The future of brick-and-mortar stores: how to get customers back into them?
In today’s world, data is tearing down barriers between banks and their clients. It is no longer the bank on one side and the client on the other. In the modern world of finance, their relationship is mutual, working in both directions. A bank can build a personalised image of each client, whom it addresses as one coherent organisation. It has a far clearer image of him, perceiving his individual needs and respecting the diversity of perceptions of the digital world.
At the end of the 20th Century, communication with customers underwent a major change. The Internet and e-mail correspondence appeared. This made it possible for large corporations to approach a larger number of customers at once. New communication channels made it possible to appeal to as large a group of clients as possible. On the other hand, communication was undifferentiated. The campaign was always uniform, as was the style of communication. Whether you were a middle-aged American businessman living in Australia, a young German studying philosophy, or a mom at a ranch in South Africa, you all received the same type of offers. Sometimes that was enough – for example, if you were all interested in Harry Potter, new Internet companies reliably supplied him to you. At the same time, you were all able to read the same on-line news, visit the same film fora, and for the first time you were even able to connect on a social network. You all encountered the same interface and advertisements. The spirit of connection and globalisation ruled the Internet. Customers were excited about the availability of products and, on the other hand, companies were excited about the availability of customers.
Data has already played a key role in the struggle for success on the market. At first, it was discrete. Data was generated as if under the surface and a few people paid attention to large log volumes. But their exponential growth could not be ignored. Whoever wanted to be competitive had to start paying attention to them. And they who were able to integrate the data into their strategy with sufficient speed, and in sufficient volume, won. Those who involved advanced and automated technologies for mining information from logs prevailed in the competitive struggle on the market.
Data gave companies possibilities to get quickly oriented and to gain an understanding. New types of analysis yielded new types of very trustworthy information. Interesting games started to be played on the vast chessboard of data and information. Key findings included information about how many people visit a company’s website and how they behave on the website, i.e., which products they buy and how many. All of a sudden, we were able to understand customer behaviour with a large degree of certainty and create customer typologies.
Those who started to process these analyses systematically were suddenly able to discover functioning business rules. Patterns of customer behaviour could be used in further customer acquisition. This success brought further interest in technologies. Not only in those that allowed reverse analysis of aggregated data, but primarily those that make it possible to process all data in real time.
In the last 20 years, data first freed companies of the need to pay one-on-one attention to their customers. Whereas now, their immense volume, in combination with Big Data technologies, allows them to rediscover clients as unique individuals, each of whom has his own story and needs.
Fig. 1: Digitisation freed companies from the need to pay one-on-one attention, whereas now it allows them to do just that.
1:1 communication: human service channels (branches, call centres, letters)
1:N communication: digital service channels have been added (web, e-mail)
1:1 communication: personalised communication across all service channels
The contemporary trend is to map individual customer paths and experience. The market is ruled by words like personalisation and a 360° view of a customer. Analytics are changing from retrospective deductions to real-time inductions. Its predictive nature is no longer sufficient; it focuses on prescriptive and adaptive models. Advance machine learning techniques and parallel data processing make all that possible.
In the spirit of innovation, successful innovative projects are being created that have the decisive word in a tough competitive struggle. They symbolise change and set the rate of market dynamism. An example is personal finance management (PFM) which has changed the world of retail banking. Banks have started to help individual clients with their personal finance, on the basis of data. Not on the basis of large financial decisions and several weeks of consultations with a private banker, but on-the-go in everyday life. Thanks to flexible mobile applications with zero latency. In them, clients can track their spending over a given period, their expenditures being automatically divided by category, or track and plan their savings towards a specific goal.
Time flies and it is a key factor. The more historical data we can process and the faster we are able to respond to client activity, the better we are able to serve the client. Because we can understand clients in a long-term perspective, we are able to accurately estimate the customer’s life cycle, i.e., estimate the client’s financial background and predict his needs. Applying prescriptive algorithms, we are able to actively manage his finance, meaning that we can, for example, help the client save, by automatically moving his money to a savings account. The transfers can be two-way and take place not only on the basis of current spending and balances, but also on the basis of expectations of regular expenditures. The better and the more diverse are the data available to us, the more appropriate a prescription we can prepare for our clients.
The success of the PFM project, however, is not based solely on the functionalities of applications, but also on communication. The better you communicate with the client (e.g., how suitable and often you inform him of news, given him tips, how you educate him), the better will be the relationship you build with the customer.
But again, communication strategies cannot be general – they require parametrisation and a rich set of communication scenarios. They work with customer preferences, choose between communication channels in different timeframes and days of the week, and deploy optimisation and rule engines, because, before each appeal, comes an evaluation of a comprehensive set of data about the client’s individual behaviour and needs These include the client’s reaction. A modern phenomenon is two-way communication that is reminiscent of a conversation.
An example may be banks that give “real-time feedback” with respect to possible settings of target spending for each category. This means that, if a client who spends money primarily on dining out approaches his limit, the application notifies him that he can buy only one more dinner at a restaurant this week to keep within his limit. In this way, banks help their clients to keep their most problematic categories under control. To another client, who buys an airplane ticket for a vacation abroad six months in advance, the bank can, for example, recommend a good time to purchase a foreign currency (including on the basis of foreign-exchange development).
The path to each customer is different. Customers are satisfied with this one-on-one approach, appreciating personalised care and natural communication with their bank. They themselves determine the scope of their relationship with the bank and to what extent the bank can intervene in their lives. They are far closer to one another than ever before, even though they see each other physically far less. A positive customer experience and loyalty is built primarily by appropriate (targeted), timely (fast), and sometimes also emotionally toned (personal) communication.
Communication with clients can be as diverse and personalised as one can wish. But it does not come free. You must invest in data and technologies, often discarding the old environment and building a brand new one. Plus, investment in changing processes is also required, as they alone can guarantee that we will be able to use technologies efficiently and with synergies throughout the organisation.
The results of large-scale innovations are already apparent. Perhaps somewhat surprisingly, this is not the case of large and rich banking houses, but, rather, primarily of fintech companies. They are building their business in a digital world and data is their greatest asset. And they find customers primarily through fast and well-targeted customised communication.
Article by Dagmar Bínová - Big Data Science Leader and Alžběta Gabalové - Data Scientist was published in the IT Systems monthly, vol. 3/2020, and on the server systemonline.cz.