According to a recent report from economic researchers at USB, it is likely that an "average Australian" requires a minimum of 40 years to afford a 10 per cent mortgage deposit in Sydney.
This calculation was based on the premise the saver was earning $80,000 a year, and was putting aside $4000 a year, and that their savings goal was for a $1.2 million Sydney home, the value of which was tied to an annual increase in price of 3 per cent.
Although Sydney is an outlier, the researchers found similar grim news for the typical first-home buyer as viewed through a national lens. If they started this year, they would need to save until 2028 to afford a 10 per cent deposit on the average $400,000 home. The time frame more than doubled to 2041 if the buyer needed a 20 per cent deposit. Of course this situation is exacerbated by a torpid wage progression, across the board.
It's little wonder then that millennials are struggling to cope with the burden of financial stress and are dissuaded from pursuing what for previous generations was a given - a family home as bedrock of financial well-being. In its Employee financial Wellness Survey of 2017 in the USA pWc paint a portrait of a generation in a dire state of mind with debt burdens that are dissuasive if not plain destructive of their plans for a financial future.
A staggering 79% of the American millennials in the survey indicated that their student loan has a moderate to significant impact on their ability to meet their other financial goals. What's worse is 50% of these people believe they will probably need to use their retirement savings to meet non-retirement expenses and 30% use credit cards to pay for monthly necessities.
Of course, where we find these dark canyons of despair we tend to find a plethora of would-be app developers purporting to be light-bearing saviors for this generation of lost financial hope. Blind Freddy can see that apps are merely tools for problem-solving and not a fix-all for what are chronic failures of macroeconomic policy. That said, they are not without their place but only when considered as an ingredient in a recipe of social and political change that addresses both the supply and demand sides of the housing affordability crisis.
Day to Day Financial Management
Whilst Mint.com leads the way in the US I'd like to mention LEVEL-money as it proactively employs the behavioral economic principle of default. The app replaces a notional bank balance with an algorithm-derived Spendable Number for the month. With this number you'll be well anchored to act frugally, knowing how much you can comfortably spend on non-essentials, gating you toward a minimalist spending behavior. An equivalent, if somewhat functionally more rounded tool comes from the Australians at MONEY-BRILLIANT . This app reflects the best of the genre in a concise, if somewhat conventional manner.
Little to no use is made of true machine learning other than in MINT in this grab bag of #fintech app. That's OK, for now as it is a solution element that is over-hyped and under-delivered. Oft the case, it's a solution looking for a problem. This will change once vendors have accumulated sufficient data to drive their classification models.
At the heart of this, at least from the would-be micro-investor's perspective is peace of mind from knowing they are doing SOMETHING for retirement. That, given their debt burdens and fluctuating, unstable income base, they can be making some kind of small step toward a nest egg the best way they know how - by trusting assets to technology, by shifting small change to a robo-investment package and largely disengaging from the face to face dialogue with financial advisers favored by their parents. Who can blame them?
Financial advisers are largely distrusted by millennials. Scotttrade surveyed 1,030 adults who had at least $2,500 in investments.
The study concluded that 80% of people in the millennial generation wish they had “access to trustworthy retirement investment guidance, and 67% think their adviser “sometimes recommends products and solutions that are in their adviser's own best interest,”. Millennials on the face of it may have trust issues when it comes to acting on recommendations from financial advisers. It's no surprise then that their proclivity for tech-driven information and affordances sees them keen to embrace robo-advice and with it opportunities to assert independence.
There are some not inconsiderable behavioral economic factors at play with millennials and micro-investing. At the very least, delving into Prospect Theory and positive framing, developers have leveraged the propensity for most individuals to avert risk by minimising investment outlay and framing their financial decisions with an emphatic slant toward positive gain.
The fine British app MONEYBOX like its US counterpart Acorns likes to amble about quietly in the background shuffling together your loose change like an obsessive compulsive major-domo; dutifully making use of transaction rounding up to micro-invest on your behalf. It takes default design to a new level as the preferred means for which to overcome decision inertia.
I'm most familiar as a beta user with the elegant home-grown SHARESIES app which implements some choice architecture principles smoothly whilst also delivering on an unobtrusive user experience. I'm most comfortable with their treatment of attribute parsimony and labels by smart categorization of the major stock types available to micro-investors. As (Johnson et. al 487-504) explain it, smart use of categories accommodates
"a consumer's pervasive tendency toward even allocation.......judgments and choices can be strongly influenced by the particular groups or categories into which the set of possibilities is partitioned."
There are a number of app reviews of micro investment plays if you want to compare and contrast .
What is missing
1. Research (other than Google)
Who are your users? Delve a little deeper than demographics. Make the effort to accumulate and interrogate as much in-situ observational data as possible across the usual suspects of app interactions, hot-jar spotting, video-based affective testing and of course the dead boring but indispensable application of standardised scales. Please remember to t bring your team to some measure of A|B testing & design.
2. Theoretical framework
Look beyond Choice Architecture and Nudge if you have the capability; you will be pleasantly surprised that there are other models that are well instrumented and validated that you can use to enrich your app design and help it stand the test of time.
3. Machine learning
Catering for as fine a grain of user category as you can and understanding how they evolve their interactions with your IP over time using meta judgmental and operative tools including sentiment analysis and dynamic scales is something to consider. You also need to be clear about what classification approach you will use. You want stickiness?
Understand that user needs and behavior can fluctuate and may evolve or regress. Understanding this better and marrying user behavioral profiles with content, at the very least may be worth reflecting upon. You must be able to serve up a UX that aligns with user categories based on behavior profiling and these UX must be adaptive and persuasive.
A brief example
Gen Y beta user of a micro-investment app. I would be leaning toward doing something affirmative about my financial situation and want to evaluate, through experimentation, a number of strategies. For the vendor, that may be operationalised via a sand-boxed active demo with "make believe"money and stocks.
At this stage, I am seeking both information and support (reassurance). Naturally, this means slick on-boarding augmented with targeted micro-content that has a focus and touches on adult learning principles. It will likely also mean the site includes Live-CHAT help.
There are literally another dozen or so behavioural design principles I'd apply here if asked by a micro-investment vendor and this is only as it applies to one stage of behavioral engagement.
Dholakia, U., Tam, L., Yoon, S. and Wong, N. (n.d.). The Ant and the Grasshopper: Understanding Personal Saving Orientation of Consumers. SSRN Electronic Journal.
Johnson, E., Shu, S., Dellaert, B., Fox, C., Goldstein, D., Häubl, G., Larrick, R., Payne, J., Peters, E., Schkade, D., Wansink, B. and Weber, E. (2012). Beyond nudges: Tools of a choice architecture. Marketing Letters, 23(2), pp.487-504.
Kaptein, M., Markopoulos, P., de Ruyter, B. and Aarts, E. (2015). Personalizing persuasive technologies: Explicit and implicit personalization using persuasion profiles. International Journal of Human-Computer Studies, 77, pp.38-51.
KarimBarbhuiya, R., Mustafa, K. and Jabin, S. (2013). A Personalized Learning System with Adaptive Content Presentation and Affective Evaluation Facilities. International Journal of Computer Applications, 70(26), pp.10-15.
Klonek, F., Isidor, R. and Kauffeld, S. (2014). Different Stages of Entrepreneurship: Lessons from the Transtheoretical Model of Change. Journal of Change Management, 15(1), pp.43-63.