Validata Recommender

Deliver the right recommendations and build next-step strategies with clicks, not code.
Banks and financial services companies are becoming more customer-centric to keep up with the competition and survive. The focus for banks is now shifting to what customers want to buy, not what financial institutions want to sell them.

Validata Recommender leverages AI and machine learning models to produce recommendations for customers future purchase and product recommendations. It offers the capability of automatically collecting the data around customer, products and purchase history and collating the Customer 360° View of each customer individually. It recommends what is the best next product for a customer using customer characteristics, seasonality information and historical trends to provide real-time insights and the ‘single version of the truth’ of your customer data, preferences and suggestions.

It provides explainability mechanisms and justifications for the recommendations and also enables visualization of similarities among customers and products. The customer segmentation functionality enables the users to see the customers based on demographics and other conditions such as customer age, type, currency they use, gender etc. This data ranking capability provide insights on the existing customer set.
Validata Recommender’s
Data access layer
This feature is responsible for collecting the data from files or databases and provides the recommendation engine algorithmic components with data up-to-date in real time or in a scheduled way.
Work Smarter with AI
Through explainable AI, Validata Recommender defines the associations and matching of the customers and the products based on the customer features and purchase history of existing clients. The recommendations refer to both existing and new clients.
Deliver AI benefits
as limitless as your data!
Customer 360
It provides a 360° view of your customer data so that you can maximize the value of your customer base by understanding those factors that influence profitability. You have a centralized view of your customer behavior, products and balances.

It keeps the historical information of the customers transactions, and it profiles them through smart ranking algorithms based on demographics and other criteria, creating on demand the customer segmentation results.

Calculate and monitor the profitability of each customer.
  • Provide an integrated view of profitability for key dimensions including:
  • Product – Line of business/organization – Customer – Geography – Time
  • Understand customer needs and propensity to buy new products.
  • Get a 360-degree view of customers and their product profiles.
Discover Insights
Predict Outcomes
Recommend next steps

Next Best Product
The AI Recommender can handle massive amounts of data, and determine what the ‘next best product’ is for existing or new customers. In doing so, it comes up with the optimal next best actions in the form of real-time recommendations. Additionally, leveraging machine learning models, it can produce a forecasting on what the next products are for a customer and assist in the sales process, with recommendations tailored to each customer’s needs.

After collecting the data for the customers, products and purchase history, it creates automatically the similarity matrix, based on characteristics that are autonomously generated from Collaborative filtering algorithms.
With the use of Customer and Product feature extraction algorithms, it can identify new customers or products and append their closest “neighbors”. In case of existing customers, the solution identifies all the closest to our individual customers, the purchase history and its closest neighbors and based on the matching it recommends what the next best product could be for this customer.

In the case of new customers, the solution uses partial matching via similarity matrix to identify the closest “neighbors” to our new customer and based on their purchase history, it recommends the possible products that our subject could be interested in.

In both cases, it uses similarity fields that can be easily defined by the user, as well as Aging and income parameters.
This enables banks to have greater customer lifetime value,
better determine customer spending habits and create campaigns that connect with those customers,
and get them buying their products.
Increase revenue by real-time, relevant offers to customers across multiple channels, based on events and transactions.
Enhance customer satisfaction through meaningful interactions, applying corrective actions, and making offers that actually meet customer's needs.
Engage customers with seamless experiences across channels with personalized actions and offers.
Improve marketing efficiency by eliminating redundant communications and ill-targeted offers.

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