Steve Jobs once said, “Your customers dream of a happier and better life. Don’t move products. Instead, enrich lives.” Considering the degree to which Apple’s products are ingrained in people’s day-to-day lives, they’ve stuck well to Jobs’ guidance.

One of the areas of retail banking where it should be easiest to sell dreams, not products, is the mortgage business. People want to buy a house and build a future; they don’t want to buy a mortgage. Long before lifestyle reality shows conditioned us to always be thinking about remodelling, I remember walking the empty rooms of my first home in Scotland dreaming about the home it could become. Unless you’re a commercial developer, a mortgage is just a means to an end, but it’s still one of the most meaningful and intimate transactions that banks can have with their customers. However, the entanglement of mortgages with dreams and aspirations also makes it a very vulnerable product category. Customers’ dreams of a new home now tend to manifest themselves via a digital footprint that ranges from simple real estate searches, to baby announcements, and relocation research. For lenders with the right analytical tools, these digital footprints create an ideal opportunity to intersect customers before they ever think about contacting their bank. In the US, the result is that banks are now beginning to fade from the mortgage landscape, with non-banks occupying six out of the top 10 origination spots in 2016, up from just two in 2011¹.

Read the report

For banks to remain relevant in the mortgage category, they need to get into the dream-fulfilment business. That means going beyond the traditional bank credit offering to facilitate a compelling end-to-end home-buying journey. The goal must be to meet customers’ digitally groomed expectation that banks know them enough to anticipate their mortgage needs and delight them with relevant, hyper-personalized offers and service. To stay relevant, banks need to get out of their traditional reactive stance and develop a predictive mortgage acquisition strategy.

Effective predictive mortgage origination draws on three core digital tools that enable lenders to connect with borrowers and enhance their experience earlier in their journey to homeownership: programmatic marketing platforms, advanced analytics and artificial intelligence (AI). Together, these capabilities create predictive lenders who can put the customer at the center of their marketing efforts and act on moments of influence throughout the customer journey from dream to move-in date and beyond.

For example, good predictive lenders build data management platforms that store and cull rich customer attribute data to inform personalized, contextualized and precise offers. By analysing the data at a granular level, lenders learn more about customers’ device-usage patterns, behavioural trends, social media profiles, purchase history and search priorities, allowing them to respond to specific contextual cues instead of simply targeting segments. With AI, predictive lenders can automate personalized marketing to acquire customers at scale, speed processes, limit manual intervention and cut costs. This isn’t just about keyword search response where any customer behaviour vaguely related to a new home search results in them being bombarded with generic mortgage offers. That is the digital equivalent of dumb direct mail, and not surprisingly the hit rates are low and getting lower for that type of blunt-instrument marketing. Instead, sophisticated machine learning can glean real insights from patterns in customers’ behaviour and data relationships to refine and predict next-best actions to a level where bank interventions are seen as truly helpful and not annoying. The difference between the best and the rest is becoming dramatic. Those who can identify and interpret the right precursor home-buying signals from their customers have seen click-through rates improve to six times industry benchmarks.

Smart banks will help customers realize their dreams of a happier and better life in a new home by enriching the mortgage experience with greater predictability. To learn more about how, I invite you to explore our report: The power of prediction in digital mortgages

¹Wall Street Journal, November 2, 2016

One response:

  1. Machine learning has huge potential in revolutionising the way banks curate products for their consumers. It is possible that machine learning can identify trends in consumers’ behaviour that normal humans would not be identify. With this capability, products can be tailored specially to meet real needs of consumers. It would be really interesting to see how machine learning can be used for other financial products!

Submit a Comment

Your email address will not be published. Required fields are marked *