How the financial services industry can leverage customer state vectors to improve user experience

We’ve all had that experience. The one where for example you’re thinking about painting your kitchen. You mention it in passing to a friend, your phone nearby – in your pocket, or in your bag. You forget about it. Then the next time you open your browser or social media, every other ad is espousing the domestic virtues of cobalt blue or Paris grey.?

How did they know?? The answer is found in something called a customer state vector.?

In marketing, a customer state vector shifts the focus away from a view of what happened to the customer in the past, to what they are doing right now – everything from what they’re putting in their basket and where it’s being delivered, to their favourite pantone colour as overheard on Instagram.?

How do customer state vectors work with data streaming?

Customer state vectors define a set of facts that we want to know about a customer’s activities. Data about real-time customer behaviour can then be streamed into this vector, every time a new data point arrives, to be analysed –?and actioned.???

Targeted advertisements are probably the best-known example of customer state vectors at play. But industries from DIY to dentistry are taking advantage of growing volumes of instantly actionable data to personalise and target their products and services.

While the tech is not sophisticated enough yet to know that I’m not painting my kitchen for another three years, its application is already of considerable benefit in spaces where data streaming can be utilised.

I’m talking about the financial services industry.?

Where customer state vectors meet financial services

I’m building out a team of industry specialists to champion the broad range of business and technical capabilities of data streaming – or as we call it, data in motion – with Kafka.?

With my background in banking, I’ve started by talking to several specialists in financial services to understand how data streaming has disrupted the way we build, provide, and consume economic services.?

For a long time, the provision of financial services was quite the analogue affair. Data gathering was dictated by slow batch data processing, which often had to happen overnight and was then reviewed and used to inform the next day’s business decisions.?

For customers, this meant they had a limited amount of insight into their incomings or their spending habits. Any changes to their account, however minor, likely meant a trip to the bank in person.?

Even when customer state vectors allowed for a more holistic and predictive view of customer behaviour, they couldn’t keep pace with their ever-increasing activity online. We talked about having a 360-view of customers. But what did that mean if it wasn’t up to date??

With customer state vectors powered by real-time data streaming, banks are able to accurately track customer behaviour in the here and now. Any time there is activity on an account, that data point can be streamed into the vector in the same instance.?

Proactive and protective customer experiences?

Increasingly accustomed to seamless customer experiences in industries like retail and travel, we’re demanding the same from our banks – and are willing to go elsewhere if we don’t get it. In fact, in a recent survey of British banking habits, over a quarter (26%) said they switch banks every three to five years.?

Real-time data streaming is enabling financial services providers to make intelligent and automated decisions that serve up superior customer experiences.

One space that continues to be a priority for financial services is fraud protection. Scams, virtual pickpocketing, online fraud, phone hacking – the methods of fraud are constantly evolving, and rapid reaction to events is a matter of necessity.

As more and more people bank online and on their mobile devices, spreading data across a wider network of systems, opportunities for fraud increase. According to UK finance data, in 2020, internet banking fraud was up 117% and mobile banking fraud was up 48%.?

Tracking and analysing data in real-time means that banks can monitor spending and flag if there’s anything out of the ordinary. In some banking apps, customers are alerted of any suspicious activity and can freeze cards at the tap of a button, rather than having to contact and wait for their bank to act.?

The faster this data can be gathered, analysed, and implemented; the faster customers can be protected – with fraudsters stopped in seconds rather than hours.?

From fraud to credit scores?

Banks that can supply this protection in proactive notifications and alerts, as well as in-app and online guidance, are better positioned to win over brand-agnostic customers concerned about the pervasive threat of fraud.

The principle is the same for transactions where data can be leveraged proactively to safeguard accounts without causing inconvenience.?

Real-time transaction data empowers notifications to verify purchases that take you from an e-commerce website to your banking app and back again, as a powerful added layer of security that barely disrupts the purchasing process.

Geospatial data means banks can track if a customer has caught an Uber to the airport, brought a coffee there and then made a purchase abroad seven hours later. When the customer pays at the other end, that data prevents the card from being declined or frozen.?

The power of personalised data can also extend to credit risk. Credit lenders can mine data from social networks, leveraging digital reputation as a factor in your credit score and making loans accessible to people disadvantaged by a lack of borrowing experience or one-off missed payments.?

AI applications in data streaming?

Mortgage applications can also be scored and filtered by AI in the same way that resumes are now often digitally screened before reaching the recruiter, enabling fast acceptance, or adversely, rejection.?

We’re also seeing the increasing presence of Robo-advisors that go beyond basic chatbots to provide more sophisticated content like algorithm-led financial planning services through the analysis of zero-party data.?

In this way, AI combined with data streaming has allowed banks to generate new value propositions where applications, claims and offers are all generated and processed automatically.?

Banks can take advantage of a data-powered, real-time visual of customers and their needs, serving up personalised, always-on customer service while optimising operational efficiency.?

A better banking experience?

We live in a world where we are notified when our package will be delivered, if a product is back on sale, if our flight is delayed or if we should expect a restaurant to be busy at a certain time.?

We live in a world where when we want something, we’ll get an ad for it so that we don’t have to search around ourselves.

Banks must step up. Instant access to payment and balance details, and activity alerts are par for the course, as is the expectation to be able to view our account data across multiple channels and devices. And if I’m putting money away to remodel my kitchen, I want to browse the best savings accounts for that unique purpose.?

To keep up with the demands of ever-active digital customers, financial services organisations need to look for ways to consolidate and process the sheer volume of data available to them so they can action it in seconds, not hours or days.

Data in motion is the catalyst for unlocking the potential of customer state vectors. Together, the potential value for organisations and their customers is, in my mind, unlimited.?

For further information on this topic you can download the eBook I've written, here:

10 Ways Confluent Drives Transformation in Financial Firms