business intelligence

BUSINESS INTELLIGENCE

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 are better?, who will remain a loyal customer and who will not?, and many others.

Some of these solutions are described below:

  1. Customer segmentation.
  2. Recommendation systems.
  3. Sales forecasting.
  4. Risk analysis.
  5. Churn prevention.
  6. Quality improvement.

1. Customer segmentation



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.


UNDERSTAND YOUR CLIENTS


OPTIMIZE CAMPAINGS


INCREASE CONVERSION

An application example is listed below:

Optimization of telemarketing campaings in a bank
Predict if a certain client will subscribe 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.

2. Recommendation systems



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. Using historical information from product searches and purchases we will increase sales offering your products to the right customers.


INCREASE TRAFFIC


IMPROVE CUSTOMER RETENTION


INCREASE SALES

An application example is listed below:

Choosing the best credit card for a customer
The objective is to know in advance which kind of credit card is best suited for a specific customer. The input variables are very similar to those above: sex, job, salary, marital status, age...

3. Sales forecasting



Sales forecasting is the process of estimating future sales based on historical data. Companies need to know what will be their sales in order to establish an action plan.

Some of the variables involved are prior history, seasonality, market-moving events... All of them contribute making a realistic prediction. Through these studies, you can create a good income and expenditure budget which contributes having a better business strategy.


BEST ESTIMATES


FEARLESS LOGISTICS


DECISION MAKING

An application example is 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.

4. Risk analysis



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 risk analysis. It helps them to identify and mitigate these risks and minimize their impact on our decisions.


IMPROVE KNOWLEDGE


FIND UNCERTAINTY FACTORS


ASSESS HAZARD

An application example is described below:

Life insurance assessment
The objective is to develop a predictive model for an insurance company to assess accurately the risk of life insurance customers. Using this model, the company can reduce costs for each contract.

5. Churn prevention



Customer churn is a term used to describe the loss of customers. An important part of any business is to keep their 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.


UNDERSTAND MARKETS


ENHANCED SATISFACTION


IDENTIFY PROBLEMS

An application example is 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).

6. Quality improvement



The goal of any business is that their products suit customer requirements. Deficiencies in quality mean less competitiveness in the global market place.

Market research with advanced analytics will make the difference between you and your competitors. Indeed, that technique will align your products to your customers.


FIT CUSTOMER PREFERENCES


INCREASE PROFIT


REDUCE ATTRITION RATE

An application example is described 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.