
THE GOAL IS
TO GO FURTHER
ML / AI
#SERVICeS
THE FUTURE CAN BE PREDICTED, some scenarios with greater certainty than others, BIG DATA gives us the possibility of using a lot of data, and by applying statistical algorithms and MACHINE LEARNING, techniques, we can identify the probability of future results based on historical data.
The objective is to go beyond knowing what has happened, in order to provide the best evaluation of what may happen in the future.

Increase knowledge of customers, how to attract and retain them
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Creating micro-segmentations of customers with similar behaviors, traits and affinity level (Clustering)
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Influencing the transfer of customer segments towards those that produce greater benefits.
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Recovering customers who have decreased their relationship (Recovering).
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Preventing customers from becoming Inactive (Retention).
Optimize Marketing and Sales Campaign
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Shape campaigns and messages based on the likelihood of leads being effective or not.
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Personalize communications based on the tastes of one group or another and increase the effectiveness of sales activities.
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Promote cross-selling opportunities.
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Increase average customer consumption by adding an upgrade to products that already have it (Up-Selling).
Optimize operations to run more efficientlY
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Product needs can be anticipated taking into account seasonality, trends, life cycles, safety stock and inventory levels to ensure the level of stock your business needs.
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Reduction of working capital through optimization of finished product, raw material and production in-process inventories.
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Alignment of production, distribution and supply activities in the supply chain, avoiding bottlenecks in production lines.
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Get to know your employees, their productivity, and degree of satisfaction using advanced analytics techniques that help you measure, analyze and optimize decision-making about staff, supported by data.
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Predictive maintenance of assets, minimizing maintenance costs, increasing their availability and useful life.
Reduce risk, detect frauD
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Creating a score for each credit application, using historical data, in order to know the credit susceptibility of a person.
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Fraud detection, analyzing anomalies in the data.
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Other uses related to risk include claims and insurance collections.
What can be achieved with
predictive analytics?


customers
#modelS
It allows customers to be segmented into homogeneous groups using various attributes such as their demographic characteristics, products they consume, purchasing power, frequency of purchase, time since their last purchase, etc., allowing the identification of profitable niches, facilitating the characteristics of campaigns to attract new customers and the retention of the most profitable customers.
CUSTOMER SEGMENTATION
vision
What kind of clients do I have?
What is the value of these clients?
What is their behavior?
ACTIONS
What can we offer them?
Can I increase your ROI?
Can I apply a loyalty campaign?
REFINEMENT
Has my relationship with my customers improved?
Do we need to improve data quality?
CUSTOMER ABANDONMENT
It allows you to identify in advance the customers who will abandon their products or services, as well as to identify the decrease or frequency of use or consumption of the products or services offered. It is extremely important to identify customers with a high propensity to abandon, generating retention promotions based on other techniques such as cross & up selling.
vision
What customers are at risk?
What is the value of these customers?
What is the cause of churn or retention?
ACTIONS
What can we do to retain?
How much contact time do I have left?
Can I apply a retention campaign?
REFINEMENT
Has customer retention accuracy increased?
Has the percentage of customers prone to churn decreased?
Do we need to improve data quality?

