Сulture of working with data
To build a model from data, you need to collect it in a processable form. The company must have a culture of working with data. All key objects should be stored in databases: information on customers, contracts, goods, prices, promotions, shipments, service, etc.
Minimum historical data
The minimum historical data depth is the time period for which data is stored in the databases. For example, if the task is to forecast demand, then analysts will rely on data on sales of goods for the last 2-3 years. In general, the period of accumulated data from 2 to 5 years is optimal. And more does not mean better here. Thus, 10-year-old data can distort the result. This is due to the fact that many external processes surrounding and penetrating companies have changed: the level of income of the population, the number of players in the market, the current economic situation, etc.
Algorithms for working with data are part of an integrated system, which ultimately, as a tool, allows you to solve business problems – to purchase goods, change pricing, manage promotional activities, abandoning ineffective ones and optimizing costs for it. If there were errors in the initial data, then on the basis of such distorted data it is impossible to build a qualitative algorithm. If you have false data, then the prediction will be wrong.
Machine learning already solves the problem of optimizing inventory in many companies, tasks of this kind are classic. Machine learning technology predicts the assortment that will be in demand with a high probability, proposes to withdraw illiquid items and calculates the volume of raw materials or finished goods, taking into account future sales. Plus, the warehouses will be loaded exactly as much as necessary to meet the demand, exactly what the client needs. You will reduce the cost of resources for the process of forming a product matrix, purchasing unnecessary materials and overproduction of what is not in demand.
A classic challenge in machine learning is personalizing communications. The algorithm analyzes the history of communications with customers, the history of purchases and personal data of the customers themselves (gender, age, region of residence, etc.). If the client base is large and there is a lot of historical data, then it is possible to predict which promotional offer the client will most likely respond to and in which channel (SMS, e-mail, call). If the history of communications, as is often the case, is not rich, then you can act differently: distribute clients into groups that are similar in behavior, socio-demographic parameters, and make a personal recommendation to a specific segment.
Choosing the right location for the outlet
Choosing the right location for a retail outlet includes assessing, calculating traffic, analyzing competitors, and convenient location. Usually this is done by special departments, whose employees carry out all the necessary analysis, collecting information for a long time and painstakingly, and issue a conclusion with the recommended opening points. The developed model can remove this burden from the staff – it will analyze all the necessary data and determine the potential of various options for premises for newly opened salons. The company will not have to fix the loss on rent, repair and development of the exposition, the cost of the advertising budget to promote a knowingly unprofitable point of sale.
The most accurate forecast will allow you to avoid a shortage or oversupply of goods. And an adaptive strategy for working with clients, corporatization that is appropriate in time and for a specific segment of clients (launching the most effective offers and rejecting those who are not working) will give a tangible economic effect for the company. A demand forecasting model analyzes data, evaluates many factors that influence sales, and looks for relationships such as revenue and seasonality.
Work with personnel
The machine learning technology is able to analyze customer traffic at each point of sale, correlate them with the workload of personnel over the past periods, with the time that an employee spends on working with one client and draw up a staffing table with the optimal number. Also, using machine learning, you can take recruiting in a company to a new level. A specially developed algorithm can analyze candidates according to specified parameters and suggest the best ones. Machine learning allows you to collect data on employees in real time, assess the sentiment of their emails, the effectiveness of tasks, enriching the data with short surveys during the working day in order to give a signal to the manager in time for burnout or decline in employee performance.
Machine learning algorithms use data from the past relatively new, reading the already marked patterns and parameters to predict future events. How does it work? The analyst develops a mathematical model. Often you can use ready-made, box-based solutions from popular libraries. It is important to understand their differences, functionality and how the model will work in this particular task on the current data. The selected (or developed) model receives data, learns to see the relationships in them, and after a training cycle shows the result. We show some of the data to the model, and it "knows" the correct answer in advance. We show part of the data (for example, the last 2-3 months of sales) as a sample for testing – the model makes a forecast for them, and we compare the model's forecast with the actual data.
In addition to checking the values, we use a different set of quality metrics in our work, the deviations of which from the norm show the effectiveness of the model.
The algorithm analyzes a large amount of data, learns in the process, identifying patterns, receives new data and checks them against them. As a result, he makes the right decisions based solely on the laws that have already confirmed themselves. Of course, you might think that machine learning is something complex and only applies to launching space rockets.
You will be surprised, but this technology has already entered our life everywhere and surrounds us, we use it without noticing it. A music service recommends compositions and performers based on your preferences, an online clothing store will recommend accessories for selected items, and an electronics hypermarket related products – this is machine learning in action. By applying it in your work, you can avoid mistakes of the past and significantly increase the efficiency and profitability of your business.