Data-driven banking experiences certainly sound nice. What banks tend to feel uneasy about is the “data” part. But pulling customer data into a single platform doesn’t have to be painful. Here are four things you keep hearing about data integration but should stop listening to.

Demographics are prime data

Yeaaahhhno. Basic customer data, such as age, gender or marital status, are part of the data integration process, granted. But nowhere near the most important part.

In fact, demographic profiles play second fiddle to key differentiators like spending behaviour in the personalisation process as a whole. The reason for this is simple: demographic segmentation helps little to nothing when it comes to knowing customers better. More often than not, this basic information has a weak correlation with the actual needs of customers, who “are much more than the sum of their banking deposits and loans, and do not align neatly to basic or broad demographic characteristics,” an EY survey of 55,000 consumers has found.

“Transactional data is hands down the most important. Then comes the information about the type of banking products customers have, including debit and credit cards, accounts and account balances. Without these insights, you won’t be able to predict if someone will run into cash flow problems or you’ll end up sending credit card or travel insurance offers to customers who already have one,” Balázs Zotter, W.UP’s chief technology officer says. “Being consistently relevant is something most retail banks still struggle with.”

Location data is still solid gold

Location-based targeting has been all the rage in recent years and with good reason. Eighty-plus percent of geolocation data buyers have seen a growth in their customer base, response rates and engagement, a 2018 Factual survey concluded. The problem is that collecting geolocation data has become much, much harder. “In the past, people didn’t think twice about sharing their location data. Today, operating systems track and alert users of applications that collect such information and ask them if they want to turn location services off,” Balázs explains.

That being said, just because geolocation data has its limitations doesn’t mean it’s not worth the effort. Especially because, as privacy-savvy as they may be, customers are still willing to share personal location data if the application they share it with delivers value to them, Accenture points out. The key to get it right? Transparency, in Balázs’s opinion.

The best data is more data

“Your scientists were so preoccupied with whether they could, they didn’t stop to think if they should,” Dr Ian Malcolm sums up a valuable lesson in the 1993 blockbuster, Jurassic Park. One that holds true for all walks of science, whether or not they involve genetically engineering dinosaurs. Data integration can be as sophisticated as banks want it to be. Besides cards and accounts, they can include information on everything from mortgages to investment products.

But should they? “Not necessarily,” Balázs says. “Using nothing more than transaction history, banks can create use cases that offer a whole roadmap for making the most of their data. No demographics or product details are needed.” Even today’s growingly popular financial fitness and PFM 2.0 tools rely on transactional information only. Meaning that they require minimal input while making a huge difference to customer experience.

Data integration has got to be all or nothing

Merging banking data into a single platform is not as hard as it sounds. Unless you make it hard. “My advice is to start with one type of data that everyone understands, like transactional data,” the chief technology officer says. Then select use cases that only run on transactional data. The more information sources are tapped, the more overwhelming the integration process can get.

And, in most cases, it won’t take banks much closer to their personalisation goals, either. Balázs explains: “Collecting every last piece of information about customers can take a huge amount of time and it only makes things more complicated, not better. Banks should build their data analytics capabilities one step at a time so they don’t lose focus and waste resources.”