Customer experience is becoming the single most important differentiator for brands – price and product advantages be damned. This is according to Deloitte’s The Future of Retail Banking study, which also found that people demand interactions with their banks fast, sophisticated and tailored to their every need. To say that financial institutions have their work cut out for them is an understatement.
In a previous post, Finshape’s head of digital banking, Cenek Navratil brought up Netflix as an example, pointing out how the streaming giant doesn’t just wait until it dawns on you to watch, say, the Knives Out sequel. If you saw the original exactly 3.5 times and rated it with two thumbs up (like I did), its machine learning algorithm will promptly send you a nudge to check part two, followed by recommendations for similar movies.
TikTok has pushed the bar even higher. Its FYP (For You Page for the uninitiated) is what personalisation dreams are made of. Users’ default screen is populated with videos from the moment they sign up for the app, all vying for their attention. What happens next is the company’s secret sauce though, The Guardian argues. As users keep scrolling, the makeup of the content suggested to them slowly but surely becomes “uncannily good” at predicting what videos are going to pique their interest.
Services like TikTok and Netflix want to do more than just cater to consumer needs.
They want to cater to them before they even arise. So do banks, to be fair, yet 94%can’t deliver on the immense hyper-personalisation potential big data analytics holds. Without further ado, here are seven mistakes that might keep them from creating next-level banking experiences for users and locking in long-term growth.
1. Thinking that all data is good data
Far from it. Crucial to translating big data into big knowledge (and results), data quality refers to the accuracy and completeness of an organisation’s data assets. In other words, data analytics expert Leon Gordon explains on Forbes, it’s a measure of how well-informed you are about whatever you’re looking at when using your dataset.
Personalisation starts with finding out who your customers are, what they like (and what they don’t) and how they live their lives based on data. This is done through merging data from a bank’s account management, CRM and card management systems and blending in other, less traditional information sources, such as geolocation or channel data.
Giving datasets a thorough cleansing to make sure that all records are complete, correct, accurate and relevant should always come next. According to research by McKinsey, 48% of companies who see the highest bottom-line impact from AI technology have protocols in place to ensure appropriate levels of data quality.
2. Underestimating the power of data enhancement
A key step of data prepping for personalisation engines, enrichment essentially means filling in the blanks in datasets. For instance, in addition to just differentiating between income and expenses in a customer’s account, banks will also be able to see if a transaction is regular or a one-off. Results of such analyses vary greatly. A transaction can be ordinary for one customer and once in a lifetime for another.
Categorisation and profiling add further nuance to customer interactions, allowing banks to understand people’s habits, motivations and preferenceson a much deeperlevel than traditional segmentation ever has.
Customers have long been labelled as mass retail, premium or private banking clients based on how much money they have in their accounts. However, behavioural analysis often yields wildly different results when run on the same customer cluster. For example, people categorised as premium clients based on their income or balance often behave more like mass retail customers in terms of transactions and vice versa.
3. Not speaking the language of digital natives
Here’s a harsh truth about engaging customers, banking or otherwise, in the digital space: you’re either super relevant or spam. Salesforce has found that 66% of consumers expect companies to understand their unique needs, and 52% expect all offers to be personalised. Making this a reality, however, requires a two-pronged approach.
Tailored, timely content, of course, is one of them. For example, letting a customer know about investment opportunities or offering to set up regular deposits into their savings account when a bank notices a sudden bump in their salary. For Raiffeisen Bank, personalised in-app and email blasts generated an average conversion uplift of 68%, and in some cases, as high as 150%.
Engaging users where and how they want to be engaged, however, is just as key to building deeper relationships with customers.
Today, this is increasingly in the form of stories, a content format that was trailblazed by Snapchat and took the social media world by storm. Using a simple embeddable widget, banks can translate complex transactional and behavioural data into single-screen, 7-10-second snapshots to help customers, individuals and business, stay on top of their money matters and make their financial goals a reality.
Stories displayed can range from daily highlights to yearly summaries, creating a steady stream of money updates for users to interact with. Daily stories might highlight unusually large transactions, double charges or balance drops, while weekly ones can offer insight into top spending categories or budget consumption. Small business owners can get updates on key financial metrics, including monthly cash flow trends or a breakdown of expenses by category, from salaries to utilities.
Like what you’ve read so far? Stay tuned for part two of this post.
