Online transaction services owned by business entities dealing with financial means are becoming commonplace. Internet platforms enjoy a growing popularity among private and corporate users alike. This may be credited to the numerous benefits this approach offers. First and foremost, it’s a time saver. Moreover, the costs of transactions are reduced, the access to one’s bank account is unhindered and the money can be managed remotely, in real time, no matter your whereabouts.

E-consumers of banking services growing in numbers

The electronic banking market has seen a considerable increase in the number of clients over the recent years. In January 2009 Polish banks’ websites were visited by 42 per cent of users, but two years later (January 2011), that share went up to 56 per cent. As seen in the July 2014 data, online banking is used by 58 per cent of all internet audience in Poland (12.6 m). As a result, the financial institutions are provided with vast amounts of data, concerning not only the transactions made on accounts, but also the number of website visits paid by users who sought information on deposits or loans, etc.

Big data storages can hold massive amounts of data

The current IT solutions used by the financial industry are based on the cutting-edge and highly secure technology, involving complex encoding methods and intuitive account management. What’s more, the growing range of online banking services means a ballooning mass of data, which quickly fills up the servers owned by organizations dealing with money transfers.

Such sets of data: large, highly varied and versatile, requiring the implementation of new processing methods, are called “big data”. With the right analytical tools at hand, the process of drawing proper conclusions is supported, which in consequence aids decision-making, helps to discover new phenomena and optimize procedures.

Increasingly well-tailored offers thanks to big data

Furthermore, information flowing from the cyclical gemiusAudience research supports accurate description of internet users’ behaviour on financial websites and segmentation of e-consumers by a set of chosen socio-demographic characteristics (such as age, education, social status, place of residence, income brackets, but also the type of device used to access the web, etc.). This means an offer can be cut out for concrete groups of individuals.

What is behavioural targeting?

Patrick Dixon, a visionary, business advisor and development consultant for the largest companies in the world, such as Google or Microsoft, claims that “Future is not about technology, it’s about what people feel”. In order to truly understand your consumers, you must know their needs and habits. Big data sets are a precious source of such information, as they contain not only current, but also historical data, providing a comprehensive picture of selected internet users.

This data, including cookie files, is recorded, then analysed and interpreted in real time. Such knowledge helps divide internet population into narrowly defined groups according to the type of content they view on website right on the spot. This method of categorising internet users - that takes into account the internet users’ behaviour online - is called “behavioural targeting”. For example, thanks to the stored cookies, you know that a given person has visited particular sub-sections of a website, when was the last time this happened, how often, and how much time such individual devotes to a content of interest, etc.

To illustrate this process, let me provide an example of behavioural targeting in practice. Imagine an internet user leafing through a bank’s website in search for information concerning deposits. Their interest would be demonstrated in the fact that the individual checks the offer in two sessions within the last four days, for example. One could assume that such person wants to establish a savings deposit. What should be done then is make sure the same person is displayed an ad emphasising the advantages of a relevant banking product on subsequent websites visited by them, or upon their next visit to the very bank’s website. This is a way of boosting the chances on hitting the customer’s needs bull’s eye. So if you are going to categorize e-consumers not only by their socio-demographic profile, but also by specific patterns of behaviour, tapping into big data proves indispensable.

Big data helps build up dialogue with the customer

Active and conscious use of big data in e-banking has a positive impact on building business advantage. This mainly means increased competitiveness compared to other market players of similar profile, and greater flexibility in meeting the needs of potential clients.

The persons using the internet leave an “electronic trail” behind them. By following their footprints, getting your offer through to concrete audience becomes easier.

However, monitoring customer online behaviour and drawing conclusions based on enormous pools of data is a true challenge. What does behavioural targeting look like on an example case?

Bank should adjust its offer to different clients

Let us assume that a bank intends to communicate its new loan product to clients. If only one message is prepared to be shown to all viewers and it’s presented in the same manner to everyone, the effectiveness will be lower than in case of communication tailored to the needs of a particular group. When preparing an offer, one should bear in mind that the bank’s website is the primary source of product information (about the loan). Hence it may prove beneficial to group the clients into “potential”, “seeking” and “decided” ones based on the data gathered in the big data storages.

The potential client doesn’t yet know what he/she wants. They enter the bank’s website without a particular aim, quickly flip through the offer, just to leave the online service shortly after. To propel such internet user to take out a loan, the financial institution’s actions should be mainly focused on prolonging their interest, for example by parading a variety of products available.

The seeking client can be identified by the fact that they visit a bank website more than once, take longer than most other users, clicks on many sub-pages, meticulously browses through the offer, and then seeks further information about the loan on the web. The bank’s interaction with such client should be founded on skilful handling of their attention by providing hints (e.g. links to product pages) and informing them about the possibility to contact a consultant.

Finally, the decided client will hone in on a concrete product, which in this case a loan. If such user comes back to the bank’s website, he/she will browse through those elements (sub-pages and the like) that deal with the loan in question. What’s more, they will devote more of their time to read the content, provide their contact details with a view to interaction with a consultant, and download PDFs with the terms of service, forms to fill in, etc. The bank’s reaction should consist in establishing contact with the client through a staff member, with an aim to finalize the transaction.

Virtual data storages hold genuine potential

As seen above, planning and running a marketing campaign based on analysis of data from big data storages and continuous monitoring of internet users’ actions offer banks a possibility to quickly boost the number of clients, and to strike durable relationships with them. Online activity of a bank entails an array of opportunities when it comes to presentation of the product range and obtaining the desired groups of clients. The knowledge on internet users behaviour is the starting point for any further sales strategy building and adaptation of products to the needs of persons who have identified requirements. Most importantly, however, the awareness of that opportunity should be a part of everyday business decision-making.