Using data to improve the customer experience
Our world is changing rapidly.
Consumer expectations have been elevated by immersive technologies and the competition for time is intense. No wonder then, that understanding and upgrading the customer experience has become a key objective for financial services organisations.
Experience and with it, personalisation are sought after by consumers like never before. Gone are the days where it was possible to segment your consumer base by geographical region or earning power alone. Digital experiences, notably through apps, have set the bar for personalisation – personalised interfaces, games that adjust to the customer’s playing style, fully customisable app interfaces – are just some of the tools savvy technology-focused companies use to make their products more personalised.
Financial services are not exempt from this current state.
Most firms do understand the importance of delighting their customers. They are investing heavily in this enterprise, but making it happen is another thing entirely. This disconnect between intention and delivery is hard to overcome and that’s because investments in improving customer journeys are too often based on intuition and incomplete data or focusing on the wrong thing.
It doesn’t have to be this way – by adopting a data-centric approach and operating with a wide-scope and customer-focused lens organisations can make best-in-class customer experiences a reality.
Real-world segmentation
Delighting customers is about understanding their needs and desires. To do this, you need to know who they are and what they’re looking to achieve by using your offering. In other words, everything starts from a base of customer knowledge.
An analytics process known as segmentation can be used to segment different types of customer demographics according to their behaviours and needs. Understanding that your customers aren’t a homogenous grouping but are, in fact, a diverse collection of sub-groupings and individuals with unique needs, is the first step to designing experiences that engage them and keep them coming back.
Segmentation is not simply about “bucketing” clients into reductive categories as has been the case in the past. Instead, it’s about acknowledging that customers are real people, with unique intentions and behaviours, each deserving of individual treatment. Traditional segmentation models based on locality, age, earnings and other factors are derived from surveys carried out across the country. But even large sample sizes may not be suitable for your customer base, especially when dealing with seasonal or time-sensitive products.
Traditional segmentation models often prove costly and inefficient when the same targeting, product offering, messaging and more are sent out to a wide range of customers. Data science and Machine Learning modelling can overcome these inefficiencies yet financial services organisations can go (when appropriate) further towards providing a more personalized experience from their customers.
This is because they enjoy access to proprietary, domain-specific data derived from customers’ transaction histories. Instead of estimating what customer segments might look like, banks have a clear view of real-life behaviour. This sophisticated form of customer analytics yields commercial applications that are practically endless.
Learning from the challengers
The past ten years have seen a wave of challengers entering financial services. These niche players have benefitted from the opening up of the financial services ecosystem, leveraging the technical infrastructure of Open Banking to access the data of larger banks.
Innovative players are embellishing open-source data with their own, creating rich user experiences in verticals like insurance, private wealth and retail banking. By building new layers of data about customers that banks don’t have access to, they can construct personalised profiles of their users in order to cross-sell products and tailor experiences from client to client. Digitally native brands know that segmentation begets growth and monetisation.
Incumbents can learn from this approach. Large retail banks, in particular, have unparalleled access to customer data, but often fail to leverage it. This isn’t necessarily about cross-selling or up-selling customers (although this is a clear benefit of using data to better understand your customers); it’s about providing a better experience by building products that are tailored to customers’ needs. Furthermore, blanket messaging and marketing simply does not work in today’s digital-savvy landscape. You risk alienating customers by marketing the wrong products to them or dealing with them in the wrong tone or about the wrong topic.
Data, done correctly, can bridge the gap between brand, in this case, the financial services institution, and customer. This can ultimately lead to improved relationships between the organisation and consumer, improving retention, brand recognition and loyalty, increasing the likelihood of cross and upselling of products and services and more.
Customer analytics in practice
At Mudano, we believe that incorporating scientific insight from personality traits is the key to better understanding your customers.
There is no such thing as a standard customer journey. Every customer is unique and their behaviour is complex and unpredictable. But personality traits can be inferred using a combination of existing customer data and personality surveys. Machine learning models can learn how existing patterns in customer data predict personality traits for a subset of customers surveyed. The model then identifies the traits of the remaining customers, giving a reasonable estimate of their personality traits.
This isn’t just data science theory; it works in practice. We recently helped a financial institution identify new potential users of their digital offering by profiling the interaction habits of users in low participation segments. By using in-house data, we found a large population of people with the potential to use the service. These users showed technology capability, such as having online digital media subscriptions and doing lots of online shopping. Using traditional segmentations alone, these users would have been excluded, since it was predicted that they had little digital participation.
There are many ways and approaches to tackle the company/customer relationship challenge and by having a multi-layered approach financial services organisations can utilise both the market segmentation approach as well as provide a personalised experience where necessary.
This approach allows organisations to strike a balance between the quasi-creepiness of hyper-personalisation and the inefficiencies of traditional ‘blind’ segmentation methods.
The journey ahead
The banking industry is on a journey. As new data science techniques emerge, so too will new commercial opportunities. Financial services are just one potential offering for banks in our new data-centric world. As banks look to offer more personalised services and products, we will inevitably see them start to offer non-traditional financial services. For instance, we have seen a number of companion apps offered by incumbents and challenger banks, offering features such as budgeting and savings recommendations. This is great to see because data does not mean you need to sell to your consumers all of the time, but rather use that data to better customers’ lives.
Banks occupy a privileged position in consumers’ lives, with access to unparalleled volumes of high-quality data that can yield profound behavioural insights. It’s no exaggeration to say that your bank might know you better than you know yourself! No wonder then, that so many big tech companies are entering financial services, and so many banks are embracing technology as a matter of the highest priority.
Here at Mudano, we believe in extracting every last drop of value from clients’ existing data to understand and better serve the business. Intelligent treatment of your own data is the key to finding the key hidden demographics everybody else may have missed. We can help your organisation to deploy machine learning models that generate personalised recommendations based on personality and behavioural change triggers. This in turn facilitates the development of new propositions by providing data-driven views of individual customer segments, identifying opportunities across the business to improve retention and acquisition.
Better understanding of the data landscape means better understanding your customer.
If you’d like to find out more about upgrading the customer experience using data, please get in touch with our team.