization module independently predicts and creates product prices. In traditional business, pricing is based on intuition and experience. These are two properties that are very important for the success of the response to price formation, but they are also insufficient - the price at which the highest profit is generated is influenced by too many factors in order for a person to understand their influence, and in particular to determine the profits mathematically. The Price Optimizer allows a person to combine his intuition and experience with an insight that delivers a large amount of data and thus determines the ideal price for each item.
A neural network that learns from each transaction in the background determines how each item is sold if a certain price is determined and a possible discount. The network understands the connections between a huge number of factors that can affect sales (from internal ones such as previous sales to external ones such as the economic situation in the city and the country or weather forecasts) and thus shows which price and discount lead to the greatest profit.
The inventory optimization module predicts what stock status will be. The Neural Network predicts the demand for several months in advance with exceptional precision (+ - one). On this basis, it is possible to define how many goods need to be purchased and in what period of time.
The module works in the following way: based on the results obtained, the intelligent system monitors which items are not enough in stock (and the demand is expected) and alerts the user about it, thus preventing the possibility of missing the stock of needed goods.
The Sales and Personalized Campaign module allows you to build sales campaigns. By analyzing the vast amount of data that a human being could not even process in a few days / months / years, machine learning algorithms note:
Based on the above, the module helps managers quickly and efficiently identify possible sales campaigns by selling units (sales campaigns), and by a particular loyal customer (personalized campaigns). The behaviors of the sellers are also monitored, and they can see where they often buy related purchases, and where they do not.
Effective tracking of buying habits of buyers gives advantages and benefits over the market and on the basis of that action can offer some assortment even before the buyer finds out that he really wants to buy it. All in all, results in a significant increase in profits, with an optimal distribution of business processes.
The model is excellent for the Sales Department and the Telecommunications Sector.
The Customer Clustering Module helps precisely defining customer groups and classifies them according to their behavior, influenced by the amount of the account, the length of the network, articles, brands and other factors in the purchase. Clusters are different to each other, while customers who form a cluster are similar. The model in the sales sector gives the best results to loyal customers, since it is fair for loyal customers of the database, and that other customers can not identify the parameters that make the customer. In other sectors, such as for example telecommunications, the model is excellent for all types of users, since databases are given to fine granulation. Clustering helps create campaigns, which with a large number of users become almost impossible at the personalized level.
asw: maximus possesses machine learning and artificial intelligence models that run automatically and give predictions of a given client metric (for example: traffic, sales volume, price difference, etc.). The result is a precise forecast at the weekly or monthly level for the next year. Predictions are from the level of an organizational unit, brand, or commodity group to the level of an individual item in one organizational unit.
These models use historical business data, to the level of a single account, learning and taking into account all the hidden accuracies and trends in them, to give precise predictions. They are based on the latest artificial intelligence technologies like neural networks and boosting algorithms.
Using the prediction capability of an individual item in a particular location by asw: maximus, this module predicts at the weekly or monthly level which items will be the most and which is the least to be taken from the warehouse, thus creating the optimal layout of the items within the warehouse. Thus, the processes in the warehouse are optimized and the time needed for their execution is significantly reduced.
The reflection of a vital and flexible company is its ability to adapt to market innovations including technologies that significantly improve business. In this way, it can easily be separated from the competition.
The company needs to know well its loyal customers and their buying habits. All this information is contained in the data, and it measures in the number of items purchased, the type of items, the method of payment, etc. By interpreting this data we come to the so-called. personalized sales where companies are able to meet each of their customers and be proactive in meeting their needs.
Another advantage is the ability to increase profits without increasing the volume of work. Achieving efficiency and efficiency in business processes leads to a rationalization of costs that directly affects business results.
For more detailed information on the implementation of ASW: MachineLearning and your company, contact our development team at e-mail: firstname.lastname@example.org or contact phone number 011 / 2071-400.