Are you guilty of running a hidden data factory? According to Thomas Redman, advisor to several Fortune 100 companies on data quality, chances are that the answer is yes.
The term refers to the insane amount of extra work people in today’s organisations – of all sizes and industries – need to put into correcting data errors as a part of their daily routine. It goes something like this: “Salespeople waste time dealing with erred prospect data; service delivery people waste time correcting flawed customer orders received from sales. Data scientists spend an inordinate amount of time cleaning data; IT expends enormous effort lining up systems that ‘don’t talk’. Senior executives hedge their plans because they don’t trust the numbers from finance.”
One by one, these hidden data factories contribute to a loss of some $3.1 trillion per year for the US economy only. That’s how much bad data costs businesses as it becomes embedded in the daily work and decision-making processes of everyone from boards and C-level executives through sales managers to knowledge workers and data scientists. In 2020, big data is no longer king. Smart and fast data is. In other words, data with an added layer of intelligence that turns statistics into actionable, as-it-happens insights. And ultimately, better decisions, products and workflows.
One of the biggest benefits of a banking personalisation platform is that banks don’t need to build such data capabilities from scratch. All they need to do is figure out how pre-built models and use cases can be configured in a way that suits their data, product, marketing and sales strategies best.
How a personalisation platfrom works by W.UP
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. So the first step is to gather as much information about them as possible, from as many data sources as possible, and integrate it into a single platform. Data integration helps banks merge account management system, CRM and card management system data together and also blend in other, less traditional data sources. Think geolocation data collected through mobile phones or information on how and how often people use various banking channels or other financial service providers.
Taking care of data hygiene is an absolute must so giving datasets a thorough cleansing to make sure that all records are complete, correct, accurate and relevant is next. Then comes data enrichment, aka refining raw data to the point that it finally makes sense. In the case of transactions, for instance, data enhancement means that they can be categorised by type or frequency or marked as regular or outlier transactions. Results of such analyses vary greatly. A transaction can be ordinary for one customer and once in a lifetime for another.
Once data is prepped, banks can start building customer profiles to get a better picture of what their customer base looks like, both as a whole and on an individual level. Do they have a car or use public transportation? Are they into fine dining or fast food? Where and how often do they do their grocery shopping? This is carried out with the help of machine learning clustering algorithms. Based on pre-defined search criteria, personalisation platforms use algorithms to identify relevant groups of customers, down to micro-segments or personas, so banks can engage them with super-customised, super-relevant messaging.