Information services multinational Experian has established innovation laboratories, which it terms DataLabs, in the US, Brazil and the UK to stimulate organic product development, says Experian VP Eric Haller.

Since the first laboratory was launched in San Diego, in the US, in 2011, products developed by the laboratories have generated 63% of the revenue for the company over the past five years, he emphasises.

The broad aim of the laboratories is to bring data to life and derive maximum value from it using advanced analytics. This focus has produced many new services that Experian clients are using, including customer experience management systems.

Business opportunities are transient and any unnecessary delays in serving a client can easily result in the client moving to another company permanently, he avers.

“Therefore, systems that can identify customers, manage their data for rapid on-boarding or transaction processing and serve them instantly at any time are important tools for companies to function in highly competitive markets.”

About 70% of the data scientists at the Experian laboratories have PhDs. The laboratories are using, among other technologies, machine learning to produce predictive models that can derive value from unstructured and anonymised data. By combining Experian data assets with those of clients, Experian’s data scientist teams are able to present a holistic picture and experiment with new ways of analysing data to deliver competitive advantages.

“What these innovations provide are ways for companies to dramatically improve their customer experiences by, for example, prepopulating documentation with a customer’s known information.”

Experian has developed a machine learning-based system for credit providers in the US to help them identify potential clients, gather as much information as possible about them from public sources – including social media profiles – and then use this information to offer specific products and services or credit facilities.

“The system aims to reduce the risks involved in extending credit to Millennials and other clients who do not have credit records that can inform their risk profile. It does this by analysing sentiments contained in public and social media posts, as well as using location-based information to estimate the average affluence of the region from which the post was made, or using location-based data to determine whether a person has a consistent place of employment.”

Further, these typically younger clients will also not visit branches in person and are averse to lengthy on-boarding processes.

“Therefore, the system, after profiling the potential client, uses context-based marketing to provide specific offerings of services or products. If the response is positive, the system will then generate all the necessary documentation and prepopulate the fields with verified information. The final step is to provide users with a view of all the documentation and ask them to confirm the details, fill in missing details and then submit – all on their phones and within minutes.”

The aim of these types of customer experience management systems is to make the experience as seamless and effortless as possible, with approval or denial within minutes, Haller concludes.