SALES marketing
#models
Using quantitative techniques, it allows future sales to be estimated over a given period of time. Forecasting algorithms try to find a regular pattern in measurements that can be continued in the future, observing seasonality or temporality, recent values, trends, etc. If this weapon is used correctly, inventories can be reduced, the risks of product obsolescence can be reduced, coordination between the different business processes can be improved by having a common starting point, reacting to crises with greater anticipation and improving customer service.
SALES FORECASTING
vision
What products will increase in sales?
What product will stop selling?
What is the investment in the forecast?
ACTIONS
Is the required stock available?
What product should we invest in more?
Can forecasts and acquisitions be optimized?
REFINEMENT
How accurate was the prediction?
Has the overstock decreased?
Do we need to improve the quality of the data?
cross & upselling
It allows generating new cross-selling opportunities for products and services, using historical customer data, understanding their preferences and those of people with common behavior. Cross-sell models allow offering products from another product category that the customer might be interested in. Up-sell models allow offering additional products that complement the product sold.
vision
What do similar customers consume?
What is the value of these customers?
What is the value of their purchases?
ACTIONS
What else can I offer you?
Can I increase its value?
What product value (pricing) can I recommend to you?
REFINEMENT
Has my customers' consumption increased?
Which product had the highest acceptance in the recommendation?
Do we need to improve the quality of the data?
market basket analYsis
A technique widely used by several retailers to discover associative patterns between items sold, looking for combinations of items and brands that are frequently sold together. These associative techniques allow exploring which items to promote together, predicting the impact of these combinations, making it easier for the marketing department to generate promotions.
vision
What products are purchased together?
What is the value of these products?
What offers can I make?
ACTIONS
What combination should be promoted?
Can I increase sales?
Which product is suitable for seasonality?
REFINEMENT
Has consumption of the products increased?
Do we need to improve the quality of the data?
price optimization
Using mathematical analysis, retailers are able to determine how customers will respond to different prices for their products and services across different channels, as well as to determine the prices that the company considers will best meet its objectives, such as maximizing operating profit. Price optimization strategies allow retailers to maximize the sales potential of their products by planning realistic objectives over time and adjusting their decisions to the changes that are imposed on the market at every minute.
vision
Which products need to maximize margin?
Which product needs to boost sales?
Can sales prices be optimized?
ACTIONS
What is the target margin vs. sales quantity per product?
What is the seasonality and life cycle of each product?
REFINEMENT
How much has the margin improved?
What is the market perception?
Do we need to improve data quality?

logistics operation
#models
Understanding what products you have in your inventory, where they are located, and when you might run out of stock or have too much stock can help increase sales, save costs, improve replenishment, and prevent fraud. This model is designed to integrate with ERPs to provide visibility into inventory levels and investments, producing quality predictions and recommendations for optimal replenishment levels. Using sophisticated predictive analytics and machine learning algorithms, you can predict product needs taking into account seasonality, trends, life cycles, safety stock, and inventory levels to ensure the level of stock your business needs.
stock optimization
vision
What products are at risk?
What is the value of these products?
How much replenishment is needed?
ACTIONS
What can we do to replenish automatically?
How much availability time do I have left?
Are Lost Sales decreasing?
REFINEMENT
Has replenishment improved?
Have we reduced product stockout?
Do we need to improve data quality?

finance
#models
With loan products, repeatedly targeting the wrong customer can lead to dissatisfaction, or targeting the right customer too late can be very valuable to competitors. The history of accepted and rejected loans allows the predictive model to identify customers who have a high chance of accepting a loan offer, finding patterns and correlations in customer characteristics and behavior.
propensity to loan
vision
What is the propensity to loan ratio?
Which customers have the required condition?
What is the value of these customers?
ACTIONS
Why would clients want a loan?
How can I increase and secure my portfolio?
Can I reduce the cost of campaigns?
REFINEMENT
Has the accuracy of crediting increased?
Do we need to improve data quality?
fraud detection
In a world where transactions and documents are recorded digitally, evidence is available to help investigators in the battle against fraudulent schemes. Detecting fraud and risk can save a lot of resources. It can benefit companies that take risks or must pay settlements for claims associated with insurance policies, companies that suffer repeated scams due to the use of credit cards. The identification of anomalous situations, such as a change in a customer's consumption profile, can prevent million-dollar losses. With the large amount of data from "normal" and "risky" transactions, patterns can be detected to identify possible new "risky" ones, generating alerts to take the corresponding actions.
vision
Can I be proactive in detecting fraud?
What losses do frauds cause me?
Am I being robbed without me noticing it?
ACTIONS
What is the % of fraud this month?
What actions have been taken with the frauds detected?
How many man-hours are we devoting to fraud management?
REFINEMENT
Has the accuracy of early detection increased?
Do we need to improve data quality?
