Data Analytics Benefits

The main benefit is to make informed decisions based on complete and reliable data.

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Most companies collect and store big volumes of data which are not properly exploited or used at all. The lack of quality information translates into misleading appreciations, leading to wrong decisions and missing business opportunities.
Hence, data processing and visualization have become a key part of business analytics. Without visualization, numbers and statistics are often very hard to interpret. The rise of Big Data stresses even more the need to build and use business dashboards as the most appropriate way to summarize big volumes of data into accessible presentations.
The benefits of using ANALYTICS are surprisingly wide and applicable to almost all industries around the world.

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Consumer goods companies

Consumer goods companies looking to understand their customers interests and buying behaviors to better tailor marketing campaigns and product offerings.

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Banks and financial institutions looking to understand how risky their clients are to offer loans and credit cards to the best ones.

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Services Companies

Services Companies looking to understand what clients are going to churn and which ones would buy different products.

About Us

We are a team of passionate and engaged professionals with more than 10 years experience on risk, business intelligence, analytics and machine learning.


Cecilia López

Actuary, Risk Specialist, Data Scientist

Lucrecia López

Industrial Engineering, Data Miner, Data Scientist


Segmentación de clientes

Customer segmentation

In order to optimise your marketing strategies, it is important to understand the needs and preferences of your customers closely. Customer segmentation is a technique that enables your business to group customers into segments that share certain characteristics such as preferences or demand. Based on customer segments and an understanding of their meaning, product offerings and marketing strategies can be better targeted by distinguishing certain categories of needs.

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Machine Learning Models

The risk or the propensity to buy associated to each of your customers can be estimated from available profile information or behavioral data.
Credit scoring enables the evaluation of the probability that one of your customers will repay his or her debts and risk based pricing. Customized machine learning models can also help identify those prospective clients who have a higher propensity to buy your products in order to take the most out of your sales efforts.

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Business Analytics

To make business decisions based on data, you need to gain insights about your market, suppliers and customers, and data is your biggest asset! When data is correctly treated, it can provide a big competitive advantage. Business analytics involves:
* Predictive analytics: analyze trend data to assess the likelihood of future outcomes
* Prescriptive analytics: uses past performance to generate recommendations for the future

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Business Intelligence

To automate and optimize business processes, it is important for your business to understand what happens internally. Many times this simple question is not easy to answer or information arrives late or is not detailed/ specific enough.
BI enables the examination of historical data to get a sense of how a business department has performed over a particular period. Through descriptive analytics which build, develop and track key performance indicators (KPIs), you can get a deeper understanding of what happens and exploit management dashboards to transform the time used by analysts to generate reports in time used to add value to your business.

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Human Resources Analytics

In order to effectively reduce your business’s turnover rate, you need to understand which are the main drivers of employee attrition to focus human resources actions on those valuable employees who want to leave the company. The data can be used to analyze trends, spot problems, report to management and prepare reduction strategies.
Workforce analytics involve exploratory analytics, predictive analytics (employee attrition models), clustering analysis. This information will allow your HR department to target and fine tune retention strategies based on data.

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Customer churn models

Part of the strategy to prevent loss of clients or customers is to correctly target future churners in a retention marketing campaign. Churn models are used to predict each customer´s likelihood of stopping doing business with a company or consuming a service. By knowing which customers are of high churn risk, your business will be able to act proactively to retain those customers.
Analyzing data related to customer attrition enables the identification of the main drivers for customers to churn and to develop an effective retention strategy in accordance with domain knowledge.


“My experience working with kydata founders has allowed me to learn how to solve real-world problems and make real impacts in a highly structured data science approach, for a credit scoring company that applies machine learning technology to mobile behavioral and psychometric data to include borrowers who are underserved for loans from the bank.
kydata founders are specialized in communicating with clients, clearly defining problems, deriving insights from data, and modeling it mathematically. With their keenness and enthusiasm, our project has had a positive impact on society by allowing people to get loans and at the same time managing the risk.
I would like to encourage anyone to work with these experts and to discover the actual impact they create!”

Wooyung Noh, Senior Data Scientist, ICS



Decisions based on quality and timely information

LL Mayo 16, 2020

Every single decision, even the most insignificant one, can potentially have a great impact on your business. Behind each decision there is always questioning about choosing the right path. What would have happened if we would have chosen B instead of A? What will my competitors do?
While it is not feasible to mitigate 100% of the risk linked to every choice, it is possible to significantly reduce the uncertainty. What do we mean? Let's assume for a second that we are in charge of the production plan of our company. The first thing we will probably do is to look at the company's reports, which indicate that the demand for our product has doubled. Then, we might feel tempted to duplicate our production in order to get sales on track. Great! Let's go for it.

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