"Lang.ai has an impressive product that we saw early on could be used as an intelligence layer on top of all our free-text data."


CaixaBank uses Lang.ai to accelerate digital transformation of free text data analysis
Digital engagement is at the forefront of CaixaBank's customer strategy

Establishing strong client relationships is critical for the success of CaixaBank. As one of the largest banks in Spain with over 20 million retail customers they recognize the importance of meeting customers at every digital touchpoint to provide the best support and experience possible. With customer engagement as a cornerstone of the bank’s digital transformation roadmap, Alberto Iriarte Lanas, the team lead for the Strategy and Big Data Projects function, has been focused on increasing CaixaBank’s capacity to understand and address the customer needs coming in from digital channels in a quick and accurate way.

“With the explosion of virtual assistants and the increase of touch-point surveys, more and more business areas like IT and Big Data asked my team for analytical capabilities to be able to analyze large volumes of text,” said Iriarte. “We already knew that there is a lot of relevant information within a free text response or transcript of customer conversations. We lacked the necessary tools to quickly analyze and extract concepts from this data and my department could not provide solutions to the huge volume of projects requested by the different business areas of the bank due to lack of specialized resources and tools for text analysis.”

CaixaBank uses Lang to create classification models to rapidly analyze and address customer needs from free text data

“We were looking for a free text analysis tool that could speed up the creation of classification models that allow us to analyze several types of free text data, such as quality survey responses, customer interactions from our virtual assistants, user reviews in Apple Store and Google Play regarding our mobile apps, conversations between agents and customers in our contact center, or any other text source,” said Iriarte. He also wanted a tool that could be implemented, configured, and used by non-technical users so that he didn’t have to burden his IT department. Ultimately the decision to choose Lang came down to its rapid categorization and labeling accuracy and language agnostic text analysis. Alberto and his team used a framework of assessing tools based on their scalability, user-friendliness, and customization capabilities; the Lang platform addressed all of these and the business team was able to go live and start building classification models within the first week of implementation.

Using Lang’s patented no-code AI platform, CaixaBank was able to speed up their free text analysis from digital customer channels and offer other lines of business such as Customer Care and the Mobile App Team the ability to build their own classification models to address customer needs and provide the best digital experience possible. Iriarte and his team were able to integrate Lang into other systems like Salesforce, Jupyter, and ServiceNow to foster greater data interoperability and cross team collaboration across several different lines of business. “This was key to being able to operationalize the use of the models created by Lang within other data processing flows that we have integrated into our data lake,” said Lanas.

Lang's flexible no code approach is empowering business users to manage their own AI

Today Lang is being leveraged by seven different lines of business that have executed more than 200 total projects on the platform. Each department uses Lang to manage their own classification models to better understand their customer needs and execute natural language projects for insights and automation. "Lang.ai has an impressive product that we saw early on could be used as an intelligence layer on top of all our free-text data to create our classification models," said Iriarte. "We're very excited for how we can continue expanding the use cases and make NLP more accessible within our lines of business going forward."