A global company routinely accumulates a huge amount of data, but maybe not enough information. Inquiries, bookings, sales, service, customer care, feedback, social media interactions, newsletter subscriptions, loyalty programmes, invoicing and payments – customers use a range of different channels, resulting in a deluge of unrelated digital records and a collection of databases. In fact, so many that the customers’ identities are often elusive. Which is of course a problem when you want to offer the best possible service to them.
The transition from having millions of unrelated digital records to knowing the people behind them allows you to understand your customer base: who has bought which products, where the best customers are, the level of service they experience, how behaviours evolve and individual preferences change. Having a full picture of your customers means being able to offer them better and more relevant services. It helps in detecting fraudulent behaviour and in designing products. In short – it’s key in business.
How can you understand which of the millions of existing disconnected profiles, collected over the years, belong to the same person?
In order to identify all the records belonging to a single customer, all the existing records must be compared to each other.
• But there may be hundreds or thousands of records for one single customer: people can buy products providing minimal personal details, or through intermediaries. They may have a loyalty programme. They may have multiple profiles and subscriptions. And of course, over the years people move, use different email addresses and payment methods, adopt new behaviours, change phone numbers, get new loyalty cards. Mathematically, for one million records, there are 500 billion possible combinations to compare. Just 100k records mean 5 billion different combinations.
• Also, one must deal with many different ways of presenting information: names can be spelled differently, dates, phone numbers and addresses come in dozens of formats, information may be abbreviated, inconsistent, mistyped, invalid, or missing … not to mention the usage of multiple languages or other alphabets such as Cyrillic or Chinese.
In short, there are too many comparisons to perform with standard methods, and they are riddled with uncertainty.
The solution Quantum designed and implemented to solve this problem is called the Matchbox. What sets it apart from other offers currently on the market is easy to tell.
Quantum is a data science and analytics company, located in the center of Zurich. We help clients to identify their most valuable customers, products, or services; determine potential risks; discover hidden potential in their markets; pinpoint and eliminate bottlenecks and inefficiencies; and provide other insights to steer their business. We do this by combining business experience and knowledge with the application, implementation and teaching of scientific methods of data analysis, data management, reporting and modern visualisation to turn data into information.
