The ability of companies to make intelligent use of their data can move them one step ahead of competitors. Artelnics offers a wide variety of solutions to face challenges such as knowing: who are the most valuable customers? Which marketing campaigns work better for my business? Who will remain a loyal customer and who will not? And many others.
Some of these solutions are described below:
Not all customers are the same; knowing which ones will buy your products and which won't is the main competitive advantage that you can have.
In the purchasing process, people interact with all kinds of complex variables. By analyzing data such as age, gender, interests, and so on, you can target specific clients and allocate resources optimally.
Optimization of telemarketing campaigns in a bank
Predict if a certain client will subscribe to a loan. The input variables are age, job, marital status, education, housing loan, last call time, month, etc. The output variable is success or failure.
Nowadays, when we buy online, we get suggestions from recommender systems. These types of systems are tools designed to automatically suggest customers products that suit their preferences.
Recommender systems are essentially predictive analytics engines. We will increase sales by using historical information from product searches and purchases by offering your products to the right customers. Some solutions are listed below:
Choosing the best credit card for a customer
The objective is to know which kind of credit card is best suited for a specific customer in advance. The input variables are very similar to those above: sex, job, salary, marital status, age...
Sales forecasting is the process of estimating future sales based on historical data. Companies need to know what their sales will be to establish an action plan.
Some of the variables involved are before history, seasonality, market-moving events... All of them contribute to making a realistic prediction. Through these studies, you can create a good income and expenditure budget, contributing to a better business strategy. Some applications are listed below:
Forecast tourism demand in a certain location
Estimate the number of tourist arrivals in a certain location in a given period. Input variables: service price, average hotel, foreign exchange, population, marketing expenses... Output variable: number of tourists.
Risk analysis is the study of uncertainties that we encounter in business.
Advanced analytics has become an essential tool for companies who want to make a risk analysis. It helps them to identify and mitigate these risks and minimize their impact on our decisions. Some uses are listed below:
Life insurance assessment
The objective is to develop a predictive model for an insurance company to accurately assess life insurance customers' risk. Using this model, the company can reduce costs for each contract.
Customer churn is a term used to describe the loss of customers. An important part of any business is to keep its customers; indeed, attracting new ones is much more expensive than keeping old ones.
Data mining techniques allow us to understand the reasons why our customers are not loyal. Knowing these reasons, we can take action to retain them. Some solutions are listed below:
Reduce the risk of customer churn
The objective is to know what variables are influencing the churn of our customers. The input variables are gender, salary, age, product price, quality, warranty... The output variable is the churn (yes/no).
The goal of any business is that its products suit customer requirements. Deficiencies in quality mean being less competitive in the global marketplace.
Market research with advanced analytics will make the difference between you and your competitors. Indeed, that technique will align your products with your customers. Some applications are listed below:
Enhance the quality of wine
Improve wine quality of a given wine based on chemical analysis and wine tasting. Input variables include fixed acidity, collative acidity, citric acid, residual sugar, chlorides, etc. The output variable is the quality, scored between 0 and 10.